May 14, 2026

Clinical mNGS Pathogen Detection for Culture-Negative Infection

Clinical microbiology is moving from targeted detection toward broader, patient-specific interpretation. Culture can miss fastidious organisms, syndromic PCR panels only detect their predefined targets, and antimicrobial resistance genes are not always easy to translate into a phenotype. In this Metagenomics Micro-Webinar, Prof. Dr. John W. A. Rossen explains how metagenomic next-generation sequencing (mNGS) can support precise pathogen detection by profiling the pathogenome, resistome, virome, microbiome, and host response from clinical samples.

What Is Clinical mNGS Pathogen Detection?

Clinical mNGS pathogen detection is an untargeted sequencing approach that looks for microbial nucleic acids in a patient sample instead of testing only for organisms named in a culture workup, PCR panel, or gene probe assay. It is most useful when the clinical question is broader than “is this one target present?”

Use clinical mNGS when the likely pathogen may be rare, mixed, fastidious, unexpected, or missed by the first diagnostic test. The result is strongest when sample preparation reduces host background, negative controls flag contamination, and the analysis pipeline links microbial reads to the specimen, syndrome, and treatment question.

Compared with targeted gene probes or PCR panels, mNGS can widen pathogen detection, but it also requires more interpretation. A detected organism should be reviewed against sample type, read depth, controls, resistance markers, host response, and clinical presentation before it is treated as the cause of disease.

When Clinical mNGS Changes the Diagnostic Answer

Clinical mNGS is most useful when the likely pathogen is not captured by the first test: culture-negative infection, encephalitis or pneumonia with an incomplete panel result, immunocompromised patients with unexpected organisms, or samples where host DNA background hides low-abundance microbial signal. In those cases, mNGS can broaden the search beyond culture and PCR, while sample preparation and interpretation decide whether the result is actionable.

If host background is the sensitivity limit, start with Devin Host Depletion. If the challenge is interpreting mixed microbial reads, review PaRTI-Seq Analysis. If your lab is evaluating where clinical mNGS fits in the diagnostic workflow, contact Micronbrane to discuss sample type, turnaround, and reporting needs.

Watch the full webinar: Precise Pathogen Detection: Personalizing Clinical Microbiology with mNGS

Key Takeaways

Targeted molecular tests are fast and specific, but they miss organisms outside the assay design.
mNGS can identify unexpected pathogens in encephalitis, meningitis, pneumonia, sepsis, UTI, and other unresolved infectious syndromes.
DNA and RNA results can separate pathogen presence from active replication, which can change treatment decisions.
Host DNA background remains one of the main sensitivity limits for clinical mNGS, especially in blood, respiratory, and urine samples.
Resistance genes require context: gene presence, expression, linkage to a pathogen, mobile elements, and phenotype prediction all matter.
Standardization, health economics, AI-assisted interpretation, and global access are as important as the sequencing technology itself.

Why Targeted Molecular Testing Is Not Enough

Prof. Rossen began with the strengths of molecular diagnostics: automation, high throughput, broad specimen compatibility, rapid turnaround, and the ability to detect resistance or virulence genes. These capabilities make molecular testing central to modern infectious disease diagnostics.

The limitation is that targeted testing is hypothesis-driven. A multiplex PCR panel can only detect the pathogens and resistance markers it was designed to include. That specificity is useful when the suspected pathogen is on the panel, but it becomes a blind spot when the true cause is rare, novel, mixed, or clinically unexpected. For a broader primer on this diagnostic gap, see mNGS: Culture-Independent Pathogen Detection.

A gene probe pathogen detection assay has the same constraint: it can be sensitive for a known target, but it cannot explain an infection caused by an organism outside the probe design. Clinical mNGS is not a replacement for every targeted assay. It is the escalation path when the target list is uncertain, the sample is difficult to recollect, or the diagnostic result must account for bacteria, viruses, fungi, parasites, resistance genes, and host background together.

Clinical mNGS changes the question from “is this target present?” to “what nucleic acid is in this sample, and what part of it explains the patient’s disease?” That broader question is why mNGS is increasingly discussed for encephalitis, meningitis, pneumonia, sepsis, and other cases where conventional microbiology does not find the cause.

DNA, RNA, And The Difference Between Presence And Activity

One of the strongest clinical examples in the webinar involved a four-year-old girl after stem cell transplantation for pre-B cell acute lymphoblastic leukemia. EBV was detected in spinal fluid, and antiviral therapy was started, but imaging still suggested an inflammatory process.

Shotgun metagenomic sequencing detected EBV and HHV-7 in DNA, but not in RNA. Prof. Rossen interpreted this as evidence that the viruses were present without active replication. The infectious disease physician reduced antiviral treatment, and lymph-node biopsy later confirmed post-transplant lymphoproliferative disorder.

The case highlights why metagenomics and metatranscriptomics can be complementary. DNA can show what is present. RNA can help determine whether a virus, resistance marker, or virulence program is active. That distinction matters when the clinical question is not only pathogen identification, but treatment prioritization.

Finding Unexpected Pathogens In Pneumonia

Prof. Rossen also presented a pneumonia case involving a 12-year-old boy with obesity and type 2 diabetes. A respiratory viral panel detected seasonal human coronavirus OC43, but the clinical presentation suggested that this did not fully explain the lower respiratory disease. After discharge and rapid readmission, pleural fluid culture was negative.

Metagenomics detected the coronavirus and an unexpected anaerobic bacterium consistent with a pleural-space infection. Antibiotic coverage was adjusted to include anaerobes, and the patient improved.

This is the practical value of unbiased detection: it can uncover organisms that were not suspected at ordering time. In clinical microbiology, that does not mean every detected organism is causal. It means the laboratory and clinicians get a broader evidence set for deciding what fits the patient.

UTI Metagenomics Shows Why Host Background Matters

The webinar then moved to urinary tract infection diagnostics. In the study discussed, routine culture-positive and culture-negative urine samples were spiked with T4 and T7 phages as internal controls, sequenced by shotgun metagenomics, and filtered against a urinary-pathogen set. For high bacterial-load urine samples, mNGS reached roughly 95% positive agreement with culture.

The misses were informative. In some samples, organisms were detected by sequencing but fell below the reporting threshold. In others, anaerobic flora or human DNA background dominated the sequencing output, reducing the ability to call the expected pathogen. This mirrors a broader mNGS limitation: when host DNA consumes read depth, microbial signal becomes harder to recover.

That is why sample preparation remains central to clinical mNGS. Micronbrane’s Devin Host Depletion Filter is designed to remove mammalian nucleated cells before extraction, allowing microbial material to pass through. In a complete workflow, host depletion can be paired with Devin Microbial DNA Enrichment, low-input library preparation, and PaRTI-Seq Analysis to increase usable microbial signal before interpretation.

For a urine-focused feasibility study using host depletion in clinical samples, see Host Depletion in Clinical mNGS: A Feasibility Study.

Targeted Enrichment Can Improve Sensitivity

Prof. Rossen described targeted enrichment as another way to improve sensitivity after library preparation. His team designed hybrid-capture probes for urinary pathogens, then enriched sequencing libraries for pathogen-related sequences.

The enrichment-based approach increased positivity, especially in culture-negative samples. It also improved detection of resistance markers, including ESBL, MRSA, and vancomycin-resistant enterococci. This is not a contradiction of unbiased mNGS; it is a practical acknowledgment that clinical laboratories may use different sequencing strategies depending on sample type, pathogen load, turnaround requirements, and cost.

For labs evaluating workflow design, the key question is where sensitivity is lost. If the bottleneck is host background, upstream host depletion is critical. If the bottleneck is target abundance after library preparation, enrichment may help. If the bottleneck is interpretation, the answer is better bioinformatics and reporting.

AMR Interpretation Needs More Than Gene Presence

The Q&A returned several times to antimicrobial resistance. Prof. Rossen stressed that detecting a resistance gene does not always equal phenotypic resistance. A gene may not be expressed, may not be linked to the pathogen causing disease, or may sit on a mobile element that changes its clinical meaning.

Read-based approaches can show whether a gene is present. Assembly-based approaches can add context: which organism the gene may belong to, what mobile genetic element surrounds it, and whether nearby promoters or mutations might affect expression. Long-read sequencing can help with this linkage problem because longer fragments make assembly easier.

This has direct implications for stewardship. If a resistance gene is present anywhere in a patient sample, even outside the organism currently being treated, clinicians may need to consider whether antibiotic pressure could select for transfer. For a focused discussion of AMR in low-bacterial-load samples, see Detecting AMR from Low Bacterial Load Clinical Samples.

AI, Standardization, And Clinical Adoption

Prof. Rossen framed AI as an interpretation layer, not a replacement for clinical microbiology expertise. Large mNGS datasets need fast and accurate analysis, and AI may help predict antimicrobial resistance from genotype, prioritize findings, and make reports more usable.

But the barrier is not only technical. He emphasized health technology assessment, health economics, organizational factors, patient factors, training, and capacity building. Clinical metagenomics must prove where it adds value: which patients should receive it, which sample types are appropriate, and whether it should be ordered first-line or after conventional tests fail.

Standardization is also unresolved. Without common approaches to validation, thresholds, controls, reporting, and reproducibility, published studies remain hard to compare. That is why Prof. Rossen argued in the Q&A that scientists, laboratories, companies, and authorities all need to contribute to mNGS guideline development.

Who Benefits Most From mNGS?

When asked which patients benefit most, Prof. Rossen identified critically ill ICU patients and immunocompromised or transplant patients. ICU patients need rapid answers because disease can progress quickly and empiric therapy may fail. Immunocompromised patients need broader detection because pathogens that are often low priority in healthy hosts can become clinically serious after transplantation or intensive therapy.

That does not mean mNGS should be used indiscriminately. It means clinical microbiology needs diagnostic algorithms that match patient risk, sample type, timing, and expected clinical action. The future of mNGS in clinical microbiology depends on combining the right wet-lab workflow, the right analysis pipeline, and the right stewardship process.

About the Speaker

Prof. Dr. John W. A. Rossen is Endowed Professor of Medical Microbiology and Infection Control at the University of Groningen and Isala Hospital Zwolle, Medical Head of Innovation and Science at Isala Hospital, and affiliated with the University of Utah. His research focuses on personalized molecular microbiology, antimicrobial resistance, genomics, epidemiology, metagenomics, and metatranscriptomics.