The immune system can mount T cell responses against tumors; however, the antigen specificities of tumor-infiltrating lymphocytes (TILs) are not well understood. We used yeast-display libraries of peptide-human leukocyte antigen (pHLA) to screen for antigens of "orphan" T cell receptors (TCRs) expressed on TILs from human colorectal adenocarcinoma. Four TIL-derived TCRs exhibited strong selection for peptides presented in a highly diverse pHLA-A∗02:01 library. Three of the TIL TCRs were specific for non-mutated self-antigens, two of which were present in separate patient tumors, and shared specificity for a non-mutated self-antigen derived from U2AF2. These results show that the exposed recognition surface of MHC-bound peptides accessible to the TCR contains sufficient structural information to enable the reconstruction of sequences of peptide targets for pathogenic TCRs of unknown specificity. This finding underscores the surprising specificity of TCRs for their cognate antigens and enables the facile indentification of tumor antigens through unbiased screening.

Cells communicate with each other via receptor-ligand interactions. Here, we describe lentiviral-mediated cell entry by engineered receptor-ligand interaction (ENTER) to display ligand proteins, deliver payloads, and record receptor specificity. We optimize ENTER to decode interactions between T cell receptor (TCR)-MHC peptides, antibody-antigen, and other receptor-ligand pairs. A viral presentation strategy allows ENTER to capture interactions between B cell receptor and any antigen. We engineer ENTER to deliver genetic payloads to antigen-specific T or B cells to selectively modulate cellular behavior in mixed populations. Single-cell readout of ENTER by RNA sequencing (ENTER-seq) enables multiplexed enumeration of antigen specificities, TCR clonality, cell type, and states of individual T cells. ENTER-seq of CMV-seropositive patient blood samples reveals the viral epitopes that drive effector memory T cell differentiation and inter-clonal vs. intra-clonal phenotypic diversity targeting the same epitope. ENTER technology enables systematic discovery of receptor specificity, linkage to cell fates, and antigen-specific cargo delivery.

B cells are capable of a wide range of effector functions including antibody secretion, antigen presentation, cytokine production, and generation of immunological memory. A consistent strategy for classifying human B cells by using surface molecules is essential to harness this functional diversity for clinical translation. We developed a highly multiplexed screen to quantify the co-expression of 351 surface molecules on millions of human B cells. We identified differentially expressed molecules and aligned their variance with isotype usage, VDJ sequence, metabolic profile, biosynthesis activity, and signaling response. Based on these analyses, we propose a classification scheme to segregate B cells from four lymphoid tissues into twelve unique subsets, including a CD45RB+CD27- early memory population, a class-switched CD39+ tonsil-resident population, and a CD19hiCD11c+ memory population that potently responds to immune activation. This classification framework and underlying datasets provide a resource for further investigations of human B cell identity and function.

Given the limited efficacy of clinical approaches that rely on ex vivo generated dendritic cells (DCs), it is imperative to design strategies that harness specialized DC subsets in situ. This requires delineating the expression of surface markers by DC subsets among individuals and tissues. Here, we performed a multiparametric phenotypic characterization and unbiased analysis of human DC subsets in blood, tonsil, spleen, and skin. We uncovered previously unreported phenotypic heterogeneity of human cDC2s among individuals, including variable expression of functional receptors such as CD172a. We found marked differences in DC subsets localized in blood and lymphoid tissues versus skin, and a striking absence of the newly discovered Axl+ DCs in the skin. Finally, we evaluated the capacity of anti-receptor monoclonal antibodies to deliver vaccine components to skin DC subsets. These results offer a promising path for developing DC subset-specific immunotherapies that cannot be provided by transcriptomic analysis alone.

In this work, we find that CD8+ T cells expressing inhibitory killer cell immunoglobulin-like receptors (KIRs) are the human equivalent of Ly49+CD8+ regulatory T cells in mice and are increased in the blood and inflamed tissues of patients with a variety of autoimmune diseases. Moreover, these CD8+ T cells efficiently eliminated pathogenic gliadin-specific CD4+ T cells from the leukocytes of celiac disease patients in vitro. We also find elevated levels of KIR+CD8+ T cells, but not CD4+ regulatory T cells, in COVID-19 patients, correlating with disease severity and vasculitis. Selective ablation of Ly49+CD8+ T cells in virus-infected mice led to autoimmunity after infection. Our results indicate that in both species, these regulatory CD8+ T cells act specifically to suppress pathogenic T cells in autoimmune and infectious diseases.

CD4+ T cells are critical to fighting pathogens, but a comprehensive analysis of human T-cell specificities is hindered by the diversity of HLA alleles (>20,000) and the complexity of many pathogen genomes. We previously described GLIPH, an algorithm to cluster T-cell receptors (TCRs) that recognize the same epitope and to predict their HLA restriction, but this method loses efficiency and accuracy when >10,000 TCRs are analyzed. Here we describe an improved algorithm, GLIPH2, that can process millions of TCR sequences. We used GLIPH2 to analyze 19,044 unique TCRβ sequences from 58 individuals latently infected with Mycobacterium tuberculosis (Mtb) and to group them according to their specificity. To identify the epitopes targeted by clusters of Mtb-specific T cells, we carried out a screen of 3,724 distinct proteins covering 95% of Mtb protein-coding genes using artificial antigen-presenting cells (aAPCs) and reporter T cells. We found that at least five PPE (Pro-Pro-Glu) proteins are targets for T-cell recognition in Mtb.

HLA-E molecules can present self- and pathogen-derived peptides to both natural killer (NK) cells and T cells. T cells that recognize HLA-E peptides via their T cell receptor (TCR) are termed donor-unrestricted T cells due to restricted allelic variation of HLA-E. The composition and repertoire of HLA-E TCRs is not known so far. We performed TCR sequencing on CD8+ T cells from 21 individuals recognizing HLA-E tetramers (TMs) folded with two Mtb-HLA-E-restricted peptides. We sorted HLA-E Mtb TM+ and TM- CD8+ T cells directly ex vivo and performed bulk RNA-sequencing and single-cell TCR sequencing. The identified TCR repertoire was diverse and showed no conservation between and within individuals. TCRs selected from our single-cell TCR sequencing data could be activated upon HLA-E/peptide stimulation, although not robust, reflecting potentially weak interactions between HLA-E peptide complexes and TCRs. Thus, HLA-E-Mtb-specific T cells have a highly diverse TCR repertoire.

To identify disease-relevant T cell receptors (TCRs) with shared antigen specificity, we analyzed 778,938 TCRβ chain sequences from 178 non-small cell lung cancer patients using the GLIPH2 (grouping of lymphocyte interactions with paratope hotspots 2) algorithm. We identified over 66,000 shared specificity groups, of which 435 were clonally expanded and enriched in tumors compared to adjacent lung. The antigenic epitopes of one such tumor-enriched specificity group were identified using a yeast peptide-HLA A∗02:01 display library. These included a peptide from the epithelial protein TMEM161A, which is overexpressed in tumors and cross-reactive epitopes from Epstein-Barr virus and E. coli. Our findings suggest that this cross-reactivity may underlie the presence of virus-specific T cells in tumor infiltrates and that pathogen cross-reactivity may be a feature of multiple cancers. The approach and analytical pipelines generated in this work, as well as the specificity groups defined here, present a resource for understanding the T cell response in cancer.