When a cell expires, it leaves a certain activity log. RNA was excreted into plasma, revealing changes in gene expression, cell signaling, tissue damage, and other biological processes.
Researchers at Cornell University have sifted through this cell-free RNA and developed a machine learning model that can identify key biomarkers of myopathic encephalomyelitis, also known as chronic fatigue syndrome (ME/CFS). This approach has been found to be difficult to confirm patients as diagnostic tests for debilitating diseases and patients' symptoms can be easily confused with symptoms of other diseases.
The results of the survey were published in minutes of the National Academy of Sciences on August 11th. The lead author is Anne Gardella, a doctoral student in biochemistry, molecular and cell biology at De Vlaminck Lab.
The project was a collaboration between the lab of co-authors of Iwijn de Vlaminck, an associate professor of biomedical engineering at Cornell Engineering, and Maureen Hanson of the Department of Liberty Hyde Bailey, Department of Molecular Biology and Genetics at the University of Agriculture Science.
By reading the molecular fingerprints that cells leave in the blood, I took a concrete step towards testing my/CFS. This study shows that blood tubes can provide clues regarding the biology of disease. ”
Iwijn de Vlamink, Cornell Associate Professor of Physical Engineering, Medical Engineering
De Vlaminck's lab previously used cell-free RNA technology to identify the presence of Kawasaki disease and multisystem inflammatory syndrome in children (MIS-C). After De Vlaminck presented a presentation on a project that includes cell-free DNA, Hanson, who studies the pathophysiology of ME/CFS, strived for potential collaborations.
Measuring the overall cell turnover of a patient's system using cell-free RNA is a relatively new concept and appeared particularly suitable for unraveling the mystery of ME/CFS.
“ME/CFS affects many different parts of the body,” said Hanson, who directs Cornell Center to receive neuroimmune diseases (ENID). “Nervous system, immune system, cardiovascular system. Plasma analysis gives you access to what's happening in these different parts of the world.”
There are no clinical laboratory tests for ME/CFS, so doctors need to rely on a variety of symptoms, including fatigue, dizziness, irregular sleep, and “brain fog.”
“The problem is that many of the symptoms a patient may be unhappy with his primary care physician can be a lot of different things,” Hanson said. “And what the primary care doctor really wants to have is a blood test.”
Blood samples were collected from controls of ME/CFS patients and sedentary but healthy people. De Vlaminck's team then spun blood plasma to separate and sequence RNA molecules released during cell damage and death.
They identified more than 700 significantly different transcripts between ME/CFS cases and control groups. These results were analyzed by various machine learning algorithms to develop classification tools that reveal signs of dysregulation of the immune system, extracellular matrix confusion, and T-cell fatigue in patients with ME/CFS.
Using statistical analysis methods, we were able to map where RNA molecules originate by deconvolutionizing patterns of gene expression based on known cell type-specific marker genes, as determined from previous ME/CFS single-cell RNA sequencing studies from Cornell's Grimson Lab.
“We identified six cell types that differ significantly between ME/CFS cases and controls,” Gardella said. “The top cell type of a patient is plasmacytoid dendritic cells. These are immune cells involved in the production of type 1 interferons that may exhibit patient overactivity or long-term antiviral immune responses. Differences in monocytes, platelets, and other subsets of T cells were also observed, pointing to broad immune regulation of ME/CFS.
It was found in the cell-free RNA classifier model 77% accuracy in ME/CFS detection – diagnostic testing is not yet sufficient, but there is a considerable leap in the field. Researchers hope that this approach will help them understand the complex biology behind other chronic diseases, helping to distinguish ME/CFS from Long Covid.
“Long Covid raises awareness of chronic conditions associated with infections, but being aware of ME/CFS is important because it is more common and more serious than most people realize,” Gardella said.
This study was supported by the National Institutes of Health and the We&Me Foundation.
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Journal Reference:
Gardera, ae, et al. (2025) Circulating cell-free RNA signature for characterization and diagnosis of muscular encephalomyelitis/chronic fatigue syndrome. pnas. doi.org/10.1073/pnas.2507345122.
