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Exhausted CD8 T cells (TEX) are associated with worse outcome in cancer yet better outcome in autoimmunity. Building on our past findings of increased TIGIT+KLRG1+ TEX with teplizumab therapy in type 1 diabetes (T1D), in the absence of treatment we found that the frequency of TIGIT+KLRG1+ TEX is stable within an individual but differs across individuals in both T1D and healthy control (HC) cohorts. This TIGIT+KLRG1+ CD8 TEX population shares an exhaustion-associated EOMES gene signature in HC, T1D, rheumatoid arthritis (RA), and cancer subjects, expresses multiple inhibitory receptors, and is hyporesponsive in vitro, together suggesting co-expression of TIGIT and KLRG1 may broadly define human peripheral exhausted cells. In HC and RA subjects, lower levels of EOMES transcriptional modules and frequency of TIGIT+KLRG1+ TEX were associated with RA HLA risk alleles (DR0401, 0404, 0405, 0408, 1001) even when considering disease status and cytomegalovirus (CMV) seropositivity. Moreover, the frequency of TIGIT+KLRG1+ TEX was significantly increased in RA HLA risk but not non-risk subjects treated with abatacept (CTLA4Ig). The DR4 association and selective modulation with abatacept suggests that therapeutic modulation of TEX may be more effective in DR4 subjects and TEX may be indirectly influenced by cellular interactions that are blocked by abatacept.

The analysis of longitudinal bulk and single-cell multi-omics data is a highly complex task. Here, the authors introduce PALMO, a software platform with five modules to analyse longitudinal bulk and single-cell multi-omics data, which is extensively tested in external datasets that include multiple omics modalities.

Summary: Deep immune profiling is essential for understanding the human immune system in health and disease. Successful biological interpretation of this data requires consistent laboratory processing with minimal batch-to-batch variation. Here, we detail a robust pipeline for the profiling of human peripheral blood mononuclear cells by both high-dimensional flow cytometry and single-cell RNA-seq. These protocols reduce batch effects, generate reproducible data, and increase throughput.For complete details on the use and execution of this protocol, please refer to Savage et al. (2021).

Abstract: Classical Hodgkin lymphoma (cHL) is composed of rare malignant Hodgkin Reed-Sternberg (HRS) cells within an extensive, but ineffective, inflammatory/immune cell infiltrate. HRS cells exhibit near-universal somatic copy gains of chromosome 9p/9p24.1, which increase expression of the programmed cell death protein 1 (PD-1) ligands. To define genetic mechanisms of response and resistance to PD-1 blockade and identify complementary treatment targets, we performed whole-exome sequencing of flow cytometry–sorted HRS cells from 23 excisional biopsies of newly diagnosed cHLs, including 8 Epstein-Barr virus–positive (EBV+) tumors. We identified significantly mutated cancer candidate genes (CCGs) as well as somatic copy number alterations and structural variations and characterized their contribution to disease-defining immune evasion mechanisms and nuclear factor κB (NF-κB), JAK/STAT, and PI3K signaling pathways. EBV– cHLs had a higher prevalence of genetic alterations in the NF-κB and major histocompatibility complex class I antigen presentation pathways. In this young cHL cohort (median age, 26 years), we identified a predominant mutational signature of spontaneous deamination of cytosine- phosphate-guanines (“Aging”), in addition to apolipoprotein B mRNA editing catalytic polypeptide-like, activation-induced cytidine deaminase, and microsatellite instability (MSI)–associated hypermutation. In particular, the mutational burden in EBV– cHLs was among the highest reported, similar to that of carcinogen-induced tumors. Together, the overall high mutational burden, MSI-associated hypermutation, and newly identified genetic alterations represent additional potential bases for the efficacy of PD-1 blockade in cHL. Of note, recurrent cHL alterations, including B2M, TNFAIP3, STAT6, GNA13, and XPO1 mutations and 2p/2p15, 6p21.32, 6q23.3, and 9p/9p24.1 copy number alterations, were also identified in >20% of primary mediastinal B-cell lymphomas, highlighting shared pathogenetic mechanisms in these diseases.

Summary: Multi-omic profiling of human peripheral blood is increasingly utilized to identify biomarkers and pathophysiologic mechanisms of disease. The importance of these platforms in clinical and translational studies led us to investigate the impact of delayed blood processing on the numbers and state of peripheral blood mononuclear cells (PBMC) and on the plasma proteome. Similar to previous studies, we show minimal effects of delayed processing on the numbers and general phenotype of PBMC up to 18 hours. In contrast, profound changes in the single-cell transcriptome and composition of the plasma proteome become evident as early as 6 hours after blood draw. These reflect patterns of cellular activation across diverse cell types that lead to progressive distancing of the gene expression state and plasma proteome from native in vivo biology. Differences accumulating during an overnight rest (18 hours) could confound relevant biologic variance related to many underlying disease states.

Single-cell measurements of cellular characteristics have been instrumental in understanding the heterogeneous pathways that drive differentiation, cellular responses to signals, and human disease. Recent advances have allowed paired capture of protein abundance and transcriptomic state, but a lack of epigenetic information in these assays has left a missing link to gene regulation. Using the heterogeneous mixture of cells in human peripheral blood as a test case, we developed a novel scATAC-seq workflow that increases signal-to-noise and allows paired measurement of cell surface markers and chromatin accessibility: integrated cellular indexing of chromatin landscape and epitopes, called ICICLE-seq. We extended this approach using a droplet-based multiomics platform to develop a trimodal assay that simultaneously measures transcriptomics (scRNA-seq), epitopes, and chromatin accessibility (scATAC-seq) from thousands of single cells, which we term TEA-seq. Together, these multimodal single-cell assays provide a novel toolkit to identify type-specific gene regulation and expression grounded in phenotypically defined cell types.