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      Single-Nuclei RNA Sequencing Assessment of the Hepatic Effects of 2,3,7,8-Tetrachlorodibenzo- p-dioxin

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          Abstract

          Background and Aims

          Characterization of cell specific transcriptional responses to hepatotoxicants is lost in the averages of bulk RNA-sequencing (RNA-seq). Single-cell/nuclei RNA-seq technologies enable the transcriptomes of individual cell (sub)types to be assessed within the context of in vivo models.

          Methods

          Single-nuclei RNA-sequencing (snSeq) of frozen liver samples from male C57BL/6 mice gavaged with sesame oil vehicle or 30 μg/kg 2,3,7,8-tetrachlorodibenzo- p-dioxin (TCDD) every 4 days for 28 days was used to demonstrate the application of snSeq for the evaluation of xenobiotics.

          Results

          A total of 19,907 genes were detected across 16,015 nuclei from control and TCDD-treated livers. Eleven cell (sub)types reflected the expected cell diversity of the liver including distinct pericentral, midzonal, and periportal hepatocyte subpopulations. TCDD altered relative proportions of cell types and elicited cell-specific gene expression profiles. For example, macrophages increased from 0.5% to 24.7%, while neutrophils were only present in treated samples, consistent with histological evaluation. The number of differentially expressed genes (DEGs) in each cell type ranged from 122 (cholangiocytes) to 7625 (midcentral hepatocytes), and loosely correlated with the basal expression level of Ahr, the canonical mediator of TCDD and related compounds. In addition to the expected functions within each cell (sub)types, RAS signaling and related pathways were specifically enriched in nonparenchymal cells while metabolic process enrichment occurred primarily in hepatocytes. snSeq also identified the expansion of a Kupffer cell subtype highly expressing Gpnmb, as reported in a dietary NASH model.

          Conclusions

          We show that snSeq of frozen liver samples can be used to assess cell-specific transcriptional changes and population shifts in models of hepatotoxicity when examining freshly isolated cells is not feasible.

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          Most cited references51

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          GSVA: gene set variation analysis for microarray and RNA-Seq data

          Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.
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            Integrating single-cell transcriptomic data across different conditions, technologies, and species

            Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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              Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics

              Background Single-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve. Results We introduce Slingshot, a novel method for inferring cell lineages and pseudotimes from single-cell gene expression data. In previously published datasets, Slingshot correctly identifies the biological signal for one to three branching trajectories. Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods. Conclusions Slingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Accurate lineage inference is a critical step in the identification of dynamic temporal gene expression. Electronic supplementary material The online version of this article (10.1186/s12864-018-4772-0) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                Journal
                Cell Mol Gastroenterol Hepatol
                Cell Mol Gastroenterol Hepatol
                Cellular and Molecular Gastroenterology and Hepatology
                Elsevier
                2352-345X
                10 August 2020
                2021
                10 August 2020
                : 11
                : 1
                : 147-159
                Affiliations
                [1 ]Institute for Integrative Toxicology, Michigan State University, East Lansing, Michigan
                [2 ]Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan
                [3 ]Department of Biomedical Engineering, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, Michigan
                [4 ]Department of Pharmacology and Toxicology, Michigan State University, East Lansing, Michigan
                Author notes
                [] Correspondence Address correspondence to: Tim Zacharewski, PhD, Michigan State University, 1129 Farm Lane, Room 248, East Lansing, Michigan 48824. fax: (517) 353-9334. tzachare@ 123456msu.edu
                Article
                S2352-345X(20)30118-1
                10.1016/j.jcmgh.2020.07.012
                7674514
                32791302
                bce644dd-6357-4e24-a667-a9fb9bd2db05
                © 2020 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 28 April 2020
                : 31 July 2020
                Categories
                Original Research

                transcriptomics,tcdd,hepatotoxicant,cellular heterogeneity,ahr, aryl hydrocarbon receptor,deg, differentially expressed gene,geo, gene expression omnibus,gskb, gene set knowledgebase,kc, kupffer cell,lncrna, long noncoding rna,mrna, messenger rna,nam, nonalcoholic steatohepatitis–associated macrophage,nash, nonalcoholic steatohepatitis,npc, nonparenchymal cell,pnd, postnatal day,rna-seq, rna sequencing,scseq, single-cell rna sequencing,snseq, single-nuclei rna sequencing,tcdd, 2,3,7,8-tetrachlorodibenzo-p-dioxin

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