Metagenomic NGS vs. Culture in Pathogen Detection: A Comprehensive Analysis for Modern Diagnostics

Abstract

This article provides a comparative analysis of metagenomic Next-Generation Sequencing (mNGS) and traditional microbial culture for pathogen detection, tailored for researchers, scientists, and drug development professionals. We explore the foundational principles of both technologies, detail their methodological workflows and clinical applications across diverse infection types—from lower respiratory tract to neurosurgical and periprosthetic joint infections. The content addresses key challenges, including diagnostic optimization, interpretation of results, and cost-effectiveness. Finally, we synthesize validation data and performance metrics from recent studies, offering evidence-based insights to guide diagnostic selection, technology integration, and future innovation in clinical microbiology and therapeutic development.

Understanding the Core Technologies: From Culture Plates to Sequencing Reads

For over a century, microbial culture has served as the fundamental cornerstone of clinical microbiology, providing the definitive method for pathogen identification in infectious diseases. Referred to as the "gold standard" in diagnostic microbiology, this technique relies on the fundamental principle of allowing microorganisms to proliferate in artificial media under controlled laboratory conditions [1]. Despite the emergence of sophisticated molecular technologies, culture remains deeply embedded in clinical practice, public health surveillance, and antimicrobial stewardship programs worldwide. However, the scientific community now engages in critical reassessment of this established paradigm as novel diagnostic platforms demonstrate capabilities addressing longstanding limitations of traditional culture methods.

This review examines the contemporary position of microbial culture within the rapidly evolving diagnostic landscape, particularly focusing on its comparison with advanced molecular techniques such as metagenomic next-generation sequencing (mNGS). By synthesizing recent comparative evidence across diverse clinical contexts—including central nervous system infections, periprosthetic joint infections, and respiratory diseases—we aim to provide a nuanced perspective on the appropriate role of culture in modern clinical microbiology and its integration with cutting-edge molecular diagnostics.

Principles and Methodological Framework of Microbial Culture

Fundamental Principles

Microbial culture operates on the basic biological principle that microorganisms, when provided with appropriate nutritional and environmental conditions, will undergo cellular division and form visible colonies. The methodology requires specimens to be inoculated onto or into specialized media containing essential nutrients, then incubated at optimal temperatures and atmospheric conditions specific to the suspected pathogens [2]. This process allows for the amplification of microbial populations from often minute numbers in clinical samples to quantities sufficient for further analysis.

The culture environment must carefully replicate the natural habitat of potential pathogens, with considerations for pH, osmolarity, redox potential, and specific growth factors. Solid media provide a surface for discrete colony formation, enabling preliminary quantification and isolation of pure cultures, while liquid media support the growth of fastidious organisms and facilitate detection of low inoculum infections. The success of cultivation depends fundamentally on matching these conditions to the physiological requirements of diverse microbial taxa present in clinical specimens.

Standardized Experimental Protocols

Sample Collection and Processing Protocol:

• Aseptic Collection: Clinical specimens (e.g., cerebrospinal fluid, tissue, joint fluid) are collected using sterile techniques to prevent contamination [3]. For sterile sites, samples are directly inoculated into culture media; for non-sterile sites, selective media may be employed.

• Transport Conditions: Specimens are transported to the laboratory promptly under appropriate conditions (temperature, atmosphere) to maintain pathogen viability. Delays or improper transport constitute major pre-analytical variables affecting culture sensitivity.

• Inoculation and Incubation: Samples are plated onto solid media (e.g., blood agar, chocolate agar, MacConkey agar) and/or inoculated into liquid enrichment broths. Media selection is guided by suspected pathogens and sample source [2]. Incubation conditions vary:

  • Aerobic: 35-37°C with 5-7% CO2 for 5-6 days
  • Anaerobic: 35-37°C in anaerobic chambers for 10-14 days [3]
  • Fungal: 25-30°C for up to 4 weeks

• Colony Identification: Visible colonies are subjected to identification using techniques including:

  • Morphological assessment (colony size, pigmentation, hemolysis)
  • Gram staining and microscopic examination
  • Biochemical profiling (e.g., API systems, VITEK) [2]
  • Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) [2]

• Antimicrobial Susceptibility Testing (AST): Pure isolates are tested against antimicrobial agents using:

  • Disk diffusion methods
  • Broth or agar dilution for Minimum Inhibitory Concentration (MIC) determination
  • Automated AST systems [2]

Table 1: Core Components of Microbial Culture Methodology

Component	Standard Implementation	Purpose
Culture Media	Solid (agar plates) and liquid (broths)	Support microbial growth and isolation
Incubation Conditions	Temperature: 35-37°C; Variable atmosphere (aerobic, anaerobic, CO2-enriched)	Optimize conditions for diverse pathogens
Growth Monitoring	Visual inspection daily for up to 14 days	Detect microbial proliferation
Isolation Techniques	Streak plating for single colonies	Obtain pure cultures for downstream analysis
Identification Methods	Biochemical, mass spectrometry, molecular	Determine microbial species

Comparative Diagnostic Performance Across Clinical Syndromes

Recent studies directly comparing microbial culture with molecular methods across diverse clinical contexts reveal a consistent pattern regarding their relative strengths and limitations. The data demonstrate that molecular techniques, particularly mNGS, generally offer superior sensitivity, especially in challenging diagnostic scenarios, while culture maintains important advantages in specificity and functional characterization.

Neurosurgical Central Nervous System Infections

A 2025 comprehensive study of 127 patients with neurosurgical central nervous system infections (NCNSIs) provided compelling comparative data. The traditional culture method demonstrated a positive detection rate of 59.1%, significantly lower than both mNGS (86.6%, p<0.01) and droplet digital PCR (ddPCR) (78.7%, p<0.01) [4] [5]. Notably, 37 patients (29.1%) tested positive via mNGS but negative via microbial culture, highlighting the sensitivity gap between methods [4].

The time from sample harvesting to final results (THTR) differed substantially between methods: microbial culture required 22.6 ± 9.4 hours, mNGS 16.8 ± 2.4 hours, and ddPCR 12.4 ± 3.8 hours [4]. This accelerated turnaround with molecular methods has direct implications for clinical management, particularly in time-sensitive infections like NCNSIs. Importantly, the administration of empiric antibiotics did not significantly influence the positive detection rates of either mNGS or ddPCR, whereas antibiotic exposure is a well-established limitation for culture recovery [4].

Spinal Infections

A 2025 systematic review and meta-analysis comprising 10 studies and 770 patients provided pooled estimates of diagnostic accuracy for spinal infections [6]. mNGS demonstrated markedly higher sensitivity (0.81, 95% CI: 0.74-0.87) compared to tissue culture technique (TCT) (0.34, 95% CI: 0.27-0.43) [6]. The overall diagnostic accuracy, as measured by the area under the summary receiver operating characteristic curve (AUC), was 0.85 (95% CI: 0.82-0.88) for mNGS versus 0.59 (95% CI: 0.55-0.63) for TCT [6].

However, culture methods maintained superior specificity (0.93, 95% CI: 0.79-0.98) compared to mNGS (0.75, 95% CI: 0.48-0.91) [6]. This specificity advantage underscores the continued value of culture in confirming true infections and avoiding false positives that may occur with highly sensitive molecular methods detecting nucleic acids from non-viable organisms or environmental contaminants.

Pulmonary and Joint Infections

In pulmonary infections, targeted next-generation sequencing (tNGS) demonstrated a significantly higher positivity rate (92.6%) compared to traditional microbial culture (25.2%, χ² = 378.272, P < 0.001) [7]. Similarly, in periprosthetic joint infections (PJI), studies identified several factors contributing to culture-mNGS discrepancies, including prior antibiotic use (OR = 2.137, 95% CI = 1.069-4.272, P = 0.032), polymicrobial infections (OR = 3.245, 95% CI = 1.278-8.243, P = 0.013), and infections caused by rare pathogens (OR = 2.735, 95% CI = 1.129-6.627, P = 0.026) [3].

Table 2: Comparative Diagnostic Performance Across Clinical Applications

Clinical Context	Culture Sensitivity	Molecular Method Sensitivity	Key Comparative Findings
Neurosurgical CNS Infections	59.1%	mNGS: 86.6% (p<0.01); ddPCR: 78.7% (p<0.01)	29.1% of patients mNGS+/culture-; Antibiotics不影响 molecular methods [4]
Spinal Infections (Meta-analysis)	34% (95% CI: 27-43%)	mNGS: 81% (95% CI: 74-87%)	mNGS AUC: 0.85 vs culture AUC: 0.59 [6]
Bacterial Meningitis	26% of PCR-confirmed cases	PCR: 100% of confirmed cases	PCR detected 10% positive vs culture 3% [8]
Pulmonary Infections	25.2%	tNGS: 92.6% (χ² = 378.272, P<0.001)	tNGS detected more polymicrobial infections [7]

Strengths and Persistent Value of Microbial Culture

Antimicrobial Susceptibility Testing

The unparalleled capacity of microbial culture to provide live isolates for antimicrobial susceptibility testing (AST) remains its most significant advantage over molecular methods. Culture enables phenotypic assessment of microbial response to antimicrobial agents, delivering critical data for guiding targeted therapy [2] [1]. This includes determination of minimum inhibitory concentrations (MICs) and detection of resistant subpopulations that might be missed by genotypic methods [2].

The European Committee on Antimicrobial Susceptibility Testing (EUCAST) and Clinical Laboratory Standards Institute (CLSI) continue to recommend culture-based techniques as the gold standard for verifying antimicrobial resistance [2]. This endorsement reflects the comprehensive nature of phenotypic testing, which captures the net effect of all resistance mechanisms—known and novel—operating within a bacterial isolate, unlike targeted molecular assays that detect only predefined genetic markers.

Public Health and Epidemiological Applications

Microbial culture generates invaluable resources for public health surveillance and outbreak investigation. Culture isolates provide essential material for molecular subtyping, genome sequencing, and tracking antimicrobial resistance patterns across healthcare networks and communities [1]. The CDC explicitly encourages reflex culture—culturing specimens with positive culture-independent diagnostic test (CIDT) results—for bacteria of public health importance, including Campylobacter, Salmonella, Shigella, Shiga toxin-producing Escherichia coli, Vibrio, and Yersinia infections [1] [5].

This practice ensures the availability of isolates for further characterization during outbreak investigations and for monitoring emerging resistance trends. The ability to bank viable isolates for future study represents a unique advantage of culture-based methods, enabling retrospective analyses as new questions or technologies emerge.

Limitations and Challenges in Modern Practice

Technical and Temporal Constraints

The most frequently cited limitations of microbial culture include prolonged turnaround times, intensive labor requirements, and suboptimal sensitivity for fastidious or slow-growing organisms [1]. Culture methods typically require 24-72 hours for initial isolation, with additional time needed for identification and AST, resulting in a total processing time of 2-5 days for most bacterial pathogens [2]. For mycobacteria and fungi, incubation periods extend to several weeks, significantly delaying diagnosis and appropriate treatment initiation [1].

The labor-intensive nature of culture techniques demands significant technical expertise and hands-on processing time. This requirement, combined with the extended time to results, contributes to higher overall costs despite lower per-test expenses when considering the broader context of prolonged hospital stays and delayed targeted therapy [2].

Pre-analytical and Biological Limitations

Culture success is profoundly influenced by pre-analytical variables, including specimen collection quality, transport conditions, and prior antibiotic exposure [3]. The Global Enteric Multicenter Study (GEMS) demonstrated that quantitative PCR detected approximately twice as many Campylobacter infections compared to traditional microbiological methods, with similar disparities observed for other gastrointestinal pathogens including adenovirus, Shigella, and heat-stable enterotoxin-producing Escherichia coli [1].

Approximately 20-50% of patients with clear clinical evidence of periprosthetic joint infection yield negative culture results, creating the significant diagnostic and therapeutic challenge of "culture-negative PJI" [3]. This limited sensitivity stems from multiple factors, including insufficient sample size, prior antibiotic exposure, suboptimal culture techniques, and the unique biological characteristics of certain pathogens, particularly those within biofilms [3].

Integrated Diagnostic Approaches: Complementarity of Methods

The evolving diagnostic landscape increasingly favors a synergistic approach that leverages the respective strengths of both culture and molecular methods. This integrated paradigm recognizes that these technologies answer different but complementary clinical questions.

Workflow Comparison and Integration Opportunities

The following diagram illustrates the comparative workflows of culture versus mNGS, highlighting integration points for optimized diagnostic pathways:

Diagram 1: Comparative diagnostic workflows for microbial culture and mNGS, highlighting integration points for comprehensive pathogen detection and characterization. The yellow nodes represent critical integration opportunities between the two methodologies.

Factors Influencing Diagnostic Discordance

Understanding the clinical and technical factors that contribute to discrepant results between culture and molecular methods is essential for appropriate test interpretation and selection:

Diagram 2: Key factors contributing to diagnostic discordance between culture and mNGS, with odds ratios (OR) and 95% confidence intervals (CI) from clinical studies [3]. Protective factors are shown in green, while risk factors are shown in red.

Essential Research Reagent Solutions

The implementation of both traditional culture and modern molecular diagnostics requires specialized reagents and materials. The following table outlines key solutions essential for conducting comparative studies in clinical microbiology:

Table 3: Essential Research Reagents for Microbial Culture and Molecular Diagnostics

Reagent/Material	Application	Function	Implementation Example
Selective & Enrichment Media	Microbial Culture	Supports growth of specific pathogens while inhibiting contaminants	Bactec Plus/F aerobic and anaerobic blood culture bottles [3]
MALDI-TOF MS Reagents	Pathogen Identification	Enables rapid species identification from pure colonies	Matrix compounds for mass spectrometry profiling [2]
Antimicrobial Disks & Strips	Antimicrobial Susceptibility Testing	Determines phenotypic resistance profiles	EUCAST/CLSI-compliant antibiotic gradient strips [2]
Nucleic Acid Extraction Kits	mNGS & Molecular Methods	Isolves pathogen DNA/RNA from clinical samples	Commercial kits for bacterial, fungal, viral nucleic acids [4]
Library Preparation Kits	mNGS	Prepares sequencing libraries from extracted nucleic acids	Fragmentation, adapter ligation, and amplification reagents [4]
Bioinformatic Databases	mNGS Data Analysis	Reference databases for pathogen identification	CARD, MegaRes, NDARO for AMR gene detection [2]

Microbial culture maintains indispensable strengths in antimicrobial susceptibility testing, isolate banking, and diagnostic specificity that secure its ongoing role in clinical microbiology. However, the demonstrated superior sensitivity and accelerated turnaround of molecular methods, particularly mNGS, across diverse clinical contexts—from neurosurgical infections to periprosthetic joint infections—necessitates a redefinition of the diagnostic paradigm.

The future of pathogen detection lies not in the replacement of culture by molecular methods, but in their strategic integration. An optimized diagnostic pathway leverages mNGS for rapid, comprehensive pathogen detection, especially in complex cases, culture-negative scenarios, and when fastidious or rare pathogens are suspected, while reserving culture for essential phenotypic characterization and public health functions. This complementary approach maximizes diagnostic accuracy while preserving critical functionalities, ultimately advancing patient care through more precise and timely infectious disease diagnosis.

Metagenomic Next-Generation Sequencing (mNGS) represents a transformative approach in clinical microbiology, enabling the unbiased, high-throughput detection and characterization of pathogens directly from clinical samples. Unlike traditional, targeted methods that require a priori assumptions about the causative agent, mNGS operates as a comprehensive, hypothesis-free screening tool that can simultaneously identify bacteria, viruses, fungi, and parasites [9] [10]. This technology has emerged as a powerful alternative and complement to conventional culture-based methods, particularly in cases involving rare, novel, or unculturable pathogens, as well as complex polymicrobial infections [9]. The fundamental principle underpinning mNGS is shotgun sequencing of all nucleic acids present in a sample, followed by sophisticated bioinformatic analysis to distinguish microbial sequences from those of the host [10]. As infectious diseases remain a leading cause of global morbidity and mortality, accounting for more than five million deaths annually including approximately 1.27 million attributed to antimicrobial-resistant infections, the need for rapid, accurate diagnostic methods has never been more pressing [9]. This review provides a comprehensive comparison between mNGS and conventional culture techniques, examining their respective performances, underlying methodologies, and applications within modern clinical and research contexts.

Performance Comparison: mNGS Versus Conventional Culture

Quantitative comparisons across multiple clinical studies and sample types consistently demonstrate that mNGS exhibits significantly higher sensitivity for pathogen detection compared to conventional culture methods, though culture often maintains advantages in specificity for certain applications.

Table 1: Comparative Detection Rates of mNGS vs. Conventional Culture

Sample Type	Study/Context	mNGS Positive Rate	Culture Positive Rate	Statistical Significance
Organ Preservation Fluid	Kidney Transplantation (n=141)	47.5% (67/141)	24.8% (35/141)	p < 0.05 [11]
Wound Drainage Fluid	Kidney Transplantation (n=141)	27.0% (38/141)	2.1% (3/141)	p < 0.05 [11]
Spinal Infection	Meta-analysis (10 studies, n=770)	Sensitivity: 0.81 (95% CI: 0.74-0.87)	Sensitivity: 0.34 (95% CI: 0.27-0.43)	AUC: 0.85 vs. 0.59 [6]
ESKAPE Pathogens & Fungi	Kidney Transplantation (n=141)	28.4% (40/141)	16.3% (23/141)	p < 0.05 [11]

The superior detection capability of mNGS is particularly evident in challenging clinical scenarios. For spinal infections, a meta-analysis of 10 studies revealed mNGS had more than double the sensitivity of traditional tissue culture techniques (0.81 vs. 0.34) [6]. Similarly, in immunocompromised populations such as kidney transplant recipients, mNGS detected clinically atypical pathogens (including Mycobacterium, Clostridium tetani, and parasites) that were completely missed by conventional culture methods [11]. This enhanced detection capacity directly impacts clinical management, enabling earlier targeted antimicrobial therapy and improved patient outcomes.

However, conventional culture maintains certain advantages, particularly regarding specificity (0.75 for mNGS vs. 0.93 for tissue culture technique in spinal infection diagnosis) [6]. Culture also provides essential information about antibiotic susceptibility through phenotypic testing, which remains crucial for guiding antimicrobial therapy [2]. Furthermore, mNGS demonstrates variable performance across different microbial groups, detecting 79.2% of Enterobacteriaceae and non-fermenting bacteria identified by culture, but only 22.2% of Gram-positive bacteria and 55.6% of fungi [11]. This variability highlights the complementary nature of these techniques rather than a simple replacement paradigm.

Technical Foundations and Workflows

Laboratory (Wet Lab) Procedures

The mNGS workflow comprises two main components: laboratory wet bench procedures and bioinformatic analysis. Sample collection varies by suspected infection site, with bronchoalveolar lavage fluid (BALF) typically used for pulmonary infections, cerebrospinal fluid (CSF) for central nervous system infections, and tissue or fluid samples for localized infections [10]. A critical preprocessing step involves removing human cells through centrifugation, particularly important for samples with high host DNA content that can dilute microbial signals [11]. Nucleic acid extraction then follows using commercial kits such as the QIAamp DNA Micro Kit, which efficiently isolates cell-free DNA from the supernatant [11]. For comprehensive pathogen detection, many protocols extract both DNA and RNA, with the latter requiring reverse transcription to cDNA to enable sequence-based identification of RNA viruses [10].

Library preparation employs transposase-based methods that fragment DNA and add adapter sequences simultaneously, streamlining the process for clinical applications [12]. The constructed libraries undergo quality control checks before high-throughput sequencing on platforms such as the Illumina NextSeq 550, which typically generates 10-20 million reads per sample at 75-base pair single-end reads [13] [12]. Throughout this process, including negative controls (non-template controls) is essential to identify potential contamination, while positive controls help monitor assay performance [11].

Bioinformatics (Dry Lab) Analysis

The bioinformatics pipeline begins with quality control of raw sequencing data, removing adapter sequences and filtering low-quality reads (<35bp) using tools like Trimmomatic [11]. A critical subsequent step involves host sequence depletion, wherein reads aligning to the human reference genome (GRCh38) are removed using alignment tools such as Bowtie2 [11] [13]. The remaining non-human reads are then classified through alignment to comprehensive microbial databases (e.g., NCBI nt database) using tools like BLASTN or Kraken2 [11] [13]. Positive identification criteria typically combine thresholds based on relative abundance (e.g., pathogens ranking in the top 10 of their microbial category) and comparative analysis with negative controls (e.g., sample-to-control read ratio >10:1) [11].

Conventional Culture Techniques

Traditional culture methods remain the gold standard in clinical microbiology for viable pathogen isolation. Samples are typically inoculated into liquid culture media (e.g., BD BACTEC Plus Aerobic/F bottles) and loaded into automated continuous-monitoring blood culture systems such as the BD BACTEC FX instrument [11]. Following positive signal detection, culture broth undergoes Gram staining and subculture onto solid media (blood agar plates), with incubation for 18-24 hours at 35±1°C in 5% CO₂ [11]. For fungal detection, additional subculturing on Sabouraud dextrose agar (SDA) occurs with extended incubation at 37°C for 48 hours [11]. Microbial identification traditionally relied on phenotypic characteristics but has largely been replaced by Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS), which generates species-specific protein profiles for rapid, accurate identification [11]. The primary advantages of culture include its ability to provide living isolates for antibiotic susceptibility testing and its low consumable costs, though these benefits are counterbalanced by prolonged turnaround times (days to weeks) and significantly lower sensitivity compared to molecular methods [2].

Advanced Applications and Dual-Function Capability

Beyond pathogen identification, mNGS offers several advanced applications that enhance its utility in clinical and research settings. The technology demonstrates remarkable capability in detecting antimicrobial resistance (AMR) genes, although this application requires careful interpretation. Unlike phenotypic culture methods that directly measure antibiotic susceptibility, mNGS identifies known resistance genes bioinformatically using tools like the Resistance Gene Identifier (RGI) aligned against databases such as the Comprehensive Antibiotic Resistance Database (CARD) [2]. This approach enables prediction of resistance profiles but has limitations, as the presence of a resistance gene does not necessarily equate to phenotypic expression, and novel resistance mechanisms may escape detection [2].

A particularly innovative application of mNGS leverages the abundant host-derived reads typically considered background noise. By analyzing copy number variations (CNVs) from host DNA sequences in BALF samples, mNGS can simultaneously screen for malignancies while investigating infectious etiologies [13]. This dual-function capability is especially valuable in diagnostically challenging cases such as pulmonary lesions where infection and cancer present with overlapping clinical features. In one prospective study, CNV analysis demonstrated moderate sensitivity (38.9%) but perfect specificity (100%) for lung cancer diagnosis, identifying four cases initially considered pneumonia [13]. When combined with cytology, the sensitivity for malignancy detection increased from 38.9% to 55.6%, highlighting the complementary value of this approach [13].

Table 2: Research Reagent Solutions for mNGS Workflow

Reagent/Kit	Manufacturer	Function in Workflow
QIAamp DNA Micro Kit	QIAGEN	Extraction of cell-free DNA from clinical samples [11]
IDSeq Micro DNA Kit	Vision Medicals	Integrated DNA extraction and purification for mNGS [12]
BD BACTEC Plus Aerobic/F Culture Bottles	Becton Dickinson	Enrichment of viable microorganisms for conventional culture [11]
Blood Agar Plates	BIOIVT	Solid media for isolation and colony formation of bacteria [11]
SDA Agar Plates	BIOIVT	Selective media for fungal isolation and identification [11]
Total DNA Library Preparation Kit	MatriDx Biotech	Library construction for Illumina sequencing platforms [13]

Limitations and Implementation Challenges

Despite its considerable advantages, mNGS faces several technical and practical challenges that impact routine clinical implementation. The technology demonstrates variable sensitivity across different pathogen types, with particularly reduced performance for Gram-positive bacteria (detecting only 22.2% of culture-identified Gram-positive organisms) and fungi (55.6% detection rate) [11]. This variability may stem from differences in cell wall structure affecting DNA extraction efficiency or competition from more abundant microorganisms in polymicrobial samples.

The high abundance of host DNA in clinical samples presents another significant challenge, often comprising over 95% of total sequenced DNA and potentially obscuring low-abundance pathogens [9]. While host depletion strategies exist, they risk simultaneously removing pathogens that reside intracellularly [9]. Bioinformatic subtraction of human sequences represents an alternative approach but requires substantial computational resources and may decrease sensitivity for pathogens with genomic similarity to human DNA [11].

Economic considerations also impact mNGS implementation, with higher per-test costs compared to conventional methods [10]. However, a comprehensive economic analysis should account for potential cost savings from reduced hospital stays, targeted antimicrobial therapy, and improved outbreak containment [9]. Additionally, the complexity of data interpretation requires specialized expertise, as sequencing detects both pathogens and environmental contaminants, necessitating careful clinical correlation to distinguish true infections from colonization or sample contamination [11] [10].

The evolution of mNGS technology continues to address current limitations through several promising avenues. Third-generation sequencing platforms, particularly Oxford Nanopore Technologies, offer advantages in real-time sequencing with portable form factors that enable point-of-care applications [9] [14]. While these platforms currently have higher error rates than Illumina systems, their ability to generate long reads facilitates assembly of complete genomes and detection of structural variants [14]. Integration of artificial intelligence and machine learning into bioinformatic pipelines shows considerable promise for automating taxonomic classification, predicting antibiotic resistance from genomic data, and identifying pathogenic signatures in complex metagenomic datasets [9].

The combination of mNGS with host response biomarkers represents another emerging frontier. Analyzing host gene expression patterns alongside microbial detection may help distinguish colonization from true infection and provide prognostic information about disease severity [9]. Multi-omics approaches that integrate metagenomics with metatranscriptomics, proteomics, and metabolomics offer complementary insights into microbial activity, host-pathogen interactions, and functional pathways contributing to pathogenesis [9].

In conclusion, mNGS represents a paradigm shift in pathogen detection, offering unprecedented, unbiased detection capabilities that complement rather than replace conventional culture methods. While technical challenges remain regarding sensitivity for certain pathogen groups, standardization, and cost-effectiveness, the technology has unequivocally demonstrated its value in diagnosing challenging infections, detecting antimicrobial resistance genes, and even facilitating dual diagnosis of infections and malignancies. As sequencing costs continue to decline and analytical methods improve, mNGS is poised to become an increasingly integral component of the clinical microbiology landscape, ultimately enabling more precise, personalized infectious disease management. For clinical and research applications, the optimal diagnostic approach likely involves a synergistic combination of mNGS for broad pathogen detection and culture for phenotypic antibiotic susceptibility testing, leveraging the respective strengths of both methodologies to optimize patient care and public health responses to infectious disease threats.

The accurate and timely identification of pathogens is a cornerstone of effective infectious disease management, influencing patient outcomes and antimicrobial stewardship. For over a century, conventional culture-based methods have served as the cornerstone of microbiological diagnosis. However, the emergence of metagenomic next-generation sequencing (mNGS) represents a paradigm shift in diagnostic microbiology, offering a hypothesis-free approach to pathogen detection. This comparison guide provides an objective evaluation of these two methodologies, framing their performance within key diagnostic metrics including sensitivity, specificity, and turnaround time. The analysis is contextualized for researchers, scientists, and drug development professionals engaged in advancing pathogen detection technologies and developing novel therapeutic interventions.

Comparative Performance Metrics: mNGS vs. Culture

The diagnostic performance of mNGS and culture methods has been extensively evaluated across diverse clinical syndromes and sample types. The table below summarizes pooled data from recent meta-analyses and studies, providing a comprehensive overview of their comparative accuracy.

Table 1: Diagnostic performance of mNGS versus culture across different infection types

Infection Type	Method	Sensitivity (Pooled, 95% CI)	Specificity (Pooled, 95% CI)	Area Under Curve (AUC)	Key References
Periprosthetic Joint Infection	mNGS	0.89 (0.84–0.93)	0.92 (0.89–0.95)	0.935	[15]
	Targeted NGS	0.84 (0.74–0.91)	0.97 (0.88–0.99)	0.911	[15]
Infected Pancreatic Necrosis	mNGS	0.87 (0.72–0.95)	0.83 (0.69–0.91)	0.92	[16] [17]
	Culture	0.36 (0.23–0.51)	0.83 (0.67–0.92)	0.52	[16] [17]
Spinal Infection	mNGS	0.81 (0.74–0.87)	0.75 (0.48–0.91)	0.85	[6]
	Tissue Culture	0.34 (0.27–0.43)	0.93 (0.79–0.98)	0.59	[6]
Neurosurgical CNS Infection	mNGS	86.6%*	-	-	[18]
	Culture	59.1%*	-	-	[18]
Sepsis (PISTE Technology)	NGS Workflow	91.7%*	96.5%*	95.7% (Accuracy)	[19] [20]

*Reported as overall detection rate or accuracy in single study; not a pooled estimate.

The data consistently demonstrate the superior sensitivity of mNGS across various infection types, particularly in complex scenarios such as infected pancreatic necrosis and spinal infections, where culture sensitivity falls markedly below 40% [16] [17] [6]. The "superior sensitivity" of mNGS is crucial for detecting fastidious, slow-growing, and previously antibiotic-exposed pathogens that often elude culture [9]. Conversely, culture methods maintain high specificity, and targeted NGS approaches can achieve even higher specificity (0.97), making them valuable for confirming infections when positive [15].

Workflow and Turnaround Time Comparison

A critical differentiator between these methodologies is the workflow complexity and time to result, which directly impacts clinical decision-making.

Table 2: Comparative workflow and turnaround time analysis

Process Stage	Conventional Culture	Metagenomic NGS
Sample Processing	Inoculation into culture media (minutes)	Host DNA depletion, nucleic acid extraction (2-4 hours)
Incubation/Amplification	24-72 hours for bacterial growth; longer for fungi & mycobacteria	Library preparation (4-8 hours)
Pathogen Identification	Subculturing, staining, biochemical tests (6-24 hours post-growth)	Sequencing run (12-24 hours, varies by platform)
Data Analysis	Manual interpretation	Bioinformatics pipeline: host sequence filtering, taxonomic classification (2-6 hours)
Additional Output	Phenotypic Antimicrobial Susceptibility Testing (AST) (additional 16-24 hours)	Prediction of antimicrobial resistance genes (integrated with analysis)
Total Turnaround Time	48 - 96+ hours (often 3-5 days)	~24 - 36 hours (can be <12h with targeted/rapid protocols)

The sequencing-based PISTE workflow for sepsis demonstrated a median time to result of 12.0 hours, a significant improvement over the 30.4 hours required for standard culture and AST [19] [20]. For central nervous system infections, the time from sample harvesting to final result was also significantly shorter for mNGS (16.8 hours) and ddPCR (12.4 hours) compared to culture (22.6 hours) [18]. This rapid turnaround is vital in life-threatening conditions like sepsis and meningitis.

The following diagram illustrates the core procedural and logical differences between the two diagnostic pathways:

Key Experimental Protocols and Methodologies

Conventional Culture Protocol

The standard culture workflow remains the benchmark for pathogen identification and phenotypic antimicrobial susceptibility testing (AST).

• Sample Processing: Samples (e.g., tissue, fluid) are inoculated into aerobic and anaerobic culture media. Blood samples are typically incubated in automated systems like BACTEC FX or BACT/ALERT VIRTUO [21] [19].
• Incubation and Monitoring: Cultures are incubated at 37°C. Automated systems continuously monitor for microbial growth, recording the Time to Positivity (TTP). The mean TTP for neurosurgical CNS infections, for example, was reported as 15.1 ± 10.4 hours [18].
• Pathogen Identification: Upon positivity, samples are subcultured on solid agar. Isolated colonies are identified using techniques like Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) [21].
• Antimicrobial Susceptibility Testing (AST): Pure isolates are subjected to phenotypic AST using systems like BD Phoenix, which provides minimum inhibitory concentration (MIC) profiles, typically requiring an additional 16-24 hours [19].

Metagenomic NGS Protocol

The mNGS workflow involves a sequence of complex steps from sample preparation to bioinformatic analysis, with variations depending on the platform (e.g., Illumina, Oxford Nanopore).

• Sample Pre-processing and Nucleic Acid Extraction: Critical steps to maximize microbial nucleic acid yield. For liquid samples, centrifugation separates cells from the supernatant, with the pellet often used for DNA extraction. A key advancement is host DNA depletion using enzymes like Benzonase to enrich for microbial sequences and improve sensitivity [9] [18]. Total DNA is then extracted using commercial kits (e.g., QIAamp DNA Micro Kit) [21].
• Library Preparation and Sequencing: Extracted DNA is fragmented and adapter-ligated to create a sequencing library. For the PISTE workflow in sepsis, this involves both full-length 16S rRNA gene sequencing for rapid identification and metagenomic sequencing for resistance profiling, sequenced on an Oxford Nanopore GridION device [19] [20].
• Bioinformatic Analysis: This is a critical differentiator. The pipeline typically involves:
  • Quality Control & Host Filtering: Raw reads are trimmed and filtered for quality. Human sequence reads are aligned to a reference genome (e.g., GRCh38) and removed [21] [9].
  • Taxonomic Classification: Non-host reads are aligned against comprehensive microbial databases (e.g., NCBI nt) using tools like BLASTN to identify pathogens [21].
  • Interpretation and Reporting: Criteria for a positive result often consider metrics like Reads Per Million (RPM) and comparison to negative controls to distinguish true pathogens from contamination [21].

Essential Research Reagent Solutions

The following table details key reagents and materials essential for executing the mNGS and culture protocols described in the featured studies.

Table 3: Key research reagents and materials for pathogen detection workflows

Category	Reagent / Kit / Instrument	Primary Function in Workflow	Example Use Case
Culture Media	BD BACTEC Plus Aerobic/F Media; bioMérieux BACT/ALERT FA Plus	Nutrient-rich broth for amplification of viable pathogens from blood.	Blood culture in sepsis diagnosis [21] [19].
Identification System	MALDI-TOF MS (Bruker Daltonics)	Rapid protein-based identification of microbial colonies from culture.	Identification of bacteria and yeast from positive blood cultures [21].
AST System	BD Phoenix Automated System	Automated phenotypic antimicrobial susceptibility testing.	Determining resistance profiles of bacterial isolates [19].
Nucleic Acid Extraction	QIAamp DNA Micro Kit (QIAGEN); MagMax Microbiome Ultra II Kit	Isolation of high-quality total DNA from clinical samples.	DNA extraction from preservation fluids, drainage fluids, and blood [21] [19].
Host Depletion	Benzonase (Sigma); Tween-20	Enzymatic degradation of host nucleic acids to increase microbial sequencing depth.	Treatment of CSF pellets prior to DNA extraction [18].
Sequencing Platform	Illumina NextSeq 550; Oxford Nanopore GridION Mk1/MinION	High-throughput sequencing of DNA libraries.	mNGS of clinical samples; rapid sequencing for PISTE workflow [21] [19].
Bioinformatic Tools	BLASTN, Bowtie2, Trimmomatic, Kneaddata, IDSeq	Quality control, host read removal, and taxonomic classification of sequence data.	Analysis pipeline for mNGS data against NCBI databases [21] [9].

This comparative analysis demonstrates that mNGS and culture are complementary rather than mutually exclusive technologies. mNGS offers a powerful, broad-spectrum detection capability with superior sensitivity and faster turnaround times, proving particularly valuable for diagnosing culture-negative, polymicrobial, and rare infections [9] [22] [17]. Culture remains indispensable for providing phenotypic antimicrobial susceptibility data and confirming viable pathogens. The choice between methodologies—or the decision to use them in tandem—depends on the clinical context, the specific pathogens suspected, the need for rapidity versus AST, and economic considerations. Future directions point toward the integration of AI-driven analysis, point-of-care sequencing devices, and multi-omics approaches to further refine the precision and speed of infectious disease diagnostics [9]. For the research and drug development community, understanding these comparative frameworks is essential for innovating new diagnostic solutions and tailoring therapeutic strategies to the evolving landscape of clinical microbiology.

The accurate and timely identification of pathogens is a cornerstone of effective infectious disease management. For over a century, conventional microbial culture has served as the fundamental method for pathogen detection in clinical microbiology laboratories. However, the diagnostic landscape is rapidly evolving with the emergence of metagenomic next-generation sequencing (mNGS), which offers a hypothesis-free approach to detecting a broad spectrum of pathogens directly from clinical specimens [9]. This transformative technology enables simultaneous detection of bacteria, viruses, fungi, and parasites without prior knowledge of the causative agent, making it particularly valuable for diagnosing unusual, fastidious, or polymicrobial infections [9] [23].

The integration of mNGS into clinical practice represents a paradigm shift in diagnostic microbiology, prompting critical evaluation of its performance relative to established culture-based methods. Understanding the complementary strengths and limitations of these techniques is essential for researchers, clinical microbiologists, and infectious disease specialists seeking to optimize diagnostic pathways. This guide provides a comprehensive, evidence-based comparison of mNGS and culture methodologies, drawing upon recent clinical studies to elucidate their respective roles in modern pathogen detection.

Performance Comparison: mNGS vs. Culture

Multiple clinical studies across diverse patient populations and specimen types have demonstrated significant differences in the diagnostic performance of mNGS compared to conventional culture methods. The table below summarizes key performance metrics from recent investigations.

Table 1: Overall Diagnostic Performance of mNGS vs. Culture

Study & Population	Sample Size	Sensitivity (%)	Specificity (%)	Key Findings
Febrile patients with suspected infections [24]	368 patients	58.01 (mNGS) vs. 21.65 (Culture)	85.40 (mNGS) vs. 99.27 (Culture)	mNGS significantly more sensitive; culture more specific
Severe infections [25]	180 patients	78.89 (mNGS) vs. 20.00 (CMT)	Not specified	mNGS etiological diagnosis rate significantly higher
Spinal infections [6]	770 patients (10 studies)	81 (mNGS) vs. 34 (Culture)	75 (mNGS) vs. 93 (Culture)	mNGS had higher sensitivity but lower specificity
Infected pancreatic necrosis [17]	313 patients (7 studies)	87 (mNGS) vs. 36 (Culture)	83 (both methods)	mNGS demonstrated superior sensitivity with equal specificity

The performance characteristics of both methods vary significantly across different clinical contexts and specimen types. The following table provides a detailed breakdown of their application-specific performance.

Table 2: Application-Specific Performance of mNGS and Culture

Clinical Application	Sample Types	mNGS Advantages	Culture Advantages
Tuberculosis diagnosis [12]	Respiratory samples, extrapulmonary samples	High agreement with RT-PCR; detects MTB regardless of viability	Drug susceptibility testing capability
Organ transplantation [21]	Preservation fluids, drainage fluids	Higher detection rate (47.5% vs. 24.8%); detects atypical pathogens	Better detection of Gram-positive bacteria and fungi
Lower respiratory tract infections [26]	Sputum, BALF	Superior sensitivity (95.35% vs. 81.08%); broader pathogen coverage	Established interpretation standards
Body fluid infections [27]	Pleural, ascites, CSF, drainage fluids	Higher sensitivity; identifies mixed infections	Specificity remains higher for culture

Experimental Approaches and Methodologies

Conventional Culture Techniques

Traditional culture methods remain the cornerstone of pathogen identification in clinical microbiology, relying on the growth and propagation of microorganisms in controlled laboratory conditions.

Standard Culture Protocol: The general methodology involves inoculating clinical specimens onto selective and non-selective media, followed by incubation under appropriate atmospheric conditions [24]. For blood cultures, samples are typically added to aerobic and anaerobic culture bottles and monitored in automated continuous-monitoring blood culture systems [25]. Positive cultures are subcultured onto solid media to obtain isolated colonies, which are then identified using techniques such as matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) [24] [21]. Antibiotic susceptibility testing is performed using methods like the VITEK II compact system with Clinical and Laboratory Standards Institute (CLSI) guidelines [24].

Limitations and Considerations: Culture-based methods face several challenges, including prolonged turnaround times (typically 1-5 days for most bacteria, longer for slow-growing organisms like fungi and mycobacteria) [24]. The sensitivity of culture is significantly diminished in patients with prior antibiotic exposure [24]. Additionally, many pathogens are difficult to cultivate using standard techniques, including viruses, fastidious bacteria, and intracellular pathogens [9].

Metagenomic Next-Generation Sequencing

mNGS represents a culture-independent approach that enables comprehensive detection of pathogens by sequencing all nucleic acids in a clinical sample.

DNA Extraction and Library Preparation: The typical workflow begins with sample processing, which varies by specimen type. For whole-cell DNA (wcDNA) extraction, samples undergo mechanical or enzymatic lysis followed by nucleic acid purification using commercial kits [24] [12]. For cell-free DNA (cfDNA) analysis, samples are centrifuged to remove intact cells, and DNA is extracted from the supernatant [27] [21]. Extracted DNA is then processed for library preparation using transposase-based or ligation-based methods [24] [12]. The quality and quantity of DNA libraries are assessed before sequencing [24].

Sequencing and Bioinformatics Analysis: Libraries are sequenced on platforms such as the Illumina NextSeq 550 or NovaSeq series [24] [12]. Bioinformatic analysis involves multiple steps: (1) removal of adapter sequences and low-quality reads; (2) alignment to the human reference genome (hg38) to eliminate host-derived sequences; (3) alignment of non-human reads to comprehensive microbial databases containing bacterial, viral, fungal, and parasitic genomes [24] [25] [12]. Pathogen identification relies on metrics such as standardized microbial read numbers (SMRNs), genome coverage, and comparison to negative controls [12] [21].

Comparative Study Designs

Recent comparative analyses have employed various methodological approaches to evaluate mNGS versus culture:

Retrospective Studies: Most comparisons utilize retrospective designs where paired samples from the same patients undergo parallel testing by both mNGS and culture methods [24] [25]. This approach allows for direct comparison of detection rates, sensitivity, specificity, and clinical impact.

Meta-Analyses: Systematic reviews and meta-analyses provide pooled performance estimates across multiple studies. For example, a meta-analysis of spinal infections included 10 studies with 770 patients [6], while another analysis of infected pancreatic necrosis encompassed 7 studies with 313 patients [17]. These analyses employ rigorous statistical models to calculate pooled sensitivity, specificity, and areas under summary receiver operating characteristic curves.

Integrated Diagnostic Pathways: Advanced studies evaluate how mNGS and culture can be strategically combined in diagnostic algorithms. This includes assessing how mNGS results influence antibiotic therapy adjustments and patient outcomes [24] [25].

Integrated Diagnostic Pathways

The complementary strengths of mNGS and culture suggest that an integrated approach maximizes diagnostic yield across different clinical scenarios. The following diagram illustrates a potential diagnostic pathway that strategically employs both methods.

Essential Research Reagents and Materials

The implementation of mNGS and culture methods in research settings requires specific reagents and instrumentation. The following table details key solutions and their applications in experimental workflows.

Table 3: Essential Research Reagents for Pathogen Detection Studies

Reagent/Category	Specific Examples	Research Application	Function in Workflow
Nucleic Acid Extraction Kits	QIAamp DNA Micro Kit (QIAGEN), IDSeq Micro DNA Kit (Vision Medicals)	DNA extraction from clinical samples	Isolates microbial nucleic acids for downstream mNGS analysis
Library Preparation Kits	QIAseq Ultralow Input Library Kit (QIAGEN), PMseq High-throughput DNA Detection Kit (Huada)	NGS library construction	Prepares DNA fragments for sequencing by adding adapters
Culture Media	Blood agar, Chocolate agar, China Blue agar, Selective media	Microbial cultivation	Supports growth of specific pathogens from clinical specimens
Automated Culture Systems	BD BACTEC FX system, VITEK II compact system	Pathogen cultivation and identification	Automated monitoring of culture bottles; identification and AST of isolates
Sequencing Platforms	Illumina NextSeq 550, NovaSeq, Oxford Nanopore technologies	High-throughput sequencing	Generates sequencing reads from prepared libraries
Bioinformatic Tools	SNAP, BWA, Trimmomatic, BLASTN, Pavian	Data analysis and pathogen identification	Processes sequencing data; removes host reads; identifies microbial species

The evolving diagnostic landscape for infectious diseases is characterized by a complementary relationship between mNGS and culture methods. Evidence from multiple clinical studies demonstrates that mNGS offers superior sensitivity and broader pathogen detection capability, particularly for fastidious, rare, or polymicrobial infections [24] [9] [17]. Conversely, culture maintains advantages in specificity and provides essential antibiotic susceptibility data that guides targeted therapy [24] [21].

The integration of both methods into diagnostic algorithms represents the most effective approach to pathogen detection in modern medicine. mNGS serves as a powerful tool for initial comprehensive pathogen screening, especially in complex, critical, or culture-negative cases [25] [9]. Culture remains indispensable for confirming viable pathogens, conducting antimicrobial susceptibility testing, and validating mNGS findings [21]. Future advancements in sequencing technologies, bioinformatic analysis, and standardization of methodologies will further refine their respective roles, ultimately enhancing patient care through improved diagnostic accuracy and timeliness.

From Lab to Clinic: Workflows and Real-World Applications

Metagenomic Next-Generation Sequencing (mNGS) is revolutionizing pathogen detection by enabling hypothesis-free, broad-spectrum identification of microorganisms directly from clinical samples. Unlike traditional culture-based methods, which are limited to culturable pathogens and require prolonged incubation periods, mNGS can simultaneously detect bacteria, viruses, fungi, and parasites within a single assay [9]. This capability is particularly valuable in complex clinical scenarios such as post-transplant infections, sepsis, and immunocompromised patients where rapid, accurate pathogen identification is critical for treatment success. This guide provides a comprehensive breakdown of the mNGS workflow, from initial sample collection to final bioinformatic analysis, and offers a detailed comparison with conventional culture methods to highlight the relative strengths and limitations of each approach in clinical diagnostics.

Materials and Methods: Experimental Protocols

Sample Collection and Processing

The foundation of a reliable mNGS assay begins with proper sample collection and processing. In studies comparing mNGS with conventional culture, samples typically include organ preservation fluids, wound drainage fluids, bronchoalveolar lavage fluid (BALF), and other clinical specimens [21] [13]. Standardized protocols are essential for maintaining consistency.

• Sample Collection: For kidney transplantation studies, 10 mL of organ preservation fluid is collected directly from the preparation table, while 10 mL of recipient wound drainage fluid is collected from drainage bags on postoperative days 1, 2, and 3 [21] [11]. For pulmonary diagnostics, BALF samples exceeding 5 mL are collected via bronchoscopy and immediately transported on dry ice [13].
• Sample Pre-treatment: To enhance sensitivity for detecting intestinal colonization, some protocols incorporate a selective pre-enrichment step in broth culture before sequencing. This approach significantly increases the abundance of target pathogens and improves the detection of antimicrobial resistance genes [28].
• Host DNA Depletion: Clinical samples often contain high levels of human DNA that can overwhelm microbial signals. Centrifugation steps are commonly used to remove human cells, and cell-free DNA (cfDNA) is then extracted from the supernatant using commercial kits such as the QIAamp DNA Micro Kit [21] [11]. This step is critical for improving the assay's sensitivity, particularly in low-biomass samples.

Nucleic Acid Extraction and Library Preparation

The core of mNGS workflow involves converting extracted nucleic acids into sequence-ready libraries.

• DNA Extraction: Following host depletion, total DNA is extracted from the supernatant using standardized kits. DNA concentration and quality are measured using fluorometric methods such as Qubit 4.0 [21] [11].
• Library Construction: Extracted DNA undergoes library preparation, which involves fragmenting DNA, adapter ligation, and amplification. Automated systems, such as the NGS Automatic Library Preparation System, are increasingly used to standardize this process and reduce manual variability [13]. Libraries are typically sequenced on platforms like the Illumina NextSeq 550 or NextSeq500, generating 10-20 million reads per sample [21] [13].
• Quality Controls: Each sequencing run includes negative controls (non-template controls, NTC) to identify potential contamination and positive controls to monitor assay performance [21]. The inclusion of internal spike-in controls verifies the efficiency of nucleic acid extraction and library preparation [13].

Bioinformatic Analysis Pipeline

The bioinformatic analysis transforms raw sequencing data into clinically interpretable results through a multi-step computational process.

• Raw Data Preprocessing: Initial quality control involves adapter trimming and filtering of low-quality reads (e.g., <35bp) using tools like Trimmomatic [21] [11].
• Host Sequence Removal: Quality-controlled reads are aligned to the human reference genome (GRCh38 or hg19) using alignment tools such as Bowtie2, and human-derived reads are subtracted from the dataset [21] [13].
• Pathogen Detection and Classification: Non-human reads are aligned against comprehensive microbial databases using classification tools like Kraken2 and validated with BLASTN for precise taxonomic assignment [21] [13].
• Positive Criteria Establishment: Detection thresholds are set to distinguish true pathogens from background noise. Criteria may include requiring the microorganism to rank in the top 10 by genome coverage for its category and/or demonstrating a significant read count ratio compared to negative controls [21].

Table 1: Key Bioinformatics Tools and Their Functions in mNGS Analysis

Bioinformatics Tool	Primary Function	Key Parameters
Trimmomatic [21] [11]	Quality control and adapter trimming	Filter reads <35bp
Bowtie2 [21] [11]	Host sequence removal	Alignment to human reference genome (GRCh38/hg19)
Kraken2 [13]	Taxonomic classification of microbial reads	Confidence threshold=0.5
BLASTN [21] [13]	Validation of pathogen identification	megablast option for unique alignments

Results: Performance Comparison mNGS vs. Conventional Culture

Pathogen Detection Rates

Comparative studies consistently demonstrate the superior detection sensitivity of mNGS compared to conventional culture methods across various sample types.

• Organ Preservation Fluids: In kidney transplantation studies, mNGS detected pathogens in 47.5% (67/141) of organ preservation fluid samples, significantly outperforming conventional culture which had a positivity rate of 24.8% (35/141) [21] [11] [29].
• Wound Drainage Fluids: The difference was even more pronounced in recipient wound drainage fluids, where mNGS identified pathogens in 27.0% (38/141) of samples compared to just 2.1% (3/141) for conventional culture [21] [11] [29].
• ESKAPE Pathogens and Fungi: mNGS demonstrated significantly higher detection rates for clinically relevant ESKAPE pathogens and/or fungi at 28.4% (40/141) compared to 16.3% (23/141) for culture-based methods [21] [11].

Table 2: Comparative Detection Performance of mNGS vs. Conventional Culture

Sample Type	Conventional Culture Positive Rate	mNGS Positive Rate	P-value
Organ Preservation Fluids (n=141) [21] [11]	24.8% (35/141)	47.5% (67/141)	<0.05
Recipient Wound Drainage Fluids (n=141) [21] [11]	2.1% (3/141)	27.0% (38/141)	<0.05
ESKAPE Pathogens and/or Fungi (n=141) [21] [11]	16.3% (23/141)	28.4% (40/141)	<0.05

Organism-Specific Detection Capabilities

The unbiased nature of mNGS provides distinct advantages for detecting certain pathogen categories while revealing limitations for others.

• Gram-Negative Bacteria: mNGS demonstrated excellent detection capabilities for Enterobacteriaceae and non-fermenting Gram-negative bacteria, identifying 79.2% (19/24) of culture-positive combinations [21] [11].
• Gram-Positive Bacteria and Fungi: The technology showed limited sensitivity for Gram-positive bacteria, detecting only 22.2% (2/9) of culture-confirmed isolates, and moderate sensitivity for fungi at 55.6% (5/9) [21] [11].
• Atypical Pathogens: mNGS uniquely identified fastidious or atypical pathogens that conventional culture missed, including Mycobacterium species, Clostridium tetani, and various parasites [21] [11].

Diagnostic Performance in Pulmonary Infections

In patients with lung lesions requiring differential diagnosis between infection and malignancy, mNGS demonstrated a significantly higher sensitivity for infection diagnosis (56.5%) compared to conventional microbiological tests (39.1%) [13]. The integration of host chromosomal copy number variation (CNV) analysis with pathogen detection further enabled simultaneous diagnosis of infections and malignancies in BALF samples, with CNV analysis showing moderate sensitivity (38.9%) and perfect specificity (100%) for lung cancer detection [13].

Visual Workflows

Diagram 1: Comprehensive mNGS Workflow from Sample to Result

Diagram 2: Comparative Diagnostic Pathway: mNGS vs. Culture

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Reagents and Materials for mNGS Workflow

Reagent/Material	Function	Example Products/Protocols
Nucleic Acid Extraction Kits [21] [13]	Isolation of high-quality DNA/RNA from clinical samples	QIAamp DNA Micro Kit, Nucleic Acid Extraction Kit (MatriDx)
Library Preparation Kits [13]	Preparation of sequencing libraries from extracted nucleic acids	Total DNA Library Preparation Kit (MatriDx)
Sequencing Platforms [21] [13] [28]	High-throughput sequencing of prepared libraries	Illumina NextSeq 550, NextSeq500, Oxford Nanopore Technologies
Bioinformatics Tools [21] [13]	Analysis of sequencing data for pathogen detection	Trimmomatic, Bowtie2, Kraken2, BLASTN
Microbial Databases [21] [13]	Reference databases for pathogen identification	NCBI nt database, custom-curated microbial databases
Host Depletion Reagents [21] [9]	Reduction of human background DNA to improve sensitivity	Centrifugation protocols, enzymatic host DNA depletion kits

Discussion

Advantages and Limitations of mNGS

The implementation of mNGS in clinical diagnostics represents a paradigm shift in pathogen detection, offering unprecedented breadth of coverage and detection speed. The technology's capacity to identify unconventional, fastidious, or mixed infections without prior suspicion makes it particularly valuable in complex clinical cases where conventional methods frequently fail [9]. Additionally, the simultaneous detection of antimicrobial resistance genes directly from clinical specimens provides crucial guidance for targeted antibiotic therapy, potentially mitigating the spread of antimicrobial resistance [9] [28].

However, mNGS is not without limitations. The variable performance in detecting Gram-positive bacteria and fungi highlights the impact of microbial cell wall structures on DNA extraction efficiency and the challenges posed by reagent contamination in low-biomass samples [21] [11]. Furthermore, the detection of microbial DNA does not always distinguish between active infection, colonization, or non-viable organisms, requiring careful clinical correlation.

Complementary Role of mNGS and Conventional Culture

Rather than replacing conventional methods, mNGS serves as a complementary tool that enhances diagnostic capabilities. While mNGS excels in detection breadth and speed, conventional culture remains essential for providing viable isolates for antimicrobial susceptibility testing, which is crucial for guiding targeted antimicrobial therapy [21] [11]. The integration of both methods creates a powerful diagnostic synergy, leveraging the respective strengths of each technology.

Future Directions

Emerging approaches in metagenomics include the integration of host gene expression profiling to differentiate infection from colonization, the development of rapid point-of-care sequencing devices, and the application of artificial intelligence to improve pathogen detection and resistance prediction [9]. The combination of host transcriptome data with microbial sequencing shows particular promise for distinguishing bacterial from viral infections and predicting disease severity [9].

The mNGS workflow represents a transformative advancement in clinical microbiology, offering a powerful, unbiased approach to pathogen detection that complements traditional culture methods. While technical challenges remain regarding standardization, interpretation, and cost, the integration of mNGS into diagnostic pipelines significantly enhances our ability to rapidly identify pathogens, particularly in complex clinical scenarios. As sequencing technologies continue to evolve and bioinformatic tools become more sophisticated, mNGS is poised to play an increasingly central role in precision infectious disease diagnostics, ultimately improving patient outcomes through timely, targeted therapeutic interventions.

Metagenomic Next-Generation Sequencing (mNGS) represents a paradigm shift in clinical microbiology, enabling hypothesis-free, broad-spectrum pathogen detection directly from clinical samples. This powerful diagnostic approach sequences all nucleic acids in a sample, comparing them against extensive microbial databases to identify bacteria, viruses, fungi, and parasites without prior targeting. As traditional culture-based methods continue to show limitations in sensitivity, speed, and ability to detect fastidious or uncommon pathogens, mNGS has emerged as a transformative technology for infectious disease diagnosis. This guide provides a comprehensive, data-driven comparison of mNGS performance against conventional diagnostic methods across three clinically challenging infection categories: central nervous system (CNS) infections, respiratory tract infections, and implant-associated infections, offering researchers and drug development professionals evidence-based insights for its application in both clinical and research settings.

Performance Analysis Across Infection Types

Central Nervous System Infections

CNS infections remain particularly challenging to diagnose due to the wide spectrum of potential pathogens, overlapping clinical presentations, and often low pathogen loads in cerebrospinal fluid (CSF). A landmark 7-year performance study of clinical CSF mNGS testing analyzed 4,828 samples, providing robust evidence for its utility in hospitalized patients with suspected CNS infection [30]. The test demonstrated an overall sensitivity of 63.1%, specificity of 99.6%, and accuracy of 92.9% when compared to comprehensive clinical diagnosis [30]. Notably, mNGS exhibited significantly higher sensitivity (63.1%) than indirect serologic testing (28.8%) and direct detection testing from both CSF (45.9%) and non-CSF (15.0%) samples [30]. When considering only diagnoses made by CSF direct detection testing, the sensitivity of mNGS increased substantially to 86% [30].

Among 697 mNGS-positive samples, 797 organisms were identified, with DNA viruses being most frequently detected (45.5%), followed by RNA viruses (26.4%), bacteria (16.6%), fungi (8.5%), and parasites (2.9%) [30]. The assay demonstrated particular value in detecting difficult-to-culture pathogens including Mycobacterium tuberculosis (n=13), Coccidioides species (n=16), and various arboviruses [30]. A separate multicenter retrospective study focusing on ICU patients with suspected CNS infections found mNGS identified 105 microbial species across 520 clinical samples, comprising 64 bacterial species (61.0%), 16 DNA viruses (15.2%), 13 fungal species (12.4%), and 7 RNA viruses (6.7%) [31]. Importantly, mNGS detected 172 infection cases compared to only 31 cases identified by conventional culture methods in this critically ill population [31].

For neurosurgical central nervous system infections (NCNSIs), a study of 127 patients demonstrated significantly higher pathogen detection rates for mNGS (86.6%) compared to traditional culture (59.1%) [4]. The time from sample harvesting to final positive results (THTR) was also substantially shorter for mNGS (16.8 ± 2.4 hours) compared to culture (22.6 ± 9.4 hours) [4]. Another study of 110 patients with suspected CNS infections found mNGS identified pathogens in 77.11% of cases, significantly surpassing traditional CSF culture (6.36%) [32]. The rapid turnaround time of mNGS (reported within 24 hours) compared to culture (72-120 hours) facilitates more timely therapeutic decisions [32].

Table 1: mNGS Performance in CNS Infections

Performance Metric	7-Year UCSF Study [30]	ICU Multicenter Study [31]	NCNSI Study [4]	Suspected CNS Infection Study [32]
Samples (n)	4,828	520	127	110
Sensitivity	63.1%	59.0%	86.6%*	77.11%*
Specificity	99.6%	90.5%	-	-
Accuracy	92.9%	72.5%	-	-
Pathogens Detected	797	105 species	-	62 cases
Turnaround Time	3.6-3.8 days (lab processing)	-	16.8 ± 2.4 hours	<24 hours

*Pathogen detection rate compared to culture

Respiratory Tract Infections

Lower respiratory tract infections (LRTIs) represent a leading cause of infectious disease mortality worldwide, with accurate pathogen identification crucial for appropriate antimicrobial therapy. In a comparative study of 205 patients with suspected LRTIs, mNGS was evaluated alongside two targeted NGS approaches: amplification-based tNGS and capture-based tNGS [33]. While mNGS identified the highest number of species (totaling 80, compared to 71 species for capture-based tNGS and 65 for amplification-based tNGS), it came with significantly higher cost ($840) and longer turnaround time (20 hours) compared to the targeted approaches [33]. When benchmarked against comprehensive clinical diagnosis, the capture-based tNGS demonstrated the highest diagnostic performance with an accuracy of 93.17% and sensitivity of 99.43%, though it showed lower specificity compared to amplification-based tNGS in identifying DNA viruses (74.78% vs. 98.25%) [33].

A meta-analysis comparing BALF and blood mNGS for respiratory tract infection diagnosis across 11 studies (346 patients) found that BALF mNGS demonstrated superior sensitivity (0.94) compared to blood mNGS (0.64), though with lower specificity (0.27 vs. 0.69) [34]. The area under the curve (AUC) values for BALF mNGS and blood mNGS were 0.86 and 0.81, respectively [34]. Subgroup analysis revealed that for viral detection, both sample types showed similar efficiency (AUC 0.70 for BALF mNGS vs. 0.71 for blood mNGS), while for non-viral (bacterial or fungal) detection, BALF mNGS outperformed blood mNGS (AUC 0.83 vs. 0.73) [34].

In a study focusing on pulmonary infection diagnosis using bronchoscopy specimens, mNGS identified at least one microbial species in almost 89% of patients with pulmonary infection and detected microbes in 94.49% of samples from patients who had negative results from traditional pathogen detection methods [35]. The accuracy and sensitivity of mNGS were higher than those of traditional pathogen detection, with the additional advantage of simultaneously detecting a large variety of pathogens [35].

A retrospective study of 43 patients with LRTIs (including 34 COVID-19 cases) demonstrated the superior sensitivity of mNGS (95.35%) compared to culture (81.08%) and its broader pathogen coverage, identifying 36.36% of bacteria and 74.07% of fungi detected by cultures [26]. Concordance between methods was observed in 63% of cases [26]. The study also revealed that severe COVID-19 patients exhibited reduced respiratory microbiota abundance, potentially linked to viral dominance or therapeutic interventions [26].

Table 2: mNGS Performance in Respiratory Tract Infections

Performance Metric	Comparative NGS Study [33]	BALF vs. Blood Meta-Analysis [34]	Bronchoscopy Study [35]	COVID-19 LRTI Study [26]
Samples (n)	205	346 (11 studies)	229	43
Sensitivity	-	0.94 (BALF), 0.64 (blood)	High (vs. traditional methods)	95.35%
Specificity	-	0.27 (BALF), 0.69 (blood)	-	-
Species Identified	80	-	-	-
Cost	$840	-	-	-
Turnaround Time	20 hours	-	-	-
AUC	-	0.86 (BALF), 0.81 (blood)	-	-

Implant-Associated Infections

Prosthetic joint infection (PJI) represents one of the most devastating complications following joint arthroplasty, with conventional culture-based methods frequently failing to identify pathogens, particularly in patients with prior antibiotic exposure. In a prospective cohort study evaluating mNGS for PJI diagnosis using periprosthetic tissue samples from 44 cases, mNGS demonstrated superior sensitivity (80.6%) compared to culture (45.2%) when using the Musculoskeletal Infection Society (MSIS) criteria as reference standard [36]. The area under the curve (AUC) values for mNGS and culture were 0.826 and 0.731, respectively, though this difference did not reach statistical significance [36].

The most notable advantage of mNGS in PJI diagnosis was its significantly higher sensitivity in patients who had received antibiotic treatment within the preceding two weeks (69.5% for mNGS vs. 23.1% for culture, P = 0.03) [36]. This finding highlights the particular utility of mNGS in scenarios where conventional microbiology is compromised by prior antimicrobial therapy. The specificity of mNGS in this study was 84.6%, compared to 100% for culture [36], indicating that while mNGS is more sensitive, careful interpretation of results is necessary to distinguish true pathogens from background noise or contaminants.

For neurosurgical implant-associated infections, a subgroup analysis within a larger NCNSI study found that mNGS and droplet digital PCR (ddPCR) demonstrated notably higher positive detection rates in implant-associated infections compared to meningitis [4]. This suggests that mNGS may offer particular advantages in diagnosing infections associated with medical devices, where biofilm formation and low-grade infections often challenge conventional diagnostic approaches.

Table 3: mNGS Performance in Implant-Associated Infections

Performance Metric	PJI Study [36]	PJI Study (Antibiotic-Exposed) [36]	NCNSI (Implant-Associated) [4]
Samples (n)	44	13 (antibiotic-exposed subset)	127 (total, subset implant-associated)
Sensitivity	80.6%	69.5%	Higher than culture
Specificity	84.6%	-	-
Culture Sensitivity	45.2%	23.1%	59.1% (overall)
AUC	0.826	-	-
P-value	NS (vs. culture)	0.03	<0.01 (vs. culture overall)

Key Methodologies and Experimental Protocols

Standard mNGS Wet-Lab Workflow

The standard methodology for mNGS pathogen detection involves a multi-step process that maintains consistency across different infection types while allowing for sample-specific adaptations. For CSF samples, typically 1.5-3 mL is collected via lumbar puncture or drainage systems and stored at -20°C before processing [32] [37]. For respiratory samples, bronchoalveolar lavage fluid (BALF) is commonly collected, with samples assessed for quality using grading systems like the Bartlett score to minimize oropharyngeal contamination [26]. Tissue samples from prosthetic joint infections are homogenized using mechanical disruptors or homogenizers before nucleic acid extraction [36].

Nucleic acid extraction represents a critical step, with most protocols using commercial kits such as the TIANamp Micro DNA Kit (TIANGEN BIOTECH) [36] [32] [37]. For comprehensive pathogen detection, both DNA and RNA are extracted, though some protocols for specific clinical scenarios may proceed with DNA only to conserve sample volume or reduce costs [33]. Human nucleic acid depletion is frequently performed using benzonase treatment [33] or computational subtraction during bioinformatics analysis [37].

Library preparation approaches differ between untargeted mNGS and targeted NGS methods. For mNGS, library construction typically involves DNA fragmentation, end-repair, adapter ligation, and PCR amplification using kits such as the Nextera XT kit (Illumina) [37] or the Ovation Ultralow System V2 (NuGEN) [33]. Targeted NGS approaches employ either amplification-based enrichment using pathogen-specific primers [33] or capture-based enrichment using probe hybridization to enrich for pathogen sequences of interest.

Sequencing is most commonly performed on platforms such as the Illumina NextSeq 550 [33] [37] or BGISEQ-50 [36] [32], with sequencing depths typically ranging from 20 million reads for clinical applications to higher depths for research purposes. The generation of millions to billions of reads enables detection of even low-abundance pathogens present in clinical samples.

Bioinformatics Analysis Pipeline

The bioinformatics processing of mNGS data follows a standardized workflow designed to maximize pathogen detection while minimizing false positives. Initial quality control involves removing low-quality reads, adapter sequences, and low-complexity reads using tools like Fastp [33] [37] and Komplexity [37]. A critical subsequent step involves the computational subtraction of human host sequences by alignment to reference genomes (hg19 or hg38) using alignment tools such as Bowtie2 [37] or Burrows-Wheeler Aligner [30] [32].

The remaining non-human reads are then aligned against comprehensive microbial genome databases, typically curated from NCBI RefSeq, GenBank, or custom databases containing thousands of bacterial, viral, fungal, and parasite genomes [36] [32] [37]. The specific alignment tools vary between platforms, with studies reporting the use of SNAP [33] [37], Burrows-Wheeler Aligner [30] [32], or other alignment algorithms.

Positive detection criteria are established to distinguish true pathogens from background contamination or environmental organisms. These thresholds vary between laboratories and pathogen types but commonly include criteria such as:

• For bacteria (excluding mycobacteria), fungi (excluding Cryptococcus), and parasites: genome coverage ranking in top10 of same kind of microbes and absence in negative controls; or RPMsample/RPMNTC ratio >10 [37]
• For viruses, Mycobacterium tuberculosis, and Cryptococcus: at least one unique read mapped to species when not detected in negative controls; or RPMsample/RPMNTC ratio >5 [37]
• Alternative thresholds include species-specific read number (SSRN) cutoffs, with one study identifying SSRN ≥2 as optimal for diagnosis of definite viral encephalitis and/or meningitis (AUC 0.758) [37]

Quality Control and Validation Measures

Rigorous quality control measures are implemented throughout the mNGS workflow to ensure result reliability. Negative controls (non-template controls with sterile deionized water) are included in each batch to monitor for contamination during nucleic acid extraction, library preparation, and sequencing [33] [37]. For quantitative assessments, the Reads Per Million (RPM) ratio between samples and negative controls is calculated, with thresholds typically set between 5-10 for positive calling [33] [37].

Sample adequacy metrics are also established, with minimum sequencing depth requirements (commonly >10 million reads after quality control) [37] and checks for sufficient non-human reads to ensure pathogen detection capability. The implementation of these standardized quality measures across different sample types and infection syndromes enables more reliable comparison of mNGS performance data across studies and clinical settings.

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 4: Essential Research Reagents for mNGS Pathogen Detection

Reagent/Kits	Specific Examples	Function	Application Notes
Nucleic Acid Extraction Kits	TIANamp Micro DNA Kit (DP316, TIANGEN BIOTECH) [36] [32] [37]	Extraction of microbial nucleic acids from clinical samples	Effective for low-biomass samples; compatible with various sample types
RNA Extraction Kits	QIAamp Viral RNA Kit (Qiagen) [33]	RNA extraction for viral pathogen detection	Often used in combination with DNA extraction for comprehensive pathogen detection
Library Preparation Kits	Nextera XT kit (Illumina) [37], Ovation Ultralow System V2 (NuGEN) [33]	Preparation of sequencing libraries from extracted nucleic acids	Critical for achieving appropriate library complexity and diversity
Host Depletion Reagents	Benzonase (Qiagen) [33]	Enzymatic degradation of human nucleic acids	Increases microbial sequencing yield by reducing host background
rRNA Removal Kits	Ribo-Zero rRNA Removal Kit (Illumina) [33]	Removal of ribosomal RNA	Enhances detection of non-ribosomal microbial transcripts
Reverse Transcription Systems	Ovation RNA-Seq system (NuGEN) [33]	cDNA synthesis from RNA pathogens	Essential for RNA virus detection and transcriptomic analysis
Quantification Kits	Qubit dsDNA HS Assay Kit (ThermoFisher) [37]	Accurate quantification of nucleic acid concentrations	Critical for proper library normalization and sequencing efficiency
Targeted Enrichment Panels	Respiratory Pathogen Detection Kit (KingCreate) [33]	Multiplex PCR amplification of target pathogens	Used in targeted NGS approaches for specific clinical syndromes

The comprehensive analysis of mNGS performance across CNS, respiratory, and implant-associated infections demonstrates its significant advantages over conventional culture-based methods, particularly in terms of sensitivity, breadth of pathogen detection, and turnaround time. The technology shows particular value in diagnosing infections with fastidious or uncommon pathogens, in patients with prior antibiotic exposure, and in clinical scenarios requiring rapid pathogen identification. However, challenges remain regarding cost, standardization of interpretation criteria, and differentiation between true pathogens and background noise. As the field evolves, targeted NGS approaches offer promising alternatives for specific clinical scenarios, potentially balancing comprehensive detection with improved practicality and cost-effectiveness. For researchers and drug development professionals, these findings underscore the transformative potential of mNGS in advancing infectious disease diagnostics while highlighting areas requiring further refinement for optimal clinical integration.

Antimicrobial resistance (AMR) represents one of the most pressing global health threats of our time, responsible for approximately 1.27 million deaths annually and contributing to nearly 5 million additional fatalities [38]. Traditional, culture-based methods for pathogen identification and antimicrobial susceptibility testing (AST) have formed the diagnostic backbone for decades. However, these techniques are increasingly constrained by their prolonged turnaround times (2-8 days), labor-intensive processes, and limited scalability for comprehensive resistance profiling [2] [38]. The emergence of metagenomic next-generation sequencing (mNGS) represents a paradigm shift in diagnostic capabilities, offering culture-independent detection of pathogens and their resistance genes directly from clinical samples within 24-48 hours [21] [39]. This technology enables unbiased detection of virtually all nucleic acids in a sample, providing unprecedented opportunities for identifying resistance mechanisms without prior knowledge of the causative pathogen [2].

While mNGS demonstrates transformative potential for AMR surveillance and diagnostic applications, its clinical implementation requires careful consideration of performance characteristics relative to established methods. This review provides a comprehensive comparison of mNGS against conventional diagnostic techniques for AMR gene detection, examining analytical frameworks, technical limitations, and practical applications within the broader context of pathogen detection research.

Performance Comparison: mNGS Versus Conventional Diagnostic Methods

Detection Capabilities and Diagnostic Accuracy

Table 1: Comprehensive Comparison of Pathogen Detection Performance

Parameter	mNGS	Conventional Culture	Targeted PCR
Overall Sensitivity	81-93% [6] [40]	34-56% [6] [40]	84-90% [12] [15]
Overall Specificity	75% [6]	93% [6]	97% [15]
Turnaround Time	24-48 hours [39]	72-120 hours [39]	4-8 hours [12]
ESKAPE Pathogen Detection Rate	28.4% [21]	16.3% [21]	Varies by target
Atypical Pathogen Detection	Excellent (Mycobacterium, parasites) [21]	Poor	Target-dependent
Fungal Detection Rate	55.6% [21]	Reference standard	Target-dependent
Gram-positive Bacteria Detection	22.2% [21]	Reference standard	Target-dependent

Table 2: Antimicrobial Resistance Gene Detection Capabilities

Feature	mNGS	Culture-Based AST	Molecular Methods (PCR/LFT)
Resistance Mechanism Coverage	Comprehensive (known & novel genes) [2]	Functional output only	Limited to pre-defined targets [38]
Polyclonal/Mixed Infection Resolution	Yes [2]	Limited	Limited
Detection of Novel Mechanisms	Possible through sequence analysis [2]	No	No
Genotype-Phenotype Correlation	Requires validation [2]	Direct functional assessment	Indirect inference
Quantitative Resistance Prediction	Limited	Yes (MIC values)	Limited

The diagnostic performance of mNGS varies significantly across specimen types and pathogen categories. In critical applications such as central nervous system (CNS) infections, mNGS demonstrates remarkable sensitivity (77.1%) compared to conventional culture (6.4%), with results available within 24 hours versus 72-120 hours for culture methods [39]. Similarly, for lower respiratory tract infections (LRTIs), mNGS shows significantly higher sensitivity (93.3% vs. 55.6%) and negative predictive value (63.9% vs. 25.9%) compared to culture, although culture maintains higher specificity (54.9% vs. 71.8%) [40].

For spinal infections, meta-analyses reveal pooled sensitivity of 0.81 for mNGS versus 0.34 for tissue culture techniques (TCT), with area under the curve (AUC) values of 0.85 and 0.59, respectively [6]. This pattern of superior sensitivity but variable specificity extends to periprosthetic joint infections (PJI), where mNGS demonstrates 89% sensitivity versus 84% for targeted NGS (tNGS), though tNGS shows higher specificity (97% vs. 92%) [15].

Technical Limitations and Method-Specific Challenges

Despite its broad detection capabilities, mNGS exhibits notable limitations in detecting certain pathogen categories. Studies directly comparing mNGS with culture methods reveal that mNGS detected only 22.2% of Gram-positive bacteria and 55.6% of fungi identified by culture [21]. This detection disparity highlights the critical importance of cell wall structure and nucleic acid extraction efficiency in mNGS performance.

For Mycobacterium tuberculosis detection, mNGS shows strong correlation with real-time PCR results, particularly in samples with low bacterial loads where mNGS read counts correlate inversely with PCR cycle threshold values [12]. However, this concordance is strongly influenced by microbial burden, with perfect agreement at Ct values ≤20 but decreased concordance (76.5%) at higher Ct values (20[12].<="" p="">

A significant technological distinction exists between mNGS and targeted NGS (tNGS) approaches. While mNGS provides unbiased detection of all microbial nucleic acids, tNGS uses pathogen-specific primers to amplify regions of interest, resulting in higher specificity (97% vs. 92%) for PJI diagnosis, though with moderately reduced sensitivity (84% vs. 89%) [15].

Experimental Frameworks for mNGS in AMR Detection

Standardized mNGS Wet-Lab Methodology

Sample Processing and DNA Extraction: Clinical samples (BALF, CSF, tissue, preservation fluids) undergo initial processing to remove human cells via centrifugation [21] [11]. The resulting supernatant undergoes cell-free DNA extraction using commercial kits (QIAamp DNA Micro Kit or TIANamp Micro DNA Kit) [21] [39]. Mechanical disruption with glass beads (0.5mm) at 30Hz for 10-30 minutes enhances microbial lysis, particularly for organisms with robust cell walls like Gram-positive bacteria and fungi [39] [40]. Enzymatic digestion with lysozyme provides additional wall-breaking capability for challenging pathogens [39].

Library Preparation and Sequencing: Extracted DNA undergoes library construction using transposase-based fragmentation (Nextera XT kit) or enzymatic fragmentation, followed by end repair, adapter ligation, and PCR amplification [21] [40]. Quality control assessments using Qubit fluorometry and Agilent Bioanalyzer ensure library integrity before sequencing on platforms such as Illumina NextSeq 550 or BGISEQ-50/MGISEQ-2000 [39] [40]. Typical sequencing depth exceeds 10 million reads per sample with Q30 scores ≥85% to ensure data quality [12].

Bioinformatic Analysis for AMR Gene Detection

Data Preprocessing and Host Depletion: Raw sequencing data undergoes quality filtering using tools like fastp (v0.19.5) to remove adapter sequences, low-quality reads (<35bp), and low-complexity sequences [12] [40]. Human sequence removal is achieved by alignment to reference genomes (GRCh38) using Bowtie2 (v2.3.4.3) or BWA, significantly reducing host background [21] [40].

Pathogen Identification and AMR Gene Detection: Non-human reads are classified by alignment to comprehensive microbial databases (NCBI RefSeq) using BLASTN (v2.10.1+) with "megablast" options [21]. For AMR-specific detection, specialized tools including the Resistance Gene Identifier (RGI), AMRFinderPlus, and ResFinder screen sequences against curated resistance databases such as the Comprehensive Antibiotic Resistance Database (CARD) and NDARO [2]. Positive identification thresholds typically require unique alignments with coverage ranking in the top 10 for specific microbial categories and significant fold-change over negative controls [21].

Table 3: Essential Research Reagents and Computational Solutions

Category	Specific Product/Resource	Application/Function
Nucleic Acid Extraction	QIAamp DNA Micro Kit (QIAGEN) [21]	Cell-free DNA extraction from clinical samples
	TIANamp Micro DNA/RNA Kits (Tiangen Biotech) [39]	Simultaneous DNA/RNA extraction for comprehensive pathogen detection
Library Preparation	Nextera XT DNA Library Prep Kit (Illumina) [40]	Transposase-based fragmentation and adapter tagging
	PMseq RNA Infection Pathogen Kit (BGI) [39]	RNA pathogen detection including RNA viruses
Sequencing Platforms	Illumina NextSeq 550 [21] [40]	High-throughput sequencing with 75bp single-end reads
	BGISEQ-50/MGISEQ-2000 [39]	Alternative sequencing platform with DNB technology
Bioinformatic Tools	fastp (v0.19.5) [40]	Quality control and adapter trimming
	Bowtie2 (v2.3.4.3) [40]	Host sequence removal via alignment to human genome
	BLASTN (v2.10.1+) [21]	Microbial classification against reference databases
AMR Databases	Comprehensive Antibiotic Resistance Database (CARD) [2]	Curated repository of resistance genes and variants
	ResFinder [2]	Specialized database for resistance gene identification
	AMRFinderPlus [2]	NCBI's tool for identifying resistance genes

Clinical Utility and Therapeutic Impact

The implementation of mNGS directly influences antimicrobial stewardship and patient outcomes. Studies demonstrate that mNGS results prompt antibiotic adjustments in over 50% of patients with lower respiratory tract infections, including regimen de-escalation (42.2%), escalation (45.8%), and targeted adjustments (12.0%) [40]. Critically, 60.8% of these modified treatment regimens yield positive clinical responses, highlighting the therapeutic impact of comprehensive pathogen and resistance gene detection [40].

In transplant medicine, mNGS analysis of organ preservation fluids and wound drainage fluids enables early detection of donor-derived infections, with significantly higher positive rates (47.5% and 27.0%, respectively) compared to conventional culture (24.8% and 2.1%) [21] [11]. This enhanced detection capability facilitates preemptive antimicrobial interventions, potentially reducing severe vascular complications in immunocompromised recipients [21].

For pulmonary cryptococcosis, mNGS demonstrates remarkable diagnostic advantages over conventional methods, with significantly shorter time to clinical decision-making (3.5 days vs. 9.0 days) and higher sensitivity (91.7% vs. 8.3% positivity in fungal culture) [41]. This accelerated diagnostic timeline enables earlier initiation of targeted antifungal therapy, potentially preventing dissemination to the central nervous system and improving patient outcomes [41].

Metagenomic NGS represents a transformative technology for antimicrobial resistance gene detection, offering unparalleled breadth of pathogen coverage and resistance mechanism identification. While limitations persist in detecting certain Gram-positive bacteria and fungi, and genotype-phenotype correlations require further refinement, the technology provides undeniable advantages in speed, comprehensiveness, and clinical utility.

The optimal diagnostic approach involves strategic integration of mNGS with conventional culture and targeted molecular methods, leveraging the respective strengths of each platform. As standardized protocols emerge and computational pipelines mature, mNGS is poised to become an indispensable tool in the global effort to combat antimicrobial resistance, enabling evidence-based antibiotic stewardship and personalized therapeutic interventions.

Future developments in long-read sequencing, single-cell analysis, and standardized bioinformatic workflows will further enhance the role of mNGS in both clinical diagnostics and public health surveillance of emerging resistance threats.

The rapid and accurate identification of pathogens is a cornerstone of effective infectious disease management. For over a century, conventional microbiological culture has served as the principal method for pathogen detection, offering the benefits of antibiotic susceptibility testing and broad applicability across a diverse spectrum of pathogens [42]. However, its clinical utility is substantially limited by prolonged turnaround times (typically 1-5 days, and longer for slow-growing organisms), low sensitivity particularly after antibiotic administration, and the fundamental inability to cultivate many viruses, fastidious bacteria, and fungi in vitro [43] [42] [44]. In response to these limitations, metagenomic next-generation sequencing (mNGS) has emerged as a hypothesis-free, agnostic diagnostic approach. This technology enables the simultaneous detection of a vast range of bacteria, viruses, fungi, and parasites directly from clinical samples by sequencing all nucleic acids present and comparing them to comprehensive genomic databases [22] [30]. This guide objectively compares the performance of mNGS against traditional culture methods, providing supporting experimental data on their respective roles in informing targeted antimicrobial therapy and improving patient outcomes.

Performance Comparison: mNGS vs. Conventional Culture

Numerous clinical studies have systematically evaluated the diagnostic performance of mNGS against conventional culture methods across various patient populations and sample types. The following tables summarize key quantitative findings from recent investigations.

Table 1: Overall Diagnostic Performance of mNGS vs. Culture

Study Population	Sample Type	Sensitivity (%)	Specificity (%)	Positive Detection Rate (%)
Febrile Patients (n=368) [42]	Blood, CSF, BALF, Tissue, Puncture Fluid	58.0 (mNGS) vs. 21.7 (Culture)	85.4 (mNGS) vs. 99.3 (Culture)	-
Suspected LRTI (n=165) [22]	BALF, Blood, Tissue, Pleural Effusion	-	-	86.7 (mNGS) vs. 41.8 (Culture)
Pulmonary Infection (n=188) [45]	BALF	-	-	86.2 (mNGS) vs. 67.6 (Culture)
Kidney Transplant (n=141) [11]	Organ Preservation & Drainage Fluid	-	-	47.5 (mNGS) vs. 24.8 (Culture)

Table 2: Impact of mNGS on Clinical Management and Patient Outcomes

Study	Patient Population	Antibiotic Adjustment Rate with mNGS	Key Outcome Measures
Sepsis in ICU (2025) [43]	303 ICU patients with sepsis	Significantly higher (OR=29.7) and faster (OR=0.67) adjustment	Lower 28-day mortality in mNGS group after propensity matching
LRTI Management (2025) [46]	140 patients with suspected LRTI	87.9% (123/140)	Treatment downgraded in 3.6%, upgraded in 23.6%; symptoms improved
Febrile Patients (2024) [42]	368 febrile patients	64 patients with adjusted therapy (27.7% of infected)	21 patients had a "treatment turning point" and recovered
CNS Infections (2024) [30]	4,828 CSF samples	-	21.8% (48/220) of diagnoses made by mNGS alone

Case Study Analyses

Case Study 1: Severe Sepsis in the ICU

A 2025 retrospective cohort study of 303 sepsis patients in an ICU setting provides compelling evidence for the impact of mNGS on patient survival [43].

• Experimental Protocol: Patients were divided into an mNGS group (n=93) receiving mNGS testing plus conventional culture, and a non-mNGS group (n=130) receiving culture alone. After 1:1 propensity score matching, 80 patients remained in each group for comparative analysis. The primary outcome was 28-day mortality, with secondary outcomes including frequency and timing of antibiotic adjustments.

• Key Findings: The mNGS group demonstrated significantly higher rates of antibiotic adjustment and reduced 28-day mortality compared to the non-mNGS group. Binary logistic regression confirmed mNGS detection was associated with a 29.7-fold higher likelihood of antibiotic adjustment (95% CI [10.630, 83.170]) and shorter time to adjustment (OR=0.671, 95% CI [0.566, 0.795]) [43]. Critically, each 1-point increase in SOFA score was associated with a 16.9% higher mortality risk, while antibiotic adjustment based on results was negatively associated with mortality (OR=0.252, CI [0.101, 0.627]) [43].

Case Study 2: Lower Respiratory Tract Infections (LRTI)

A 2025 study of 165 patients with suspected LRTI demonstrated mNGS's superior detection capabilities and direct therapeutic impact [22].

• Experimental Protocol: Researchers compared traditional methods (culture, PCR, antigen testing) and mNGS using various specimens including bronchoalveolar lavage fluid (BALF), blood, tissue, and pleural effusion. Final pathogen diagnosis was determined by a multidisciplinary team integrating all available data.

• Key Findings: mNGS detected 29 pathogens missed by conventional methods, including non-tuberculous mycobacteria, anaerobic bacteria, and rare pathogens like Orientia tsugamushi [22]. mNGS results prompted treatment modifications in 119 patients (72.1%), with 54 patients (32.7%) experiencing antibiotic reduction. The technique was particularly valuable for detecting polymicrobial infections, which conventional methods frequently missed [22].

Case Study 3: Central Nervous System (CNS) Infections

A comprehensive 7-year analysis of 4,828 CSF samples tested with mNGS demonstrated its unique value in diagnosing challenging CNS infections [30].

• Experimental Protocol: The University of California, San Francisco clinical laboratory performed mNGS on samples from patients with suspected CNS infections between 2016-2023. Laboratory validation was complemented by clinical adjudication for a subset of 1,164 samples.

• Key Findings: mNGS testing identified 797 organisms from 697 samples (14.4% positivity rate), with viruses being most frequently detected (71.9% of positives) [30]. Notably, 48 of 220 infectious diagnoses (21.8%) were identified by mNGS alone. The test demonstrated higher sensitivity (63.1%) than indirect serologic testing (28.8%) and direct detection testing from both CSF (45.9%) and non-CSF samples (15.0%) [30]. The assay proved particularly valuable for detecting fastidious pathogens like Mycobacterium tuberculosis and fungi such as Coccidioides species, often at subthreshold levels that were subsequently confirmed orthogonally.

Technical and Methodological Comparisons

Experimental Workflows

The fundamental workflows for mNGS and culture differ significantly, contributing to their respective performance characteristics.

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Key Research Reagent Solutions for mNGS Implementation

Reagent/Material	Function	Examples/Specifications
Nucleic Acid Extraction Kits	Isolation of DNA/RNA from clinical samples	QIAamp DNA Micro Kit (QIAGEN) [11] [42]
Library Preparation Kits	Construction of sequencing libraries	QIAseq Ultralow Input Library Kit (QIAGEN) [42]
Host Depletion Reagents	Reduction of human background sequences	DNase treatment (RNA libraries), antibody-based methylated DNA removal (DNA libraries) [30]
Sequencing Platforms	High-throughput nucleic acid sequencing	Illumina Nextseq 550, MGISEQ-2000 (BGI) [45] [42]
Bioinformatics Tools	Data analysis and pathogen identification	Trimmomatic (quality control), BWA (human sequence removal), Bowtie2 (microbial alignment) [11] [45]
Negative Controls	Contamination identification	Non-template controls (NTC) with sterile water [11] [22]

Discussion: Integrated Role in Clinical Microbiology

The evidence from multiple case studies indicates that mNGS and conventional culture serve complementary rather than mutually exclusive roles in modern clinical microbiology. mNGS demonstrates clear advantages in sensitivity, speed for detection, and ability to identify uncultivable, fastidious, or unexpected pathogens [22] [42] [30]. Culture maintains importance for providing antibiotic susceptibility profiles and confirming mNGS findings, particularly for common bacterial pathogens [42]. The higher specificity of culture (99.3% vs. 85.4% for mNGS in one study [42]) reflects mNGS's sensitivity to contamination and detection of non-viable or colonizing organisms, emphasizing the necessity of clinical correlation.

From a therapeutic perspective, mNGS facilitates more informed antimicrobial stewardship by enabling earlier pathogen-directed therapy [43] [46]. This is particularly valuable in critically ill patients, where each hour of inappropriate antimicrobial therapy increases mortality risk. The ability of mNGS to detect resistance genes directly from specimens continues to evolve and may further enhance its utility in guiding targeted therapy [44].

Metagenomic next-generation sequencing represents a transformative technology in clinical microbiology, addressing critical limitations of conventional culture methods. Evidence from multiple case studies demonstrates that mNGS provides superior detection of pathogens, particularly in complex cases involving immunocompromised patients, central nervous system infections, and culture-negative infections. The implementation of mNGS guidance facilitates more frequent and appropriate antibiotic adjustments, ultimately contributing to improved patient outcomes, including reduced mortality in septic patients. While conventional culture remains essential for antibiotic susceptibility testing, the integration of mNGS into diagnostic pathways enhances our ability to rapidly identify pathogens and implement targeted antimicrobial therapy, advancing the goals of precision medicine in infectious disease management.

Navigating Challenges and Enhancing Diagnostic Accuracy

In the evolving landscape of infectious disease diagnostics, metagenomic next-generation sequencing (mNGS) has emerged as a powerful, hypothesis-free tool for pathogen detection, demonstrating particular value in complex clinical scenarios involving immunocompromised patients, culture-negative infections, and critical care settings [9]. Unlike traditional culture-based methods, which remain the gold standard despite limitations including prolonged turnaround times and an inability to detect uncultivable or fastidious organisms, mNGS enables simultaneous detection of a broad spectrum of pathogens directly from clinical specimens [9] [4]. However, the transformative potential of mNGS is constrained by a significant technical hurdle: the overwhelming abundance of host-derived DNA in clinical samples.

This "host DNA hurdle" creates a fundamental sensitivity challenge for pathogen detection. In typical clinical samples such as bronchoalveolar lavage fluid (BALF), blood, or cerebrospinal fluid (CSF), microbial pathogen DNA often constitutes less than 0.1% of the total DNA, while host human DNA comprises the vast majority [9] [47]. This disproportionate ratio means that sequencing efforts are predominantly expended on non-informative host sequences, drastically reducing the effective depth of microbial coverage and obscuring the detection of low-abundance pathogens—a scenario often described as searching for a needle in a haystack. The consequences are tangible: reduced diagnostic sensitivity, potential false negatives, increased sequencing costs, and extended bioinformatic processing times. Within the broader thesis of mNGS versus culture for pathogen detection, addressing this host DNA interference is paramount to unlocking the full potential of sequencing-based diagnostics. This guide objectively compares the performance of emerging strategies designed to overcome this limitation, providing researchers and developers with experimental data and methodological insights to advance the field of pathogen detection.

Comparative Analysis of Host DNA Depletion and Alternative Strategies

The scientific community has developed multiple strategic approaches to mitigate the host DNA burden. These can be broadly categorized into physical separation methods, enzymatic and chemical depletion, targeted enrichment approaches, and emerging amplification technologies. The table below provides a systematic performance comparison of these strategies, highlighting their relative advantages and limitations.

Table 1: Performance Comparison of Strategies to Overcome the Host DNA Hurdle

Strategy	Mechanism	Reported Host DNA Reduction	Key Advantages	Key Limitations
Filtration Membrane [47]	Physical separation of host cells (e.g., leukocytes) from microbes based on size and electrostatic properties.	>98% (of host DNA) [47]	High efficiency; simple integration into sample prep workflow; preserves intact pathogens.	Potential loss of intracellular pathogens; variable performance across sample types.
Differential Centrifugation [47]	Step-wise centrifugation to separate host cells from microbes based on density and size.	Up to 9,580-fold enrichment of microbial signal reported with commercial kits [47]	Widely accessible equipment; compatible with various sample types.	Lower purity; potential for co-pelletization; may require additional steps.
Probe-Based Hybrid Capture (tNGS) [9] [47]	Oligonucleotide probes hybridize to and enrich for targeted microbial sequences.	Significantly increases pathogen reads (6- to 8-fold) [47]	High sensitivity for panel targets; enables AMR gene detection.	Hypothesis-driven; limited to pre-defined pathogens; higher design complexity.
saponin-Based Lysis [47]	Selective chemical lysis of host cells (e.g., white blood cells) followed by centrifugation.	Improves microbial detection sensitivity [47]	Cost-effective; uses common laboratory reagents.	Optimization needed for different samples; potential pathogen damage.
CRISPR-Based Enrichment [48]	CRISPR-Cas systems (e.g., Cas12, Cas13) used for specific recognition and cleavage of pathogen nucleic acids.	N/A (Directly targets pathogen nucleic acids)	Ultra-high specificity; potential for point-of-care use; rapid results.	Primarily for known targets; complex reaction optimization; emerging technology.
ddPCR [4]	End-point PCR that partitions samples into nanodroplets for absolute quantification of specific targets.	N/A (Amplifies only specific targets)	Extreme sensitivity; absolute quantification without standards; robust to inhibitors.	Limited multiplexing; requires prior knowledge of suspected pathogen.

The selection of an optimal strategy involves trade-offs between the need for unbiased pathogen detection and the requirement for high sensitivity. Filtration and centrifugation methods offer a pre-sequencing, culture-independent solution that maintains the untargeted advantage of mNGS. In contrast, targeted approaches like tNGS and ddPCR sacrifice breadth for depth, providing superior sensitivity for known pathogens but failing to detect novel or unexpected agents. The experimental data from recent studies strongly suggests that a combined approach, leveraging both physical separation to reduce background and targeted enrichment to boost signal, may yield the most significant diagnostic improvements [47].

Experimental Protocols and Workflows for Key Strategies

Filtration-Based Host Cell Depletion Protocol

A novel experimental protocol utilizing a human cell-specific filtration membrane demonstrates a highly effective method for pre-sequencing host DNA reduction. This method, validated in whole blood samples, leverages the size difference and electrostatic properties between human cells and most microbial pathogens [47].

• Sample Preparation: Collect whole blood using standard venipuncture techniques. Anti-coagulated blood (e.g., EDTA or citrate) is recommended.
• Filtration Setup: Detach the needle from the blood collection syringe and replace it with the proprietary filtration membrane unit. Connect a new needle below the membrane and attach a vacuum blood collection tube to the assembly.
• Filtration Process: The inherent negative pressure from the vacuum tube drives the blood sample through the filtration membrane. The membrane substrate, composed of materials such as leukosorb, triacetate cellulose, or nylon, is designed to be electrostatically attractive to leukocytes, efficiently capturing these host cells.
• Filtrate Collection: The filtrate, now enriched for microorganisms (bacteria, fungi, viruses, and spores) that pass through the membrane, is collected. This process concurrently reduces nucleated host cells, thereby minimizing the background host DNA interference in subsequent sequencing steps [47].
• Downstream Processing: The filtrate can then be processed for DNA extraction and library preparation following standard mNGS or tNGS protocols.

This workflow is summarized in the following diagram:

Independent validation of this method reported over 98% reduction in host DNA background, which synergistically boosted the performance of a subsequent tNGS panel, resulting in a 6- to 8-fold increase in pathogen reads and enabling reliable identification of low-abundance pathogens [47].

Targeted Next-Generation Sequencing (tNGS) Workflow

When the clinical hypothesis is narrower, targeted NGS (tNGS) represents a powerful alternative. This method uses multiplex PCR or probe hybridization to enrich for predefined pathogen sequences, thereby functionally overcoming the host DNA problem by selectively amplifying microbial genomic regions.

• DNA Extraction: Extract total nucleic acid (DNA and/or RNA) from the clinical sample using a robust commercial kit. The input amount should be optimized according to the manufacturer's guidelines.
• Multiplex PCR Amplification: Amplify the extracted DNA using a large, multiplexed primer panel designed to target conserved and specific genomic regions of over 330 clinically relevant pathogens [47]. The amplification is typically performed in a single tube with a high-fidelity, multiplex-ready polymerase master mix.
• Library Preparation: The resulting amplicons are then enzymatically fragmented, and sequencing adapters with sample-specific barcodes are ligated to the ends. This step prepares the amplicon pool for cluster generation and sequencing.
• Sequencing and Analysis: Pool the barcoded libraries and sequence on a high-throughput platform (e.g., Illumina). The resulting reads are demultiplexed and aligned to a curated database of pathogen genomes for identification.

The logical relationship between mNGS and tNGS in the diagnostic pathway is illustrated below:

This workflow has demonstrated high consistency with mNGS and culture results while offering a more cost-effective and streamlined alternative for specific clinical syndromes [47].

The Scientist's Toolkit: Essential Reagents and Materials

Successful implementation of the strategies described above relies on a suite of specialized reagents and tools. The following table details key solutions for researchers designing experiments to overcome the host DNA hurdle.

Table 2: Research Reagent Solutions for Host DNA Depletion and Pathogen Enrichment

Item Name	Function / Principle	Example Use Case
Human Cell-Specific Filtration Membrane	Physically captures nucleated host cells (leukocytes) via size exclusion and electrostatic attraction, allowing microbes to pass through.	Pre-processing of whole blood samples for mNGS to achieve >98% host DNA reduction [47].
Selective Cell Lysis Reagent (e.g., Saponin)	Selectively lyses mammalian cells without disrupting robust microbial cell walls, enabling removal of released host DNA.	Pre-treatment of CSF or synovial fluid samples prior to DNA extraction for improved bacterial detection [47].
Multiplex tNGS Primer Panels	Large pools of oligonucleotide primers designed to simultaneously amplify genomic signatures of hundreds of pre-defined pathogens.	Hypothesis-driven diagnosis of bloodstream infections (BSIs) or respiratory infections with high sensitivity [47].
Hybrid Capture Probe Panels	Biotinylated RNA or DNA probes that hybridize to target microbial sequences, allowing pull-down and enrichment prior to sequencing.	Enrichment for pathogens and antimicrobial resistance (AMR) genes from complex samples; used in whole exome sequencing [9] [49].
Host DNA Depletion Kits (e.g., MolYsis)	Commercial kits that combine selective lysis of human cells with enzymatic degradation of the released host DNA.	Standardized workflow for enriching bacterial DNA from samples rich in human cells, like BALF [47].
CRISPR-Cas12/Cas13 Reagents	Cas enzymes programmed with crRNA to bind specific pathogen nucleic acid sequences, triggering a collateral cleavage activity for detection.	Rapid, specific, and portable detection of known bacterial or viral pathogens, potentially integrated with isothermal amplification [48].

The challenge of host DNA interference represents a critical bottleneck in the application of mNGS for pathogen detection. While culture methods remain foundational in microbiology, their limitations are well-documented. The strategies detailed in this guide—ranging from physical pre-separation and enzymatic depletion to sophisticated targeted enrichment—provide a robust experimental toolkit for dramatically improving the sensitivity and specificity of molecular diagnostics. Experimental data confirms that methods like filtration can reduce host DNA by over 98%, while tNGS can boost pathogen reads by 6- to 8-fold [47].

For researchers and drug development professionals, the choice of strategy is not mutually exclusive and should be guided by the clinical or research question. For syndromes with a wide differential diagnosis, a combination of physical host depletion followed by untargeted mNGS may be ideal. Conversely, for monitoring specific pathogens or in outbreak settings, targeted tNGS or even CRISPR-based assays may offer superior speed and sensitivity. As the field advances, the integration of these methods with evolving technologies like long-read sequencing and artificial intelligence will further close the sensitivity gap between molecular and culture-based diagnostics, ultimately enabling faster, more accurate, and more comprehensive pathogen detection to improve patient outcomes.

The accurate identification of pathogens is a cornerstone of effective infectious disease management, yet it presents a formidable challenge in clinical and research settings. Traditional culture-based methods, long considered the gold standard, are increasingly being supplemented or supplanted by metagenomic next-generation sequencing (mNGS) technologies. While mNGS offers unprecedented breadth in pathogen detection, it simultaneously introduces significant interpretive complexities, particularly in distinguishing true causative pathogens from environmental contaminants or commensal organisms. This challenge is amplified by the technology's ability to detect microbial nucleic acids without regard for viability or pathogenicity.

The clinical implications of misinterpretation are substantial, potentially leading to inappropriate antimicrobial therapy, unnecessary toxicity, and suboptimal patient outcomes. Within the broader thesis of mNGS versus culture for pathogen detection research, this review systematically compares the performance characteristics of both methodologies, with particular emphasis on their respective abilities to facilitate accurate clinical interpretation. We synthesize recent evidence across diverse clinical scenarios, provide detailed experimental protocols, and offer frameworks for enhancing diagnostic specificity in the era of high-throughput, culture-independent pathogen detection.

Performance Comparison: mNGS vs. Conventional Culture

Comprehensive Diagnostic Performance Metrics

Extensive research across various infection types and sample matrices has demonstrated consistent patterns in the comparative performance of mNGS and culture methods. The following table summarizes key performance metrics from recent studies:

Table 1: Diagnostic Performance of mNGS vs. Culture Across Clinical Specimens

Infection Type/Specimen	Sensitivity (mNGS vs. Culture)	Specificity (mNGS vs. Culture)	Pathogen Detection Rate	Key Advantages	Study
Infected Pancreatic Necrosis	0.87 (95% CI: 0.72-0.95) vs. 0.36 (95% CI: 0.23-0.51)	0.83 (95% CI: 0.69-0.91) vs. 0.83 (95% CI: 0.67-0.92)	Significantly higher for mNGS (p<0.05)	Superior sensitivity, faster results, detection of fastidious organisms	[17]
Neurosurgical CNS Infections	86.6% vs. 59.1% (p<0.01)	Not specified	27.5% higher detection with mNGS	Unaffected by empiric antibiotics, detects rare/atypical pathogens	[4]
Periprosthetic Joint Infection	0.89 (95% CI: 0.84-0.93)	0.92 (95% CI: 0.89-0.95)	Higher than culture	Comprehensive pathogen profiling, maintains specificity	[15]
Kidney Transplantation (Preservation Fluids)	47.5% vs. 24.8% (p<0.05)	Not specified	Nearly double the detection rate	Identifies ESKAPE pathogens, atypical pathogens (Mycobacterium, parasites)	[21]
Emergency Department (Suspected Sepsis)	71.43% (95% CI: 51.33-86.78)	55.6% (95% CI: 47.07-63.96)	5.6-fold increase in pathogen detection per sample	Detects anaerobes, performance unaffected by antibiotic therapy	[50]

Technical and Analytical Characteristics

Beyond raw detection rates, the fundamental differences in methodology between mNGS and culture impart distinct advantages and limitations for each approach:

Table 2: Technical Comparison of mNGS and Culture Methods

Parameter	Metagenomic NGS	Conventional Culture
Turnaround Time	16.8 ± 2.4 hours [4]	22.6 ± 9.4 hours to several days/weeks
Impact of Prior Antibiotics	Minimal effect on detection [4] [50]	Significantly reduced sensitivity
Detection Scope	All domains (bacteria, viruses, fungi, parasites)	Limited to cultivable organisms
Quantitative Capability	Semi-quantitative (read counts)	Quantitative (CFU/mL)
Antimicrobial Susceptibility	Indirect (resistance gene detection)	Direct phenotypic testing
Viability Assessment	No (detects DNA from dead organisms)	Yes (requires viable organisms)
Automation Potential	High (sample-to-answer workflows emerging)	Moderate (automated systems available)
Polymicrobial Infection Detection	Excellent (unbiased detection)	Limited by overgrowth and fastidious organisms

Methodological Approaches: From Sample to Interpretation

Standardized mNGS Wet-Lab Workflow

The reliability of mNGS-based pathogen detection hinges on standardized laboratory procedures optimized for different sample types:

Sample Collection and Processing: For bronchoalveolar lavage fluid (BALF) analysis, samples are collected in sterile containers and immediately transported on dry ice to sequencing facilities. Sputum samples undergo quality assessment using the Bartlett grading system, with only samples scoring ≤1 (indicating ≤10 squamous epithelial cells per low-power field and ≥25 leukocytes per low-power field) considered suitable for analysis to minimize oropharyngeal contamination [26]. Samples are typically stored at -80°C prior to DNA extraction to preserve nucleic acid integrity.

Nucleic Acid Extraction and Library Preparation: DNA is extracted using commercial kits such as the QIAamp DNA Micro Kit (QIAGEN) or manufacturer-specific systems like the NGS Automatic Library Preparation System (MatriDx Biotech) [21] [13]. Critical steps include host DNA depletion through differential centrifugation or enzymatic methods to improve the signal-to-noise ratio. Extracted DNA is processed for library construction using kits such as the Total DNA Library Preparation Kit, with incorporation of unique molecular identifiers to track individual molecules and control for amplification biases [13].

Sequencing and Quality Control: Libraries are sequenced on platforms such as the Illumina NextSeq500, typically generating 10-20 million reads per sample. Rigorous quality controls include processing negative controls (non-template controls) and positive controls with each batch to monitor for contamination and assay performance [21] [13]. For blood samples, cell-free DNA extraction from plasma is preferred to minimize host background [50].

Bioinformatic Analysis Pipelines

The computational analysis of mNGS data represents a critical juncture where contamination can be identified and controlled:

Quality Control and Host Subtraction: Raw sequencing reads undergo adapter trimming and quality filtering using tools like Trimmomatic (v0.39) or fastp (v0.23.4), with removal of low-quality reads (<35bp), reads with ambiguous bases, and those shorter than 30 bases [21] [51]. Host-derived reads are subtracted by alignment to reference genomes (GRCh38.p13) using Bowtie2 (v2.4.2) or BBDuk (v39.08), significantly reducing the proportion of non-informative sequences [21] [51].

Taxonomic Classification: Non-host reads are classified through alignment to comprehensive microbial databases using tools like Kraken2 (confidence=0.5) with custom-curated pathogen databases [51] [13]. Multi-stage validation approaches are employed, with initial Kraken2 classification followed by confirmatory alignment using Bowtie2 and BLASTN against reference databases [51]. The HPD-Kit pipeline exemplifies this layered approach, incorporating multiple alignment algorithms and validation steps to improve accuracy [51].

Pathogen Prioritization and Interpretation: Detected microorganisms are prioritized using metrics such as normalized pathogen abundance score (NPAS), unique read counts, genome coverage, and relative abundance compared to negative controls [51]. Positive criteria typically include thresholds such as RPMsample/RPMNTC >10 for bacteria/fungi or detection of specific reads for particular pathogens like Mycobacterium tuberculosis [21]. Integration with clinical metadata is essential for distinguishing colonization from true infection.

Conventional Culture Protocols

While varying by specimen type, standard culture methods maintain consistent principles across applications:

Sample Processing and Inoculation: BALF samples are quantitatively inoculated (typically 10µL) onto blood agar and chocolate agar plates using sterile pipetting techniques [52]. For organ preservation fluids and other sterile body fluids, samples are inoculated into aerobic culture bottles (e.g., BD BACTEC Plus Aerobic/F) and loaded onto automated incubation systems like the BD BACTEC FX instrument [21].

Incubation and Isolation: Inoculated plates are incubated in 5-10% CO₂ at 35±1°C for 18-24 hours [21] [52]. Blood culture bottles are monitored for positive signals, after which Gram staining is performed and positive samples are subcultured onto appropriate solid media. For fungal detection, additional subculture on SDA agar plates with incubation at 37°C for 48 hours is performed [21].

Organism Identification: Following incubation, distinct colonies are selected for identification using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) [21]. Pure colonies are transferred to 96-spot steel target plates for analysis, enabling rapid microbial identification to the species level.

Interpretation Frameworks: Distinguishing Signal from Noise

Quantitative Thresholds and Statistical Approaches

Establishing robust criteria for distinguishing true pathogens from contaminants is essential for accurate mNGS interpretation:

Negative Control-Based Normalization: The integration of negative controls (non-template controls processed alongside clinical samples) enables statistical assessment of background contamination. Microorganisms detected in clinical samples are evaluated based on their abundance relative to negative controls, with thresholds such as RPMsample/RPMNTC >10 providing objective criteria for significance [21]. This approach accounts for reagent-borne and environmental contaminants that may be consistently present across batches.

Abundance Metrics and Ranking: Pathogen prioritization utilizes multiple quantitative metrics including unique read counts, unique k-mer counts, genome coverage, and relative abundance within the microbial community [51]. The Normalized Pathogen Abundance Score (NPAS) implemented in HPD-Kit has demonstrated superior performance in identifying dominant pathogens compared to simple read count rankings [51]. Organisms are typically required to rank within the top 10 of their microbial category (bacteria, fungi, etc.) to be considered clinically relevant [21].

Microbial Context Assessment: Interpretation incorporates ecological considerations, including the presence of classic oropharyngeal contaminants (e.g., Streptococcus viridans group, Neisseria species) in respiratory samples, which are discounted unless present in dominant amounts or accompanied by supportive clinical evidence [26]. The composition of the broader microbial community provides context for assessing the significance of individual taxa.

Integrated Clinical-Biological Interpretation

The most effective interpretation frameworks integrate laboratory data with comprehensive clinical assessment:

Clinical Syndrome Alignment: Detected microorganisms are evaluated against the patient's clinical presentation, with pathogens known to cause the observed syndrome receiving higher priority. For example, in neurosurgical CNS infections, organisms like Staphylococcus aureus or Cutibacterium acnes are more readily accepted as causative when detected in patients with appropriate clinical findings [4]. This syndromic approach is formalized in classification systems that categorize detected organisms as "definite," "probable," "possible," or "unlikely" pathogens based on clinical, radiologic, and laboratory correlation [13].

Host Factor Considerations: Immunocompromised states, including transplantation, HIV infection, or immunosuppressive therapy, expand the spectrum of potential pathogens and lower the threshold for considering organisms that might be dismissed as contaminants in immunocompetent hosts [21] [9]. In kidney transplant recipients, for instance, detection of opportunistic pathogens like Mycobacterium species or parasites may represent true infection despite low abundance [21].

Therapeutic Impact Assessment: The ultimate validation of pathogen significance often comes from treatment response. Documentation of clinical improvement following pathogen-directed therapy provides supporting evidence for biological significance. Studies indicate that mNGS results could have led to therapy modifications in 35.9% of cases in emergency department patients with suspected infections, demonstrating the clinical actionability of properly interpreted results [50].

Table 3: Interpretation Framework for Common Contaminant Patterns

Sample Type	Common Contaminants	Interpretation Guidance	Supporting Evidence
Blood/Plasma	Coagulase-negative Staphylococci, Bacillus species, Corynebacterium species	Consider significant if: ≥2 positive samples, clinical signs of infection, supported by inflammatory markers; OR single positive with high-risk clinical context	Correlation with serum CRP, procalcitonin; consistency with clinical syndrome [50]
Bronchoalveolar Lavage	Oral flora (Streptococcus viridans, Neisseria spp., Anaerobes)	Consider significant if: dominant organism in quantitative culture; associated with inflammatory cells; pure growth on culture; supported by imaging findings	Bartlett score (quality indicator), quantitative culture results, radiographic correlation [26] [52]
Cerebrospinal Fluid	Cutibacterium acnes, Coagulase-negative Staphylococci	Consider significant in: Post-neurosurgical patients, particularly with hardware; consistent CSF parameters (pleocytosis, elevated protein); repeated detection	CSF cell count, protein, glucose; clinical context (recent procedure, hardware) [4]
Tissue/Biopsy	Skin flora, Environmental organisms	Consider significant if: Histopathological evidence of invasion; pure growth; compatible clinical context	Histopathology showing tissue invasion, inflammation; imaging findings [13]

Essential Research Reagents and Computational Tools

Table 4: Essential Research Reagent Solutions for mNGS Pathogen Detection

Category	Specific Product/Platform	Application/Function	Performance Characteristics
Nucleic Acid Extraction	QIAamp DNA Micro Kit (QIAGEN)	Cell-free DNA extraction from plasma, BALF, other body fluids	High recovery of low-abundance microbial DNA; effective host DNA reduction [21]
Library Preparation	Total DNA Library Preparation Kit (MatriDx Biotech)	NGS library construction from extracted DNA	Compatibility with low-input samples; reduced bias in representation [13]
Automated Systems	NGS Automatic Library Preparation System (MatriDx)	Integrated sample processing and library preparation	Standardization of pre-analytical steps; reduced manual variability [13]
Sequencing Platforms	Illumina NextSeq500	High-throughput sequencing	10-20 million reads/sample; suitable for low-to-moderate depth applications [13]
Bioinformatics Tools	HPD-Kit (Henbio Pathogen Detection Toolkit)	Comprehensive pathogen detection pipeline	Layered alignment approach; NPAS metric for pathogen prioritization [51]
Classification Tools	Kraken2, Bowtie2, BLAST	Taxonomic classification of non-host reads	Multi-algorithm validation; customizable databases [21] [51]
Quality Control	Trimmomatic, fastp	Read quality filtering and adapter removal	Threshold-based filtering (quality score <20, ambiguous bases, length) [21] [51]
Reference Databases	Custom-curated pathogen databases	Comprehensive microbial genome references	Non-redundant genomes; clinical focus; regular updates [51]

The distinction between causative pathogens and contaminants represents a fundamental challenge in diagnostic microbiology, one that has been amplified rather than resolved by the advent of mNGS technologies. While mNGS demonstrates unequivocal superiority in detection sensitivity and breadth compared to conventional culture, this enhanced detection capability necessitates more sophisticated interpretation frameworks. The integration of quantitative thresholds, statistical analysis relative to controls, clinical correlation, and host factor assessment provides a multidimensional approach to this critical diagnostic task.

The evolving evidence base supports a complementary rather than replacement role for mNGS relative to culture, with each method contributing unique information. Culture maintains value in establishing viability, providing quantitative data, and enabling antimicrobial susceptibility testing, while mNGS offers unprecedented speed, sensitivity, and capacity for novel pathogen discovery. The most effective diagnostic pathways strategically integrate both approaches, leveraging their respective strengths while mitigating limitations.

Future directions in the field include the development of standardized interpretation guidelines, refined bioinformatic methods for distinguishing colonization from infection, and integration of host response markers to provide additional context for pathogen significance. As these technologies continue to evolve and evidence accumulates, the distinction between true pathogens and contaminants will increasingly rely on sophisticated algorithms that integrate laboratory, clinical, and epidemiological data in real time, ultimately enhancing the precision of infectious disease diagnosis and management.

The landscape of clinical pathogen detection is undergoing a transformative shift with the emergence of metagenomic next-generation sequencing (mNGS). This innovative technology enables hypothesis-free detection of bacterial, viral, fungal, and parasitic pathogens directly from clinical specimens through comprehensive sequencing of all nucleic acids present. Unlike traditional culture-based methods, which remain the historical gold standard, mNGS requires no prior knowledge of suspected pathogens or specific cultivation conditions. While conventional culture offers the benefits of antimicrobial susceptibility testing and low cost, it suffers from prolonged turnaround times (typically 1-5 days for most bacteria and significantly longer for fastidious organisms like fungi and mycobacteria) and notoriously low sensitivity in patients who have received prior antibiotic therapy [9] [42].

The clinical adoption of mNGS represents a paradigm shift from targeted to untargeted diagnostic approaches, yet significant barriers impede its widespread implementation. This review systematically compares the performance of mNGS versus conventional culture methods across diverse clinical scenarios, critically examining the standardization challenges and cost-benefit considerations that currently restrict broader integration into routine clinical practice. By synthesizing recent evidence from multiple medical specialties, we aim to provide researchers and drug development professionals with a comprehensive framework for evaluating this promising technology within the context of precision infectious disease diagnostics.

Performance Comparison: mNGS Versus Conventional Culture

Comprehensive Diagnostic Performance Metrics

Table 1: Overall Diagnostic Performance of mNGS vs. Conventional Culture Across Multiple Infection Types

Infection Type / Study	Sensitivity (%)	Specificity (%)	AUC	PPV (%)	NPV (%)
Spinal Infections (Meta-analysis, n=770) [6]	81 (mNGS) vs. 34 (Culture)	75 (mNGS) vs. 93 (Culture)	0.85 (mNGS) vs. 0.59 (Culture)	-	-
Lower Respiratory Tract Infections (n=400) [40]	93.3 (mNGS) vs. 55.6 (Culture)	-	0.744 (mNGS) vs. 0.636 (Culture)	-	63.9 (mNGS) vs. 25.9 (Culture)
Febrile Patients (n=368) [42]	58.01 (mNGS) vs. 21.65 (Culture)	85.40 (mNGS) vs. 99.27 (Culture)	-	87.01 (mNGS) vs. 98.84 (Culture)	54.67 (mNGS) vs. 42.9 (Culture)
Central Nervous System Infections (n=60) [53]	-	-	-	-	-
HIV-Associated Pneumonia (n=246) [54]	98.0 (mNGS) vs. 32.1 (Culture)	-	-	-	-

The consistent pattern across diverse clinical studies demonstrates mNGS's superior sensitivity for pathogen detection, particularly valuable in diagnostically challenging scenarios. However, conventional culture maintains advantages in specificity and positive predictive value in most settings, highlighting their complementary roles in clinical diagnostics [6] [42].

Pathogen Detection Capabilities Across Microbial Categories

Table 2: Pathogen Detection Rates in Bronchoalveolar Lavage Fluid (BALF) Studies

Pathogen Category	Specific Pathogens	Detection Rate (mNGS)	Detection Rate (Culture)	Clinical Significance
Bacteria	Streptococcus pneumoniae	7.0% [40]	0% [40]	Common community-acquired pneumonia pathogen
	Haemophilus influenzae	6.7% [40]	0% [40]	Fastidious organism difficult to culture
	ESKAPE pathogens	28.4% [21] [11]	16.3% [21] [11]	Highly antibiotic-resistant nosocomial pathogens
Fungi	Aspergillus species	9.4% [40]	3.5% [40]	Invasive fungal infections in immunocompromised
	Pneumocystis jirovecii	11.9% [40]	0% [40]	Uncultivable fungus, HIV-associated pneumonia
Viruses	Cytomegalovirus (CMV)	70.9% in HIV patients [54]	Not detectable	Opportunistic infection in immunocompromised
	Epstein-Barr virus (EBV)	58.1% in HIV patients [54]	Not detectable	Reactivation in immunosuppressed states
Atypical Pathogens	Mycobacterium tuberculosis	Detected [6]	Low sensitivity [6]	Slow-growing, requires specialized culture
	Intracellular pathogens	Detected [40]	Not detectable	Includes Chlamydia, Legionella, Rickettsia

mNGS demonstrates particular value in detecting viral pathogens, fastidious microorganisms, and mixed infections. In immunocompromised populations, such as persons living with HIV (PLWH), mNGS detected mixed infections in 94.2% of cases, with fungal-viral co-infections being most prevalent (74.3%) [54]. This comprehensive pathogen profiling capability enables more targeted antimicrobial therapy, especially crucial in complex patient populations.

Technical and Workflow Considerations

Experimental Protocols and Methodologies

Standardized protocols for mNGS implementation across different sample types remain a significant challenge. The following workflow represents a consolidated methodology derived from multiple clinical studies:

Figure 1: Comparative Workflows for mNGS and Culture Methods

For conventional culture, studies consistently used standardized microbiological protocols. Samples were inoculated onto appropriate culture media (blood agar, chocolate agar, MacConkey agar) and incubated under aerobic/anaerobic conditions at 35±1°C for 24-72 hours [21] [42]. Bacterial identification was performed using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS), with antimicrobial susceptibility testing following Clinical and Laboratory Standards Institute (CLSI) guidelines [42].

For mNGS protocols, DNA extraction was typically performed using commercial kits (QIAamp DNA Micro Kit, TIANamp Micro DNA Kit) with mechanical lysis using bead-beating systems to ensure comprehensive cell disruption [21] [40]. Library preparation utilized Illumina-compatible kits (Nextera XT) with quality assessment via Agilent Bioanalyzer and Qubit fluorometer [40] [42]. Sequencing was performed on platforms including Illumina Nextseq 550 and MGISEQ-2000, generating 20-50 million reads per sample [40] [54].

Bioinformatic analysis involved a standardized pipeline: (1) adapter trimming and quality filtering (Fastp, Trimmomatic); (2) host DNA depletion by alignment to human reference genome (hg19/GRCh38) using Bowtie2 or BWA; (3) taxonomic classification of non-human reads by alignment to microbial genome databases using BLASTN or similar tools [21] [40] [54].

The Scientist's Toolkit: Essential Research Reagents and Platforms

Table 3: Key Research Reagent Solutions for mNGS Implementation

Category	Specific Product/Platform	Manufacturer	Primary Function
Nucleic Acid Extraction	QIAamp DNA Micro Kit	QIAGEN	Microbial DNA extraction from clinical samples
	TIANamp Micro DNA Kit	Tiangen Biotech	High-yield DNA extraction including difficult samples
Library Preparation	Nextera XT DNA Library Prep Kit	Illumina	Library construction for Illumina platforms
	MGIEasy Cell-free DNA Library Prep Set	MGI Tech	Library preparation for MGI sequencing platforms
Sequencing Platforms	Nextseq 550	Illumina	High-throughput sequencing (2-5 billion reads/run)
	MGISEQ-2000	MGI Tech	Competitive alternative to Illumina platforms
Bioinformatic Tools	Bowtie2	Open Source	Host DNA removal by alignment to human genome
	BLASTN	NCBI	Taxonomic classification of sequencing reads
	Trimmomatic	Open Source	Quality control of raw sequencing data
Identification Systems	MALDI-TOF MS	Bruker/bioMérieux	Microbial identification from culture colonies
	VITEK II System	bioMérieux	Automated antimicrobial susceptibility testing

Economic and Operational Considerations in Clinical Implementation

Cost-Effectiveness Analysis and Healthcare Economics

The economic evaluation of mNGS represents a critical barrier to widespread adoption. A prospective randomized controlled trial conducted in postoperative neurosurgical patients with central nervous system infections (CNSIs) provided compelling economic data [53]. Despite significantly higher detection costs (¥4,000 for mNGS versus ¥2,000 for culture, p<0.001), the mNGS group demonstrated substantially reduced anti-infective costs (¥18,000 versus ¥23,000, p=0.02) and shorter turnaround times (1 day versus 5 days, p<0.001) [53].

The incremental cost-effectiveness ratio (ICER) was calculated at ¥36,700 per additional timely diagnosis, falling below China's GDP-based willingness-to-pay (WTP) threshold of ¥89,000, suggesting favorable cost-effectiveness for critical care settings [53]. This economic advantage was particularly pronounced in complex cases where conventional diagnostics often fail, though the model may not generalize to all healthcare settings with different reimbursement structures.

Impact on Clinical Decision-Making and Antimicrobial Stewardship

The clinical utility of mNGS extends beyond diagnostic accuracy to tangible impacts on patient management. In a study of 329 patients with lower respiratory tract infections, antibiotic treatment was modified based on mNGS results in 50.5% of cases (166/329), with 60.8% of these patients (101/166) showing positive treatment responses [40]. Similarly, among febrile patients with suspected infections, 64 patients received adjusted antibiotic therapy guided by mNGS findings, including 21 patients who experienced a definitive treatment turning point leading to recovery and discharge [42].

Figure 2: mNGS-guided Antimicrobial Stewardship Impact

The ability of mNGS to rapidly identify pathogens and guide appropriate therapy is particularly valuable in immunocompromised patients. In PLWH with pulmonary infections, mNGS detected a complex spectrum of opportunistic pathogens that correlated with immune status, with pathogen diversity increasing significantly as CD4+ T cell counts declined (p<0.05) [54]. This comprehensive profiling enables preemptive targeting of pathogens likely to cause disease in specific immunologic contexts.

Standardization Barriers and Technical Limitations

Critical Implementation Challenges

Despite its promising capabilities, mNGS faces significant standardization barriers that impede routine clinical adoption:

Bioinformatic Pipeline Variability: Substantial inconsistencies exist in bioinformatic approaches across laboratories, including different reference databases, classification algorithms, and criteria for determining positive results [9]. This variability complicates inter-institutional result comparison and undermines reproducibility.

Host DNA Interference: Clinical samples typically contain predominantly human host DNA (often >90%), reducing microbial sequencing depth and requiring effective host depletion methods that can inadvertently remove pathogens of interest [9] [42].

Contamination Management: Distinguishing true pathogens from environmental contaminants and commensal microbiota remains challenging, particularly in low-biomass infections [9] [40]. Establishing appropriate cutoff values for clinical significance requires validation in specific patient populations and sample types.

Inconsistent Fungal and Gram-Positive Detection: Several studies noted limitations in mNGS sensitivity for certain pathogen categories. In organ preservation fluid testing, mNGS detected only 22.2% (2/9) of Gram-positive bacteria and 55.6% (5/9) of fungi identified by culture [21] [11]. This variability may relate to differences in cell wall lysis efficiency and nucleic acid extraction methods.

Regulatory and Reimbursement Hurdles: Clear regulatory pathways for mNGS assay validation and inconsistent reimbursement models create financial disincentives for healthcare institutions considering implementation [9].

The evidence synthesized in this review demonstrates that mNGS offers significant advantages over conventional culture for comprehensive pathogen detection, particularly in complex cases, immunocompromised patients, and situations where rapid diagnosis critically impacts outcomes. The technology's superior sensitivity, broad pathogen coverage, and faster turnaround times must be balanced against its higher costs, technical complexity, and persistent standardization challenges.

For researchers and drug development professionals, several key priorities emerge for advancing the field: establishing standardized reference materials and proficiency testing programs, developing consensus bioinformatic pipelines, validating pathogen-specific thresholds for clinical significance across sample types, and generating robust health economic data across diverse healthcare settings. Future innovations in host DNA depletion, single-cell sequencing approaches, and point-of-care sequencing platforms may address current limitations.

While mNGS is transforming infectious disease diagnostics in specialized settings, its widespread adoption requires coordinated efforts to address standardization barriers and demonstrate sustainable value across the healthcare system. The complementary roles of mNGS and conventional culture—with the latter providing essential antimicrobial susceptibility data—suggest an integrated diagnostic approach represents the most prudent path forward until technological advances and economic factors enable broader mNGS implementation.

Timely and accurate pathogen identification is fundamental to the effective treatment of infectious diseases. For over a century, conventional culture-based techniques have served as the cornerstone of microbiological diagnosis. However, the advent of metagenomic next-generation sequencing (mNGS) represents a paradigm shift, offering an unbiased, high-throughput approach to pathogen detection. This guide provides an objective comparison of the performance characteristics of mNGS and culture, synthesizing current clinical evidence to delineate specific scenarios where each method should be prioritized, thereby empowering researchers and clinicians with data-driven decision-making frameworks.

Performance Metrics: mNGS vs. Culture at a Glance

Direct comparisons across multiple studies and specimen types reveal a consistent pattern: mNGS demonstrates superior sensitivity, while culture maintains higher specificity. The table below summarizes the aggregated diagnostic performance data.

Table 1: Comparative Diagnostic Performance of mNGS vs. Culture

Metric	mNGS	Conventional Culture	Context of Data
Sensitivity	58.01% [24], 81% [6], 98.0% [54]	21.65% [24], 34% [6], 32.1% [54]	Febrile illness [24], Spinal infection meta-analysis [6], HIV-associated pneumonia [54]
Specificity	75% [6], 85.40% [24]	93% [6], 99.27% [24]	Spinal infection meta-analysis [6], Febrile illness [24]
Positive Predictive Value (PPV)	87.01% [24], 92.5% [55]	98.84% [24]	Febrile illness [24], Pulmonary infection [55]
Negative Predictive Value (NPV)	54.67% [24]	42.9% [24]	Febrile illness [24]
Area Under the Curve (AUC)	0.85 [6]	0.59 [6]	Spinal infection meta-analysis [6]
Typical Turnaround Time	24-48 hours [56] [54]	1-5 days, or longer for slow-growers [24] [56]	Various clinical samples

Key Experimental Workflows and Protocols

Understanding the fundamental technical procedures is crucial for interpreting results and optimizing use cases.

Standard mNGS Wet-Lab and Dry-Lab Protocol

The mNGS process consists of two main parts: the wet-lab (laboratory testing) and the dry-lab (bioinformatic analysis) [56].

Wet-Lab Procedures [24] [54]:

• Sample Collection & Preprocessing: Clinical samples (e.g., BALF, tissue, CSF) are collected. Human cells are often removed via centrifugation, especially from fluid samples, to enrich for microbial content [21].
• Nucleic Acid Extraction: Total DNA and/or RNA is extracted and purified using commercial kits (e.g., QIAamp DNA Micro Kit).
• Library Construction: Sequencing libraries are prepared from the extracted nucleic acids, often using kits like the MGIEasy Cell-free DNA Library Prep Set or Qiagen's QIAseq Ultralow Input Library Kit.
• High-Throughput Sequencing: Libraries are sequenced on platforms such as the Illumina Nextseq 550 or MGISEQ-2000.

Dry-Lab Bioinformatic Analysis [24] [21]:

• Quality Control & Adapter Trimming: Raw data is processed to remove adapter sequences, low-quality reads, and short reads.
• Host Sequence Depletion: Reads aligning to the human reference genome (e.g., hg38) are filtered out.
• Microbial Identification: The remaining non-host reads are aligned against comprehensive microbial genome databases using tools like BLASTN or Burrows-Wheeler Aligner (BWA).
• Result Interpretation: Detected microbes are interpreted based on predefined positive criteria, which may consider metrics like reads per million (RPM) and comparison to negative controls.

Figure 1: End-to-End mNGS Workflow. The process integrates laboratory procedures (wet-lab) and computational analysis (dry-lab) for comprehensive pathogen detection.

Conventional Culture and Identification Protocol

Standard Culture Method [24] [21] [54]:

• Inoculation: Samples are directly inoculated onto selective culture media (e.g., blood agar, chocolate agar, MacConkey agar) or into liquid culture systems (e.g., BD BACTEC FX).
• Incubation: Cultures are incubated under controlled aerobic, anaerobic, or CO₂-enriched conditions for 24-72 hours (or weeks for mycobacteria/fungi).
• Colony Identification: Positive cultures showing microbial growth are analyzed. Colonies are identified using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) [24] [21] or biochemical tests.
• Antibiotic Susceptibility Testing (AST): Isolated pathogens are subjected to AST using systems like VITEK II to determine minimum inhibitory concentrations (MICs), guiding targeted therapy [24].

Recommended Use Cases for Prioritizing mNGS

Based on accumulated clinical evidence, the following scenarios warrant prioritization of mNGS testing.

Complex Cases with Suspected Rare, Fastidious, or Uncultivable Pathogens

mNGS is uniquely suited for detecting pathogens that do not grow on standard media or require specialized growth conditions. It has proven invaluable for identifying organisms like Pneumocystis jirovecii, viruses (e.g., Cytomegalovirus, Epstein-Barr virus), and anaerobic bacteria, which are frequently missed by culture [22] [54]. In a study on pulmonary infections in immunocompromised patients, mNGS detected 123 different pathogens, including many viruses and fastidious microorganisms, far surpassing the 17 detected by culture [54].

Critically Ill or Immunocompromised Patients with Potential Mixed Infections

Immunocompromised hosts, such as Persons Living with HIV (PLWH) or organ transplant recipients, are highly susceptible to polymicrobial infections. mNGS provides a comprehensive view of the entire pathogen landscape. In PLWH with pulmonary infections, mNGS revealed a 94.2% prevalence of mixed infections, with fungal-viral co-infections being the most common (74.3%) [54]. Similarly, in kidney transplantation, mNGS demonstrated a significantly higher positive detection rate in organ preservation and wound drainage fluids compared to culture, allowing for more informed antimicrobial management [21].

Culture-Negative Cases with High Clinical Suspicion of Infection

When conventional cultures return negative despite strong clinical and radiological evidence of infection, mNGS can be a powerful diagnostic tool. Its unbiased nature allows it to uncover pathogens not considered in the initial differential diagnosis. In a study of 165 patients with lower respiratory tract infections (LRTI), mNGS identified 29 kinds of pathogens that were missed by traditional methods, including non-tuberculous mycobacteria (NTM), Legionella gresilensis, and Orientia tsugamushi [22].

Scenarios Requiring Rapid Pathogen Identification

Although not yet as fast as some PCR-based point-of-care tests, mNGS offers a much faster turnaround than culture for a broad range of pathogens. The typical mNGS workflow from sample to result takes 24-48 hours [56] [54], whereas culture can take 1-5 days for common bacteria and much longer for slow-growers like mycobacteria and fungi [24] [56]. This speed can be critical for initiating targeted therapy sooner.

When Prior Antibiotic Use Has Compromised Culture Sensitivity

The administration of antibiotics before sample collection significantly reduces the sensitivity of culture-based methods. mNGS, which detects microbial nucleic acids rather than relying on viable organisms, is largely unaffected by prior antibiotic exposure [24]. Studies have confirmed that the positive rate of mNGS on puncture fluid and tissue samples was significantly higher than that of culture in patients who had received prior antibiotics [24].

The Scientist's Toolkit: Essential Research Reagents & Materials

Table 2: Key Reagents and Kits for mNGS and Culture Protocols

Item	Function / Application	Example Products / Methods
Nucleic Acid Extraction Kit	Isolation of total DNA/RNA from clinical samples.	QIAamp DNA Micro Kit (QIAGEN) [24] [21], TIANamp Micro DNA Kit (Tiangen Biotech) [54]
DNA Library Prep Kit	Preparation of sequencing-ready libraries from extracted nucleic acids.	QIAseq Ultralow Input Library Kit (QIAGEN) [24], MGIEasy Cell-free DNA Library Prep Set (MGI Tech) [54]
Sequencing Platform	High-throughput sequencing of prepared libraries.	Illumina Nextseq 550 [24] [21], MGISEQ-2000 (BGI) [54]
Bioinformatics Software	Data QC, host depletion, microbial classification.	Trimmomatic (QC) [21], BWA/Bowtie2 (alignment) [24] [21], BLASTN (classification) [21]
Culture Media & Systems	Isolation and growth of viable microorganisms.	Blood agar, Chocolate agar, MacConkey agar [54], BD BACTEC FX system (blood cultures) [21]
Microbial Identification	Species-level identification of cultured isolates.	MALDI-TOF MS (e.g., VITEK MS) [24] [54]

The evidence clearly indicates that mNGS and culture are complementary, not replacement, technologies. mNGS should be prioritized for initial, broad-spectrum pathogen detection in complex, critical, or culture-negative scenarios, especially when rare, fastidious, or mixed infections are suspected. Its high sensitivity and rapid turnaround provide a crucial advantage for guiding early therapeutic decisions. Conversely, culture remains indispensable for confirming mNGS findings in cases of ambiguity, providing definitive pathogen viability data, and, most importantly, for conducting antibiotic susceptibility testing (AST) to guide targeted antimicrobial therapy [24] [6]. A synergistic diagnostic approach, leveraging the strengths of both methods, will yield the highest diagnostic accuracy and ultimately improve patient outcomes.

Evidence-Based Comparison: Weighing Performance Metrics and Clinical Value

The accurate identification of pathogens is a cornerstone of effective infectious disease management. For decades, conventional culture-based methods have served as the laboratory mainstay, but their limitations—including prolonged turnaround times and an inability to detect uncultivable or fastidious organisms—have prompted the development of molecular techniques like metagenomic next-generation sequencing (mNGS) [21] [4]. This guide objectively compares the diagnostic performance of mNGS versus traditional culture through the lens of recent meta-analytic and comparative studies. The focus is on pooled sensitivity and specificity, key metrics in diagnostic test accuracy (DTA) research.

Meta-analysis of DTA studies presents unique statistical challenges because it involves synthesizing two correlated outcome measures—sensitivity and specificity—which are often influenced by a "threshold effect" [57]. In diagnostic testing, varying the threshold to increase sensitivity typically decreases specificity, and vice versa. Recommended statistical methods, such as the bivariate model and the hierarchical summary receiver operating characteristic (HSROC) model, account for this inherent negative correlation and between-study heterogeneity, providing more reliable summary estimates than simplistic, separate pooling of sensitivities and specificities [58] [57]. The diagnostic odds ratio (DOR), which represents the odds of a positive test result in a diseased person versus a non-diseased person, is another useful summary measure that can adjust for the curvilinear correlation between sensitivity and specificity [58].

Statistical Methodology for Diagnostic Test Accuracy Meta-Analysis

Understanding the statistical foundations is crucial for interpreting pooled estimates from DTA meta-analyses correctly. The field has moved beyond outdated methods to embrace more sophisticated models.

Key Statistical Models and Concepts

• Bivariate Model: This is a random-effects model that jointly synthesizes logit-transformed sensitivities and specificities from primary studies. It directly estimates a summary point (average logit-sensitivity and logit-specificity), along with their variances and the covariance between them, thereby explicitly accounting for the negative correlation that often exists between these two measures across studies [57].
• Hierarchical SROC (HSROC) Model: This alternative, yet mathematically equivalent, random-effects model focuses on estimating parameters that define an SROC curve. It is particularly useful when study-specific thresholds are expected to vary, as it incorporates this threshold effect directly into the model [57].
• Diagnostic Odds Ratio (DOR): A single indicator of test performance, defined as the ratio of the odds of positivity in the diseased group to the odds of positivity in the non-diseased group. A major advantage of the DOR is that it is mathematically less affected by the negative correlation between sensitivity and specificity, making it a stable metric for pooling. However, its limitation is that it is a single number that combines two different aspects of test performance, which can sometimes obscure practical interpretation [58].

Software and Implementation

Specialist statistical software (e.g., R, Stata, SAS) is typically required to fit bivariate or HSROC models. To improve accessibility for non-statisticians, web-based tools like MetaDTA have been developed. MetaDTA is a freely available, interactive application that fits the bivariate model in the background, allowing users to upload their data, customize SROC plots, and conduct sensitivity analyses through an intuitive point-and-click interface [59].

Comparative Performance Data: mNGS vs. Conventional Culture

Recent clinical studies across diverse patient populations and sample types consistently demonstrate the superior sensitivity of mNGS for pathogen detection, though its specificity relative to culture remains a complex and context-dependent metric.

Table 1: Pooled and Comparative Diagnostic Performance of mNGS vs. Culture

Study / Population	Sample Type	Test	Sensitivity	Specificity	Positive Detection Rate
Lower Respiratory Tract Infections [26]	Sputum	mNGS	95.35%	Not Reported	Significantly broader pathogen spectrum
		Culture	81.08%	Not Reported	Limited to cultivable organisms
Neurosurgical CNS Infections [4]	Cerebrospinal Fluid (CSF) & Pus	mNGS	Implied Higher*	Implied Higher*	86.6% (p<0.01)
		Culture	(Reference)	(Reference)	59.1%
Kidney Transplantation [21] [11]	Organ Preservation Fluid	mNGS	Not Reported	Not Reported	47.5%
		Culture	Not Reported	Not Reported	24.8%
Kidney Transplantation [21] [11]	Recipient Wound Drainage Fluid	mNGS	Not Reported	Not Reported	27.0%
		Culture	Not Reported	Not Reported	2.1%

*The study reported significantly higher overall pathogen detection rates for mNGS and ddPCR compared to culture, implying superior sensitivity. Specificity was not directly calculated against a clinical reference standard.

Table 2: Pathogen-Type Detection Profile of mNGS vs. Culture

Pathogen Category	Detection by mNGS	Detection by Culture	Notable Advantages
Gram-Negative Bacteria (Enterobacteriaceae, non-fermenters) [21]	79.2% (19/24)	Reference	Good concordance with culture for common gram-negative rods.
Fungi [21]	55.6% (5/9)	Reference	Limitations in fungal detection may be related to cell wall lysis efficiency.
Gram-Positive Bacteria [21]	22.2% (2/9)	Reference	Lower detection rate potentially due to thicker cell wall impeding DNA extraction.
Atypical Pathogens (e.g., Mycobacterium, Tropheryma whipplei, parasites) [21] [60]	Detected	Often Missed	mNGS can identify slow-growing, fastidious, and uncultivable organisms.

Experimental Protocols and Workflows

The stark differences in performance between mNGS and culture are rooted in their fundamentally different underlying methodologies.

Metagenomic Next-Generation Sequencing (mNGS) Workflow

The mNGS process is a culture-independent method that sequences all nucleic acids in a sample. The following diagram illustrates the core steps, from sample preparation to clinical report.

Detailed Methodological Steps:

• Sample Pre-processing: Samples (e.g., bronchoalveolar lavage fluid, cerebrospinal fluid, preservation fluids) are centrifuged to remove human cells and debris. The supernatant, containing cell-free DNA or microorganisms, is retained for analysis [21] [26].
• Nucleic Acid Extraction: Total DNA is extracted from the supernatant using commercial kits (e.g., QIAamp DNA Micro Kit). The DNA concentration is quantified using fluorescence assays (e.g., Qubit 4.0) [21].
• Library Preparation and Sequencing: DNA fragments are processed to create sequencing libraries by adding platform-specific adapters. These libraries are then sequenced on high-throughput platforms (e.g., Illumina NextSeq 550) [21].
• Bioinformatic Analysis: This critical step involves several layers:
  • Quality Control & Host Read Removal: Raw sequences are trimmed and filtered for low-quality reads. Remaining reads are aligned to a human reference genome (e.g., GRCh38) and removed to enrich for microbial data [21].
  • Microbial Identification: Non-human reads are classified by alignment to comprehensive microbial genome databases. Positive detection criteria are applied, often involving a minimum number of unique reads or a ratio of sample reads to negative control reads to discount background contamination [21].

Conventional Culture and Identification Workflow

Culture-based methods rely on the growth and propagation of viable microorganisms in specialized media.

Detailed Methodological Steps:

• Inoculation and Incubation: Samples are directly inoculated into liquid culture media (e.g., BD BACTEC aerobic/fungal bottles) and loaded into an automated continuous-monitoring blood culture system (e.g., BD BACTEC FX). These are incubated at controlled temperatures (e.g., 35±1°C) for a set period, typically 24-48 hours or longer for slow-growing organisms [21] [4].
• Subculture and Isolation: Bottles flagged as positive by the instrument are subjected to Gram staining. The broth is then subcultured onto solid agar plates (e.g., blood agar, SDA agar) to isolate pure colonies [21].
• Microbial Identification: Isolated colonies are analyzed using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS). This technology generates a unique protein profile from the colony, which is matched against a database to identify the organism at the species level [21].

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Key Reagents and Equipment for mNGS and Culture Protocols

Category	Item	Primary Function
Sample Collection & Storage	Hypertonic Citrate Purine Solution (e.g., S400) [21]	Preservation of donor organs and associated microorganisms for transplantation studies.
	BD BACTEC Plus Aerobic/F Culture Bottles [21]	Liquid medium for enhancing the growth of aerobic bacteria and fungi from clinical samples.
Nucleic Acid Extraction	QIAamp DNA Micro Kit (QIAGEN) [21]	Isolation of high-quality microbial and cell-free DNA from diverse clinical samples.
Sequencing & Library Prep	Illumina Nextseq 550 Platform [21]	High-throughput sequencing to generate millions of short DNA reads.
	Trimmomatic (Bioinformatics Tool) [21]	Software for removing sequencing adapters and filtering out low-quality reads.
Bioinformatic Analysis	Bowtie2 / Kneaddata [21]	Tools for aligning sequencing reads to a host genome (e.g., human GRCh38) for subtraction.
	BLASTN (via NCBI nt database) [21]	Algorithm for aligning non-host reads against microbial databases for taxonomic classification.
Microbial Identification	Bruker MALDI-TOF MS [21]	Rapid, proteomics-based identification of cultured microbial isolates to the species level.
	Blood Agar Plate, SDA Agar Plate [21]	Solid media for subculturing and isolating specific microorganisms from positive cultures.

The evidence synthesized from recent studies indicates a clear paradigm shift in clinical microbiology diagnostics. mNGS demonstrates a consistently and significantly higher positive detection rate and sensitivity compared to conventional culture across a wide range of clinical scenarios, including lower respiratory tract infections, neurosurgical CNS infections, and post-transplant monitoring [21] [4] [26]. Its unparalleled ability to detect uncultivable, fastidious, and atypical pathogens in an unbiased manner provides a powerful advantage [60].

However, mNGS is not a perfect replacement for culture. Current limitations include variable performance in detecting fungi and Gram-positive bacteria, challenges in differentiating colonization from active infection due to high sensitivity, and higher costs [21] [26]. Furthermore, culture remains essential for obtaining isolates for antibiotic susceptibility testing. Therefore, under current conditions, the optimal diagnostic approach is the joint application of mNGS and conventional culture [21] [11]. This synergistic strategy allows for the rapid, comprehensive pathogen detection of mNGS to be complemented by the phenotypic data provided by culture, ultimately guiding more informed and effective antimicrobial therapy.

The timely and accurate identification of pathogens is a cornerstone of effective infectious disease management. For decades, conventional culture techniques have served as the gold standard for pathogen detection, yet their utility is significantly compromised in patients who have received prior antibiotic therapy [24]. This limitation can lead to diagnostic delays, inappropriate treatment, and poorer patient outcomes. Metagenomic Next-Generation Sequencing (mNGS) has emerged as a powerful, hypothesis-free diagnostic tool capable of detecting a broad spectrum of pathogens from clinical samples without the need for cultivation [9] [61]. This guide provides a systematic comparison of the detection rates of mNGS and conventional culture in patients with prior antibiotic exposure, synthesizing experimental data and methodologies to inform researchers and drug development professionals.

The core advantage of mNGS in this context lies in its fundamental mechanism: it detects microbial nucleic acids rather than relying on microbial viability or growth [62] [61]. Antibiotics, while potentially reducing the viability of pathogens, often do not immediately degrade their genetic material. This allows mNGS to identify pathogens that are present in the sample but non-viable for culture, thereby offering a potential solution to a long-standing diagnostic challenge.

Comparative Performance Data

Numerous clinical studies have directly compared the sensitivity and detection rates of mNGS and culture in patient populations with recent antibiotic exposure. The data consistently demonstrate mNGS's superior ability to identify pathogens in this critical scenario.

Table 1: Comparative Sensitivity of mNGS vs. Culture in Antibiotic-Exposed Patients

Infection Type / Patient Population	Sample Type	mNGS Sensitivity	Culture Sensitivity	Statistical Significance (p-value)	Source (Citation)
Febrile Patients (Suspected Infection)	Blood, Puncture Fluid, Tissue, BALF, CSF	58.01%	21.65%	p < 0.001	[24]
Joint Infections (JI)	Synovial Fluid	68.1%	25.5%	p < 0.01	[62]
Spinal Infections	Tissue/Puncture Fluid	81% (Pooled)	34% (Pooled)	Not Reported	[6]
Critically Ill Patients on ECMO	Various (Blood, BALF, etc.)	79.6% (Positive Rate)	30.4% (Positive Rate)	Not Reported	[63]

The elevated detection rate of mNGS has direct clinical implications. In one study of febrile patients, 21 individuals experienced a critical treatment turning point based solely on mNGS results, leading to targeted antibiotic adjustments and subsequent recovery [24]. Similarly, in joint infection cases, patients with positive pathogen identification (largely via mNGS) had a significantly lower reoperation rate (3.03%) compared to those with negative results (28.6%) [62].

Table 2: Diagnostic Accuracy Metrics from Meta-Analyses

Analysis Focus	Number of Studies/Patients	Metric	mNGS Performance	Conventional Method Performance	Source
Spinal Infection (Meta-Analysis)	10 Studies / 770 patients	Pooled Sensitivity	0.81 (95% CI: 0.74–0.87)	0.34 (95% CI: 0.27–0.43)	[6]
Spinal Infection (Meta-Analysis)	10 Studies / 770 patients	Area Under the Curve (AUC)	0.85 (95% CI: 0.82–0.88)	0.59 (95% CI: 0.55–0.63)	[6]
Infectious Diseases (Meta-Analysis)	85 Studies	Overall AUC	0.88 (95% CI: 0.85-0.90)	Not Applicable	[64]

Experimental Protocols and Methodologies

To critically appraise the comparative data, an understanding of the underlying experimental workflows for both mNGS and culture is essential.

Conventional Microbiological Culture Protocol

The culture methods referenced in the studies generally follow a standardized clinical microbiology workflow [24] [62]:

• Sample Inoculation: Clinical samples (e.g., synovial fluid, tissue homogenate, blood) are inoculated onto selective and non-selective culture media, including blood agar plates for aerobic and anaerobic bacteria.
• Incubation: Inoculated media are incubated under appropriate atmospheric conditions (aerobic, anaerobic, or microaerophilic) for a prescribed period. This can range from 24-48 hours for common bacteria to several weeks for slow-growing organisms like fungi or mycobacteria.
• Pathogen Identification: Growing colonies are identified using techniques such as matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) or biochemical profiling systems (e.g., VITEK II) [24] [62].
• Antibiotic Susceptibility Testing (AST): Positive cultures are often subjected to AST using methods like broth microdilution to guide targeted antibiotic therapy.

A key limitation is that this process is entirely dependent on the viability and cultivability of microorganisms in the sample, which is severely hampered by prior antibiotic exposure [24].

Metagenomic Next-Generation Sequencing (mNGS) Protocol

The mNGS protocol is a multi-step process that transforms a clinical sample into actionable diagnostic information [24] [61]:

• Sample Processing and Nucleic Acid Extraction: Sample is homogenized and subjected to mechanical lysis (e.g., using glass beads). Total nucleic acid (DNA and RNA) is extracted and purified using commercial kits (e.g., TIANamp Micro DNA Kit, QIAamp DNA Micro Kit) [24] [62] [63].
• Library Preparation: The extracted DNA is fragmented, end-repaired, and adapter-ligated to create a sequencing library. For RNA-based detection, an additional reverse transcription step is included to create complementary DNA (cDNA) [30].
• Sequencing: The qualified library is loaded onto a high-throughput sequencing platform, such as the Illumina NextSeq 550 or BGISEQ-200, to generate millions of short sequence reads [24] [62].
• Bioinformatic Analysis: This critical step involves:
  • Quality Control & Host Depletion: Low-quality sequences and reads aligning to the human reference genome (hg38) are removed to enhance microbial signal [24] [30].
  • Taxonomic Classification: The remaining reads are aligned against comprehensive microbial genome databases to identify the species present.
  • Result Interpretation: Detected organisms are evaluated against pre-established thresholds and clinical context to distinguish pathogens from contaminants [30].

The following diagram illustrates the core contrast between the two diagnostic pathways and their susceptibility to antibiotic exposure.

The Scientist's Toolkit: Key Research Reagents and Platforms

The implementation and advancement of mNGS technology rely on a suite of specialized reagents, instruments, and software.

Table 3: Essential Research Solutions for mNGS Workflow

Category	Item	Primary Function in mNGS Protocol	Example Products/Brands
Nucleic Acid Extraction	DNA/RNA Extraction Kit	Purifies total nucleic acid from complex clinical samples; critical for lysis efficiency and yield.	TIANamp Micro DNA Kit (Tiangen), QIAamp DNA Micro Kit (QIAGEN) [62] [24]
Library Preparation	Library Prep Kit	Fragments DNA, adds adapters, and amplifies the library for sequencing.	QIAseq Ultralow Input Library Kit (QIAGEN) [24]
Sequencing Platform	NGS Sequencer	High-throughput instrument that generates millions to billions of DNA sequence reads.	Illumina Nextseq 550, BGISEQ-200, NovaSeq X [24] [62] [65]
Bioinformatics	Analysis Software/Pipeline	Classifies sequence reads, removes host background, and identifies microbial species.	IDSeq, PathoScope, One Codex [9]
Quality Control	Bioanalyzer / Qubit Fluorometer	Assesses the quality, size, and concentration of DNA libraries prior to sequencing.	Agilent 2100 Bioanalyzer, Qubit Fluorometer (Thermo Fisher) [24] [62]

The body of evidence unequivocally demonstrates that prior antibiotic exposure drastically reduces the sensitivity of conventional culture methods, while mNGS maintains a significantly higher detection rate. This performance advantage is rooted in the fundamental difference between detecting microbial nucleic acids (mNGS) and relying on pathogen viability (culture). For researchers and clinicians, this translates to mNGS's unique value in diagnosing complex infections where empirical antibiotics have been administered, ultimately enabling more targeted therapies and improving patient outcomes.

The integration of mNGS into clinical practice and clinical trial protocols for infectious diseases requires careful consideration of its higher cost and computational demands compared to culture. However, its ability to identify polymicrobial, rare, and fastidious infections—even after antibiotic initiation—positions it as an indispensable tool in modern pathogen detection and antimicrobial stewardship. Future efforts to standardize protocols, reduce costs, and automate bioinformatic analyses will further solidify its role in precision infectious disease diagnostics.

The accurate and timely identification of pathogens is a critical challenge in clinical diagnostics, particularly for severe infections such as postoperative pneumonia and central nervous system infections. For years, conventional microbial culture has served as the gold standard, despite limitations in sensitivity and turnaround time, which can critically delay appropriate treatment [17]. Metagenomic Next-Generation Sequencing (mNGS) emerged as a powerful alternative, offering rapid, comprehensive pathogen detection by sequencing all nucleic acids in a sample without prior targeting [66] [67]. However, mNGS faces challenges including high costs, significant human DNA interference, and complex data interpretation [68].

Targeted Next-Generation Sequencing (tNGS) represents an evolutionary advancement designed to overcome these limitations. By focusing sequencing efforts on pre-defined pathogen targets, tNGS offers a balanced approach between the comprehensive nature of mNGS and the practicality required for routine clinical use [69]. This guide provides an objective comparison of these technologies, focusing specifically on their performance characteristics and cost-effectiveness in clinical pathogen detection, to inform researchers, scientists, and drug development professionals in their diagnostic strategy selection.

Metagenomic NGS (mNGS)

mNGS employs an untargeted approach to sequence all DNA and/or RNA in a sample, enabling theoretically comprehensive detection of bacteria, viruses, fungi, and parasites [67]. This hypothesis-free method is particularly valuable for identifying rare, novel, or unexpected pathogens without prior suspicion [17]. The technology has demonstrated remarkable success in diagnosing complex infections, with one study on formalin-fixed paraffin-embedded (FFPE) tissues identifying at least one potentially pathogenic and plausible microorganism in 36.8% of samples, detecting diverse pathogens including a novel human circovirus and Coccidioides posadasii [67].

Targeted NGS (tNGS)

tNGS utilizes a targeted approach, enriching specific genomic regions of interest through multiplex PCR or probe-based hybridization prior to sequencing [68] [69]. This focused strategy significantly reduces the sequencing space required, thereby improving sensitivity for targeted pathogens and reducing costs and computational burden. tNGS panels are typically designed to detect clinically relevant pathogens and, increasingly, antibiotic resistance genes, making them particularly suited for syndrome-specific diagnostics [69]. A key advantage is the reduction of human host DNA background, which often plagues mNGS assays [68].

Comparative Performance Analysis: Experimental Data

Diagnostic Performance in Respiratory Infections

A retrospective observational study directly compared tNGS and mNGS in 91 infants with severe postoperative pneumonia after congenital heart surgery, providing robust comparative data [68]. The study demonstrated comparable overall detection rates between the two methods, with tNGS detecting pathogens in 84.6% (77/91) of cases compared to 81.3% (74/91) for mNGS (P = 0.55) [68]. No significant differences were found in sensitivity, specificity, positive percentage agreement (PPA), and negative percentage agreement (NPA) between the two methods (P > 0.05) [68].

Table 1: Performance Comparison of tNGS vs. mNGS in Postoperative Pneumonia

Parameter	tNGS	mNGS	P-value
Detection Rate	84.6% (77/91)	81.3% (74/91)	0.55
Time to Result	12 hours	24 hours	Not reported
Cost per Test	$150	$500	Not reported
Resistance Genes Detected	5 strains	1 strain	Not reported

Beyond comparable detection capabilities, tNGS demonstrated superior performance in detecting antimicrobial resistance markers, identifying five strains with resistance genes compared to only one detected by mNGS [68]. This enhanced capability for resistance gene detection has significant implications for guiding targeted antimicrobial therapy, potentially improving patient outcomes and combating antimicrobial resistance.

Broader Diagnostic Applications

The superior sensitivity of NGS technologies compared to traditional culture methods extends beyond respiratory infections. A recent systematic review and meta-analysis of infected pancreatic necrosis (IPN) diagnosis found that mNGS showed significantly higher sensitivity (0.87, 95% CI: 0.72–0.95) than culture (0.36, 95% CI: 0.23–0.51), while maintaining comparable specificity (0.83 for both) [17]. The area under the curve (AUC) for mNGS (0.92, 95% CI: 0.79–0.94) substantially surpassed that of culture (0.52, 95% CI: 0.27–0.86), confirming the diagnostic superiority of sequencing-based approaches [17].

Similarly, in central nervous system infections (CNSIs), mNGS demonstrated remarkable diagnostic efficiency with significantly shorter turnaround time (1 day vs. 5 days for culture; P<0.001) despite higher detection costs (¥4,000 vs. ¥2,000; P<0.001) [53]. The incremental cost-effectiveness ratio (ICER) of ¥36,700 per additional timely diagnosis suggested cost-effectiveness at China's GDP-based willingness-to-pay threshold, demonstrating the health economic value of advanced diagnostic sequencing in critical care settings [53].

Methodological Workflows: From Sample to Result

Experimental Protocols

tNGS Workflow

The tNGS methodology employs a targeted enrichment approach through the following detailed steps [68]:

• Sample Preparation: Bronchoalveolar lavage fluid (BALF) samples are centrifuged at 12,000 rpm for 5 minutes and homogenized using a tissue grinder at 70 Hz for 5 minutes.
• Nucleic Acid Extraction: DNA/RNA extraction and purification is performed using commercial kits (e.g., Zymo BIOMICS DNA Miniprep Kit) following manufacturer's instructions.
• Library Construction: A multiplex PCR capture library construction kit is utilized with two rounds of PCR amplification. Specific primers target highly conserved regions of pre-defined pathogens - typically including 65 bacteria, 14 fungi, 68 viruses, 6 mycoplasma/chlamydia, and 15 drug-resistance genes.
• Sequencing: The denatured library is sequenced on a platform such as the Illumina MiniSeqDx-CN using a MiniSeq Rapid Kit with sequencing read length = SE100.
• Data Analysis: Generated sequences are filtered for quality and aligned against a pathogen database to confirm pathogen type and identify resistance genes.

mNGS Workflow

The mNGS approach follows a broader, untargeted protocol [68]:

• Sample Preparation: 0.5 mL of BALF sample is combined with 1g of 0.5-mm diameter glass beads and vigorously agitated at 3000 rpm for approximately 30 minutes.
• DNA Extraction: DNA is extracted from 0.3 mL of the lysed sample using the TIANamp Micro DNA Isolation Kit per manufacturer's recommendations.
• Library Construction: DNA libraries are constructed through fragmentation, end repair, adapter ligation, and PCR amplification without target enrichment.
• Sequencing: Quality-controlled libraries are sequenced on platforms such as the BGISEQ-50.
• Bioinformatic Analysis: Raw sequencing data undergoes quality control, followed by alignment to the human reference genome (hg38) to remove human sequences. The remaining fragments are mapped to comprehensive microbial databases for pathogen identification.

Key Research Reagent Solutions

Both tNGS and mNGS workflows require specialized reagents and equipment for optimal performance. The table below outlines essential materials and their functions in the experimental process.

Table 2: Essential Research Reagents and Equipment for NGS Pathogen Detection

Item	Function	Application in tNGS/mNGS
Nucleic Acid Extraction Kits (e.g., Zymo BIOMICS, TIANamp)	Isolation and purification of DNA/RNA from clinical samples	Essential for both methods; sample quality critical for success
Multiplex PCR Panels	Simultaneous amplification of multiple target pathogen sequences	Core component of tNGS for targeted enrichment
Library Preparation Kits	Fragment end-repair, adapter ligation, and amplification for sequencing	Required for both methods; platform-specific kits available
Pathogen-Specific Primers/Probes	Targeted enrichment of conserved genomic regions	Essential for tNGS; typically cover 100+ pathogens and resistance genes
Sequencing Platforms (e.g., Illumina MiniSeq, BGISEQ-50)	High-throughput nucleotide sequencing	Method-specific platforms with varying throughput requirements
Bioinformatics Pipelines	Data quality control, pathogen identification, and interpretation	Critical for both; complexity higher for mNGS due to larger datasets

Cost-Effectiveness and Practical Considerations

Economic Analysis

The economic evaluation of diagnostic technologies must consider both direct costs and broader health economic impacts. In the infant pneumonia study, tNGS demonstrated substantial cost advantages at $150 per test compared to $500 for mNGS [68]. This significant price difference, combined with faster turnaround times (12 hours vs. 24 hours), positions tNGS as a more accessible option for routine clinical use, particularly in resource-limited settings [68].

However, a broader perspective on cost-effectiveness must consider the clinical context and downstream outcomes. A prospective study on central nervous system infections (CNSIs) found that although mNGS had higher detection costs (¥4,000 vs. ¥2,000; P<0.001), it resulted in lower anti-infective costs (¥18,000 vs. ¥23,000; P=0.02) due to more targeted therapeutic interventions [53]. The incremental cost-effectiveness ratio (ICER) of ¥36,700 per additional timely diagnosis suggested mNGS was cost-effective at China's GDP-based willingness-to-pay threshold, highlighting the importance of considering total treatment costs rather than just diagnostic expenses [53].

Technical Limitations and Biases

Each technology presents distinct limitations that must be considered in experimental design and clinical implementation:

tNGS Limitations [68] [69]:

• Target restriction: Cannot detect pathogens outside the predefined panel, limiting utility for novel or unexpected pathogens
• PCR amplification biases: May affect relative abundance measurements of detected pathogens
• Interpretation challenges: Results dependent on established criteria that may not cover all clinical scenarios

mNGS Limitations [68] [67]:

• Human DNA interference: Host nucleic acids can dominate sequencing reads, reducing sensitivity for pathogen detection
• Higher costs and longer turnaround times: Reduced feasibility for rapid, routine diagnostics
• Complex data interpretation: Requires sophisticated bioinformatics pipelines and expertise
• Detection sensitivity issues: May miss low-abundance pathogens despite comprehensive approach

Targeted NGS represents a significant advancement in clinical pathogen detection, offering a balanced approach that maintains high sensitivity while addressing the cost and practicality limitations of mNGS. The experimental data demonstrates that tNGS provides comparable detection rates to mNGS for predefined pathogens, with the advantages of faster turnaround times, lower costs, and enhanced capability for detecting antimicrobial resistance genes [68] [69].

The choice between tNGS and mNGS ultimately depends on clinical context and diagnostic needs. tNGS appears ideally suited for syndrome-specific diagnostics where the range of potential pathogens is reasonably defined, such as hospital-acquired pneumonia or meningitis [68] [69]. In contrast, mNGS remains valuable for complex cases where conventional diagnostics have failed, or when novel or unexpected pathogens are suspected [67] [17].

As sequencing technologies continue to evolve and costs decline, the clinical NGS market is projected to grow significantly from USD 6.2 billion in 2024 to USD 15.2 billion by 2032 [70]. This growth will likely drive further innovation in both tNGS and mNGS technologies, potentially converging into integrated diagnostic approaches that combine the practicality of targeted sequencing with the comprehensiveness of metagenomics. For researchers and clinicians, understanding the specific advantages, limitations, and appropriate applications of each technology is essential for optimizing pathogen detection and ultimately improving patient outcomes.

The advent of metagenomic Next-Generation Sequencing (mNGS) has introduced a powerful, hypothesis-free approach to pathogen detection, challenging the long-standing reign of culture-based methods as the diagnostic gold standard. This guide provides an objective comparison of the diagnostic performance of mNGS versus traditional culture, synthesizing current evidence to inform research and development. Quantitative synthesis reveals that while mNGS demonstrates superior sensitivity and is invaluable for detecting uncultivable or fastidious pathogens, traditional culture maintains an advantage in specificity. The integration of both methods, leveraging their complementary strengths, presents the most robust framework for advanced pathogen detection and antimicrobial resistance research.

Diagnostic Performance Metrics: A Quantitative Synthesis

The evaluation of any diagnostic method rests on key performance metrics. The table below synthesizes pooled data from meta-analyses and large-scale studies to provide a direct comparison between mNGS and culture.

Table 1: Pooled Diagnostic Performance of mNGS vs. Culture

Metric	mNGS Performance	Culture Performance	Context & Implications
Overall Sensitivity	75% (95% CI: 72–77%) [71]	21.65% [42]	mNGS is significantly more likely to identify the causative agent in a true infection.
Overall Specificity	68% (95% CI: 66–70%) [71]	99.27% [42]	Culture is superior at ruling out non-infectious cases; mNGS's lower specificity requires careful result interpretation.
Diagnostic Odds Ratio (DOR)	11.94 (95% CI: 6.11–23.34) [71]	Data not fully pooled, but high specificity suggests a high DOR.	mNGS shows a strong overall ability to distinguish between infected and non-infected individuals.
Area Under Curve (AUC)	0.85 (Excellent) [71]	Not Available	Confirms the overall excellent diagnostic accuracy of mNGS.
Positivity Rate in Febrile Patients	58.01% [42]	21.65% [42]	mNGS can identify pathogens in a larger proportion of clinically infected patients.

The Diagnostic Odds Ratio (DOR) is a key metric that combines sensitivity and specificity into a single indicator of test performance. A higher DOR indicates a better ability to distinguish between diseased and non-diseased states. The mNGS DOR of 11.94 is strong, confirming its utility as a diagnostic tool [71].

Performance varies significantly by sample type and clinical scenario. For instance, in Central Nervous System (CNS) infections, mNGS demonstrated a sensitivity of 63.1% and a specificity of 99.6% in a large 7-year study, outperforming indirect serologic testing (28.8% sensitivity) and direct detection tests from non-CSF samples (15.0% sensitivity) [30]. Another study on body fluids reported a sensitivity of 74.07% and a specificity of 56.34% for mNGS when using culture as a reference [27].

Analysis of Performance Disparities and Clinical Impact

The Sensitivity-Specificity Trade-off

The inverse relationship between the sensitivity of mNGS and the specificity of culture is the central trade-off in modern pathogen diagnostics. This disparity stems from the fundamental principles of each method.

• Breadth vs. Specificity: mNGS detects microbial nucleic acids in an unbiased manner, identifying pathogens that are dead, difficult to grow, or present in low biomass. This breadth increases sensitivity but also increases the detection of environmental contaminants or clinically irrelevant colonizers, thereby reducing specificity [9] [71]. Culture, by contrast, requires a viable, growing microorganism, which serves as a natural confirmation of its pathogenic potential and leads to very high specificity [42].

• Impact of Prior Antibiotic Use: Culture is highly susceptible to false negatives when patients have received antimicrobial therapy before sample collection. mNGS, detecting DNA rather than relying on viability, is largely unaffected by this pre-analytical factor. Studies have shown that the positive rate of mNGS on puncture fluid and tissue samples remains high despite prior antibiotic use, a setting where culture performance drops significantly [42].

Impact on Clinical Decision-Making

The superior sensitivity of mNGS has a direct and measurable impact on patient management. In a study of 368 febrile patients, 64 individuals had their antibiotic therapy adjusted based on positive mNGS results that were not available from culture. This included transitions to targeted therapy, antibiotic downgrading, and combination therapy. For 21 of these patients, the mNGS result was identified as the turning point in their treatment, leading to recovery and discharge [42]. This demonstrates the value of mNGS in enabling precision medicine for infectious diseases.

Experimental Protocols and Workflows

Standard mNGS Workflow for Body Fluids

The following diagram illustrates the core steps in a standard mNGS protocol for clinical body fluid samples like cerebrospinal fluid (CSF), plasma, or drainage fluid.

Detailed Protocol Steps:

• Sample Collection & Pre-processing: Clinical samples (e.g., CSF, blood, tissue) are collected aseptically. For blood samples, plasma is separated via centrifugation to analyze cell-free DNA (cfDNA), which significantly reduces host DNA background compared to using whole blood [72]. For other fluids, centrifugation is used to separate whole-cells (precipitate) from cell-free DNA (supernatant) [27].
• Nucleic Acid Extraction: Total nucleic acid (DNA and RNA) is extracted from the sample. For RNA viruses, a reverse transcription step is included to create complementary DNA (cDNA) [32] [30]. Commercial kits, such as the TIANamp Micro DNA Kit or Qiagen DNA Mini Kit, are typically used [32] [27].
• Library Preparation: The extracted DNA and cDNA are mechanically or enzymatically fragmented to a uniform size (e.g., 200-300 bp). Sequencing adapters are then ligated to the fragments, and the library is amplified via PCR. Kits like the VAHTS Universal Pro DNA Library Prep Kit for Illumina are commonly employed [27] [72].
• High-Throughput Sequencing: The pooled libraries are sequenced on platforms such as the Illumina NovaSeq or BGISEQ-50, generating millions to billions of short reads [32] [42].
• Bioinformatic Analysis: This critical phase involves:
  • Quality Control & Host Depletion: Low-quality sequences and reads aligning to the human reference genome (e.g., hg38) are removed using tools like Fastp and BWA [32] [72].
  • Taxonomic Classification: The remaining non-host reads are aligned against comprehensive microbial genome databases (e.g., NCBI RefSeq) using tools like BLAST or BWA. The output is a list of detected microorganisms and their relative read abundances [30] [27].
• Interpretation & Reporting: Detected microbes are interpreted using predefined criteria to distinguish pathogens from contaminants. Criteria may include the number of unique reads, genomic coverage, and the ratio of a microbe's abundance in the sample versus negative controls [32] [30]. The final report details pathogens identified as likely causative agents.

Traditional Culture Protocol

The culture workflow remains the foundational method for pathogen identification and phenotypic antibiotic susceptibility testing.

Table 2: Standard Culture Protocol for Sterile Body Fluids

Step	Protocol Description	Key Equipment & Reagents
1. Sample Inoculation	Sample is inoculated into liquid enrichment media (e.g., blood culture bottles like BD BACTEC or bioMérieux BacT/ALERT) and/or directly onto solid agar plates (e.g., blood, chocolate, MacConkey agar).	Blood culture bottles, Solid agar plates, Incubators
2. Incubation	Inoculated media are incubated aerobically and/or anaerobically at 35±2°C for a defined period (typically 1-5 days, up to 6 weeks for certain fungi/mycobacteria).	CO2 Incubator, Anaerobic chamber
3. Subculture & Isolation	Once growth is detected (automatically or visually), a subculture is performed from liquid media to solid agar to obtain pure, isolated colonies for identification.	Inoculation loops, Agar plates
4. Pathogen Identification	Isolated colonies are identified using phenotypic methods (e.g., gram stain, biochemical tests) or automated systems like MALDI-TOF Mass Spectrometry.	MALDI-TOF MS, VITEK II compact system, Biochemical test strips
5. Antimicrobial Susceptibility Testing (AST)	The pure isolate is subjected to AST to determine the Minimum Inhibitory Concentration (MIC) of relevant antibiotics, guiding targeted therapy.	AST cards/broths, Incubators, Automated AST systems

The Scientist's Toolkit: Essential Research Reagents and Solutions

Successful implementation and development of pathogen detection methods rely on a suite of specialized reagents and tools.

Table 3: Key Research Reagent Solutions for mNGS and Culture

Category	Item	Function & Research Application
Sample Preparation	Nucleic Acid Extraction Kits (e.g., Qiagen DNA Mini Kit, TIANamp Micro DNA Kit)	Isolate high-quality, PCR-amplifiable DNA/RNA from diverse clinical matrices for downstream sequencing [32] [27].
	Host DNA Depletion Kits (e.g., NEBNext Microbiome DNA Enrichment Kit)	Selectively deplete abundant human host DNA to improve the signal-to-noise ratio and sensitivity for low-biomass microbial pathogens [9].
Library Construction & Sequencing	Library Prep Kits (e.g., VAHTS Universal Pro DNA Library Prep Kit, PMseq Pathogen Detection Kit)	Prepare fragmented and adapter-ligated DNA for sequencing on platforms like Illumina. Essential for generating sequence-compatible libraries [32] [27].
	Internal Control DNA (e.g., Artificial synthetic DNA)	Spiked into samples to monitor the efficiency of the entire workflow from extraction to sequencing, ensuring result reliability [72].
Bioinformatics	Reference Databases (e.g., NCBI RefSeq, GenBank)	Curated databases used for taxonomic classification of sequencing reads. Database comprehensiveness directly impacts detection capability [30].
	Analysis Pipelines & Platforms (e.g., IDSeq, PathoScope, One Codex)	Automated bioinformatic platforms that streamline data analysis, from raw read processing to pathogen reporting, reducing the bioinformatics burden [9].
Traditional Culture	Enrichment & Culture Media (e.g., BD BACTEC bottles, BacT/ALERT bottles, Blood/Chocolate agar)	Support the growth and viability of a wide range of fastidious and non-fastidious microorganisms from clinical specimens [42] [72].
	Identification & AST Systems (e.g., MALDI-TOF MS, VITEK II)	Enable rapid, accurate microbial identification and generate phenotypic antibiotic susceptibility profiles for guiding treatment [42].

The evidence clearly demonstrates that mNGS and traditional culture are not mutually exclusive but are complementary diagnostics. mNGS offers a powerful, broad-net approach with high sensitivity, revolutionizing the diagnosis of difficult-to-culture and novel pathogens. Culture remains indispensable for its high specificity and provision of live isolates for antimicrobial susceptibility testing. The choice of method depends on the clinical or research question: mNGS for broad pathogen detection in complex cases, and culture for confirmation and phenotypic analysis.

Future developments will focus on integrating these methodologies into unified diagnostic pathways. Emerging trends include the use of AI and machine learning to improve bioinformatic interpretation of mNGS data [9], the development of rapid, portable sequencing technologies for point-of-care testing [9], and the application of metagenomics directly to positive blood culture bottles to speed up pathogen identification. For researchers and drug development professionals, understanding the distinct advantages and limitations of each method is paramount for designing robust studies, developing next-generation diagnostics, and ultimately advancing the field of infectious disease management.

Conclusion

The evidence confirms that mNGS and microbial culture are not mutually exclusive but complementary diagnostics. mNGS offers a powerful, hypothesis-free tool for rapid, sensitive detection of rare, fastidious, and culture-negative pathogens, significantly impacting patient management. However, culture remains indispensable for phenotypic antibiotic susceptibility testing and as a high-specificity benchmark. Future directions must focus on standardizing mNGS protocols, reducing costs, validating antimicrobial resistance prediction from genomic data, and establishing clear diagnostic thresholds. For researchers and drug developers, this synergy paves the way for more precise infectious disease diagnostics, tailored therapeutic strategies, and robust surveillance of emerging pathogens, ultimately strengthening the foundation of clinical microbiology and antimicrobial development.

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