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      Spatiotemporal single-cell regulatory atlas reveals neural crest lineage diversification and cellular function during tooth morphogenesis

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          Abstract

          Cranial neural crest cells are an evolutionary innovation of vertebrates for craniofacial development and function, yet the mechanisms that govern the cell fate decisions of postmigratory cranial neural crest cells remain largely unknown. Using the mouse molar as a model, we perform single-cell transcriptome profiling to interrogate the cell fate diversification of postmigratory cranial neural crest cells. We reveal the landscape of transcriptional heterogeneity and define the specific cellular domains during the progression of cranial neural crest cell-derived dental lineage diversification, and find that each domain makes a specific contribution to distinct molar mesenchymal tissues. Furthermore, IGF signaling-mediated cell-cell interaction between the cellular domains highlights the pivotal role of autonomous regulation of the dental mesenchyme. Importantly, we reveal cell-type-specific gene regulatory networks in the dental mesenchyme and show that Foxp4 is indispensable for the differentiation of periodontal ligament. Our single-cell atlas provides comprehensive mechanistic insight into the cell fate diversification process of the cranial neural crest cell-derived odontogenic populations.

          Abstract

          The mechanisms that govern cell fate decisions of postmigratory cranial neural crest cells remain largely unknown. Here the authors present a spatiotemporal single-cell regulatory atlas tracking these cells’ dental lineage diversification.

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          Most cited references60

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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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            Integrated analysis of multimodal single-cell data

            Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
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              SCENIC: Single-cell regulatory network inference and clustering

              Although single-cell RNA-seq is revolutionizing biology, data interpretation remains a challenge. We present SCENIC for the simultaneous reconstruction of gene regulatory networks and identification of cell states. We apply SCENIC to a compendium of single-cell data from tumors and brain, and demonstrate that the genomic regulatory code can be exploited to guide the identification of transcription factors and cell states. SCENIC provides critical biological insights into the mechanisms driving cellular heterogeneity.
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                Author and article information

                Contributors
                ychai@usc.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                16 August 2022
                16 August 2022
                2022
                : 13
                : 4803
                Affiliations
                [1 ]GRID grid.42505.36, ISNI 0000 0001 2156 6853, Center for Craniofacial Molecular Biology, , University of Southern California, ; Los Angeles, CA 90033 USA
                [2 ]GRID grid.13291.38, ISNI 0000 0001 0807 1581, State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, ; Chengdu, Sichuan 610041 China
                Author information
                http://orcid.org/0000-0003-2477-7247
                Article
                32490
                10.1038/s41467-022-32490-y
                9381504
                35974052
                3dfececb-c3e0-4dfb-9ef3-a53be1843d25
                © The Author(s) 2022

                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
                : 22 November 2021
                : 2 August 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000072, U.S. Department of Health & Human Services | NIH | National Institute of Dental and Craniofacial Research (NIDCR);
                Award ID: R01 DE022503
                Award ID: R01 DE012711
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute of Dental and Craniofacial Research (NIDCR)
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                cell lineage,stem-cell differentiation,rna sequencing,developmental neurogenesis,nervous system

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