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      A spatially resolved timeline of the human maternal–fetal interface

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          Abstract

          Beginning in the first trimester, fetally derived extravillous trophoblasts (EVTs) invade the uterus and remodel its spiral arteries, transforming them into large, dilated blood vessels. Several mechanisms have been proposed to explain how EVTs coordinate with the maternal decidua to promote a tissue microenvironment conducive to spiral artery remodelling (SAR) 13 . However, it remains a matter of debate regarding which immune and stromal cells participate in these interactions and how this evolves with respect to gestational age. Here we used a multiomics approach, combining the strengths of spatial proteomics and transcriptomics, to construct a spatiotemporal atlas of the human maternal–fetal interface in the first half of pregnancy. We used multiplexed ion beam imaging by time-of-flight and a 37-plex antibody panel to analyse around 500,000 cells and 588 arteries within intact decidua from 66 individuals between 6 and 20 weeks of gestation, integrating this dataset with co-registered transcriptomics profiles. Gestational age substantially influenced the frequency of maternal immune and stromal cells, with tolerogenic subsets expressing CD206, CD163, TIM-3, galectin-9 and IDO-1 becoming increasingly enriched and colocalized at later time points. By contrast, SAR progression preferentially correlated with EVT invasion and was transcriptionally defined by 78 gene ontology pathways exhibiting distinct monotonic and biphasic trends. Last, we developed an integrated model of SAR whereby invasion is accompanied by the upregulation of pro-angiogenic, immunoregulatory EVT programmes that promote interactions with the vascular endothelium while avoiding the activation of maternal immune cells.

          Abstract

          A multiomics approach is used to produce a spatiotemporal atlas of the human maternal–fetal interface in the first half of pregnancy, revealing relationships among gestational age, extravillous trophoblasts and spiral artery remodelling.

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          Most cited references64

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          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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            Single-cell reconstruction of the early maternal–fetal interface in humans

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              FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data.

              The number of markers measured in both flow and mass cytometry keeps increasing steadily. Although this provides a wealth of information, it becomes infeasible to analyze these datasets manually. When using 2D scatter plots, the number of possible plots increases exponentially with the number of markers and therefore, relevant information that is present in the data might be missed. In this article, we introduce a new visualization technique, called FlowSOM, which analyzes Flow or mass cytometry data using a Self-Organizing Map. Using a two-level clustering and star charts, our algorithm helps to obtain a clear overview of how all markers are behaving on all cells, and to detect subsets that might be missed otherwise. R code is available at https://github.com/SofieVG/FlowSOM and will be made available at Bioconductor.
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                Author and article information

                Contributors
                greenbaumsh@gmail.com
                mangelo0@stanford.edu
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                19 July 2023
                19 July 2023
                2023
                : 619
                : 7970
                : 595-605
                Affiliations
                [1 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Pathology, , Stanford University, ; Stanford, CA USA
                [2 ]GRID grid.17788.31, ISNI 0000 0001 2221 2926, Department of Obstetrics and Gynecology, , Hadassah-Hebrew University Medical Center, ; Jerusalem, Israel
                [3 ]GRID grid.168010.e, ISNI 0000000419368956, Immunology Program, , Stanford University, ; Stanford, CA USA
                [4 ]Department of Pathology, University of Californica San Francisco, San Francisco, CA USA
                [5 ]GRID grid.168010.e, ISNI 0000000419368956, Cancer Biology Program, , Stanford University, ; Stanford, CA USA
                [6 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Department of Obstetrics Gynecology and Reproductive Sciences, , University of California San Francisco, ; San Francisco, CA USA
                [7 ]GRID grid.20861.3d, ISNI 0000000107068890, Division of Biology and Bioengineering, , California Institute of Technology, ; Pasadena, CA USA
                [8 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Obstetrics and Gynecology, , Stanford University, ; Stanford, CA USA
                [9 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Department of Pathology, , Memorial Sloan Kettering Cancer Center, ; New York, NY USA
                [10 ]GRID grid.13992.30, ISNI 0000 0004 0604 7563, Department of Molecular Cell Biology, , Weizmann Institute of Science, ; Rehovot, Israel
                Author information
                http://orcid.org/0000-0002-0680-7652
                http://orcid.org/0000-0003-1863-205X
                http://orcid.org/0000-0003-3052-0253
                http://orcid.org/0000-0002-7836-4379
                http://orcid.org/0000-0003-4912-8059
                http://orcid.org/0000-0001-8131-9125
                http://orcid.org/0000-0002-4264-9479
                http://orcid.org/0000-0001-7534-7621
                http://orcid.org/0000-0003-1136-2907
                http://orcid.org/0000-0002-6799-6303
                http://orcid.org/0000-0003-1531-5067
                Article
                6298
                10.1038/s41586-023-06298-9
                10356615
                37468587
                5d2df7a8-b4cb-4d54-8ddc-11c1feed4ffa
                © The Author(s) 2023

                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
                : 3 September 2021
                : 8 June 2023
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2023

                Uncategorized
                immunology,developmental biology,reproductive biology
                Uncategorized
                immunology, developmental biology, reproductive biology

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