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Raman Spectrometry Based Biomarker Discovery for Myalgic Encephalomyelitis (RASPBERRY-ME)

The overarching goal of RASPBERRY-ME project is the characterization of the biomolecular signature of Myalgic Encephalomyelitis using Label-free Raman Spectroscopy (RS) and machine learning models.

  • Alain Moreau, PhD
  • Mathieu Dehaes, PhD
  • Frédéric Leblond, PhD
  • Raman spectral data acquisition for the entire cohort has been completed.
  • Preliminary analysis is complete on plasma samples from 15 ME/CFS patients and 23 sedentary healthy controls.
  • Machine learning techniques were employed to identify significant features allowing for the differentiation of ME/CFS patients from healthy controls at baseline.

STUDY HYPOTHESIS AND DESCRIPTION

Woman working with computer in the office of a science laboratoryRaman spectroscopy is a non-destructive, rapid, and low-cost technique allows the study of the molecular composition of biological fluids like blood, or inside a cell when combined with confocal microscopy. This innovative approach could lead to the development of diagnostic tools to better stratify ME patients and find the underlying causes of different symptoms like post-exertional malaise as well as clinical tools to validate the therapeutic potential of pharmacological treatments to treat, stop or mitigate ME through precision medicine.

We hypothesize that our approach will allow the identification of a biomolecular signature of ME both at baseline and in response to the application of a post-exertional stress challenge. We expect to stratify patients by differentiating severe cases from mild forms of ME. Results from this study will be further combined to ongoing proteomic and metabolomic profiling approaches to better understand the pathophysiology of ME.

Objective

  1. Characterize the biomolecular signature of ME patients and healthy age-matched controls using label-free Raman spectroscopy in plasma samples acquired at baseline and after PEM induction
  2. Determine cellular metabolite alterations between ME patients and age-matched controls using Raman spectroscopy combined with confocal microscopy with peripheral blood mononuclear cells (PBMCs) acquired at baseline and after PEM induction
  3. Develop molecular feature detection and machine-learning models capable to predict PEM response (baseline vs. post stress-test) and the disease (ME vs. control).
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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