First steps towards developing a new diagnostic test to accurately identify hallmarks of chronic fatigue syndrome in blood cells

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating condition that is estimated to affect more than 1.25 million people in the UK. It is characterised by extreme symptoms of persistent and unexplained fatigue that profoundly impacts patients’ lives. With no single sensitive and specific test, diagnosis of the condition is difficult, with most patients relying on self-report, questionnaires, and subjective measures to receive a diagnosis.

Now a new diagnostic test shows a promising result, accurately identifying hallmarks of chronic fatigue syndrome in blood cells. Researchers at the University of Oxford led by Dr Karl Morten (Nuffield Department of Women's & Reproductive Health), and Professor of Biological Engineering Wei Huang have developed the test, which has an accuracy rate of 91%.

The non-invasive Raman-based test characterizes features of blood cells known as peripheral blood mononuclear cells, or PBMCs, that are unique to those suffering from chronic fatigue syndrome. Raman spectroscopy is a technique used to determine vibrational modes of molecules, commonly used in chemistry to provide a ‘structural fingerprint’ by which molecules can be identified.

When a laser beam is directed at a cell, some of the scattered photons undergo frequency shifts due to energy exchanges with the cell’s molecular components. Raman micro-spectroscopy detects these shifted photons, providing a non-invasive method for single cell analysis. The resulting single cell Raman spectra serve as a unique fingerprint, revealing the intrinsic and biochemical properties and indicating the physiological and metabolic state of the cell.

Previous studies identified PBMCs in ME/CFS patients as exhibiting reduced energetic function compared to healthy controls. With this evidence, the team proposed that single-cell analysis of PBMCs might reveal differences in the structure and morphology in ME/CFS patients compared to healthy controls and other disease groups such as Multiple Sclerosis. They used AI to generate high accuracy by combining the results from individual machine learning models.

Their study, published in Advanced Science, analyzed blood cells from 98 human subjects, including 61 ME/CFS patients of varying disease severity and 37 healthy and disease controls. 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%).