Breakthrough genomic test identifies virtually any infection in one go

A single lab test that can detect almost any pathogen — bacteria, viruses, fungi, or parasites — from one patient sample has shown remarkable accuracy in a large clinical trial. Researchers at the University of California San Francisco have validated a genomic sequencing method that correctly identified 86% of neurological infections across nearly 5,000 patients, and the U.S. Food and Drug Administration has granted it breakthrough device designation.

At a glance

  • mNGS test: Metagenomic Next-Generation Sequencing analyzes a single cerebrospinal fluid sample against a database of more than 68,000 known pathogens, returning results in as little as 48 hours.
  • Clinical trial scale: UCSF professor Charles Chiu and his team tested 4,828 patient samples over seven years, publishing their findings in Nature Medicine.
  • Pandemic surveillance: An adapted respiratory fluid version of the test can detect novel virus strains and flag potential outbreak threats, cutting processing time from up to seven days down to 12–24 hours.

Why diagnosing infections is so hard

Most infectious disease testing works by elimination. A doctor observes symptoms, makes an educated guess about the likely cause, and orders a targeted test. If the first guess is wrong, the process starts over.

For common illnesses, this works well enough. But for neurological infections like meningitis — which can have rare or unexpected causes and worsen rapidly — every hour of uncertainty carries real risk. Patients may spend days or weeks in the hospital on broad-spectrum treatments while clinicians wait for answers that conventional tests may never provide.

That delay has consequences. It extends hospitalization, increases exposure to unnecessary medications, and in serious cases can mean the difference between full recovery and permanent damage.

What the genomic approach changes

The mNGS method sidesteps the guesswork entirely. A sample of cerebrospinal fluid — the liquid surrounding the brain and spinal cord — is collected, and DNA and RNA are extracted from it. A computational pipeline then processes millions of genetic sequences per minute, matching them against a database of more than 68,000 pathogens.

The result is a comprehensive picture of what is actually present in the sample, not what a clinician suspected might be there. Chiu, who has spent roughly a decade developing this approach, also co-founded Delve Bio, a company that produces mNGS test kits and delivers analyzed results within 48 hours.

For conditions like meningitis or encephalitis, that turnaround can compress a diagnostic timeline from weeks to days — allowing targeted treatment to begin far sooner.

A tool for the next pandemic

The implications extend well beyond individual patient care. The same sequencing logic that identifies a known bacterium in a spinal fluid sample can also detect novel or emerging viral strains in respiratory fluid — including pathogens that have never been catalogued before.

The UCSF team has adapted mNGS to work with respiratory samples and largely automated the process, bringing turnaround time down to 12–24 hours. That speed matters enormously for early pandemic detection. The test identified SARS-CoV-2 and influenza strains in validation studies, suggesting it could serve as an early-warning system for novel respiratory threats before they reach widespread transmission.

Researchers are also working to adapt mNGS for plasma and other body fluids, which would broaden its diagnostic reach considerably. The FDA’s breakthrough device designation for both the cerebrospinal fluid and respiratory fluid versions means formal review could happen within months.

The limits worth naming

An 86% detection rate is genuinely impressive for a method covering such a wide range of pathogens, but it also means roughly one in seven neurological infections went unidentified in the trial. Cost and access remain open questions — genomic sequencing at scale is still expensive, and the infrastructure required to run these tests is not evenly distributed across health systems globally.

The Nature Medicine publication marks a significant milestone in moving mNGS from research tool to clinical standard. But equitable deployment — ensuring the test reaches patients in under-resourced hospitals and lower-income countries, where diagnostic delays are often most severe — will require deliberate effort from regulators, health systems, and funders alike.

Chiu’s team has spent a decade building toward this moment. The evidence now exists. What happens next depends on whether the broader health infrastructure can move as fast as the science.

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For more on this story, see: New Atlas

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