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      Single-cell sequencing reveals the evolution of immune molecules across multiple vertebrate species

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          Graphical abstract

          Highlights

          • Macrophages develop evolutionary versatility in expressed genes.

          • B cell immunity is relatively conserved among vertebrate species.

          • Compensatory TCR cascade components are utilized by different species.

          • Mouse species shows the highest similarity in immune transcriptomes to human among multiple vertebrates.

          Abstract

          Introduction

          Both innate and adaptive immune system undergo evolution from low to high vertebrates. Due to the limitation of conventional approaches in identifying broader spectrum of immune cells and molecules from various vertebrates, it remains unclear how immune molecules evolve among vertebrates.

          Objectives

          Here, we utilized carry out comparative transcriptome analysis in various immune cells across seven vertebrate species.

          Methods

          Single-cell RNA sequencing (scRNA-seq).

          Results

          We uncovered both conserved and species-specific profiling of gene expression in innate and adaptive immunity. Macrophages exhibited highly-diversified genes and developed sophisticated molecular signaling networks along with evolution, indicating effective and versatile functions in higher species. In contrast, B cells conservatively evolved with less differentially-expressed genes in analyzed species. Interestingly, T cells represented a dominant immune cell populations in all species and unique T cell populations were identified in zebrafish and pig. We also revealed compensatory TCR cascade components utilized by different species. Inter-species comparison of core gene programs demonstrated mouse species has the highest similarity in immune transcriptomes to human.

          Conclusions

          Therefore, our comparative study reveals gene transcription characteristics across multiple vertebrate species during the evolution of immune system, providing insights for species-specific immunity as well as the translation of animal studies to human physiology and disease.

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          Most cited references87

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          Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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            Cytoscape: a software environment for integrated models of biomolecular interaction networks.

            Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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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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                Author and article information

                Contributors
                Journal
                J Adv Res
                J Adv Res
                Journal of Advanced Research
                Elsevier
                2090-1232
                2090-1224
                04 March 2023
                January 2024
                04 March 2023
                : 55
                : 73-87
                Affiliations
                [a ]Department of Pathogenic Microbiology and Immunology, School of Basic Medical Sciences, Xi’an Jiaotong University, Xi’an, Shaanxi 710061, China
                [b ]Institute of Infection and Immunity, Translational Medicine Institute, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi 710061, China
                [c ]Key Laboratory of Environment and Genes Related to Diseases (Xi’an Jiaotong University), Ministry of Education, Xi’an, Shaanxi 710061, China
                [d ]Xi’an Key Laboratory of Immune Related Diseases, Xi’an, Shaanxi 710061, China
                [e ]The First Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi 710061, China
                [f ]Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, China
                [g ]Suzhou Institute of Systems Medicine, Suzhou 215123, China
                Author notes
                [1]

                These authors contributed equally to this work.

                Article
                S2090-1232(23)00069-3
                10.1016/j.jare.2023.02.017
                10770119
                36871615
                e7428dbe-7741-47eb-bf66-427863a22155
                © 2023 The Authors. Published by Elsevier B.V. on behalf of Cairo University.

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

                History
                : 7 June 2022
                : 11 February 2023
                : 26 February 2023
                Categories
                Original Article

                innate immunity,adaptive immunity,scrna-seq,across species,evolution

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