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      Revealing the molecular landscape of human placenta: a systematic review and meta-analysis of single-cell RNA sequencing studies

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          Abstract

          BACKGROUND

          With increasing significance of developmental programming effects associated with placental dysfunction, more investigations are devoted to improving the characterization and understanding of placental signatures in health and disease. The placenta is a transitory but dynamic organ adapting to the shifting demands of fetal development and available resources of the maternal supply throughout pregnancy. Trophoblasts (cytotrophoblasts, syncytiotrophoblasts, and extravillous trophoblasts) are placental-specific cell types responsible for the main placental exchanges and adaptations. Transcriptomic studies with single-cell resolution have led to advances in understanding the placenta’s role in health and disease. These studies, however, often show discrepancies in characterization of the different placental cell types.

          OBJECTIVE AND RATIONALE

          We aim to review the knowledge regarding placental structure and function gained from the use of single-cell RNA sequencing (scRNAseq), followed by comparing cell-type-specific genes, highlighting their similarities and differences. Moreover, we intend to identify consensus marker genes for the various trophoblast cell types across studies. Finally, we will discuss the contributions and potential applications of scRNAseq in studying pregnancy-related diseases.

          SEARCH METHODS

          We conducted a comprehensive systematic literature review to identify different cell types and their functions at the human maternal–fetal interface, focusing on all original scRNAseq studies on placentas published before March 2023 and published reviews (total of 28 studies identified) using PubMed search. Our approach involved curating cell types and subtypes that had previously been defined using scRNAseq and comparing the genes used as markers or identified as potential new markers. Next, we reanalyzed expression matrices from the six available scRNAseq raw datasets with cell annotations (four from first trimester and two at term), using Wilcoxon rank-sum tests to compare gene expression among studies and annotate trophoblast cell markers in both first trimester and term placentas. Furthermore, we integrated scRNAseq raw data available from 18 healthy first trimester and nine term placentas, and performed clustering and differential gene expression analysis. We further compared markers obtained with the analysis of annotated and raw datasets with the literature to obtain a common signature gene list for major placental cell types.

          OUTCOMES

          Variations in the sampling site, gestational age, fetal sex, and subsequent sequencing and analysis methods were observed between the studies. Although their proportions varied, the three trophoblast types were consistently identified across all scRNAseq studies, unlike other non-trophoblast cell types. Notably, no marker genes were shared by all studies for any of the investigated cell types. Moreover, most of the newly defined markers in one study were not observed in other studies. These discrepancies were confirmed by our analysis on trophoblast cell types, where hundreds of potential marker genes were identified in each study but with little overlap across studies. From 35 461 and 23 378 cells of high quality in the first trimester and term placentas, respectively, we obtained major placental cell types, including perivascular cells that previously had not been identified in the first trimester. Importantly, our meta-analysis provides marker genes for major placental cell types based on our extensive curation.

          WIDER IMPLICATIONS

          This review and meta-analysis emphasizes the need for establishing a consensus for annotating placental cell types from scRNAseq data. The marker genes identified here can be deployed for defining human placental cell types, thereby facilitating and improving the reproducibility of trophoblast cell annotation.

          Graphical abstract

          Graphical Abstract

          Following the integration of existing single-cell transcriptomic data and a comprehensive literature review, we successfully identified marker genes crucial for defining distinct placental cell types. CTB: cytotrophoblast; EVT: extravillous trophoblast .

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          Most cited references188

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          SCANPY : large-scale single-cell gene expression data analysis

          Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells (https://github.com/theislab/Scanpy). Along with Scanpy, we present AnnData, a generic class for handling annotated data matrices (https://github.com/theislab/anndata).
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            The GTEx Consortium atlas of genetic regulatory effects across human tissues

            (2020)
            The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the version 8 data, examining 15,201 RNA-sequencing samples from 49 tissues of 838 postmortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue specificity of genetic effects and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.
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              DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors

              Single-cell RNA sequencing (scRNA-seq) data are commonly affected by technical artifacts known as “doublets,” which limit cell throughput and lead to spurious biological conclusions. Here, we present a computational doublet detection tool—Doublet-Finder—that identifies doublets using only gene expression data. DoubletFinder predicts doublets according to each real cell’s proximity in gene expression space to artificial doublets created by averaging the transcriptional profile of randomly chosen cell pairs. We first use scRNA-seq datasets where the identity of doublets is known to show that DoubletFinder identifies doublets formed from transcriptionally distinct cells. When these doublets are removed, the identification of differentially expressed genes is enhanced. Second, we provide a method for estimating DoubletFinder input parameters, allowing its application across scRNA-seq datasets with diverse distributions of cell types. Lastly, we present “best practices” for DoubletFinder applications and illustrate that DoubletFinder is insensitive to an experimentally validated kidney cell type with “hybrid” expression features. scRNA-seq data interpretation is confounded by technical artifacts known as doublets—single-cell transcriptome data representing more than one cell. Moreover, scRNA-seq cellular throughput is purposefully limited to minimize doublet formation rates. By identifying cells sharing expression features with simulated doublets, DoubletFinder detects many real doublets and mitigates these two limitations.
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                Author and article information

                Contributors
                Journal
                Hum Reprod Update
                Hum Reprod Update
                humupd
                Human Reproduction Update
                Oxford University Press
                1355-4786
                1460-2369
                Jul-Aug 2024
                13 March 2024
                13 March 2024
                : 30
                : 4
                : 410-441
                Affiliations
                Department of Physiology and Pharmacology, Karolinska Institutet , Solna, Stockholm, Sweden
                Department of Physiology and Pharmacology, Karolinska Institutet , Solna, Stockholm, Sweden
                Department of Physiology and Pharmacology, Karolinska Institutet , Solna, Stockholm, Sweden
                INRAE, BREED, Université Paris-Saclay, UVSQ , Jouy-en-Josas, France
                Ecole Nationale Vétérinaire d’Alfort, BREED , Maisons-Alfort, France
                Department of Physiology and Pharmacology, Karolinska Institutet , Solna, Stockholm, Sweden
                Center for Molecular Medicine, Karolinska University Hospital , Solna, Stockholm, Sweden
                Author notes
                Correspondence address. Department of Physiology and Pharmacology, Karolinska Institutet, Solna, 17165 Stockholm, Sweden. E-mail: qiaolin.deng@ 123456ki.se

                Emilie Derisoud and Hong Jiang authors contributed equally to this work and share first authorship.

                Author information
                https://orcid.org/0000-0001-5934-7816
                Article
                dmae006
                10.1093/humupd/dmae006
                11215163
                38478759
                5ddaa5ea-26ae-40af-8fd2-510f216843c1
                © The Author(s) 2024. Published by Oxford University Press on behalf of European Society of Human Reproduction and Embryology.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 18 May 2023
                : 12 February 2024
                : 18 February 2024
                Page count
                Pages: 32
                Funding
                Funded by: Wallenberg Academy Fellow Grant, Swedish Medical Research Council;
                Award ID: no.2018-02557
                Award ID: 2020-00253
                Categories
                Review
                AcademicSubjects/MED00460
                AcademicSubjects/MED00905

                Human biology
                marker genes,cell type annotation,single-nucleus rna sequencing,single-cell rna sequencing,trophoblasts

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