0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Targeted expression profiling by RNA-Seq improves detection of cellular dynamics during pregnancy and identifies a role for T cells in term parturition

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Development of maternal blood transcriptomic markers to monitor placental function and risk of obstetrical complications throughout pregnancy requires accurate quantification of gene expression. Herein, we benchmark three state-of-the-art expression profiling techniques to assess in maternal circulation the expression of cell type-specific gene sets previously discovered by single-cell genomics studies of the placenta. We compared Affymetrix Human Transcriptome Arrays, Illumina RNA-Seq, and sequencing-based targeted expression profiling (DriverMap TM) to assess transcriptomic changes with gestational age and labor status at term, and tested 86 candidate genes by qRT-PCR. DriverMap identified twice as many significant genes (q < 0.1) than RNA-Seq and five times more than microarrays. The gap in the number of significant genes remained when testing only protein-coding genes detected by all platforms. qRT-PCR validation statistics (PPV and AUC) were high and similar among platforms, yet dynamic ranges were higher for sequencing based platforms than microarrays. DriverMap provided the strongest evidence for the association of B-cell and T-cell gene signatures with gestational age, while the T-cell expression was increased with spontaneous labor at term according to all three platforms. We concluded that sequencing-based techniques are more suitable to quantify whole-blood gene expression compared to microarrays, as they have an expanded dynamic range and identify more true positives. Targeted expression profiling achieved higher coverage of protein-coding genes with fewer total sequenced reads, and it is especially suited to track cell type-specific signatures discovered in the placenta. The T-cell gene expression signature was increased in women who underwent spontaneous labor at term, mimicking immunological processes at the maternal-fetal interface and placenta.

          Related collections

          Most cited references55

          • Record: found
          • Abstract: not found
          • Article: not found

          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Systematic determination of genetic network architecture.

            Technologies to measure whole-genome mRNA abundances and methods to organize and display such data are emerging as valuable tools for systems-level exploration of transcriptional regulatory networks. For instance, it has been shown that mRNA data from 118 genes, measured at several time points in the developing hindbrain of mice, can be hierarchically clustered into various patterns (or 'waves') whose members tend to participate in common processes. We have previously shown that hierarchical clustering can group together genes whose cis-regulatory elements are bound by the same proteins in vivo. Hierarchical clustering has also been used to organize genes into hierarchical dendograms on the basis of their expression across multiple growth conditions. The application of Fourier analysis to synchronized yeast mRNA expression data has identified cell-cycle periodic genes, many of which have expected cis-regulatory elements. Here we apply a systematic set of statistical algorithms, based on whole-genome mRNA data, partitional clustering and motif discovery, to identify transcriptional regulatory sub-networks in yeast-without any a priori knowledge of their structure or any assumptions about their dynamics. This approach uncovered new regulons (sets of co-regulated genes) and their putative cis-regulatory elements. We used statistical characterization of known regulons and motifs to derive criteria by which we infer the biological significance of newly discovered regulons and motifs. Our approach holds promise for the rapid elucidation of genetic network architecture in sequenced organisms in which little biology is known.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Comprehensive comparative analysis of strand-specific RNA sequencing methods

              Strand-specific, massively-parallel cDNA sequencing (RNA-Seq) is a powerful tool for novel transcript discovery, genome annotation, and expression profiling. Despite multiple published methods for strand-specific RNA-Seq, no consensus exists as to how to choose between them. Here, we developed a comprehensive computational pipeline to compare library quality metrics from any RNA-Seq method. Using the well-annotated Saccharomyces cerevisiae transcriptome as a benchmark, we compared seven library construction protocols, including both published and our own novel methods. We found marked differences in strand-specificity, library complexity, evenness and continuity of coverage, agreement with known annotations, and accuracy for expression profiling. Weighing each method’s performance and ease, we identify the dUTP second strand marking and the Illumina RNA ligation methods as the leading protocols, with the former benefitting from the current availability of paired-end sequencing. Our analysis provides a comprehensive benchmark, and our computational pipeline is applicable for assessment of future protocols in other organisms.
                Bookmark

                Author and article information

                Contributors
                atarca@med.wayne.edu
                prbchiefstaff@med.wayne.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                29 January 2019
                29 January 2019
                2019
                : 9
                : 848
                Affiliations
                [1 ]ISNI 0000 0000 9635 8082, GRID grid.420089.7, Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services (NICHD/NIH/DHHS), ; Bethesda, Maryland, and Detroit, Michigan USA
                [2 ]ISNI 0000 0001 1456 7807, GRID grid.254444.7, Department of Obstetrics and Gynecology, Wayne State University School of Medicine, ; Detroit, Michigan USA
                [3 ]ISNI 0000 0001 1456 7807, GRID grid.254444.7, Department of Computer Science, , Wayne State University College of Engineering, ; Detroit, Michigan USA
                [4 ]ISNI 0000000086837370, GRID grid.214458.e, Department of Obstetrics and Gynecology, , University of Michigan, ; Ann Arbor, Michigan USA
                [5 ]ISNI 0000 0001 2150 1785, GRID grid.17088.36, Department of Epidemiology and Biostatistics, , Michigan State University, ; East Lansing, Michigan USA
                [6 ]ISNI 0000 0001 1456 7807, GRID grid.254444.7, Center for Molecular Medicine and Genetics, , Wayne State University, ; Detroit, Michigan USA
                [7 ]ISNI 0000 0004 0378 8294, GRID grid.62560.37, Channing Laboratory, , Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, ; Boston, Massachusetts USA
                [8 ]ISNI 0000 0001 1456 7807, GRID grid.254444.7, Department of Immunology, , Microbiology and Biochemistry, Wayne State University School of Medicine, Detroit, ; Michigan, USA
                [9 ]Department of Obstetrics and Gynecology, Soroka University Medical Center, School of Medicine, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-sheba, Israel
                [10 ]ISNI 0000 0001 1456 7807, GRID grid.254444.7, Department of Physiology, , Wayne State University School of Medicine, ; Detroit, Michigan USA
                Article
                36649
                10.1038/s41598-018-36649-w
                6351599
                30696862
                b3702ae9-d642-439d-b3e6-828e775c8da7
                © The Author(s) 2019

                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
                : 6 July 2018
                : 26 November 2018
                Funding
                Funded by: This research was supported, in part, by the Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services (NICHD/NIH/DHHS); and, in part, with Federal funds from NICHD/NIH/DHHS under Contract No. HHSN275201300006C. ALT was also supported by the Perinatal Initiative of the Wayne State University School of Medicine.
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                Uncategorized

                Comments

                Comment on this article