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suck abstract from ncbi


10.1038/s41467-021-24730-4

http://scihub22266oqcxt.onion/10.1038/s41467-021-24730-4
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34312385!8313584!34312385
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suck abstract from ncbi

pmid34312385
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  • Single-cell RNA sequencing reveals ex vivo signatures of SARS-CoV-2-reactive T cells through reverse phenotyping #MMPMID34312385
  • Fischer DS; Ansari M; Wagner KI; Jarosch S; Huang Y; Mayr CH; Strunz M; Lang NJ; D'Ippolito E; Hammel M; Mateyka L; Weber S; Wolff LS; Witter K; Fernandez IE; Leuschner G; Milger K; Frankenberger M; Nowak L; Heinig-Menhard K; Koch I; Stoleriu MG; Hilgendorff A; Behr J; Pichlmair A; Schubert B; Theis FJ; Busch DH; Schiller HB; Schober K
  • Nat Commun 2021[Jul]; 12 (1): 4515 PMID34312385show ga
  • The in vivo phenotypic profile of T cells reactive to severe acute respiratory syndrome (SARS)-CoV-2 antigens remains poorly understood. Conventional methods to detect antigen-reactive T cells require in vitro antigenic re-stimulation or highly individualized peptide-human leukocyte antigen (pHLA) multimers. Here, we use single-cell RNA sequencing to identify and profile SARS-CoV-2-reactive T cells from Coronavirus Disease 2019 (COVID-19) patients. To do so, we induce transcriptional shifts by antigenic stimulation in vitro and take advantage of natural T cell receptor (TCR) sequences of clonally expanded T cells as barcodes for 'reverse phenotyping'. This allows identification of SARS-CoV-2-reactive TCRs and reveals phenotypic effects introduced by antigen-specific stimulation. We characterize transcriptional signatures of currently and previously activated SARS-CoV-2-reactive T cells, and show correspondence with phenotypes of T cells from the respiratory tract of patients with severe disease in the presence or absence of virus in independent cohorts. Reverse phenotyping is a powerful tool to provide an integrated insight into cellular states of SARS-CoV-2-reactive T cells across tissues and activation states.
  • |Aged[MESH]
  • |Aged, 80 and over[MESH]
  • |CD4-Positive T-Lymphocytes/metabolism/virology[MESH]
  • |COVID-19/epidemiology/*immunology/virology[MESH]
  • |Cells, Cultured[MESH]
  • |Cohort Studies[MESH]
  • |Female[MESH]
  • |Gene Expression Profiling/*methods[MESH]
  • |Humans[MESH]
  • |Male[MESH]
  • |Middle Aged[MESH]
  • |Pandemics[MESH]
  • |Receptors, Antigen, T-Cell/genetics/immunology/metabolism[MESH]
  • |SARS-CoV-2/physiology[MESH]
  • |Sequence Analysis, RNA/*methods[MESH]
  • |Single-Cell Analysis/*methods[MESH]
  • |T-Lymphocytes/*metabolism/virology[MESH]


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  • suck abstract from ncbi

    4515 1.12 2021