AUTHOR=Hertzman Rebecca J. , Deshpande Pooja , Leary Shay , Li Yueran , Ram Ramesh , Chopra Abha , Cooper Don , Watson Mark , Palubinsky Amy M. , Mallal Simon , Gibson Andrew , Phillips Elizabeth J. 

TITLE=Visual Genomics Analysis Studio as a Tool to Analyze Multiomic Data

JOURNAL=Frontiers in Genetics

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.642012

DOI=10.3389/fgene.2021.642012

ISSN=1664-8021

ABSTRACT=Type B adverse drug reactions (ADRs) are immune-mediated syndromes with undefined etiologies. Some of the most severe ADRs, including delayed drug hypersensitivity reactions (DHRs), are T-cell mediated, restricted by human leukocyte antigen risk alleles and sometimes dominant T-cell receptors (TCR), central to the immunopathogenesis. However, the specific cellular signatures of effector, regulatory and accessory populations which define reaction phenotype and severity have not been defined. Single-cell platforms now provide opportunity to simultaneously examine the full transcriptome, TCRs and surface proteins of heterogenous immune cell populations. However, the requirement for advanced bio-informatic expertise, computational hardware and software has limited the ability of investigators to exploit these new approaches.
Here we describe a state-of-the-art, fully integrated application for analysis and visualization of multi-omic single-cell data called Visual Genomics Analysis Studio (VGAS). This unique user-friendly, windows-based graphical user interface (GUI) is designed to enable investigators to interrogate their own data. We focus on its application to single-cell TCR-RNA-Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE)-seq; enabling holistic cellular characterisation by unbiased transcriptome and select surface proteome. Critically, VGAS does not require user-directed coding or high-performance computers; instead, incorporating performance-optimised hidden code to provide intuitive tools for analyses and production of high-resolution graphics on standard laptops. Specifically, it allows comprehensive analyses of single-cell TCR sequencing (scTCR-seq) data, detailing (i) functional pairings of alpha-beta heterodimer TCRs, (ii) one-click histograms to display entropy and gene rearrangements, and (iii) Circos and Sankey plots to visualize clonality and dominance. For single-cell RNA sequencing (scRNA-seq), users extract cell transcriptome signatures according to global structure with overlay of scTCR-seq enabling selection of the immunodominant TCR-expressing populations. Integration with sequence-based detection of surface protein using oligo-labelled antibodies provides comparative understanding of surface protein expression, with differential gene or protein analyses visualized using volcano or heatmap functions. These data compared to reference cell atlases or suitable controls reveal discrete disease-specific subsets, from epithelial to tissue-resident memory T-cells, and activation status, from senescence through exhaustion, with finite expression displayed as violin and box plots. Guided tutorial videos are available, as are regular application updates based on the latest advances and user feedback.