Multimodal single-cell approaches shed light on T cell heterogeneity

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.

Figure 1:. Different paradigms for the collection and analysis of multimodal singlecell data.

Traditionally, immune populations of interest have been studied by flow sorting on a surface marker and then characterizing the cell with a follow-up assay. Now, multiple modalities of data can instead be collected in parallel, either from separate samples of cells or from the same aliquot.

Figure 2:. Methods to collect multimodal data from the same cells.

Current advancements in single-cell sequencing technology have focused on measuring multiple modalities of data in individual cells. The methods shown here yield highdimensional data that link different types of features for higher-resolution immunophenotyping.