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

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). Graphical Highlights • 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 Motivation 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.

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

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).

Profiling B cell immunodominance after SARS-CoV-2 infection reveals antibody evolution to non-neutralizing viral targets.

Dissecting the evolution of memory B cells (MBCs) against SARS-CoV-2 is critical for understanding antibody recall upon secondary exposure. Here, we used single-cell sequencing to profile SARS-CoV-2-reactive B cells in 38 COVID-19 patients. Using oligo-tagged antigen baits, we isolated B cells specific to the SARS-CoV-2 spike, nucleoprotein (NP), open reading frame 8 (ORF8), and endemic human coronavirus (HCoV) spike proteins. SARS-CoV-2 spike-specific cells were enriched in the memory compartment of acutely infected and convalescent patients several months post symptom onset. With severe acute infection, substantial populations of endemic HCoV-reactive antibody-secreting cells were identified and possessed highly mutated variable genes, signifying preexisting immunity. Finally, MBCs exhibited pronounced maturation to NP and ORF8 over time, especially in older patients. Monoclonal antibodies against these targets were non-neutralizing and non-protective in vivo. These findings reveal antibody adaptation to non-neutralizing intracellular antigens during infection, emphasizing the importance of vaccination for inducing neutralizing spike-specific MBCs.

SARS-CoV-2 Infection Severity Is Linked to Superior Humoral Immunity against the Spike.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently causing a global pandemic. The antigen specificity of the antibody response mounted against this novel virus is not understood in detail. Here, we report that subjects with a more severe SARS-CoV-2 infection exhibit a larger antibody response against the spike and nucleocapsid protein and epitope spreading to subdominant viral antigens, such as open reading frame 8 and nonstructural proteins. Subjects with a greater antibody response mounted a larger memory B cell response against the spike, but not the nucleocapsid protein. Additionally, we revealed that antibodies against the spike are still capable of binding the D614G spike mutant and cross-react with the SARS-CoV-1 receptor binding domain. Together, this study reveals that subjects with a more severe SARS-CoV-2 infection exhibit a greater overall antibody response to the spike and nucleocapsid protein and a larger memory B cell response against the spike.IMPORTANCE With the ongoing pandemic, it is critical to understand how natural immunity against SARS-CoV-2 and COVID-19 develops. We have identified that subjects with more severe COVID-19 disease mount a more robust and neutralizing antibody response against SARS-CoV-2 spike protein. Subjects who mounted a larger response against the spike also mounted antibody responses against other viral antigens, including the nucleocapsid protein and ORF8. Additionally, this study reveals that subjects with more severe disease mount a larger memory B cell response against the spike. These data suggest that subjects with more severe COVID-19 disease are likely better protected from reinfection with SARS-CoV-2.

Distinct B cell subsets give rise to antigen-specific antibody responses against SARS-CoV-2.

Discovery of durable memory B cell (MBC) subsets against neutralizing viral epitopes is critical for determining immune correlates of protection from SARS-CoV-2 infection. Here, we identified functionally distinct SARS-CoV-2-reactive B cell subsets by profiling the repertoire of convalescent COVID-19 patients using a high-throughput B cell sorting and sequencing platform. Utilizing barcoded SARS-CoV-2 antigen baits, we isolated thousands of B cells that segregated into discrete functional subsets specific for the spike, nucleocapsid protein (NP), and open reading frame (ORF) proteins 7a and 8. Spike-specific B cells were enriched in canonical MBC clusters, and monoclonal antibodies (mAbs) from these cells were potently neutralizing. By contrast, B cells specific to ORF8 and NP were enriched in naïve and innate-like clusters, and mAbs against these targets were exclusively non-neutralizing. Finally, we identified that B cell specificity, subset distribution, and affinity maturation were impacted by clinical features such as age, sex, and symptom duration. Together, our data provide a comprehensive tool for evaluating B cell immunity to SARS-CoV-2 infection or vaccination and highlight the complexity of the human B cell response to SARS-CoV-2.