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

Area of Science:

• Immunology
• Computational Biology
• Genomics

Background:

• Single-cell transcriptomics identifies immune cells across tissues and diseases.
• Deeper understanding requires integrating multiple single-cell omics data (transcriptomic, proteomic, cell-receptor).

Purpose of the Study:

• To enhance cell type identification and classification accuracy in multi-omics single-cell datasets.
• To develop a novel analysis workflow for improved resolution and discovery of cell subsets.

Main Methods:

• Developed SuPERR, a multi-omics single-cell analysis workflow.
• Incorporated cell-surface proteins and immunoglobulin transcript counts.
• Improved clustering resolution and accuracy, doublet removal, and cell-type classification.

Main Results:

• SuPERR increases resolution and accuracy of clustering in multi-omics data.
• The workflow effectively removes cell doublets and prevents misclassification.
• Identified previously hidden immune cell subsets, including rare antibody-secreting cells in bone marrow.

Conclusions:

• SuPERR offers a novel approach to analyze multi-omics single-cell data.
• The workflow enhances the identification of heterogeneous cell types and states in the human immune system.
• Enables discovery of rare immune cell populations with high accuracy.