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Spatially resolved immune exhaustion within the alloreactive microenvironment predicts liver transplant rejection.
Barbetta, Arianna; Rocque, Brittany; Bangerth, Sarah; Street, Kelly; Weaver, Carly; Chopra, Shefali; Kim, Janet; Sher, Linda; Gaudilliere, Brice; Akbari, Omid; Kohli, Rohit; Emamaullee, Juliet.
Afiliación
  • Barbetta A; University of Southern California.
  • Rocque B; University of Southern California.
  • Bangerth S; University of Southern California.
  • Street K; Keck School of Medicine of U.
  • Weaver C; University of Southern California.
  • Chopra S; University of Southern California.
  • Kim J; University of Southern California.
  • Sher L; University of Southern California Keck School of Mdicine.
  • Gaudilliere B; Stanford University.
  • Akbari O; University of Southern California, Keck School of Medicine.
  • Kohli R; University of Southern California.
  • Emamaullee J; University of Southern California.
Res Sq ; 2023 Jul 03.
Article en En | MEDLINE | ID: mdl-37461437
ABSTRACT
Allograft rejection is a frequent complication following solid organ transplantation, but defining specific immune subsets mediating alloimmunity has been elusive due to the scarcity of tissue in clinical biopsy specimens. Single cell techniques have emerged as valuable tools for studying mechanisms of disease in complex tissue microenvironments. Here, we developed a highly multiplexed imaging mass cytometry panel, single cell analysis pipeline, and semi-supervised immune cell clustering algorithm to study archival biopsy specimens from 79 liver transplant (LT) recipients with histopathological diagnoses of either no rejection (NR), acute T-cell mediated rejection (TCMR), or chronic rejection (CR). This approach generated a spatially resolved proteomic atlas of 461,816 cells derived from 98 pathologist-selected regions of interest relevant to clinical diagnosis of rejection. We identified 41 distinct cell populations (32 immune and 9 parenchymal cell phenotypes) that defined key elements of the alloimmune microenvironment (AME), identified significant cell-cell interactions, and established higher order cellular neighborhoods. Our analysis revealed that both regulatory (HLA-DR+ Treg) and exhausted T-cell phenotypes (PD1+CD4+ and PD1+CD8+ T-cells), combined with variations in M2 macrophage polarization, were a unique signature of TCMR. TCMR was further characterized by alterations in cell-to-cell interactions among both exhausted immune subsets and inflammatory populations, with expansion of a CD8 enriched cellular neighborhood comprised of Treg, exhausted T-cell subsets, proliferating CD8+ T-cells, and cytotoxic T-cells. These data enabled creation of a predictive model of clinical outcomes using a subset of cell types to differentiate TCMR from NR (AUC = 0.96 ± 0.04) and TCMR from CR (AUC = 0.96 ± 0.06) with high sensitivity and specificity. Collectively, these data provide mechanistic insights into the AME in clinical LT, including a substantial role for immune exhaustion in TCMR with identification of novel targets for more focused immunotherapy in allograft rejection. Our study also offers a conceptual framework for applying spatial proteomics to study immunological diseases in archival clinical specimens.
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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Año: 2023 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Año: 2023 Tipo del documento: Article