Medrxiv has published the following research paper ‘Developing a blood cell-based diagnostic test for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) using peripheral blood mononuclear cells.'
A blood-based diagnostic test for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and multiple sclerosis (MS) would be of great value in both conditions, facilitating more accurate and earlier diagnosis, helping with current treatment delivery, and supporting the development of new therapeutics.
Here we use Raman micro-spectroscopy to examine differences between the spectral profiles of blood cells of ME/CFS, MS and healthy controls. We were able to discriminate the three groups using ensemble classification models with high levels of accuracy (91%) with the additional ability to distinguish mild, moderate, and severe ME/CFS patients from each other (84%).
To our knowledge, this is the first research using Raman micro-spectroscopy to discriminate specific subgroups of ME/CFS patients on the basis of their symptom severity. Specific Raman peaks linked with the different disease types with the potential in further investigations to provide insights into biological changes associated with the different conditions.
ME Association Comment
Katrina Pears, Research Correspondent at the ME Association covered this pre-print research in her weekly roundup:
This study used a total of 98 subjects, which included 61 with ME/CFS, 21 with MS (multiple sclerosis) and 16 healthy controls. The ME/CFS cohort was further divided into Severe (n = 21), Moderate (n = 15) and Mild (n = 25). The study used peripheral blood mononuclear cells (PBMCs).
The principal method of this study was the fairly new application of the technique Raman spectroscopy. This technique involves cell imaging, where a light (usually from a laser) shining on a cell results in changed frequencies of photons – due to the energy exchange between the incident light and vibrations of biomolecules in cells – which are then detected and observed in the form of a Raman spectrum, named after Indian Physicist Sir C. V. Raman who earned the 1930 Nobel Prize for the discovery.
Each biomolecule has a unique ‘fingerprint’ on the Raman spectrum (shown as different length bands) and the sum of all biomolecular fingerprints in a cell can be used as a phenotype of the single cell. These fingerprints can be used to indicate changes in cellular metabolism and identify disease related biomarkers.
This non-invasive biochemical analysis technique has an advantage over other biomarker-identifying methods as it can be performed on living cells. Also, compared to other techniques no radio-active labelling is needed so the cells remain close to their natural state, reflecting the intrinsic biochemical profiles of the cells with less manipulation. The initial pilot study which was conducted by this research group back in 2018, can be found in a previous MEA research review.
Results from this study included:
- Healthy individuals, disease controls, and ME/CFS patients to be distinguished between with high accuracy (91%).
- The two disease cohorts differed from each other (with different spectra produced) and the cells in the ME/CFS cohorts differed more than those in the MS cohort in terms of their metabolic profiles, probably because of a higher number of subjects involved and the broad range of symptom severities from mild, moderate and severe.
- Results allowed differentiation between mild, moderate, and severe ME/CFS patients with 84% accuracy.
- Important biomarkers which allowed the differentiation between cohorts was through the quantification of aromatic amino acids (AAAs), namely tryptophan, tyrosine, and phenylalanine. The levels of these three AAAs differed between the cohorts, e.g. intracellular phenylalanine suggested metabolic subtypes exist in the ME/CFS patients, with the moderate and severe groups having significantly reduced phenylalanine and the mild ME and MS having increased levels relative to controls.
- Altered lipid metabolism was also found, with elevated glycerol levels compared to the controls, suggesting different lipidic profiles in the cohorts.
- Energy fuelling biomarkers were also different between cohorts, glycogen levels were significantly reduced in the mild and severe ME (and MS). Glucose quantification showed a decrease in all ME subgroups and the MS cohort had the lowest glucose accumulation.
Results from this study are exciting, as there is a desperate need to develop a diagnostic test. Results are reassuring as the majority of metabolic changes which were found in this study relating to energy strain and lipid metabolism are in agreement with previous research. Furthermore, these results showed clear cellular differences with different ME/CFS severities and allowed different phenotypes to be observed, which could in turn lead to the development of targeted therapies.
The study has a number of strengths as it uses the well-defined ME/CFS cohort from the ME UK Biobank. The research team have a vast amount of experience and knowledge of ME/CFS, with Karl Morten being funded by the MEA.
Unfortunately, there are a few problems with this research currently as this technique is not widely clinically accepted, therefore, the researchers are desperately trying to get others involved to verify results and validate the findings. This needs other research groups to carry out the same research on samples from the ME UK Biobank and also on separate ME/CFS samples, there is hope that this preprint paper will help to bring other researchers together. Additionally, bigger sample numbers are needed.
Read more on this week's Research Round up:
Dr Katrina Pears
The ME Association.