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      Comparative analysis of cell lineage differentiation during hepatogenesis in humans and mice at the single-cell transcriptome level

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          Abstract

          During embryogenesis, the liver is the site of hepatogenesis and hematopoiesis and contains many cell lineages derived from the endoderm and mesoderm. However, the characteristics and developmental programs of many of these cell lineages remain unclear, especially in humans. Here, we performed single-cell RNA sequencing of whole human and mouse fetal livers throughout development. We identified four cell lineage families of endoderm-derived, erythroid, non-erythroid hematopoietic, and mesoderm-derived non-hematopoietic cells, and defined the developmental pathways of the major cell lineage families. In both humans and mice, we identified novel markers of hepatic lineages and an ID3 + subpopulation of hepatoblasts as well as verified that hepatoblast differentiation follows the “default-directed” model. Additionally, we found that human but not mouse fetal hepatocytes display heterogeneity associated with expression of metabolism-related genes. We described the developmental process of erythroid progenitor cells during human and mouse hematopoiesis. Moreover, despite the general conservation of cell differentiation programs between species, we observed different cell lineage compositions during hematopoiesis in the human and mouse fetal livers. Taken together, these results reveal the dynamic cell landscape of fetal liver development and illustrate the similarities and differences in liver development between species, providing an extensive resource for inducing various liver cell lineages in vitro.

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            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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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Contributors
                cxu@pku.edu.cn
                Journal
                Cell Res
                Cell Res
                Cell Research
                Springer Singapore (Singapore )
                1001-0602
                1748-7838
                20 July 2020
                20 July 2020
                December 2020
                : 30
                : 12
                : 1109-1126
                Affiliations
                [1 ]GRID grid.11135.37, ISNI 0000 0001 2256 9319, Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Department of Human Anatomy, Histology, and Embryology, and School of Basic Medical Sciences, , Peking University Health Science Center, Peking University, ; Beijing, 100871 China
                [2 ]Haidian Maternal & Child Health Hospital, Beijing, 100080 China
                Author information
                http://orcid.org/0000-0002-0583-4464
                Article
                378
                10.1038/s41422-020-0378-6
                7784864
                32690901
                59dd7cad-ebbf-4879-a759-c70bd81b9a1e
                © Center for Excellence in Molecular Cell Science, CAS 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 6 November 2019
                : 23 June 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100002855, Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology);
                Award ID: 2019YFA0801500
                Award ID: 2015CB942800
                Award Recipient :
                Funded by: Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 31521004
                Award ID: 31522036
                Award Recipient :
                Funded by: SLS-Qidong Innovation Fund Peking-Tsinghua Center for Life Sciences
                Categories
                Article
                Custom metadata
                © Center for Excellence in Molecular Cell Science, CAS 2020

                Cell biology
                developmental biology,stem-cell differentiation
                Cell biology
                developmental biology, stem-cell differentiation

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