{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T20:30:21Z","timestamp":1788985821834,"version":"build-2803163510"},"reference-count":66,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T00:00:00Z","timestamp":1714003200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Systemic autoimmune diseases (SADs) are a significant burden on the healthcare system. Understanding the complexity of the peripheral immunophenotype in SADs may facilitate the differential diagnosis and identification of potential therapeutic targets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Single-cell mass cytometric immunophenotyping was performed on peripheral blood mononuclear cells (PBMCs) from healthy controls (HCs) and therapy-naive patients with rheumatoid arthritis (RA), progressive systemic sclerosis (SSc), and systemic lupus erythematosus (SLE). Immunophenotyping was performed on 15,387,165 CD45\n                      <jats:sup>+<\/jats:sup>\n                      live single cells from 52 participants (13\u00a0cases\/group), using an antibody panel to detect 34 markers.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Using the t-SNE (t-distributed stochastic neighbor embedding) algorithm, the following 17 main immune cell types were determined: CD4\n                      <jats:sup>+<\/jats:sup>\n                      \/CD57\n                      <jats:sup>\u2013<\/jats:sup>\n                      T cells, CD4\n                      <jats:sup>+<\/jats:sup>\n                      \/CD57\n                      <jats:sup>+<\/jats:sup>\n                      T cells, CD8\n                      <jats:sup>+<\/jats:sup>\n                      \/CD161\n                      <jats:sup>\u2013<\/jats:sup>\n                      T cells, CD8\n                      <jats:sup>+<\/jats:sup>\n                      \/CD161\n                      <jats:sup>+<\/jats:sup>\n                      \/CD28\n                      <jats:sup>+<\/jats:sup>\n                      T cells, CD8\n                      <jats:sup>dim<\/jats:sup>\n                      T cells, CD3\n                      <jats:sup>+<\/jats:sup>\n                      \/CD4\n                      <jats:sup>\u2013<\/jats:sup>\n                      \/CD8\n                      <jats:sup>\u2013<\/jats:sup>\n                      T cells, TCR\u03b3\/\u03b4 T cells, CD4\n                      <jats:sup>+<\/jats:sup>\n                      NKT cells, CD8\n                      <jats:sup>+<\/jats:sup>\n                      NKT cells, classic NK cells, CD56\n                      <jats:sup>dim<\/jats:sup>\n                      \/CD98\n                      <jats:sup>dim<\/jats:sup>\n                      cells, B cells, plasmablasts, monocytes, CD11cdim\/CD172dim cells, myeloid dendritic cells (mDCs), and plasmacytoid dendritic cells (pDCs). Seven of the 17 main cell types exhibited statistically significant frequencies in the investigated groups. The expression levels of the 34 markers in the main populations were compared between HCs and SADs. In summary, 59 scatter plots showed significant differences in the expression intensities between at least two groups. Next, each immune cell population was divided into subpopulations (metaclusters) using the FlowSOM (self-organizing map) algorithm. Finally, 121 metaclusters (MCs) of the 10 main immune cell populations were found to have significant differences to classify diseases. The single-cell T-cell heterogeneity represented 64MCs based on the expression of 34 markers, and the frequency of 23 MCs differed significantly between at least twoconditions. The CD3\n                      <jats:sup>\u2013<\/jats:sup>\n                      non-T-cell compartment contained 57 MCs with 17 MCs differentiating at least two investigated groups. In summary, we are the first to demonstrate the complexity of the immunophenotype of\u00a034\u00a0markers over 15 million single cells in HCs vs. therapy-naive patients with\u00a0RA, SSc, and SLE. Disease specific population frequencies or expression patterns of peripheral immune cells provide a single-cell data resource to the scientific community.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2024.1376933","type":"journal-article","created":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T00:55:56Z","timestamp":1714006556000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Comparative single-cell multiplex immunophenotyping of therapy-naive patients with rheumatoid arthritis, systemic sclerosis, and systemic lupus erythematosus shed light on disease-specific composition of the peripheral immune system"],"prefix":"10.3389","volume":"15","author":[{"given":"J\u00f3zsef 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