In a groundbreaking advancement poised to reshape the understanding and diagnosis of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), researchers at Cornell University have pioneered a novel approach leveraging circulating cell-free RNA (cfRNA) signatures detectable in blood plasma. This cutting-edge technique utilizes machine-learning algorithms to decode the complex molecular signals that dying cells release into the bloodstream, offering unprecedented insight into the elusive pathophysiology of this debilitating, often misunderstood chronic illness.
ME/CFS, characterized by profound fatigue, cognitive dysfunction, post-exertional malaise, and multi-systemic symptoms, has long challenged clinicians due to the absence of reliable diagnostic tests. The disease’s clinical overlap with other disorders renders symptom-based diagnosis problematic. Addressing this diagnostic void, the Cornell team has developed computational models capable of discerning disease-specific cfRNA patterns, essentially reading the molecular “activity logs” cells leave behind as they undergo damage or death. By capturing these cellular footprints, the study brings the prospect of a minimally invasive blood-based assay closer to reality.
The research, recently published on August 11, 2025, in the prestigious Proceedings of the National Academy of Sciences, details how plasma samples from ME/CFS patients and sedentary healthy controls were analyzed to isolate and sequence extracellular RNA fragments. These fragments serve as proxies for gene expression profiles from diverse tissues impacted by the disease. Utilizing advanced machine-learning classifiers, the team identified over 700 transcripts significantly divergent between ME/CFS cases and controls, facilitating a molecular signature indicative of the syndrome.
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Leading the study, Anne Gardella, a doctoral candidate specializing in biochemistry, molecular, and cell biology at Cornell University, highlighted the unique capability of this method to shed light on systemic cellular alterations. “The circulating cfRNA reflects a composite snapshot of cellular turnover and stress responses occurring throughout the body,” Gardella explained. “This allows us to map disease-associated molecular changes across multiple organ systems simultaneously, which is crucial given ME/CFS’s widespread physiological impacts.”
The project was conceived through a collaboration between the De Vlaminck Lab, under associate professor Iwijn De Vlaminck, renowned for pioneering cell-free nucleic acid technologies, and Dr. Maureen Hanson’s team, leaders in ME/CFS pathophysiology research. De Vlaminck’s laboratory previously demonstrated the diagnostic power of cfRNA in identifying Kawasaki disease and multisystem inflammatory syndrome in children (MIS-C), signaling the versatility of this approach in inflammatory and immune-mediated conditions.
This interdisciplinary effort leveraged deep computational analysis, applying machine-learning algorithms adept at handling high-dimensional data to uncover patterns invisible to conventional statistical methods. These computational models not only confirmed immune dysregulation known to occur in ME/CFS but also implicated extracellular matrix disorganization and T cell exhaustion, signaling broad immune dysfunction and tissue remodeling abnormalities. Such insights provide a more nuanced biological framework for understanding ME/CFS beyond symptomatology.
Crucially, the team employed deconvolution techniques informed by cell type-specific gene expression markers, drawn from prior single-cell RNA sequencing data, to pinpoint the cellular origins of the cfRNA. This revealed six distinct cell types with differential RNA signatures in ME/CFS patients, with plasmacytoid dendritic cells—the primary producers of type I interferons—showing the most significant elevation. This finding suggests an aberrant antiviral immune activation state underpinning the disease’s chronicity.
Monocytes, platelets, and various T cell subsets also displayed altered cfRNA levels, reinforcing the hypothesis of systemic immune dysregulation. These immune perturbations may contribute to the constellation of symptoms experienced by patients, from neuroinflammation to vascular dysfunction. The data also hint at persistent immune activation or unresolved viral triggers, potentially connecting to the hypothesis of post-infectious etiologies for ME/CFS.
The cfRNA-based classifier developed achieved an accuracy rate of 77% in distinguishing ME/CFS patients from controls—a promising but preliminary figure. While this degree of precision is insufficient to constitute a standalone diagnostic tool today, it represents a significant leap forward given the historical diagnostic ambiguity surrounding ME/CFS. Improvements with larger cohorts and integration with other biomarkers could enhance diagnostic performance, ultimately aiding clinicians in making objective, timely diagnoses.
Beyond diagnostics, the technology offers a potent research instrument to dissect the multifaceted biology of ME/CFS and related chronic illnesses such as long COVID. Notably, while long COVID has recently amplified awareness of post-infectious chronic syndromes, ME/CFS remains more prevalent and, in many cases, more severely disabling. The Cornell team’s innovation could therefore serve as a reference model for studying infection-associated chronic diseases with overlapping symptomatology but distinct molecular fingerprints.
The study benefits from strong support by the National Institutes of Health and the WE&ME Foundation, underscoring the growing prioritization of ME/CFS research funding. The research’s translational potential is considerable, potentially catalyzing the development of future blood-based assays to monitor disease activity and therapeutic response, thereby personalizing patient care amidst a historically neglected field.
This research is a testament to the power of integrating molecular biology, computational science, and clinical insight to address one of medicine’s most confounding syndromes. With further validation and technological refinement, circulating cfRNA analysis stands to revolutionize not only ME/CFS diagnosis but also our understanding of chronic, systemic illnesses long shrouded in mystery, marking a new frontier in precision diagnostics.
Subject of Research: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and circulating cell-free RNA biomarkers
Article Title: Circulating cell-free RNA signatures for the characterization and diagnosis of myalgic encephalomyelitis/chronic fatigue syndrome
News Publication Date: 11-Aug-2025
Web References:
DOI: 10.1073/pnas.2507345122
Keywords: Chronic fatigue syndrome, diseases and disorders, health and medicine
Tags: blood plasma diagnosticschronic fatigue syndrome biomarkerscirculating cell-free RNA analysiscomputational models in healthcareCornell University research studydiagnostic challenges in chronic illnessextracellular RNA fragmentsgene expression profiling techniquesmachine learning in medicineminimally invasive diagnostic testsmyalgic encephalomyelitis researchpathophysiology of ME/CFS