Projecting single-cell transcriptomics data onto a reference T cell atlas to interpret immune responses

Abstract

Single-cell transcriptomics is a transformative technology to explore heterogeneous cell populations such as T cells, one of the most potent weapons against cancer and viral infections. Recent advances in this technology and the computational tools developed in their wake provide unique opportunities to build reference atlases that can be used to systematically compare new single-cell RNA-seq (scRNA-seq) datasets derived from different models or therapeutic conditions. We have developed ProjecTILs (https://github.com/carmonalab/ProjecTILs), a novel computational tool to project new scRNA-seq data into a reference map of T cells, allowing their direct comparison in a stable, annotated system of coordinates. ProjecTILs enables the classification of query cells into curated, discrete states, but also over a continuous space of intermediate states. We illustrate the projection of several datasets from recent publications over two novel cross-study murine T cell reference atlases: the first describing tumor-infiltrating T lymphocytes (TILs), the second characterizing acute and chronic viral infection. ProjecTILs accurately predicted the effects of multiple perturbations, including the ablation of genes controlling T cell differentiation, such as Tox, Ptpn2, miR-155 and Regnase-1, and identified novel gene programs that were altered in these cells (such as a Lag3-Klrc1 inhibitory module), revealing mechanisms of action behind these immunotherapeutic targets and opening new opportunities for the identification of novel targets. By comparing multiple samples over the same reference map, and across alternative embeddings, our method allows exploring the effect of cellular perturbations (e.g. as the result of therapy or genetic engineering) in terms of transcriptional states and altered genetic programs. *

STACAS: Sub-Type Anchor Correction for Alignment in Seurat to integrate single-cell RNA-seq data

“…1H ). We obtained similar, consistent results on larger-scale integration tasks toward the construction of reference T cell maps in cancer and chronic infection ( Andreatta et al , 2020 ). An interactive TIL reference atlas constructed using STACAS can be explored at: http://tilatlas.unil.ch…”

Cross-tissue, single-cell stromal atlas identifies shared pathological fibroblast phenotypes in four chronic inflammatory diseases

“…The identification of shared cell states across tissues with scRNAseq has recently become possible with advances in statistical methods for integrative clustering (Butler et al, 2018; Korsunsky et al, 2019; Tran et al, 2020) and reference mapping (Andreatta et al, 2020; Kang et al, 2020; Lotfollahi et al, 2020). Integrative clustering identifies similar cell states across a range of scRNAseq datasets, even when the datasets come from different donors, species, or tissues.…”

“…Reference mapping allows rapid comparison of data from a new study to a well annotated reference, even if the study represents a tissue, disease, or species not present in the reference atlas. For instance, Andreatta et al, 2020 mapped T cell subtypes to a scRNAseq atlas of annotated tumor infiltrating T cells, while Lotfollahi et al, 2020 found disease-related immune states by mapping PBMCs from patients with COVID19 to a healthy reference library of immune cells.…”

Multiomics reveals persistence of obesity-associated immune cell phenotypes in adipose tissue during weight loss and subsequent weight regain

“…T cell exhaustion has been recently noted in human and mouse AT CD8 + T cells, which were shown to have impaired stimulation and increased markers of T cell exhaustion following obesity 31,32,33 . This transcriptional phenotype was further validated in our model by comparing T cells identified in our study to previously published exhausted T cell atlases generated in models of viral infection and cancer 24 . We also previously reported that AT T cell clonality is increased during obesity and that the T cell repertoire likely responds to positively charged, non-polar antigens 34 .…”

“…We observed that obesity, WL, and WC were all enriched for our exhaustion module and that CD8 + T EM were most associated with an exhausted phenotype (Fig 4F). T cell exhaustion is frequently studied in models of viral infection and in the tumor microenvironment, so we utilized ProjecTILs, a published scRNA-seq reference atlas 24 , to further confirm our exhaustion profile. We observed that cells captured in our experiments projected more accurately onto an LCMV chronic infection atlas ( Concordant with our exhaustion module, more CD8 + T cells aligned with the LCMV exhausted precursor cells in obese, WL, and WC groups than in the lean control group.…”