Comprehensive multi-omics single-cell data integration reveals greater heterogeneity in the human immune system

Abstract

Single-cell transcriptomics enables the definition of diverse human immune cell types across multiple tissues and disease contexts. Still, deeper biological understanding requires comprehensive integration of multiple single-cell omics (transcriptomic, proteomic, and cell-receptor repertoire). To improve the identification of diverse cell types and the accuracy of cell-type classification in multi-omics single-cell datasets, we developed SuPERR-seq, a novel analysis workflow to increase the resolution and accuracy of clustering and allow for the discovery of previously hidden cell subsets. SuPERR-seq accurately removes cell doublets and prevents widespread cell-type misclassification by incorporating information from cell-surface proteins and immunoglobulin transcript counts. This approach uniquely improves the identification of heterogeneous cell types in the human immune system, including a novel subset of antibody-secreting cells in the bone marrow.