Visual Genomics Analysis Studio as a Tool to Analyze Multiomic Data.
Visual Genomics Analysis Studio as a Tool to Analyze Multiomic Data.
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DOI：
10.3389/fgene.2021.642012
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发表时间：
2021
影响因子：
3.7
通讯作者：
Phillips EJ
中科院分区：
文献类型：
--
作者：
Hertzman RJ;Deshpande P;Leary S;Li Y;Ram R;Chopra A;Cooper D;Watson M;Palubinsky AM;Mallal S;Gibson A;Phillips EJ
B型药物不良反应（ADR）是医源性免疫介导的综合征，其机制病因仍不完全清楚。一些最严重的ADR（包括迟发性药物超敏反应）是T细胞介导的，受特定人类白细胞抗原风险等位基因限制，有时受公共或寡克隆T细胞受体（TCR）限制，是组织损伤反应免疫发病机制的核心。然而，尚未确定介导疾病、定义反应表型和确定严重程度的效应、调节和辅助免疫群体的特异性细胞特征。最近开发的单细胞平台将基因组学和免疫学的进展结合在一起，提供了在单细胞水平上同时检查病理反应部位高度异质性免疫细胞群体的全转录组、TCR和表面蛋白标志物的工具。然而，对先进生物信息学专业知识以及计算硬件和软件的需求往往限制了了解疾病和生物模型的研究人员利用这些新方法的能力。在这里，我们描述了一个最先进的，完全集成的应用程序的功能和使用，用于分析和可视化多组单细胞数据，称为可视化基因组学分析工作室（VGAS）。这种独特的用户友好的基于Windows的图形用户界面专门设计用于使研究者能够查询自己的数据。虽然VGAS还包括用于序列比对和鉴定与宿主或生物体遗传多态性相关性的工具，但在本文中，我们重点关注其在单细胞TCR-RNA-细胞转录组和表位索引测序（CITE）-seq分析中的应用，通过无偏转录组和选择表面蛋白质组实现整体细胞表征。重要的是，VGAS不需要用户指导的编码或访问高性能计算机，而是结合性能优化的隐藏代码，为数据分析提供基于应用程序的快速和直观的工具，并在标准规格的笔记本电脑上制作高分辨率的出版就绪图形。具体而言，它允许分析全面的单细胞TCR测序（scTCR-seq）数据，详细说明（i）α-β异源二聚体TCR的功能配对，（ii）一键直方图以显示熵和基因重排，以及（iii）Circos和Sankey图以可视化克隆性和优势。对于无偏单细胞RNA测序（scRNA-seq）分析，用户通过主成分分析、t分布随机邻域嵌入或均匀流形近似和投影图根据全局结构提取细胞转录组特征，scTCR-seq的叠加使得能够识别和选择免疫显性TCR表达群体。使用寡核苷酸标记的抗体（CITE-seq）与表面蛋白标记物的类似的基于序列的检测的进一步整合提供了对表面蛋白表达的比较理解，使用火山图或热图功能可视化差异基因或蛋白质分析。这些数据可以与参考细胞图谱或合适的对照进行比较，以揭示离散的疾病特异性子集，从上皮到组织驻留记忆T细胞，以及从衰老到衰竭的激活状态，其中更有限的转录物表达显示为小提琴和箱形图。重要的是，指导教程视频可用，以及基于生物信息学和用户反馈的最新进展的定期应用程序更新。
Type B adverse drug reactions (ADRs) are iatrogenic immune-mediated syndromes with mechanistic etiologies that remain incompletely understood. Some of the most severe ADRs, including delayed drug hypersensitivity reactions, are T-cell mediated, restricted by specific human leukocyte antigen risk alleles and sometimes by public or oligoclonal T-cell receptors (TCRs), central to the immunopathogenesis of tissue-damaging response. However, the specific cellular signatures of effector, regulatory, and accessory immune populations that mediate disease, define reaction phenotype, and determine severity have not been defined. Recent development of single-cell platforms bringing together advances in genomics and immunology provides the tools to simultaneously examine the full transcriptome, TCRs, and surface protein markers of highly heterogeneous immune cell populations at the site of the pathological response at a single-cell level. However, the requirement for advanced bioinformatics expertise and computational hardware and software has often limited the ability of investigators with the understanding of diseases and biological models to exploit these new approaches. Here we describe the features and use of a state-of-the-art, fully integrated application for analysis and visualization of multiomic single-cell data called Visual Genomics Analysis Studio (VGAS). This unique user-friendly, Windows-based graphical user interface is specifically designed to enable investigators to interrogate their own data. While VGAS also includes tools for sequence alignment and identification of associations with host or organism genetic polymorphisms, in this review we focus on its application for analysis of single-cell TCR–RNA–Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE)-seq, enabling holistic cellular characterization by unbiased transcriptome and select surface proteome. Critically, VGAS does not require user-directed coding or access to high-performance computers, instead incorporating performance-optimized hidden code to provide application-based fast and intuitive tools for data analyses and production of high-resolution publication-ready graphics on standard specification laptops. Specifically, it allows analyses of comprehensive single-cell TCR sequencing (scTCR-seq) data, detailing (i) functional pairings of α–β heterodimer TCRs, (ii) one-click histograms to display entropy and gene rearrangements, and (iii) Circos and Sankey plots to visualize clonality and dominance. For unbiased single-cell RNA sequencing (scRNA-seq) analyses, users extract cell transcriptome signatures according to global structure via principal component analysis, t-distributed stochastic neighborhood embedding, or uniform manifold approximation and projection plots, with overlay of scTCR-seq enabling identification and selection of the immunodominant TCR-expressing populations. Further integration with similar sequence-based detection of surface protein markers using oligo-labeled antibodies (CITE-seq) provides comparative understanding of surface protein expression, with differential gene or protein analyses visualized using volcano plot or heatmap functions. These data can be compared to reference cell atlases or suitable controls to reveal discrete disease-specific subsets, from epithelial to tissue-resident memory T-cells, and activation status, from senescence through exhaustion, with more finite transcript expression displayed as violin and box plots. Importantly, guided tutorial videos are available, as are regular application updates based on the latest advances in bioinformatics and user feedback.
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影响因子：
3.7
作者：
Nakkam N;Konyoung P;Kanjanawart S;Saksit N;Kongpan T;Khaeso K;Khunarkornsiri U;Dornsena A;Tassaneeyakul W;Tassaneeyakul W
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