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      Reference Transcriptomes of Porcine Peripheral Immune Cells Created Through Bulk and Single-Cell RNA Sequencing

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          Abstract

          Pigs are a valuable human biomedical model and an important protein source supporting global food security. The transcriptomes of peripheral blood immune cells in pigs were defined at the bulk cell-type and single cell levels. First, eight cell types were isolated in bulk from peripheral blood mononuclear cells (PBMCs) by cell sorting, representing Myeloid, NK cells and specific populations of T and B-cells. Transcriptomes for each bulk population of cells were generated by RNA-seq with 10,974 expressed genes detected. Pairwise comparisons between cell types revealed specific expression, while enrichment analysis identified 1,885 to 3,591 significantly enriched genes across all 8 cell types. Gene Ontology analysis for the top 25% of significantly enriched genes (SEG) showed high enrichment of biological processes related to the nature of each cell type. Comparison of gene expression indicated highly significant correlations between pig cells and corresponding human PBMC bulk RNA-seq data available in Haemopedia. Second, higher resolution of distinct cell populations was obtained by single-cell RNA-sequencing (scRNA-seq) of PBMC. Seven PBMC samples were partitioned and sequenced that produced 28,810 single cell transcriptomes distributed across 36 clusters and classified into 13 general cell types including plasmacytoid dendritic cells (DC), conventional DCs, monocytes, B-cell, conventional CD4 and CD8 αβ T-cells, NK cells, and γδ T-cells. Signature gene sets from the human Haemopedia data were assessed for relative enrichment in genes expressed in pig cells and integration of pig scRNA-seq with a public human scRNA-seq dataset provided further validation for similarity between human and pig data. The sorted porcine bulk RNAseq dataset informed classification of scRNA-seq PBMC populations; specifically, an integration of the datasets showed that the pig bulk RNAseq data helped define the CD4CD8 double-positive T-cell populations in the scRNA-seq data. Overall, the data provides deep and well-validated transcriptomic data from sorted PBMC populations and the first single-cell transcriptomic data for porcine PBMCs. This resource will be invaluable for annotation of pig genes controlling immunogenetic traits as part of the porcine Functional Annotation of Animal Genomes (FAANG) project, as well as further study of, and development of new reagents for, porcine immunology.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Comprehensive Integration of Single-Cell Data

            Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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              Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

              A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                23 June 2021
                2021
                : 12
                : 689406
                Affiliations
                [1] 1Department of Animal Science, Iowa State University , Ames, IA, United States
                [2] 2Food Safety and Enteric Pathogens Research Unit, National Animal Disease Center, Agricultural Research Service, United States Department of Agriculture , Ames, IA, United States
                [3] 3Immunobiology Graduate Program, Iowa State University , Ames, IA, United States
                [4] 4Oak Ridge Institute for Science and Education, Agricultural Research Service Participation Program , Oak Ridge, TN, United States
                [5] 5Genome Informatics Facility, Iowa State University , Ames, IA, United States
                [6] 6USDA, ARS, U.S. Meat Animal Research Center , Clay Center, NE, United States
                [7] 7USDA-ARS, Beltsville Agricultural Research Center, Animal Parasitic Diseases Laboratory , Beltsville, MD, United States
                Author notes

                Edited by: Angela Cánovas, University of Guelph, Canada

                Reviewed by: Paolo Zambonelli, University of Bologna, Italy; Sergey V. Kozyrev, Uppsala University, Sweden

                *Correspondence: Crystal L. Loving, crystal.loving@ 123456usda.gov
                Christopher K. Tuggle, cktuggle@ 123456iastate.edu

                These authors have contributed equally to this work and share first authorship

                These authors have contributed equally to this work and share senior and last authorship

                This article was submitted to Livestock Genomics, a section of the journal Frontiers in Genetics

                Article
                10.3389/fgene.2021.689406
                8261551
                34249103
                8e5d611d-edab-4e9c-bd24-98af97f84a47
                Copyright © 2021 Herrera-Uribe, Wiarda, Sivasankaran, Daharsh, Liu, Byrne, Smith, Lunney, Loving and Tuggle.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 31 March 2021
                : 18 May 2021
                Page count
                Figures: 8, Tables: 5, Equations: 0, References: 119, Pages: 27, Words: 0
                Categories
                Genetics
                Original Research

                Genetics
                pig,immune cells,transcriptome,single-cell rna-seq,bulkrna-seq,faang
                Genetics
                pig, immune cells, transcriptome, single-cell rna-seq, bulkrna-seq, faang

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