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To date, biomarkers that forecast differences in clinical phenotypes and outcomes have been limited. In this context, cell-type-specific interactome analyses offer important prospects and hope for novel diagnostics and therapeutics. We report here an original study using bioinformatic analysis of MS data sets that revealed interaction profiles as well as specific hub proteins in white matter (WM) and gray matter (GM) that appear critical for disease mechanisms. First, cell-type-specific interactome analyses suggested that while interactions within the WM were focused on oligodendrocytes, interactions within the GM were mostly neuron centric. Second, hub proteins such as APP, EGLN3, PTEN, and LRRK2 were identified to be differentially regulated in MS data sets. Lastly, a comparison of the brain and peripheral blood samples identified biomarker candidates such as NRGN, CRTC1, CDC42, and IFITM3 to be differentially expressed in different types of MS. These findings offer a unique cell-type-specific cell-to-cell interaction network in MS and identify potential biomarkers by comparative analysis of the brain and the blood transcriptomics. From a study design and methodology perspective, we suggest that the cell-type-specific interactome analysis is an important systems science frontier that might offer new insights on other neurodegenerative and brain disorders as well.<\/jats:p>","DOI":"10.1089\/omi.2022.0023","type":"journal-article","created":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T16:54:07Z","timestamp":1651164847000},"page":"305-317","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Multiple Sclerosis Biomarker Candidates Revealed by Cell-Type-Specific Interactome Analysis"],"prefix":"10.1177","volume":"26","author":[{"given":"K\u00fcbra","family":"Yurduseven","sequence":"first","affiliation":[{"name":"Institute of Biotechnology, Gebze Technical University, Kocaeli, Turkey."},{"name":"Regenerative and Restorative Medical Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Turkey."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9954-4435","authenticated-orcid":false,"given":"Yigit Koray","family":"Babal","sequence":"additional","affiliation":[{"name":"Institute of Biotechnology, Gebze Technical University, Kocaeli, Turkey."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Esref","family":"Celik","sequence":"additional","affiliation":[{"name":"Regenerative and Restorative Medical Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Turkey."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bilal Ersen","family":"Kerman","sequence":"additional","affiliation":[{"name":"Regenerative and Restorative Medical Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Turkey."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5587-4763","authenticated-orcid":false,"given":"I\u015f\u0131l Aksan","family":"Kurnaz","sequence":"additional","affiliation":[{"name":"Institute of Biotechnology, Gebze Technical University, Kocaeli, Turkey."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2022,5,11]]},"reference":[{"key":"e_1_3_6_2_1","first-page":"100052","article-title":"Inferring multiple sclerosis stages from the blood transcriptome via machine learning","volume":"1","author":"Acquaviva M","year":"2020","unstructured":"Acquaviva M, , Menon R, , Di Dario M, et al. 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