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      Single-cell transcriptomics of the human placenta: inferring the cell communication network of the maternal-fetal interface

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          Abstract

          Organismal function is, to a great extent, determined by interactions among their fundamental building blocks, the cells. In this work, we studied the cell-cell interactome of fetal placental trophoblast cells and maternal endometrial stromal cells, using single-cell transcriptomics. The placental interface mediates the interaction between two semiallogenic individuals, the mother and the fetus, and is thus the epitome of cell interactions. To study these, we inferred the cell-cell interactome by assessing the gene expression of receptor-ligand pairs across cell types. We find a highly cell-type-specific expression of G-protein-coupled receptors, implying that ligand-receptor profiles could be a reliable tool for cell type identification. Furthermore, we find that uterine decidual cells represent a cell-cell interaction hub with a large number of potential incoming and outgoing signals. Decidual cells differentiate from their precursors, the endometrial stromal fibroblasts, during uterine preparation for pregnancy. We show that decidualization (even in vitro) enhances the ability to communicate with the fetus, as most of the receptors and ligands up-regulated during decidualization have their counterpart expressed in trophoblast cells. Among the signals transmitted, growth factors and immune signals dominate, and suggest a delicate balance of enhancing and suppressive signals. Finally, this study provides a rich resource of gene expression profiles of term intravillous and extravillous trophoblasts, including the transcriptome of the multinucleated syncytiotrophoblast.

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            circlize Implements and enhances circular visualization in R.

            Circular layout is an efficient way for the visualization of huge amounts of genomic information. Here we present the circlize package, which provides an implementation of circular layout generation in R as well as an enhancement of available software. The flexibility of this package is based on the usage of low-level graphics functions such that self-defined high-level graphics can be easily implemented by users for specific purposes. Together with the seamless connection between the powerful computational and visual environment in R, circlize gives users more convenience and freedom to design figures for better understanding genomic patterns behind multi-dimensional data.
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              Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples.

              Measures of RNA abundance are important for many areas of biology and often obtained from high-throughput RNA sequencing methods such as Illumina sequence data. These measures need to be normalized to remove technical biases inherent in the sequencing approach, most notably the length of the RNA species and the sequencing depth of a sample. These biases are corrected in the widely used reads per kilobase per million reads (RPKM) measure. Here, we argue that the intended meaning of RPKM is a measure of relative molar RNA concentration (rmc) and show that for each set of transcripts the average rmc is a constant, namely the inverse of the number of transcripts mapped. Further, we show that RPKM does not respect this invariance property and thus cannot be an accurate measure of rmc. We propose a slight modification of RPKM that eliminates this inconsistency and call it TPM for transcripts per million. TPM respects the average invariance and eliminates statistical biases inherent in the RPKM measure.
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                Author and article information

                Journal
                Genome Res
                Genome Res
                genome
                genome
                GENOME
                Genome Research
                Cold Spring Harbor Laboratory Press
                1088-9051
                1549-5469
                March 2017
                : 27
                : 3
                : 349-361
                Affiliations
                [1 ]Center for Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio 45229, USA;
                [2 ]Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio 45229, USA;
                [3 ]Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut 06511, USA;
                [4 ]Yale Systems Biology Institute, Yale University, West Haven, Connecticut 06516, USA;
                [5 ]Department of Obstetrics, Gynecology and Reproductive Sciences, Yale Medical School, Yale University, New Haven, Connecticut 06510, USA;
                [6 ]Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan 48201, USA;
                [7 ]Center for Fetal Cellular and Molecular Therapy, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio 45229, USA;
                [8 ]Department of Surgery, University of Cincinnati College of Medicine, Cincinnati, Ohio 45229, USA
                Author notes
                Article
                9509184
                10.1101/gr.207597.116
                5340963
                28174237
                ab96cf68-889f-4fbf-ac32-3a3ac0ae4324
                © 2017 Pavličev et al.; Published by Cold Spring Harbor Laboratory Press

                This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 26 March 2016
                : 12 January 2017
                Page count
                Pages: 13
                Funding
                Funded by: March of Dimes Prematurity Research Center Ohio Collaborative
                Award ID: 22-FY14-470
                Funded by: John Templeton Foundation http://dx.doi.org/10.13039/100000925
                Award ID: 54860
                Funded by: National Institutes of Health http://dx.doi.org/10.13039/100000002
                Award ID: R00HD068504
                Categories
                Research

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