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      Single-Cell RNA Sequencing Analysis of Chicken Anterior Pituitary: A Bird’s-Eye View on Vertebrate Pituitary

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          Abstract

          It is well-established that anterior pituitary contains multiple endocrine cell populations, and each of them can secrete one/two hormone(s) to regulate vital physiological processes of vertebrates. However, the gene expression profiles of each pituitary cell population remains poorly characterized in most vertebrate groups. Here we analyzed the transcriptome of each cell population in adult chicken anterior pituitaries using single-cell RNA sequencing technology. The results showed that: (1) four out of five known endocrine cell clusters have been identified and designated as the lactotrophs, thyrotrophs, corticotrophs, and gonadotrophs, respectively. Somatotrophs were not analyzed in the current study. Each cell cluster can express at least one known endocrine hormone, and novel marker genes ( e.g., CD24 and HSPB1 in lactotrophs, NPBWR2 and NDRG1 in corticotrophs; DIO2 and SOUL in thyrotrophs, C5H11ORF96 and HPGDS in gonadotrophs) are identified. Interestingly, gonadotrophs were shown to abundantly express five peptide hormones: FSH, LH, GRP, CART and RLN3; (2) four non-endocrine/secretory cell types, including endothelial cells (expressing IGFBP7 and CFD) and folliculo-stellate cells (FS-cells, expressing S100A6 and S100A10), were identified in chicken anterior pituitaries. Among them, FS-cells can express many growth factors, peptides ( e.g., WNT5A, HBEGF, Activins, VEGFC, NPY, and BMP4), and progenitor/stem cell-associated genes ( e.g., Notch signaling components, CDH1), implying that the FS-cell cluster may act as a paracrine/autocrine signaling center and enrich pituitary progenitor/stem cells; (3) sexually dimorphic expression of many genes were identified in most cell clusters, including gonadotrophs and lactotrophs. Taken together, our data provides a bird’s-eye view on the diverse aspects of anterior pituitaries, including cell composition, heterogeneity, cell-to-cell communication, and gene expression profiles, which facilitates our comprehensive understanding of vertebrate pituitary biology.

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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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            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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              Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.

              Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.
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                Author and article information

                Contributors
                Journal
                Front Physiol
                Front Physiol
                Front. Physiol.
                Frontiers in Physiology
                Frontiers Media S.A.
                1664-042X
                29 June 2021
                2021
                : 12
                : 562817
                Affiliations
                [1] 1Key Laboratory of Bio-Resources and Eco-Environment, Ministry of Education, College of Life Sciences, Sichuan University , Chengdu, China
                [2] 2Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, West China Second University Hospital, Sichuan University , Chengdu, China
                Author notes

                Edited by: Krystyna Pierzchala-Koziec, University of Agriculture in Krakow, Poland

                Reviewed by: Kazuyoshi Ukena, Hiroshima University, Japan; Matan Golan, Agricultural Research Organization (ARO), Israel

                *Correspondence: Yajun Wang, cdwyjhk@ 123456gmail.com

                These authors have contributed equally to this work

                This article was submitted to Avian Physiology, a section of the journal Frontiers in Physiology

                Article
                10.3389/fphys.2021.562817
                8276247
                34267669
                9319299e-187a-4be1-ac74-9ff8d09fdd2c
                Copyright © 2021 Zhang, Lv, Mo, Liu, Wan, Li and Wang.

                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
                : 16 May 2020
                : 21 May 2021
                Page count
                Figures: 10, Tables: 1, Equations: 0, References: 109, Pages: 21, Words: 0
                Funding
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Award ID: 31771375
                Award ID: 31772590
                Award ID: 31802056
                Award ID: 31572391
                Funded by: China Postdoctoral Science Foundation 10.13039/501100002858
                Award ID: 2019M653412
                Award ID: 2020T130439
                Funded by: Fundamental Research Funds for the Central Universities 10.13039/501100012226
                Categories
                Physiology
                Original Research

                Anatomy & Physiology
                chickens,anterior pituitary,endocrine cell,folliculo-stellate cell,scrna sequencing,gene expression

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