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Background Single-cell RNA sequencing (scRNA-seq) technologies offer unique opportunities for exploring heterogeneous cell populations. However, in-depth single-cell transcriptomic characterization of complex tissues often requires profiling tens to hundreds of thousands of cells. Such large numbers of cells represent an important hurdle for downstream analyses, interpretation and visualization. Results We develop a framework called SuperCell to merge highly similar cells into metacells and perform standard scRNA-seq data analyses at the metacell level. Our systematic benchmarking demonstrates that metacells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, metacells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop. Conclusions SuperCell is a framework to build and analyze metacells in a way that efficiently preserves the results of scRNA-seq data analyses while significantly accelerating and facilitating them.

Single-cell RNA sequencing (scRNA-seq) technologies offer unique opportunities for exploring heterogeneous cell populations. However, in-depth single-cell transcriptomic characterization of complex tissues often requires profiling tens to hundreds of thousands of cells. Such large numbers of cells represent an important hurdle for downstream analyses, interpretation and visualization. Here we develop a network-based coarse-graining framework where highly similar cells are merged into super-cells. We demonstrate that super-cells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, super-cells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop.

Fully functional, tumor-specific, SLAMF7-expressing cytotoxic CD4 T cells directly mediate immunity against human cancer., CD4 T cells have been implicated in cancer immunity for their helper functions. Moreover, their direct cytotoxic potential has been shown in some patients with cancer. Here, by mining single-cell RNA-seq datasets, we identified CD4 T cell clusters displaying cytotoxic phenotypes in different human cancers, resembling CD8 T cell profiles. Using the peptide-MHCII-multimer technology, we confirmed ex vivo the presence of cytolytic tumor-specific CD4 T cells. We performed an integrated phenotypic and functional characterization of these cells, down to the single-cell level, through a high-throughput nanobiochip consisting of massive arrays of picowells and machine learning. We demonstrated a direct, contact-, and granzyme-dependent cytotoxic activity against tumors, with delayed kinetics compared to classical cytotoxic lymphocytes. Last, we found that this cytotoxic activity was in part dependent on SLAMF7. Agonistic engagement of SLAMF7 enhanced cytotoxicity of tumor-specific CD4 T cells, suggesting that targeting these cells might prove synergistic with other cancer immunotherapies.

Background CD4 T cells have been implicated in cancer immunity for their helper functions. However, their direct cytotoxic potential remains elusive in cancer patients. Here, we aimed at assessing the presence, rate and cytotoxic function of tumor-specific Th-CTX directly in cancer patients. Methods We capitalized on published single cell transcriptomic analyses of patient samples, integrated with the direct phenotypic and functional characterization of clonal, tumor-specific CD4 T cell populations, using peptide-MHC class II multimers and a novel high-throughput single-cell cytotoxicity assay in picowell arrays. The direct tumor cell killing by cytolytic tumor-specific CD4 T cells in the arrays was monitored in a high-throughput manner by combining multi-channel time-lapse microscopy with deep neural networks. Results By mining single-cell RNA-seq datasets of tumor infiltrating lymphocytes, we identified CD4 T cells displaying cytotoxic phenotypes in different human tumors. The cytolytic CD4 T cells formed a distinct cluster and expressed genes related to classical cytotoxic functions, largely resembling CD8 T cell gene profiles. Using the peptide MHC class II multimer technology, we confirmed directly ex vivo the presence of cytolytic tumor antigen-specific CD4 T cells, both in the circulation and in the tumors of patients. We performed an integrated phenotypic and functional characterization of cytolytic tumor-specific CD4 T cells, down to the single cell level, through a high-throughput nanobiochip consisting of massive arrays of picowells with sub-nanoliter volumes and machine learning. We demonstrated a direct, contact-dependent, granzyme-dependent cytotoxic activity against tumor cells, with delayed kinetics compared to classical cytotoxic lymphocytes. Lastly, we discovered that this cytotoxic activity was at least in part dependent on the expression of SLAMF7, a homophylic receptor known to regulate NK cell activity. Conclusions Our work provides a deep characterization of human Th-CTX in cancer and supports their role in tumor immunity. Moreover, our results showing that agonistic engagement of SLAMF7 enhances the cytolytic capacity of tumor-specific CD4 T cells, suggests that targeting these cells might prove synergistic with the use of other immunotherapies in cancer patients.

Recent studies have proposed that tumor-specific tumor-infiltrating CD8+ T lymphocytes (CD8 TIL) can be classified into two main groups: “exhausted” TILs, characterized by high expression of the inhibitory receptors PD-1 and TIM-3 and lack of transcription factor 1 (Tcf1); and “memory-like” TILs, with self-renewal capacity and co-expressing Tcf1 and PD-1. However, a comprehensive definition of the heterogeneity existing within both tumor-specific and total CD8 TILs has yet to be clearly established.To investigate this heterogeneity at the transcriptomic level, we performed paired single-cell RNA and TCR sequencing of CD8 T cells infiltrating B16 murine melanoma tumors, including cells of known tumor specificity. Unsupervised clustering and gene signature analysis revealed four distinct CD8 TIL states - exhausted, memory-like, naïve and effector memory-like (EM-like) - and predicted novel markers, including Ly6C for the EM-like cells, that were validated by flow cytometry. Tumor-specific PMEL T cells were predominantly found within the exhausted and memory-like states but also within the EM-like state. Further, TCR repertoire sequencing revealed a large clonal expansion of exhausted, memory-like and EM-like cells with partial clonal relatedness between them. Finally, meta-analyses of public bulk and single-cell RNA-seq data suggested that anti-PD-1 treatment induces expansion of EM-like cells.Our reference map of the transcriptomic landscape of murine CD8 TILs will help interpreting future bulk and single-cell transcriptomic studies and may guide the analysis of CD8 TIL subpopulations in response to therapeutic interventions.