Multimodal single-cell approaches shed light on T cell heterogeneity
Aparna Nathan1, Yuriy Baglaenko2, Chamith Y Fonseka1
1Center for Data Sciences, Brigham and Women's Hospital, Boston, MA 02115, USA; Division of Genetics, Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA 02115, USA; Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115 USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA.
Abstract:
Single-cell methods have revolutionized the study of T cell biology by enabling the identification and characterization of individual cells. This has led to a deeper understanding of T cell heterogeneity by generating functionally relevant measurements - like gene expression, surface markers, chromatin accessibility, T cell receptor sequences - in individual cells. While these methods are independently valuable, they can be augmented when applied jointly, either on separate cells from the same sample or on the same cells. Multimodal approaches are already being deployed to characterize T cells in diverse disease contexts and demonstrate the value of having multiple insights into a cell's function. But, these data sets pose new statistical challenges for integration and joint analysis.
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