Background/Purpose: In the current work, we used a mass spectrometry based proteomics workflow to analyze isolated plasma microvesicles from systemic sclerosis (SSc) patients and from controls to search for biomarkers showing diagnostic and prognostic potential and correlation with disease severity and co-morbidities. SSc is a heterogenic autoimmune connective tissue disease affecting the skin and inner organs. High degree of morbidity and increased mortality is associated with the disease. Early diagnosis is crucial for intervening therapy. Yet, diagnosis as well as prognosis may be a challenge in systemic sclerosis and therapy may only delay—but not stop—the progression of the disease.

Methods: Microvesicles were isolated from 1 mL citrate plasma by ultracentrifugation (20,000xg, 30 min) followed by supernatant removal, addition of PBS and repeated ultracentrifugation to wash out plasma proteins (in total 4 washes). The final pellet was mixed with 8 M urea with DTT and IAA and digested using eLysC and trypsin. A fixed volume aliquot (approx. 500 ng peptides) were loaded onto EvoTips and analyzed using an Exploris 480 system equipped with an EvoSep LC-system. Peptides were eluted using a 45 min gradient and analyzed by data independent acquisition covering peptides in the range from 361-1033 Da. Collected RAW-files were analyzed using Spectronaut v16 using MS2 fragment intensities for quantification. Subsequent data analysis were performed using Perseus and R-studio.

Results: Thirty-eight plasma samples from patients diagnosed with scleroderma were compared with 25 samples collected from age and sex matched healthy controls. In total, almost 3000 proteins were quantified demonstrating the power of isolating microvesicles from plasma samples in order to obtain high analytical depth despite the challenges associated with the high dynamic range spanned by plasma proteins. After excluding 3 samples with less than 1750 proteins quantified, and removal of proteins quantified in less than 70 % of all samples, the dataset still included 2690 quantified proteins which were subject for further analysis. Among these proteins 752 proteins showed significantly differential abundance between patient and control samples. Review of published literature on biomarkers for SSc resulted in a list of 67 putative protein biomarkers in plasma. Among these 23 of the putative biomarkers were also quantified in the current dataset and the data showed significant differences in abundance between SSc patients and controls for 12 of these proteins. Among the proteins showing significant differential abundance were thrombospondin-1, thrombospondin-4, cartilage oligomeric matrix protein and CD47. This is particularly interesting as these proteins are part of a tight protein network involved in synthesis of NO. NO and associated downstream signaling is important for vasodilation of the small capillaries and is associated with Raynaud’s phenomenon – a symptom often observed years before diagnosis of systemic scleroderma.

Conclusion: The study gives insight into pathogenic processes in SSc as manifested in plasma and expand the panel of SSc biomarkers.

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/proteomic-profiling-of-plasma-microvesicles-isolated-from-patients-with-systemic-sclerosis/