New Publication: Raman Spectroscopy Combined with Machine Learning

Raman Spectroscopy Combined with Machine Learning Reveals Myalgic Encephalomyelitis–Associated Biomolecular Signatures at Rest and After Standardized Stress

Abstract dark-blue network of connected nodes and points of light converging on a robotic arm, representing machine learning analysis of biomolecular data.

Dr. Alain Moreau, Director of OMF’s Collaborative Center at Montreal, and his team recently published a paper on their work developing a screening approach to differentiate people with ME/CFS from sedentary healthy controls using Raman spectroscopy (RS) and machine learning (ML).

This project aimed to identify biomolecular changes in plasma before and after a stress test using RS-ML modeling. The team collected blood from 115 patients and 45 controls at rest and 90 min after a non-invasive stress test. The resulting model was able to identify patients with 79% and 84% accuracy before and after the stress test, respectively. This RS-ML approach moves the needle towards developing an objective diagnostic test for ME/CFS.

Want to hear more?

LIVE EVENT | OMF JOURNAL CLUB

If you want to dive deeper into this paper, join Dr. Danielle Meadows, OMF’s VP of Research Programs, for another session of OMF Journal Club on July 1 at 11am ET.

During the session, Dr. Meadows will talk through the main ideas of the paper, the figures, and the implications for people with ME/CFS and Long COVID. If you’re not able to join the session live, a recording will be sent to all registrants.

Professional headshot of Danielle Meadows smiling at the camera.

The Bigger Picture

The lack of an objective diagnostic tool for ME/CFS and Long COVID is a widely acknowledged challenge for healthcare providers and burden to patients. This initial study of a RS-ML approach shows potential as a rapid and scalable tool for diagnosis.

While RS doesn’t directly measure levels of proteins, lipids, and metabolites, the spectra identify contributions from those molecules that have biological implications. The contributions of proteins, lipids, and low-molecular-weight metabolites found in this project are consistent with evidence of altered amino acid, fatty acid, and lipid metabolism; and dysregulation of the urea and TCA cycles.

This manuscript is part of the Raman Spectrometry Based Biomarker Discovery for Myalgic Encephalomyelitis (RASPBERRY-ME) project. Read the full paper in the International Journal of Molecular Sciences

Myalgic Encephalomyelitis / Chronic Fatigue Syndrome (ME / CFS) Post Treatment Lyme Disease Syndrome (PTLDS), Fibromyalgia Leading Research. Delivering Hope.Open Medicine Foundation®

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