Advances in single-cell technologies have enabled the investigation of T-cell phenotypes and repertoires at unprecedented resolution and scale. Bioinformatic methods for the efficient analysis of these large-scale datasets are instrumental for advancing our understanding of adaptive immune responses. However, while well-established solutions are accessible for the processing of single-cell transcriptomes, no streamlined pipelines are available for the comprehensive characterization of T-cell receptors. Here, we propose single-cell immune repertoires in Python (Scirpy), a scalable Python toolkit that provides simplified access to the analysis and visualization of immune repertoires from single cells and seamless integration with transcriptomic data. Scirpy source code and documentation are available at https://github.com/icbi-lab/scirpy. Supplementary data are available at Bioinformatics online.

The accumulation of tumor-specific CD4+ and CD8+ effector T cells is key to an effective antitumor response. Locally, CD4+ T cells promote the recruitment and effector function of tumor-specific CD8+ T cells and activate innate killer cells in the tumor. Here, we show that tumor-specific CD4+ T cells were predominantly present in the CD39+ subset of tumor-infiltrating lymphocytes (TIL). The CD39+ CD4+ and CD8+ TILs were detected in three different tumor types, and displayed an activated (PD-1+, HLA-DR+) effector memory phenotype. CD4+CD39+ single-cell RNA-sequenced TILs shared similar well-known activation, tissue residency, and effector cell–associated genes with CD8+CD39+CD103+ TILs. Finally, analysis of directly ex vivo cell-sorted and in vitro expanded pure populations of CD39-positive and negative CD4+ and CD8+ TILs revealed that tumor-specific antigen reactivity was almost exclusively detected among CD39+ cells. Immunotherapy of cancer is based on the activation of tumor-reactive CD4+ and CD8+ T cells. We show that the expression of CD39 can be used to identify, isolate, and expand tumor-reactive T-cell populations in cancers.

Normothermic machine perfusion (NMP) has emerged as an innovative organ preservation technique. Developing an understanding for the donor organ immune cell composition and its dynamic changes during NMP is essential. We aimed for a comprehensive characterization of immune cell (sub)populations, cell trafficking and cytokine release during liver NMP. Single-cell transcriptome profiling of human donor livers prior to, during NMP and after transplantation shows an abundance of CXC chemokine receptor 1⁺/2⁺ (CXCR1⁺/CXCR2⁺) neutrophils, which significantly decreased during NMP. This is paralleled by a large efflux of passenger leukocytes with neutrophil predominance in the perfusate. During NMP, neutrophils shift from a pro-inflammatory state towards an aged/chronically activated/exhausted phenotype, while anti-inflammatory/tolerogenic monocytes/macrophages are increased. We herein describe the dynamics of the immune cell repertoire, phenotypic immune cell shifts and a dominance of neutrophils during liver NMP, which potentially contribute to the inflammatory response. Our findings may serve as resource to initiate future immune-interventional studies.

Significance: Blood vessels in the central nervous system possess unique barrier properties that prevent infiltration of foreign substances and allow for precise delivery of ions, molecules, and immune cells into neural networks. Barrier breakdown is associated with a host of retinal and neurological disorders but few BRB/BBB-enhancing therapies have been developed. To identify novel barrier-inducing factors, we genetically engineered a transcriptional reporter cell line with CRISPR technology for compound library screening. Using this approach, we identified compounds, including a TGF-β receptor inhibitor, RepSox, which functions in vitro to increase barrier resistance in human primary and stem cell-derived endothelial cell lines. These data may inform future therapies for BRB/BBB disorders and retinal/neurological diseases.

Advances in single-cell technology have enabled the measurement of cell-resolved molecular states across a variety of cell lines and tissues under a plethora of genetic, chemical, environmental or disease perturbations. Current methods focus on differential comparison or are specific to a particular task in a multi-condition setting with purely statistical perspectives. The quickly growing number, size and complexity of such studies require a scalable analysis framework that takes existing biological context into account. Here we present pertpy, a Python-based modular framework for the analysis of large-scale single-cell perturbation experiments. Pertpy provides access to harmonized perturbation datasets and metadata databases along with numerous fast and user-friendly implementations of both established and novel methods, such as automatic metadata annotation or perturbation distances, to efficiently analyze perturbation data. As part of the scverse ecosystem, pertpy interoperates with existing single-cell analysis libraries and is designed to be easily extended.