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

Li, Lei Dugan Haley L.; Stamper, Christopher T.; Lan, Linda Yu-Ling Asby Nicholas W.; Knight, Matthew Stovicek Olivia Zheng Nai-Ying Madariaga Maria Lucia Shanmugarajah Kumaran Jansen Maud O.; Changrob, Siriruk Utset Henry A.; Henry, Carole Nelson Christopher Jedrzejczak Robert P.; Fremont, Daved H.; Joachimiak, Andrzej Krammer Florian Huang Jun Khan Aly A.; Wilson, Patrick C.

Summary Multimodal advances in single-cell sequencing have enabled the simultaneous quantification of cell surface protein expression alongside unbiased transcriptional profiling. Here, we present LinQ-View, a toolkit designed for multimodal single-cell data visualization and analysis. LinQ-View integrates transcriptional and cell surface protein expression profiling data to reveal more accurate cell heterogeneity and proposes a quantitative metric for cluster purity assessment. Through comparison with existing multimodal methods on multiple public CITE-seq datasets, we demonstrate that LinQ-View efficiently generates accurate cell clusters, especially in CITE-seq data with routine numbers of surface protein features, by preventing variations in a single surface protein feature from affecting results. Finally, we utilized this method to integrate single-cell transcriptional and protein expression data from SARS-CoV-2-infected patients, revealing antigen-specific B cell subsets after infection. Our results suggest LinQ-View could be helpful for multimodal analysis and purity assessment of CITE-seq datasets that target specific cell populations (e.g., B cells).