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      Single-cell RNA sequencing reveals placental response under environmental stress

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          Abstract

          The placenta is crucial for fetal development, yet the impact of environmental stressors such as arsenic exposure remains poorly understood. We apply single-cell RNA sequencing to analyze the response of the mouse placenta to arsenic, revealing cell-type-specific gene expression, function, and pathological changes. Notably, the Prap1 gene, which encodes proline-rich acidic protein 1 (PRAP1), is significantly upregulated in 26 placental cell types including various trophoblast cells. Our study shows a female-biased increase in PRAP1 in response to arsenic and localizes it in the placenta. In vitro and ex vivo experiments confirm PRAP1 upregulation following arsenic treatment and demonstrate that recombinant PRAP1 protein reduces arsenic-induced cytotoxicity and downregulates cell cycle pathways in human trophoblast cells. Moreover, PRAP1 knockdown differentially affects cell cycle processes, proliferation, and cell death depending on the presence of arsenic. Our findings provide insights into the placental response to environmental stress, offering potential preventative and therapeutic approaches for environment-related adverse outcomes in mothers and children.

          Abstract

          Environmental stressors have been associated with placental dysfunction and pregnancy complications. Here, the authors reveal gene expression changes in the mouse placenta exposed to arsenic at single-cell resolution and identify a potential therapeutic target to mitigate its harmful effects on pregnancy and fetal development.

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          Most cited references140

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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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            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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              clusterProfiler 4.0: A universal enrichment tool for interpreting omics data

              Summary Functional enrichment analysis is pivotal for interpreting high-throughput omics data in life science. It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible. To meet these requirements, we present here an updated version of our popular Bioconductor package, clusterProfiler 4.0. This package has been enhanced considerably compared with its original version published 9 years ago. The new version provides a universal interface for functional enrichment analysis in thousands of organisms based on internally supported ontologies and pathways as well as annotation data provided by users or derived from online databases. It also extends the dplyr and ggplot2 packages to offer tidy interfaces for data operation and visualization. Other new features include gene set enrichment analysis and comparison of enrichment results from multiple gene lists. We anticipate that clusterProfiler 4.0 will be applied to a wide range of scenarios across diverse organisms.
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                Author and article information

                Contributors
                hae-ryung_park@urmc.rochester.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                2 August 2024
                2 August 2024
                2024
                : 15
                : 6549
                Affiliations
                [1 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Biostatistics, , Harvard T.H. Chan School of Public Health, ; Boston, MA USA
                [2 ]Department of Environmental Medicine, School of Medicine and Dentistry, University of Rochester, ( https://ror.org/022kthw22) Rochester, NY USA
                [3 ]Division of Allergy, Immunology and Rheumatology, Department of Medicine, University of Rochester, ( https://ror.org/022kthw22) Rochester, NY USA
                [4 ]Department of Obstetrics and Gynecology, School of Medicine and Dentistry, University of Rochester, ( https://ror.org/022kthw22) Rochester, NY USA
                [5 ]Department of Pediatrics, School of Medicine and Dentistry, University of Rochester, ( https://ror.org/022kthw22) Rochester, NY USA
                [6 ]Department of Statistics, Harvard University, ( https://ror.org/03vek6s52) Cambridge, MA USA
                Author information
                http://orcid.org/0009-0001-1642-9798
                http://orcid.org/0000-0002-9738-1182
                http://orcid.org/0000-0001-9268-2464
                http://orcid.org/0000-0003-4914-880X
                http://orcid.org/0009-0003-2135-1424
                http://orcid.org/0000-0001-7067-7752
                http://orcid.org/0000-0002-7531-8844
                Article
                50914
                10.1038/s41467-024-50914-9
                11297347
                39095385
                d5e92eff-cd58-4e30-872f-c029f19863bd
                © The Author(s) 2024

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 16 August 2023
                : 25 July 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000066, U.S. Department of Health & Human Services | NIH | National Institute of Environmental Health Sciences (NIEHS);
                Award ID: R00ES029548
                Award ID: P30ES001247
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute of Environmental Health Sciences (NIEHS)
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                transcriptomics,differentiation,mechanisms of disease
                Uncategorized
                transcriptomics, differentiation, mechanisms of disease

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