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

Bioinformatics
  • 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, https://doi.org/10.1073/pnas.1719264115 (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, https://doi.org/10.1073/pnas.1710470114 (2017).

  • 3.

    Alizadeh, A. A. et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511, https://doi.org/10.1038/35000501 (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, https://doi.org/10.1038/nrg2484 (2009).

  • 6.

    Levin, J. Z. et al. Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nature methods 7, 709–715, https://doi.org/10.1038/nmeth.1491 (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, https://doi.org/10.1371/journal.pone.0184094 (2017).

  • 8.

    Ngo, T. T. M. et al. Noninvasive blood tests for fetal development predict gestational age and preterm delivery. Science 360, 1133–1136, https://doi.org/10.1126/science.aar3819 (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, https://doi.org/10.1093/nar/gku555 (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, https://doi.org/10.1016/j.ajog.2010.08.009 (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, https://doi.org/10.1038/nbt1239 (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, https://doi.org/10.1093/bioinformatics/btt492 (2013).

  • 14.

    Dayarian, A. et al. Predicting protein phosphorylation from gene expression: top methods from the IMPROVER Species Translation Challenge. Bioinformatics 31, 462–470, https://doi.org/10.1093/bioinformatics/btu490 (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, https://doi.org/10.1016/j.comtox.2017.04.001 (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, https://doi.org/10.1038/nbt.2957 (2014).

  • 17.

    Teng, M. et al. A benchmark for RNA-seq quantification pipelines. Genome biology 17, 74, https://doi.org/10.1186/s13059-016-0940-1 (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, https://doi.org/10.1093/nar/gkv736 (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, https://doi.org/10.1109/TVCG.2014.2346248 (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, https://doi.org/10.1371/journal.pone.0155191 (2016).

  • 22.

    Al-Garawi, A. et al. The Role of Vitamin D in the Transcriptional Program of Human Pregnancy. PloS one 11, e0163832, https://doi.org/10.1371/journal.pone.0163832 (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, https://doi.org/10.1002/pd.4417 (2014).

  • 24.

    Everaert, C. et al. Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data. Scientific reports 7, 1559, https://doi.org/10.1038/s41598-017-01617-3 (2017).

  • 25.

    Archer, K. J. & Reese, S. E. Detection call algorithms for high-throughput gene expression microarray data. Briefings in bioinformatics 11, 244–252, https://doi.org/10.1093/bib/bbp055 (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, https://doi.org/10.1515/jpm-2013-0082 (2013).

  • 27.

    Risso, D., Schwartz, K., Sherlock, G. & Dudoit, S. GC-content normalization for RNA-Seq data. BMC bioinformatics 12, 480, https://doi.org/10.1186/1471-2105-12-480 (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, https://doi.org/10.1016/j.ajog.2017.12.234 (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, https://doi.org/10.1038/nrg3642 (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, https://doi.org/10.1101/gr.132159.111 (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, https://doi.org/10.1016/j.ajog.2007.01.008 (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, https://doi.org/10.3109/14767058.2013.860963 (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, https://doi.org/10.1002/path.2463 (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, https://doi.org/10.1016/j.ajog.2017.03.017 (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, https://doi.org/10.1186/s13059-014-0550-8 (2014).

  • 36.

    Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic acids research 43, e47, https://doi.org/10.1093/nar/gkv007 (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, https://doi.org/10.1093/nar/gkm549 (2007).

  • 38.

    Okoniewski, M. J. & Miller, C. J. Hybridization interactions between probesets in short oligo microarrays lead to spurious correlations. BMC bioinformatics 7, 276, https://doi.org/10.1186/1471-2105-7-276 (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, https://doi.org/10.1101/gr.079558.108 (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, https://doi.org/10.1038/nmeth.4197 (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, https://doi.org/10.1016/j.ajog.2017.02.037 (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, https://doi.org/10.1371/journal.pone.0079217 (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, https://doi.org/10.1186/gb-2010-11-2-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, https://doi.org/10.1038/10343 (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, https://doi.org/10.1038/ng1180 (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, https://doi.org/10.1038/ncomms13427 (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, https://doi.org/10.1016/j.ajog.2017.07.022 (2017).

  • 48.

    Wu, C. et al. BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome biology 10, R130, https://doi.org/10.1186/gb-2009-10-11-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, https://doi.org/10.1016/j.jri.2003.11.007 (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, https://doi.org/10.1016/j.placenta.2005.11.008 (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, https://doi.org/10.1016/j.humimm.2008.12.006 (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, https://doi.org/10.1016/j.jri.2009.02.004 (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, https://doi.org/10.4049/jimmunol.0903597 (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, https://doi.org/10.4049/jimmunol.1700114 (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, https://doi.org/10.1016/j.ajog.2011.04.019 (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, https://doi.org/10.1177/1933719112452473 (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, https://doi.org/10.1111/aji.12074 (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, https://doi.org/10.1111/j.1600-0897.2011.01088.x (2012).

  • 60.

    Aghaeepour, N. et al. An immune clock of human pregnancy. Science immunology 2, https://doi.org/10.1126/sciimmunol.aan2946 (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, https://doi.org/10.3389/fimmu.2017.01138 (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, https://doi.org/10.1093/molehr/gap054 (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, https://doi.org/10.1095/biolreprod.108.074294 (2009).

  • 64.

    Kim, J. S. et al. Involvement of Hofbauer cells and maternal T cells in villitis of unknown aetiology. Histopathology 52, 457–464, https://doi.org/10.1111/j.1365-2559.2008.02964.x (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, https://doi.org/10.4049/jimmunol.0803834 (2009).

  • 66.

    Ito, Y. et al. Increased expression of perforin, granzyme B, and C5b-9 in villitis of unknown etiology. Placenta 36, 531–537, https://doi.org/10.1016/j.placenta.2015.02.004 (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, https://doi.org/10.1038/modpathol.2010.73 (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, https://doi.org/10.1371/journal.pone.0016806 (2011).

  • 70.

    Lee, J. et al. Chronic chorioamnionitis is the most common placental lesion in late preterm birth. Placenta 34, 681–689, https://doi.org/10.1016/j.placenta.2013.04.014 (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, https://doi.org/10.1016/j.ajog.2015.08.041 (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, https://doi.org/10.1515/jpm-2017-0042 (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, https://doi.org/10.1016/j.placenta.2013.07.002 (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, https://doi.org/10.1038/icb.2015.63 (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, https://doi.org/10.1038/cmi.2015.22 (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, https://doi.org/10.4049/jimmunol.1501962 (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, https://doi.org/10.1111/cei.12968 (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, https://doi.org/10.1111/aji.12562 (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, https://doi.org/10.1126/scitranslmed.aan2263 (2018).

  • 80.

    Kim, J. H. et al. Comparison of three different kits for extraction of high-quality RNA from frozen blood. SpringerPlus 3, 76, https://doi.org/10.1186/2193-1801-3-76 (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, https://doi.org/10.1093/bioinformatics/btq431 (2010).

  • 83.

    Gentleman, R. C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome biology 5, R80, https://doi.org/10.1186/gb-2004-5-10-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, https://doi.org/10.12688/f1000research.7563.2 (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, https://doi.org/10.1038/nprot.2013.099 (2013).

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