Spatially resolved immune exhaustion within the alloreactive microenvironment predicts liver transplant rejection.

Allograft rejection is common following clinical organ transplantation, but defining specific immune subsets mediating alloimmunity has been elusive. Calcineurin inhibitor dose escalation, corticosteroids, and/or lymphocyte depleting antibodies have remained the primary options for treatment of clinical rejection episodes. Here, we developed a highly multiplexed imaging mass cytometry panel to study the immune response in archival biopsies from 79 liver transplant (LT) recipients with 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 (42 phenotypes) derived from 96 pathologist-selected regions of interest. Our analysis revealed that regulatory (HLADR+ Treg) and PD1+ T cell phenotypes (CD4+ and CD8+ subsets), combined with variations in M2 macrophage polarization, were a unique signature of active TCMR. These data provide insights into the alloimmune microenvironment in clinical LT, including identification of potential targets for focused immunotherapy during rejection episodes and suggestion of a substantial role for immune exhaustion in TCMR.

Spatially resolved immune exhaustion within the alloreactive microenvironment predicts liver transplant rejection.

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.

MUC1-C Dictates JUN and BAF-Mediated Chromatin Remodeling at Enhancer Signatures in Cancer Stem Cells.

The oncogenic MUC1-C protein promotes dedifferentiation of castrate-resistant prostate cancer (CRPC) and triple-negative breast cancer (TNBC) cells. Chromatin remodeling is critical for the cancer stem cell (CSC) state; however, there is no definitive evidence that MUC1-C regulates chromatin accessibility and thereby expression of stemness-associated genes. We demonstrate that MUC1-C drives global changes in chromatin architecture in the dedifferentiation of CRPC and TNBC cells. Our results show that MUC1-C induces differentially accessible regions (DAR) across their genomes, which are significantly associated with differentially expressed genes (DEG). Motif and cistrome analysis further demonstrated MUC1-C-induced DARs align with genes regulated by the JUN/AP-1 family of transcription factors. MUC1-C activates the BAF chromatin remodeling complex, which is recruited by JUN in enhancer selection. In studies of the NOTCH1 gene, which is required for CRPC and TNBC cell self-renewal, we demonstrate that MUC1-C is necessary for (i) occupancy of JUN and ARID1A/BAF, (ii) increases in H3K27ac and H3K4me3 signals, and (iii) opening of chromatin accessibility on a proximal enhancer-like signature. Studies of the EGR1 and LY6E stemness-associated genes further demonstrate that MUC1-C-induced JUN/ARID1A complexes regulate chromatin accessibility on proximal and distal enhancer-like signatures. These findings uncover a role for MUC1-C in chromatin remodeling that is mediated at least in part by JUN/AP-1 and ARID1A/BAF in association with driving the CSC state. IMPLICATIONS: These findings show that MUC1-C, which is necessary for the CRPC and TNBC CSC state, activates a novel pathway involving JUN/AP-1 and ARID1A/BAF that regulates chromatin accessibility of stemness-associated gene enhancers.

Progressive immune dysfunction with advancing disease stage in renal cell carcinoma.

The tumor immune microenvironment plays a critical role in cancer progression and response to immunotherapy in clear cell renal cell carcinoma (ccRCC), yet the composition and phenotypic states of immune cells in this tumor are incompletely characterized. We performed single-cell RNA and T cell receptor sequencing on 164,722 individual cells from tumor and adjacent non-tumor tissue in patients with ccRCC across disease stages: early, locally advanced, and advanced/metastatic. Terminally exhausted CD8+ T cells were enriched in metastatic disease and were restricted in T cell receptor diversity. Within the myeloid compartment, pro-inflammatory macrophages were decreased, and suppressive M2-like macrophages were increased in advanced disease. Terminally exhausted CD8+ T cells and M2-like macrophages co-occurred in advanced disease and expressed ligands and receptors that support T cell dysfunction and M2-like polarization. This immune dysfunction circuit is associated with a worse prognosis in external cohorts and identifies potentially targetable immune inhibitory pathways in ccRCC.

Trajectory-based differential expression analysis for single-cell sequencing data.

Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. By incorporating observation-level weights, the model additionally allows to account for zero inflation. We evaluate the method on simulated datasets and on real datasets from droplet-based and full-length protocols, and show that it yields biological insights through a clear interpretation of the data.