In-depth plasma proteome from patients with post-viral neurocognitive disorders: a pilot study

Background
There are ample examples of outbreaks of post-infectious disorders with long lasting sequels. The pathophysiology of those disorders is very much under-investigated and there is a lack of consensus diagnostic, prognostic and therapeutic guidelines. One of the more common, but still less understood disorders are myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS). Today, we think that at least 60-80% of the patients have been initially affected and triggered by an Ebstein Barr virus infection. It has also been estimated that more than 65 million individuals worldwide are suspected to be suffering from Long COVID, a complex multisystemic condition, wherein patients of all ages report fatigue, post-exertional malaise, and other symptoms resembling ME/CFS. With no current treatments or reliable diagnostic markers, there is an urgent need to define the molecular underpinnings of these conditions.
Methodology
Using a small cohort of patients (8 individuals) and matched healthy controls (8 individuals), we investigated three common sample preparation procedures for plasma proteomes, with the aim to choose the best method for a larger-scale analysis (>10000 samples). These were: standard digestion with trypsin (neat), an antibody-based depletion method known to deplete the 14 most abundant plasma proteins (Top14 depletion), and perchloric acid depletion (per-CA). Reported protein biomarkers for long covid were used to verify the applied depletion methods. We employed the Thermo Scientific™ Orbitrap™ Astral™ mass spectrometer, using DIA to evaluate depth of coverage, quantitative performance, at different throughputs, to determine both the best sample preparation protocol and address the question of whether the larger cohort measurements could be achieved in ~1 yr or 2 yrs. All files were processed with Spectronaut 18. Principal component analysis (PCA) was used for downstream analysis, and significant protein biomarkers were manually verified.
Results
Analysing three replicates of the 16 samples for each depletion method we identified 2532 protein groups (PGs) for Top14, 1842 PGs for per-CA, and 1031 PGs for neat plasma. Protein group CVs followed the same trend as number of protein groups identified (best 7.5%, worst 12.6%).
PCA was able to separate patients versus control for both neat and per-CA treated samples. Strikingly, for per-CA, PC1 contained 63% of the explained variance clearly separating the two groups. Further, we could observe a general trend of higher variability between disease samples compared to healthy controls. In contrast, top14 depletion resulted in no clear separation of the two groups as many of the assumed ME/CFS protein markers (e.g., immunoglobulins) are depleted from the sample.
Overall, we conclude that per-CA depletion followed by high-throughput DIA analysis on Orbitrap Astral enables the investigation of large sample cohorts to study post-infectious disorders.