Improved integration of single-cell transcriptome and surface protein expression by LinQ-View

Area of Science:

• Single-cell biology
• Immunology
• Bioinformatics

Background:

• Multimodal single-cell sequencing allows simultaneous measurement of gene expression and cell surface proteins.
• Accurate cell heterogeneity and purity assessment are crucial for understanding complex biological systems.

Purpose of the Study:

• To introduce LinQ-View, a novel toolkit for multimodal single-cell data visualization and analysis.
• To enhance the accuracy of cell clustering and purity assessment in single-cell datasets.

Main Methods:

• LinQ-View integrates transcriptional and cell surface protein expression data.
• It employs a quantitative metric for assessing cluster purity.
• The toolkit was validated on public CITE-seq datasets and applied to SARS-CoV-2 infection data.

Main Results:

• LinQ-View generates accurate cell clusters, outperforming existing methods for routine CITE-seq data.
• It effectively prevents single protein variations from skewing results.
• The analysis of SARS-CoV-2 data revealed antigen-specific B cell subsets.

Conclusions:

• LinQ-View provides efficient multimodal analysis and purity assessment for CITE-seq datasets.
• It is particularly useful for targeting specific cell populations, such as B cells.
• The toolkit aids in understanding immune responses to viral infections.