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Interpretation of T cell states from single-cell transcriptomics data using reference atlases (Miller et al 2019 - Projection)

Massimo Andreatta, Jesus Corria-Osorio, Soren Muller, Rafael Cubas, George Coukos

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.

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Species: mouse
Number of cells: 18287
Number of downloads: 4
Study size: 643MB
Uploaded at:

Mouse cell atlas 
T cell landscape 
Reference atlas 
Cell type prediction 
Tumor-infiltrating T cell 
lymph node