Closing the Diagnostic Gap in CNS Infections

Meningitis and encephalitis are life-threatening inflammatory conditions of the central nervous system (CNS). Timely diagnosis and appropriate treatment are critical for improved clinical outcomes. Delays in either area have been linked to prolonged hospital stays, long-term neurological damage, and increased mortality. 

Effective treatment depends on determining whether the condition is infectious or non-infectious in origin, beyond the causative pathogen. However, identifying the cause or etiology can be quite complex. In many instances, traditional diagnostic methods fall short, leaving clinicians with limited information and creating clinical dilemmas, such as whether to prescribe immunosuppressants if infection remains on the differential. These challenges highlight a growing need for diagnostic solutions that are comprehensive, fast, and capable of identifying both common and unexpected pathogens. 

Diagnostic challenges in meningitis and encephalitis 

Diagnosing meningitis and encephalitis is a time-sensitive process, complicated by clinical and diagnostic uncertainty. For instance, the symptoms of these conditions, such as headache, fever, double vision, and nausea, are common across a variety of infectious and non-infectious diseases. 

If the etiology is determined to be infectious, the source of infection must then be determined. Infections can stem from bacterial, viral, fungal, or parasitic pathogens. Altogether, this creates a substantial number of possibilities, including rare and emerging pathogens that are not covered by standard diagnostic panels. If the etiology is non-infectious, confidently excluding all possible pathogens is critical in determining appropriate therapies.

To detect (or exclude) pathogens, clinicians traditionally rely on culture-based methods, antigen and antibody tests, and targeted molecular detection. While valuable, these approaches have limitations in speed, sensitivity, and scope. For instance, culture and antibody-based methods can take days to weeks to return results, and only detect a few bacterial or viral targets, leaving clinicians to make treatment decisions without a complete picture. 

Other typical microbial diagnostic tests only target specific pathogens, meaning a test will only identify what it’s designed to look for. As a result, clinicians must a priori make educated guesses about which pathogens to test for. This piecemeal approach can quickly add up in cost and time. In one study, researchers reviewed over 1,100 cases involving patients with suspected CNS infections—including meningitis, encephalitis, and meningoencephalitis. Across this cohort, the average number of microbiological tests performed per case was 20.2, spanning a mix of culture-based, nucleic acid amplification, antigen, and serologic methods. Despite this extensive testing, nearly 45 percent of cases remained undiagnosed with unknown etiology.¹ 

Collectively, these challenges lead to delays in diagnosis, prolonged hospital stays, increased healthcare costs, poorer outcomes, and treatment with unnecessary empiric antimicrobial therapy. This has prompted a growing interest in modern diagnostic approaches, such as metagenomic next-generation sequencing (mNGS).

What is mNGS?

In 2016, the University of California, San Francisco (UCSF) developed and validated a clinical mNGS assay for diagnosing infectious causes of meningitis and encephalitis from cerebrospinal fluid (CSF). Unlike targeted tests, which are limited to detecting specific pathogens, this approach simultaneously sequences all of the genetic material present in a sample, enabling the comprehensive and unbiased detection of bacteria, fungi, viruses, and parasites.

In UCSF’s mNGS workflow (Figure 1), all nucleic acids—RNA and DNA—are extracted from the sample and fragmented. Adapter sequences are then added to the fragments to enable multiplex sequencing and generate a library. Once the fragments are sequenced, human genetic material is removed through bioinformatic methods. The remaining reads are assembled into longer contiguous sequences and compared against reference databases for taxonomic classification. 

Clinical impact of mNGS

mNGS offers several important advantages over traditional diagnostic methods, particularly for complex or unexplained CNS infections. Since it sequences nucleic acids present in a sample, no prior assumptions are needed about which pathogen might be responsible. This approach allows for the identification of unexpected, rare, and emerging pathogens—including those for which no commercially available diagnostic tests exist. 

A notable case involved a 14-year-old boy who, after three hospitalizations and approximately 40 diagnostic tests, including a brain biopsy, still had no definitive diagnosis. It wasn’t until mNGS was performed on a CSF sample that a leptospira infection was identified, providing a clinically actionable diagnosis.² 

In critically ill patients, delays in diagnosis and treatment can lead to irreversible neurological damage or death. These patients often present with non-specific symptoms and overlapping clinical issues, which makes it difficult to determine which pathogens to test for. Similar challenges exist in pediatric populations, where limited CSF sample volumes add further constraints. Standard diagnostic workups can consume large amounts of CSF and sometimes require repeat lumbar punctures. By consolidating the diagnostic process into a single, comprehensive test, mNGS avoids the need for serial testing, reduces sample volume requirements, and provides clinically actionable information earlier. 

A negative mNGS result can also guide care. When no pathogen is detected, clinicians can more confidently exclude infection as the cause of illness. This can support decisions to discontinue empiric antimicrobial therapies, limit further diagnostic testing, and pursue alternative treatments. In this way, mNGS contributes not only to confirming infections but also to reducing patient risk, minimizing resource use, and accelerating the path to appropriate care.  

mNGS in practice

To support broader clinical access, Delve Bio has exclusively licensed and optimized UCSF’s mNGS technology as a laboratory-developed test under the name Delve Detect. The test provides comprehensive detection of both RNA and DNA pathogens (bacteria, viruses, fungi, and parasites) from CSF samples in a single assay, without requiring prior assumptions about the infectious agent. 

Early implementations of CSF mNGS at UCSF had an average turnaround time of 8.2 days, which limited its usefulness in clinical decision-making. Delve Bio reduced the turnaround time to an average of 2.1 days from sample receipt. This faster processing allows patients to receive results sooner, improving the clinical actionability of mNGS testing. 

To meet the operational demands of clinical labs, Delve Detect incorporates automated extraction and library preparation, supporting scalability and ensuring consistency across samples. It also employs high-throughput sequence generation for greater read depth and improved sensitivity. 

One of the main obstacles to routine clinical use of mNGS is the volume and complexity of the data it produces. Delve Detect addresses this with Delve Decide, its proprietary bioinformatics pipeline and clinical interpretation software for automating pathogen identification. After analyzing and performing quality control checks on the millions of sequences and filtering out human genetic material, Delve Decide compares microbial sequences to a custom-curated database of over 68,000 known and emerging pathogens, enabling accurate classification across a wide taxonomic range. 

To further support clinical integration, Delve Bio’s clinical microbial sequencing board offers consultative support, facilitating direct discussion between clinical providers and infectious disease experts to guide interpretation and decision-making. 

Together, these features aim to make mNGS more accessible and actionable for clinical teams managing patients with suspected CNS infections. By reducing reliance on serial testing, minimizing sample volume requirements, and delivering timely results, Delve Detect supports earlier and more targeted treatment, thereby improving outcomes across a diverse range of patient populations. 

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References

1.    “Seven-year performance of a clinical metagenomic next-generation sequencing test for diagnosis of central nervous system infections.” https://www.nature.com/articles/s41591-024-03275-1

2.    “Actionable Diagnosis of Neuroleptospirosis by Next-Generation Sequencing.” https://www.nejm.org/doi/full/10.1056/NEJMoa1401268