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

• Published in: Cell reports methods Vol. 1; no. 4; p. 100056
• Main Authors: 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.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 23-08-2021; Elsevier
• Subjects: CITE-seq; Cluster Analysis; computational method; COVID-19 - genetics; gene expression; Humans; integrated model; Membrane Proteins; mRNA; multimodal method; protein; purity metric; SARS-CoV-2 - genetics; scRNA-seq; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; Transcriptome - genetics; scRNA-seq; integrated model; CITE-seq; purity metric; protein; mRNA; computational method; multimodal method; gene expression
• ISSN: 2667-2375; 2667-2375
• DOI: 10.1016/j.crmeth.2021.100056

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).
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•LinQ-View integrates mRNA and protein expression data to reveal cell heterogeneity•LinQ-View prevents single dominant ADT features from affecting clustering•LinQ-View presents a quantitative purity metric for CITE-seq data•LinQ-View is specialized in handling CITE-seq data with fewer ADT features
Multimodal single-cell sequencing enables multiple aspects for characterizing the dynamics of cell states and developmental processes. Properly integrating information from multiple modalities is a crucial step for interpreting cell heterogeneity. Here, we present LinQ-View, a computational workflow that provides an effective solution for integrating multiple modalities of CITE-seq data for downstream interpretation. LinQ-View balances information from multiple modalities to achieve accurate clustering results and is specialized in handling CITE-seq data with routine numbers of surface protein features.
Li et al. present LinQ-View, a computational workflow that provides an effective solution for integrating multiple modalities of CITE-seq data and quantitative assessment of cluster purity. LinQ-View could be helpful for multimodal analysis and purity assessment of CITE-seq datasets that target specific cell populations.