T cells engineered to express chimeric antigen receptors (CARs) targeting CD19 have produced impressive outcomes for the treatment of B cell malignancies, but different products vary in kinetics, persistence, and toxicity profiles based on the co-stimulatory domains included in the CAR. In this study, we performed transcriptional profiling of bulk CAR T cell populations and single cells to characterize the transcriptional states of human T cells transduced with CD3ζ, 4-1BB-CD3ζ (BBζ), or CD28-CD3ζ (28ζ) co-stimulatory domains at rest and after activation by triggering their CAR or their endogenous T cell receptor (TCR). We identified a transcriptional signature common across CARs with the CD3ζ signaling domain, as well as a distinct program associated with the 4-1BB co-stimulatory domain at rest and after activation. CAR T cells bearing BBζ had increased expression of human leukocyte antigen (HLA) class II genes, ENPP2, and interleukin (IL)-21 axis genes, and decreased PD1 compared to 28ζ CAR T cells. Similar to previous studies, we also found BBζ CAR CD8 T cells to be enriched in a central memory cell phenotype and fatty acid metabolism genes. Our data uncovered transcriptional signatures related to costimulatory domains and demonstrated that signaling domains included in CARs uniquely shape the transcriptional programs of T cells.
4-1BB Ligand/chemistry , 4-1BB Ligand/metabolism , Cell Engineering/methods , Protein Domains/genetics , RNA, Small Cytoplasmic/genetics , Receptors, Chimeric Antigen/genetics , Signal Transduction/genetics , T-Lymphocytes/metabolism , Transcriptome , HEK293 Cells , Humans , K562 Cells , RNA-Seq/methods , Single-Cell Analysis , Transduction, Genetic
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Single-cell genomics is essential to chart tumor ecosystems. Although single-cell RNA-Seq (scRNA-Seq) profiles RNA from cells dissociated from fresh tumors, single-nucleus RNA-Seq (snRNA-Seq) is needed to profile frozen or hard-to-dissociate tumors. Each requires customization to different tissue and tumor types, posing a barrier to adoption. Here, we have developed a systematic toolbox for profiling fresh and frozen clinical tumor samples using scRNA-Seq and snRNA-Seq, respectively. We analyzed 216,490 cells and nuclei from 40 samples across 23 specimens spanning eight tumor types of varying tissue and sample characteristics. We evaluated protocols by cell and nucleus quality, recovery rate and cellular composition. scRNA-Seq and snRNA-Seq from matched samples recovered the same cell types, but at different proportions. Our work provides guidance for studies in a broad range of tumors, including criteria for testing and selecting methods from the toolbox for other tumors, thus paving the way for charting tumor atlases.
Algorithms , Cell Nucleus/genetics , Genomics/methods , Neoplasms/genetics , RNA-Seq/methods , Single-Cell Analysis/methods , Adult , Animals , Cell Nucleus/chemistry , Cell Nucleus/metabolism , Child , Computational Biology/methods , Female , Freezing , Gene Expression Profiling/methods , Gene Expression Regulation, Neoplastic , Humans , Mice , Mice, Knockout , Mice, Nude , Neoplasms/metabolism , Neoplasms/pathology , Sequence Analysis, RNA/methods , Tumor Cells, Cultured , Exome Sequencing/methods
