Interpretation of T cell states from single-cell transcriptomics data using reference atlases

Abstract

Single-cell RNA sequencing (scRNA-seq) has revealed an unprecedented degree of immune cell diversity. However, consistent definition of cell subtypes and cell states across studies and diseases remains a major challenge. Here we generate reference T cell atlases for cancer and viral infection by multi-study integration, and develop ProjecTILs, an algorithm for reference atlas projection. In contrast to other methods, ProjecTILs allows not only accurate embedding of new scRNA-seq data into a reference without altering its structure, but also characterizing previously unknown cell states that “deviate” from the reference. ProjecTILs accurately predicts the effects of cell perturbations and identifies gene programs that are altered in different conditions and tissues. A meta-analysis of tumor-infiltrating T cells from several cohorts reveals a strong conservation of T cell subtypes between human and mouse, providing a consistent basis to describe T cell heterogeneity across studies, diseases, and species.

Dynamic regulatory networks of T cell trajectory dissect transcriptional control of T cell state transition

“…Combining tissue distribution and cell state composition along the trajectory, these results suggested a tendency that blood circulating naive T cells are recruited to the tumor, and persistent antigen stimulation drives intermediate state cells to pre-dysfunction cells and further differentiate to the dysfunctional state, which was similar to the CD8 + T cell differentiation model proposed by Andreatta et al. 15 Another lineage differentiated from intermediate state cells directly to normal effect cells or intratumoral bystander cells.…”

geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq

“…Similarly to mouse embryogenesis, the occasional mismapping of cells occurred between transcriptionally similar cell types, such as follicular and marginal zone B cells, as well as CD4+ and CD8+ T cells and innate lymphoid cells. Of note, the difficulty of using only the transcriptome to distinguish between different types of T cells is well known [ 44 ], and it has been suggested that a more precise annotation of T cells can be obtained by generating paired measurements of cellular transcriptomes and immunophenotypes [ 45 ].…”

Adenovirus Armed With TNFa and IL2 Added to aPD-1 Regimen Mediates Antitumor Efficacy in Tumors Refractory to aPD-1

“…In vivo determination of the refractory status could have direct translatability into the clinical arena. Furthermore, a widely used model for in vivo Oncoimmunology studies such as the B16.OVA model for C57 mice, offers reference frameworks in terms of survival to anti-PD-1 (29,34) and transcriptomic data (35,36).…”