Multi-Omics and Systems Biology

1 Rosetta Stone Study

  • Principal Investigator:: Danny Altmann, Professor of Immunology

  • Institution:: Imperial College London, United Kingdom

  • Contact/URL:: https://meassociation.org.uk/2025/12/driving-discovery-the-me-association-invests-1-1m-into-pioneering-research-programme/

  • Funder:: ME Association, Ramsay Research Fund

  • Status:: Active (recruitment began late 2025)

  • Phase:: Observational, multi-omics

  • Cohort:: 250 ME/CFS + 250 Long Covid + matched healthy controls

  • Mechanism/Focus:: Side-by-side cellular and molecular analyses of ME/CFS and Long Covid. Stool, blood, and saliva samples analysed across multiple omics platforms, with machine learning applied to discover shared molecular pathways between the two conditions. Partnership with ELAROS for digital phenotyping.

  • Primary Outcomes:: Shared and distinguishing molecular signatures between ME/CFS and Long Covid; candidate biomarkers

  • Estimated Completion:: 2028 (3-year study)

  • Timeline:: Recruitment started Q4 2025; 3-month progress update published March 2026; interim analysis expected mid-study

  • Publication Medium:: Not yet announced; likely high-impact immunology or multidisciplinary journal; open access expected given charity funding

  • Document Relevance:: Ch. 7 (immune dysfunction), Ch. 11 (gut microbiome), Ch. 13 (integrative models), Ch. 14 Speculative Cross-Disease Connections (cross-disease comparison)

The Rosetta Stone Study represents the largest single investment in ME/CFS biomedical research by a UK charity (1.1M). Its defining strength is the parallel recruitment of ME/CFS and Long Covid cohorts with identical multi-omics profiling, enabling direct molecular comparison of two conditions widely hypothesised to share pathophysiological mechanisms. If the study identifies shared immune or metabolic signatures, it could accelerate biomarker development by leveraging the larger Long Covid research infrastructure. The inclusion of stool samples positions the study to contribute microbiome data that remains scarce in well-phenotyped ME/CFS cohorts.

2 BioQuest Study

  • Principal Investigator:: Jonas Bergquist (proteomics lead); coordinated by Open Medicine Foundation (OMF) research team

  • Institution:: Uppsala University (Sweden) and Open Medicine Foundation collaborative centres

  • Contact/URL:: https://ns1.omf.ngo/bioquest-update/

  • Funder:: Open Medicine Foundation

  • Status:: Active (sample collection complete; analysis phase)

  • Phase:: Observational, multi-omics biomarker discovery

  • Cohort:: 400 ME/CFS + 400 healthy controls + 200 disease controls (50 each: multiple sclerosis, burnout syndrome, depression, Long Covid). Combines samples from the Uppsala Collaborative Centre and the NIH Chronic Fatigue Initiative biobank.

  • Mechanism/Focus:: Large-scale metabolomics, lipidomics, proteomics, and cytokine profiling (10,000 analytes) with AI-driven subgroup identification. Goal: identify a 5–20 protein/metabolite biomarker panel unique to ME/CFS.

  • Primary Outcomes:: Validated ME/CFS biomarker panel; biologically defined patient subgroups

  • Estimated Completion:: Results expected 2026–2027

  • Timeline:: Sample integration approved 2025; testing began late 2025; bioinformatic analysis ongoing

  • Publication Medium:: Expected in high-impact journal; OMF typically publishes open access

  • Document Relevance:: Ch. 6 (energy metabolism), Ch. 7 (immune dysfunction), Ch. 13 (integrative models), Ch. 20 (biomarker research)

BioQuest is the largest ME/CFS biomarker study to date by sample size (\(n=1,000\)). Its inclusion of disease controls (MS, depression, burnout, Long Covid) directly addresses a longstanding criticism that ME/CFS biomarker candidates lack disease specificity. The multi-omics approach measuring over 10,000 analytes, combined with AI-driven clustering, has realistic potential to identify biologically defined subgroupsβ€”a prerequisite for precision medicine approaches. If successful, BioQuest could provide the first blood-based diagnostic panel capable of distinguishing ME/CFS from conditions with overlapping symptom profiles.