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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). 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.

Background & Aims: The liver provides a unique niche of lymphocytes enriched with a large proportion of innate-like T cells. However, the heterogeneity and functional characteristics of the hepatic T-cell population remain to be fully elucidated., Methods: We obtained liver sinusoidal mononuclear cells from the liver perfusate of healthy donors and recipients with HBV-associated chronic liver disease (CLD) during liver transplantation. We performed a CITE-seq analysis of liver sinusoidal CD45 + cells in combination with T cell receptor (TCR)-seq and flow cytometry to examine the phenotypes and functions of liver sinusoidal CD8 + T cells., Results: We identified a distinct CD56 hi CD161 - CD8 + T-cell population characterized by natural killer (NK)-related gene expression and a uniquely restricted TCR repertoire. The frequency of these cells among the liver sinusoidal CD8 + T-cell population was significantly increased in patients with HBV-associated CLD. Although CD56 hi CD161 - CD8 + T cells exhibit weak responsiveness to TCR stimulation, CD56 hi CD161 - CD8 + T cells highly expressed various NK receptors, including CD94, killer immunoglobulin-like receptors, and NKG2C, and exerted NKG2C-mediated NK-like effector functions even in the absence of TCR stimulation. In addition, CD56 hi CD161 - CD8 + T cells highly respond to innate cytokines, such as IL-12/18 and IL-15, in the absence of TCR stimulation. We validated the results from liver sinusoidal CD8 + T cells using intrahepatic CD8 + T cells obtained from liver tissues., Conclusions: In summary, the current study found a distinct CD56 hi CD161 - CD8 + T-cell population characterized by NK-like activation via TCR-independent NKG2C ligation. Further studies are required to elucidate the roles of liver sinusoidal CD56 hi CD161 - CD8 + T cells in immune responses to microbial pathogens or liver immunopathology., Lay Summary: The role of different immune cell populations in the liver is becoming an area of increasing interest. Herein, we identified a distinct T-cell population that had features similar to those of natural killer (NK) cells - a type of innate immune cell. This distinct population was expanded in the livers of patients with chronic liver disease and could thus have pathogenic relevance., Competing Interests: Conflict of interest The authors declare no conflicts of interest that pertain to this work. Please refer to the accompanying ICMJE disclosure forms for further details., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Single-cell RNA-sequencing (scRNAseq) experiments are becoming a standard tool for bench-scientists to explore the cellular diversity present in all tissues. Data produced by scRNAseq is technically complex and requires analytical workflows that are an active field of bioinformatics research, whereas a wealth of biological background knowledge is needed to guide the investigation. Thus, there is an increasing need to develop applications geared towards bench-scientists to help them abstract the technical challenges of the analysis so that they can focus on the science at play. It is also expected that such applications should support closer collaboration between bioinformaticians and bench-scientists by providing reproducible science tools. We present SCHNAPPs, a Graphical User Interface (GUI), designed to enable bench-scientists to autonomously explore and interpret scRNAseq data and associated annotations. The R/Shiny-based application allows following different steps of scRNAseq analysis workflows from Seurat or Scran packages: performing quality control on cells and genes, normalizing the expression matrix, integrating different samples, dimension reduction, clustering, and differential gene expression analysis. Visualization tools for exploring each step of the process include violin plots, 2D projections, Box-plots, alluvial plots, and histograms. An R-markdown report can be generated that tracks modifications and selected visualizations. The modular design of the tool allows it to easily integrate new visualizations and analyses by bioinformaticians. We illustrate the main features of the tool by applying it to the characterization of T cells in a scRNAseq and Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) experiment of two healthy individuals., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Atherosclerosis is initiated by the accumulation of lipids in the arterial wall that trigger a complex and poorly understood network of inflammatory processes. At the same time, recent clinical findings reveal that targeting specific immune alterations in patients with cardiovascular disease (CVD) represents a promising approach to preventing recurrent cardiovascular events. In order to achieve these tailored therapies, it is critical to resolve the heterogenous environment of the atherosclerotic lesion and decipher the complex structural and functional changes which immune cells undergo throughout disease progression. Recently, single-cell approaches including single cell mass cytometry by time of flight (CyTOF), single cell RNA sequencing (scRNA-seq) and Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) have emerged as valuable tools in resolving cellular plasticity within atherosclerotic lesions. In this review, we will discuss the most important insights that have been gleaned from the application of these single-cell approaches to validated experimental models of atherosclerosis. Additionally, as clinical progress in treatment of the disease depends on the translation of discoveries to human tissues, we will also examine the challenges associated with the application of single-cell approaches to human vascular tissue and the discoveries made by the initial efforts in this direction. Finally, we will analyze the advantages and limitations of dissociative single-cell approaches and how novel in-situ technologies could advance the field by allowing for the investigation of individual cells while preserving the heterogenous architecture of the atherosclerotic lesion., (Copyright © 2021 Elsevier B.V. All rights reserved.)