Interpretation of T cell states from single-cell transcriptomics data using reference atlases
| Autor: | Massimo, Andreatta, Jesus, Corria-Osorio, Sören, Müller, Rafael, Cubas, George, Coukos, Santiago J, Carmona |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
T-Lymphocytes
Science T cells Cell Differentiation Article Cohort Studies Disease Models Animal Mice Lymphocytes Tumor-Infiltrating Animals Cell Differentiation/immunology Gene Expression Regulation/immunology Humans Lymphocytes Tumor-Infiltrating/immunology Neoplasms/blood Neoplasms/immunology Neoplasms/pathology RNA-Seq/methods Reference Values Single-Cell Analysis/methods Software Species Specificity T-Lymphocyte Subsets/immunology T-Lymphocytes/immunology Tumor Microenvironment/immunology Virus Diseases/blood Virus Diseases/immunology Gene Expression Regulation T-Lymphocyte Subsets Virus Diseases Neoplasms Machine learning Tumor Microenvironment Tumour immunology RNA-Seq Single-Cell Analysis |
| Zdroj: | Nature communications, vol. 12, no. 1, pp. 2965 Nature Communications, Vol 12, Iss 1, Pp 1-19 (2021) Nature Communications |
| Popis: | 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. One challenge of single cell RNA sequencing analysis is how to consistently identify cell subtypes and states across different datasets. Here the authors propose the use of a reference single-cell atlas as a stable system of coordinates to characterize T cell states across studies, diseases and species. |
| Databáze: | OpenAIRE |
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