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

  • 1.

    Hong, X. et al. Molecular signatures of circulating melanoma cells for monitoring early response to immune checkpoint therapy. Proceedings of the National Academy of Sciences of the United States of America 115, 2467–2472, (2018).

  • 2.

    Tsang, J. C. H. et al. Integrative single-cell and cell-free plasma RNA transcriptomics elucidates placental cellular dynamics. Proceedings of the National Academy of Sciences of the United States of America 114, E7786–E7795, (2017).

  • 3.

    Alizadeh, A. A. et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511, (2000).

  • 4.

    Schena, M., Shalon, D., Davis, R. W. & Brown, P. O. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470 (1995).

  • 5.

    Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nature reviews. Genetics 10, 57–63, (2009).

  • 6.

    Levin, J. Z. et al. Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nature methods 7, 709–715, (2010).

  • 7.

    Souza, M. F. et al. Circulating mRNAs and miRNAs as candidate markers for the diagnosis and prognosis of prostate cancer. PloS one 12, e0184094, (2017).

  • 8.

    Ngo, T. T. M. et al. Noninvasive blood tests for fetal development predict gestational age and preterm delivery. Science 360, 1133–1136, (2018).

  • 9.

    Saliba, A. E., Westermann, A. J., Gorski, S. A. & Vogel, J. Single-cell RNA-seq: advances and future challenges. Nucleic acids research 42, 8845–8860, (2014).

  • 10.

    Brosens, I., Pijnenborg, R., Vercruysse, L. & Romero, R. The “Great Obstetrical Syndromes” are associated with disorders of deep placentation. American journal of obstetrics and gynecology 204, 193–201, (2011).

  • 11.

    Liu, J., Walter, E., Stenger, D. & Thach, D. Effects of globin mRNA reduction methods on gene expression profiles from whole blood. The Journal of molecular diagnostics: JMD 8, 551–558 (2006).

  • 12.

    Consortium, M. et al. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nature biotechnology 24, 1151–1161, (2006).

  • 13.

    Tarca, A. L. et al. Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge. Bioinformatics 29, 2892–2899, (2013).

  • 14.

    Dayarian, A. et al. Predicting protein phosphorylation from gene expression: top methods from the IMPROVER Species Translation Challenge. Bioinformatics 31, 462–470, (2015).

  • 15.

    Sarac, O. S. et al. Species translatable blood gene signature as a marker of exposure to smoking: computational approaches of the top ranked teams in the sbv IMPROVER Systems Toxicology challenge. Comput Toxicol 5, 25–30, (2018).

  • 16.

    Consortium, S. M.-I. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nature biotechnology 32, 903–914, (2014).

  • 17.

    Teng, M. et al. A benchmark for RNA-seq quantification pipelines. Genome biology 17, 74, (2016).

  • 18.

    Peixoto, L. et al. How data analysis affects power, reproducibility and biological insight of RNA-seq studies in complex datasets. Nucleic acids research 43, 7664–7674, (2015).

  • 19.

    Lex, A., Gehlenborg, N., Strobelt, H., Vuillemot, R. & Pfister, H. UpSet: Visualization of Intersecting Sets. IEEE transactions on visualization and computer graphics 20, 1983–1992, (2014).

  • 20.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Soc B 57, 289–300 (1995).

  • 21.

    Heng, Y. J. et al. Maternal Whole Blood Gene Expression at 18 and 28 Weeks of Gestation Associated with Spontaneous Preterm Birth in Asymptomatic Women. PloS one 11, e0155191, (2016).

  • 22.

    Al-Garawi, A. et al. The Role of Vitamin D in the Transcriptional Program of Human Pregnancy. PloS one 11, e0163832, (2016).

  • 23.

    Zwemer, L. M., Hui, L., Wick, H. C. & Bianchi, D. W. RNA-Seq and expression microarray highlight different aspects of the fetal amniotic fluid transcriptome. Prenatal diagnosis 34, 1006–1014, (2014).

  • 24.

    Everaert, C. et al. Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data. Scientific reports 7, 1559, (2017).

  • 25.

    Archer, K. J. & Reese, S. E. Detection call algorithms for high-throughput gene expression microarray data. Briefings in bioinformatics 11, 244–252, (2010).

  • 26.

    Chaiworapongsa, T. et al. Differences and similarities in the transcriptional profile of peripheral whole blood in early and late-onset preeclampsia: insights into the molecular basis of the phenotype of preeclampsiaa. Journal of perinatal medicine 41, 485–504, (2013).

  • 27.

    Risso, D., Schwartz, K., Sherlock, G. & Dudoit, S. GC-content normalization for RNA-Seq data. BMC bioinformatics 12, 480, (2011).

  • 28.

    Paquette, A. G. et al. Comparative analysis of gene expression in maternal peripheral blood and monocytes during spontaneous preterm labor. American journal of obstetrics and gynecology 218, 345 e341–345 e330, (2018).

  • 29.

    Sims, D., Sudbery, I., Ilott, N. E., Heger, A. & Ponting, C. P. Sequencing depth and coverage: key considerations in genomic analyses. Nature reviews. Genetics 15, 121–132, (2014).

  • 30.

    Derrien, T. et al. The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome research 22, 1775–1789, (2012).

  • 31.

    Pineles, B. L. et al. Distinct subsets of microRNAs are expressed differentially in the human placentas of patients with preeclampsia. American journal of obstetrics and gynecology 196, 261 e261–266, (2007).

  • 32.

    Romero, R. et al. Transcriptome interrogation of human myometrium identifies differentially expressed sense-antisense pairs of protein-coding and long non-coding RNA genes in spontaneous labor at term. The journal of maternal-fetal & neonatal medicine: the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstet 27, 1397–1408, (2014).

  • 33.

    Montenegro, D. et al. Expression patterns of microRNAs in the chorioamniotic membranes: a role for microRNAs in human pregnancy and parturition. The Journal of pathology 217, 113–121, (2009).

  • 34.

    Gormley, M. et al. Preeclampsia: novel insights from global RNA profiling of trophoblast subpopulations. American journal of obstetrics and gynecology 217, 200 e201–200 e217, (2017).

  • 35.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology 15, 550, (2014).

  • 36.

    Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic acids research 43, e47, (2015).

  • 37.

    Royce, T. E., Rozowsky, J. S. & Gerstein, M. B. Toward a universal microarray: prediction of gene expression through nearest-neighbor probe sequence identification. Nucleic acids research 35, e99, (2007).

  • 38.

    Okoniewski, M. J. & Miller, C. J. Hybridization interactions between probesets in short oligo microarrays lead to spurious correlations. BMC bioinformatics 7, 276, (2006).

  • 39.

    Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M. & Gilad, Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome research 18, 1509–1517, (2008).

  • 40.

    Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nature methods 14, 417–419, (2017).

  • 41.

    Romero, R. et al. The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study. American journal of obstetrics and gynecology 217, 67 e61–67 e21, (2017).

  • 42.

    Tarca, A. L., Bhatti, G. & Romero, R. A comparison of gene set analysis methods in terms of sensitivity, prioritization and specificity. PloS one 8, e79217, (2013).

  • 43.

    Young, M. D., Wakefield, M. J., Smyth, G. K. & Oshlack, A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome biology 11, R14, (2010).

  • 44.

    Tavazoie, S., Hughes, J. D., Campbell, M. J., Cho, R. J. & Church, G. M. Systematic determination of genetic network architecture. Nature genetics 22, 281–285, (1999).

  • 45.

    Mootha, V. K. et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nature genetics 34, 267–273, (2003).

  • 46.

    Ozerov, I. V. et al. In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development. Nature communications 7, 13427, (2016).

  • 47.

    Tarui, T. et al. Amniotic fluid transcriptomics reflects novel disease mechanisms in fetuses with myelomeningocele. American journal of obstetrics and gynecology 217, 587 e581–587 e510, (2017).

  • 48.

    Wu, C. et al. BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome biology 10, R130, (2009).

  • 49.

    Sindram-Trujillo, A., Scherjon, S., Kanhai, H., Roelen, D. & Claas, F. Increased T-cell activation in decidua parietalis compared to decidua basalis in uncomplicated human term pregnancy. American journal of reproductive immunology 49, 261–268 (2003).

  • 50.

    Sindram-Trujillo, A. P. et al. Comparison of decidual leukocytes following spontaneous vaginal delivery and elective cesarean section in uncomplicated human term pregnancy. Journal of reproductive immunology 62, 125–137, (2004).

  • 51.

    Tilburgs, T. et al. Differential distribution of CD4(+)CD25(bright) and CD8(+)CD28(−) T-cells in decidua and maternal blood during human pregnancy. Placenta 27(Suppl A), S47–53, (2006).

  • 52.

    Tilburgs, T., Scherjon, S. A., Roelen, D. L. & Claas, F. H. Decidual CD8+ CD28− T cells express CD103 but not perforin. Human immunology 70, 96–100, (2009).

  • 53.

    Tilburgs, T. et al. Expression of NK cell receptors on decidual T cells in human pregnancy. Journal of reproductive immunology 80, 22–32, (2009).

  • 54.

    Tilburgs, T. et al. Human decidual tissue contains differentiated CD8+ effector-memory T cells with unique properties. Journal of immunology 185, 4470–4477, (2010).

  • 55.

    Powell, R. M. et al. Decidual T Cells Exhibit a Highly Differentiated Phenotype and Demonstrate Potential Fetal Specificity and a Strong Transcriptional Response to IFN. Journal of immunology 199, 3406–3417, (2017).

  • 56.

    Gomez-Lopez, N. et al. Specific inflammatory microenvironments in the zones of the fetal membranes at term delivery. Am J Obstet Gynecol 205(235), e215–224, (2011).

  • 57.

    Gomez-Lopez, N., Hernandez-Santiago, S., Lobb, A. P., Olson, D. M. & Vadillo-Ortega, F. Normal and premature rupture of fetal membranes at term delivery differ in regional chemotactic activity and related chemokine/cytokine production. Reproductive sciences 20, 276–284, (2013).

  • 58.

    Gomez-Lopez, N. et al. Evidence for a role for the adaptive immune response in human term parturition. American journal of reproductive immunology 69, 212–230, (2013).

  • 59.

    Xu, Y. et al. Peripheral CD300a+ CD8+ T lymphocytes with a distinct cytotoxic molecular signature increase in pregnant women with chronic chorioamnionitis. American journal of reproductive immunology 67, 184–197, (2012).

  • 60.

    Aghaeepour, N. et al. An immune clock of human pregnancy. Science immunology 2, (2017).

  • 61.

    Shah, N. M. et al. Changes in T Cell and Dendritic Cell Phenotype from Mid to Late Pregnancy Are Indicative of a Shift from Immune Tolerance to Immune Activation. Frontiers in immunology 8, 1138, (2017).

  • 62.

    Yuan, M., Jordan, F., McInnes, I. B., Harnett, M. M. & Norman, J. E. Leukocytes are primed in peripheral blood for activation during term and preterm labour. Molecular human reproduction 15, 713–724, (2009).

  • 63.

    Bizargity, P., Del Rio, R., Phillippe, M., Teuscher, C. & Bonney, E. A. Resistance to lipopolysaccharide-induced preterm delivery mediated by regulatory T cell function in mice. Biology of reproduction 80, 874–881, (2009).

  • 64.

    Kim, J. S. et al. Involvement of Hofbauer cells and maternal T cells in villitis of unknown aetiology. Histopathology 52, 457–464, (2008).

  • 65.

    Kim, M. J. et al. Villitis of unknown etiology is associated with a distinct pattern of chemokine up-regulation in the feto-maternal and placental compartments: implications for conjoint maternal allograft rejection and maternal anti-fetal graft-versus-host disease. Journal of immunology 182, 3919–3927, (2009).

  • 66.

    Ito, Y. et al. Increased expression of perforin, granzyme B, and C5b-9 in villitis of unknown etiology. Placenta 36, 531–537, (2015).

  • 67.

    Kim, C. J. et al. The frequency, clinical significance, and pathological features of chronic chorioamnionitis: a lesion associated with spontaneous preterm birth. Mod Pathol 23, 1000–1011, (2010).

  • 68.

    Khong, T. Y. et al. Chronic deciduitis in the placental basal plate: definition and interobserver reliability. Human pathology 31, 292–295 (2000).

  • 69.

    Lee, J. et al. A signature of maternal anti-fetal rejection in spontaneous preterm birth: chronic chorioamnionitis, anti-human leukocyte antigen antibodies, and C4d. PLoS One 6, e16806, (2011).

  • 70.

    Lee, J. et al. Chronic chorioamnionitis is the most common placental lesion in late preterm birth. Placenta 34, 681–689, (2013).

  • 71.

    Kim, C. J., Romero, R., Chaemsaithong, P. & Kim, J. S. Chronic inflammation of the placenta: definition, classification, pathogenesis, and clinical significance. Am J Obstet Gynecol 213, S53–69, (2015).

  • 72.

    Maymon, E. et al. Chronic inflammatory lesions of the placenta are associated with an up-regulation of amniotic fluid CXCR3: A marker of allograft rejection. J Perinat Med 46, 123–137, (2018).

  • 73.

    Tamblyn, J. A., Lissauer, D. M., Powell, R., Cox, P. & Kilby, M. D. The immunological basis of villitis of unknown etiology – review. Placenta 34, 846–855, (2013).

  • 74.

    Gomez-Lopez, N., Olson, D. M. & Robertson, S. A. Interleukin-6 controls uterine Th9 cells and CD8(+) T regulatory cells to accelerate parturition in mice. Immunol Cell Biol 94, 79–89, (2016).

  • 75.

    Arenas-Hernandez, M. et al. An imbalance between innate and adaptive immune cells at the maternal-fetal interface occurs prior to endotoxin-induced preterm birth. Cell Mol Immunol 13, 462–473, (2016).

  • 76.

    St Louis, D. et al. Invariant NKT Cell Activation Induces Late Preterm Birth That Is Attenuated by Rosiglitazone. Journal of immunology 196, 1044–1059, (2016).

  • 77.

    Gomez-Lopez, N. et al. In vivo activation of invariant natural killer T cells induces systemic and local alterations in T-cell subsets prior to preterm birth. Clin Exp Immunol 189, 211–225, (2017).

  • 78.

    Gomez-Lopez, N. et al. In vivo T-cell activation by a monoclonal alphaCD3epsilon antibody induces preterm labor and birth. American journal of reproductive immunology 76, 386–390, (2016).

  • 79.

    Frascoli, M. et al. Alloreactive fetal T cells promote uterine contractility in preterm labor via IFN-gamma and TNF-alpha. Sci Transl Med 10, (2018).

  • 80.

    Kim, J. H. et al. Comparison of three different kits for extraction of high-quality RNA from frozen blood. SpringerPlus 3, 76, (2014).

  • 81.

    Irizarry, R. A. et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 4, 249–264 (2003).

  • 82.

    Carvalho, B. S. & Irizarry, R. A. A framework for oligonucleotide microarray preprocessing. Bioinformatics 26, 2363–2367, (2010).

  • 83.

    Gentleman, R. C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome biology 5, R80, (2004).

  • 84.

    Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research 4, 1521, (2015).

  • 85.

    Smyth, G. K. In Bioinformatics and Computational Biology Solutions Using R and Bioconductor (ed. Gentleman, R. et al.) 397–420 (Springer, 2012).

  • 86.

    Anders, S. et al. Count-based differential expression analysis of RNA sequencing data using R and Bioconductor. Nature protocols 8, 1765–1786, (2013).

  • Articles You May Like

    Scientists Dig Into Hard Questions About The Fluorinated Pollutants Known As PFAS
    The buzz about bumble bees isn’t good
    Carbon dioxide from Silicon Valley affects the chemistry of Monterey Bay
    Behavioral disorders in kids with autism linked to reduced brain connectivity
    Thermodynamic magic enables cooling without energy consumption

    Leave a Reply

    Your email address will not be published. Required fields are marked *