Developing a blood cell-based diagnostic test for myalgic encephalomyelitis/chronic fatigue syndrome using peripheral blood mononuclear cells

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is characterized by debilitating fatigue that profoundly impacts patients’ lives. Diagnosis of ME/CFS remains challenging, with most patients relying on self-report, questionnaires, and subjective measures to receive a diagnosis, and many never receiving a clear diagnosis at all. ME/CFS lacks a single sensitive and specific diagnostic test making the development of a simple test with the potential for early diagnosis a critical goal. Early diagnosis would enable patients to manage their conditions more effectively, potentially leading to new discoveries in disease pathways and treatment development.

Peripheral blood mononuclear cells (PBMCs) obtained from ME/CFS patients exhibited altered mitochondrial function, indicating a difference in energetic function when compared to non-fatigued controls. As ME/CFS may have a systemic energy issue, studying PBMCs may provide a good model for understanding the pathology affecting other organ systems. We hypothesized that single-cell analysis of PBMCs might reveal differences in ME/CFS compared to healthy and other disease groups. Raman spectroscopy is a non-invasive and label-free approach to probe molecular vibrations in a sample, and when combined with confocal microscopy, it can interrogate individual cells. A single-cell Raman spectrum (SCRS) is a phenotypic fingerprint of all biomolecules in that cell and could potentially differentiate between various cell types and give insights into underlying biology

In this study, we utilized a single-cell Raman platform and artificial intelligence to analyze blood cells from 98 human subjects, including 61 ME/CFS patients of varying disease severity and 37 healthy and disease controls. Our results demonstrate that Raman profiles of blood cells can distinguish between healthy individuals, disease controls, and ME/CFS patients with high accuracy (91%), and can further differentiate between mild, moderate, and severe ME/CFS patients (84%). Additionally, we identified specific Raman peaks that correlate with ME/CFS phenotypes and have the potential to provide insights into biological changes and support the development of new therapeutics. This study presents a promising approach for aiding in the diagnosis and management of ME/CFS, and could be extended to other unexplained chronic diseases such as long COVID and post-treatment Lyme disease syndrome, which share many of the same symptoms as ME/CFS.