Underwood, M. A., Gilbert, W. M. & Sherman, M. P. Amniotic fluid: Not just fetal urine anymore. J. Perinatol. 25, 341–348. https://doi.org/10.1038/sj.jp.7211290 (2005).
Google Scholar
Fischer, R. L. Amniotic fluid: Physiology and assessment. GLOWM The Global Library of Women’s Medicine (2009).
Cho, C. K., Shan, S. J., Winsor, E. J. & Diamandis, E. P. Proteomics analysis of human amniotic fluid. Mol. Cell Proteomics 6, 1406–1415. https://doi.org/10.1074/mcp.M700090-MCP200 (2007).
Google Scholar
Hui, L. & Bianchi, D. W. Cell-free fetal nucleic acids in amniotic fluid. Hum. Reprod. Update 17, 362–371. https://doi.org/10.1093/humupd/dmq049 (2011).
Google Scholar
Tsangaris, G. T. et al. The normal human amniotic fluid supernatant proteome. In Vivo 20, 479–490 (2006).
Google Scholar
Tsangaris, G. T. et al. Application of proteomics for the identification of biomarkers in amniotic fluid: Are we ready to provide a reliable prediction?. EPMA J. 2, 149–155. https://doi.org/10.1007/s13167-011-0083-0 (2011).
Google Scholar
Page, N. M., Kemp, C. F., Butlin, D. J. & Lowry, P. J. Placental peptides as markers of gestational disease. Reproduction 123, 487–495. https://doi.org/10.1530/rep.0.1230487 (2002).
Google Scholar
Vasani, A. & Kumar, M. S. Advances in the proteomics of amniotic fluid to detect biomarkers for chromosomal abnormalities and fetomaternal complications during pregnancy. Exp. Rev. Proteom. 16, 277–286. https://doi.org/10.1080/14789450.2019.1578213 (2019).
Google Scholar
Tsangaris, G. T. et al. Proteomic analysis of amniotic fluid in pregnancies with Down syndrome. Proteomics 6, 4410–4419. https://doi.org/10.1002/pmic.200600085 (2006).
Google Scholar
Mavrou, A. et al. Proteomic analysis of amniotic fluid in pregnancies with Turner syndrome fetuses. J. Proteome Res. 7, 1862–1866. https://doi.org/10.1021/pr700588u (2008).
Google Scholar
Cen, J. et al. Comparative proteome analysis of amniotic fluids and placentas from patients with idiopathic polyhydramnios. Placenta 89, 67–77. https://doi.org/10.1016/j.placenta.2019.10.010 (2020).
Google Scholar
Romero, R. et al. Proteomic analysis of amniotic fluid to identify women with preterm labor and intra-amniotic inflammation/infection: the use of a novel computational method to analyze mass spectrometric profiling. J. Matern. Fetal Neonatal Med. 21, 367–388. https://doi.org/10.1080/14767050802045848 (2008).
Google Scholar
Vuadens, F. et al. Identification of biologic markers of the premature rupture of fetal membranes: proteomic approach. Proteomics 3, 1521–1525. https://doi.org/10.1002/pmic.200300455 (2003).
Google Scholar
Vascotto, C. et al. Oxidized transthyretin in amniotic fluid as an early marker of preeclampsia. J. Proteome Res. 6, 160–170. https://doi.org/10.1021/pr060315z (2007).
Google Scholar
Bujold, E. et al. Proteomic profiling of amniotic fluid in preterm labor using two-dimensional liquid separation and mass spectrometry. J. Matern. Fetal Neonatal Med. 21, 697–713. https://doi.org/10.1080/14767050802053289 (2008).
Google Scholar
Romero, R. et al. Amniotic fluid interleukin-6 determinations are of diagnostic and prognostic value in preterm labor. Am. J. Reprod. Immunol. 30, 167–183. https://doi.org/10.1111/j.1600-0897.1993.tb00618.x (1993).
Google Scholar
Romero, R. et al. A comparative study of the diagnostic performance of amniotic fluid glucose, white blood cell count, interleukin-6, and gram stain in the detection of microbial invasion in patients with preterm premature rupture of membranes. Am. J. Obstet. Gynecol. 169, 839–851. https://doi.org/10.1016/0002-9378(93)90014-a (1993).
Google Scholar
Romero, R. et al. Prevalence and clinical significance of sterile intra-amniotic inflammation in patients with preterm labor and intact membranes. Am. J. Reprod. Immunol. 72, 458–474. https://doi.org/10.1111/aji.12296 (2014).
Google Scholar
Romero, R. et al. Sterile and microbial-associated intra-amniotic inflammation in preterm prelabor rupture of membranes. J. Matern. Fetal Neonatal Med. 28, 1394–1409. https://doi.org/10.3109/14767058.2014.958463 (2015).
Google Scholar
Chaemsaithong, P. et al. A rapid interleukin-6 bedside test for the identification of intra-amniotic inflammation in preterm labor with intact membranes. J. Matern. Fetal Neonatal Med. 29, 349–359. https://doi.org/10.3109/14767058.2015.1006620 (2016).
Google Scholar
Leaños-Miranda, A. et al. Interleukin-6 in amniotic fluid: A reliable marker for adverse outcomes in women in preterm labor and intact membranes. Fetal Diagn. Ther. https://doi.org/10.1159/000514898 (2021).
Wilson, R. D., Committee, S. G. & Contributor, S. Prenatal screening, diagnosis, and pregnancy management of fetal neural tube defects. J. Obstet. Gynaecol. Can. 36, 927–939. https://doi.org/10.1016/S1701-2163(15)30444-8 (2014).
Google Scholar
American College of Obstetricians and Gynecologists. Amniocentesis, <https://www.acog.org/womens-health/faqs/amniocentesis> (2021, March).
Pös, O., Budiš, J. & Szemes, T. Recent trends in prenatal genetic screening and testing. F1000Res https://doi.org/10.12688/f1000research.16837.1 (2019).
Google Scholar
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).
Google Scholar
Vlková, B., Szemes, T., Minárik, G., Turna, J. & Celec, P. Does maternal saliva contain fetal DNA usable for prenatal diagnostics?. Med. Hypotheses 74, 258–260. https://doi.org/10.1016/j.mehy.2009.09.022 (2010).
Google Scholar
Tsui, N. B. et al. High resolution size analysis of fetal DNA in the urine of pregnant women by paired-end massively parallel sequencing. PLoS ONE 7, e48319. https://doi.org/10.1371/journal.pone.0048319 (2012).
Google Scholar
Perluigi, M. et al. Proteomic analysis for the study of amniotic fluid protein composition. J. Prenat. Med. 3, 39–41 (2009).
Google Scholar
Kamath-Rayne, B. D. et al. Systems biology evaluation of cell-free amniotic fluid transcriptome of term and preterm infants to detect fetal maturity. BMC Med. Genom. 8, 67. https://doi.org/10.1186/s12920-015-0138-5 (2015).
Google Scholar
Hampton, T. Comprehensive, “proteomic profile” of amniotic fluid may aid prenatal diagnosis. JAMA 298, 1751. https://doi.org/10.1001/jama.298.15.1751 (2007).
Google Scholar
Liu, X., Song, Y., Guo, Z., Sun, W. & Liu, J. A comprehensive profile and inter-individual variations analysis of the human normal amniotic fluid proteome. J. Proteom. 192, 1–9. https://doi.org/10.1016/j.jprot.2018.04.023 (2019).
Google Scholar
Romero, R. et al. The maternal plasma proteome changes as a function of gestational age in normal pregnancy: A longitudinal study. Am. J. Obstet. Gynecol. 217(67), e61-67.e21. https://doi.org/10.1016/j.ajog.2017.02.037 (2017).
Google Scholar
Nilsson, S., Ramström, M., Palmblad, M., Axelsson, O. & Bergquist, J. Explorative study of the protein composition of amniotic fluid by liquid chromatography electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry. J. Proteome Res. 3, 884–889. https://doi.org/10.1021/pr0499545 (2004).
Google Scholar
Tsangaris, G., Weitzdörfer, R., Pollak, D., Lubec, G. & Fountoulakis, M. The amniotic fluid cell proteome. Electrophoresis 26, 1168–1173. https://doi.org/10.1002/elps.200406183 (2005).
Google Scholar
Cho, C. K., Smith, C. R. & Diamandis, E. P. Amniotic fluid proteome analysis from Down syndrome pregnancies for biomarker discovery. J. Proteome Res. 9, 3574–3582. https://doi.org/10.1021/pr100088k (2010).
Google Scholar
Queloz, P. A. et al. Proteomic analyses of amniotic fluid: Potential applications in health and diseases. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 850, 336–342. https://doi.org/10.1016/j.jchromb.2006.12.006 (2007).
Google Scholar
Michaels, J. E. et al. Comprehensive proteomic analysis of the human amniotic fluid proteome: Gestational age-dependent changes. J. Proteome Res. 6, 1277–1285. https://doi.org/10.1021/pr060543t (2007).
Google Scholar
Kolialexi, A., Tsangaris, G. T. & Mavrou, A. Proteomics in prenatal diagnosis. Exp. Rev. Proteom. 6, 111–113. https://doi.org/10.1586/epr.09.6 (2009).
Google Scholar
Gold, L. et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS ONE 5, e15004. https://doi.org/10.1371/journal.pone.0015004 (2010).
Google Scholar
Davies, D. R. et al. Unique motifs and hydrophobic interactions shape the binding of modified DNA ligands to protein targets. Proc. Natl. Acad. Sci. U S A 109, 19971–19976. https://doi.org/10.1073/pnas.1213933109 (2012).
Google Scholar
Candia, J. et al. Assessment of variability in the SOMAscan assay. Sci. Rep. 7, 14248. https://doi.org/10.1038/s41598-017-14755-5 (2017).
Google Scholar
Erez, O. et al. The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study. PLoS ONE 12, e0181468. https://doi.org/10.1371/journal.pone.0181468 (2017).
Google Scholar
Tarca, A. L. et al. The prediction of early preeclampsia: Results from a longitudinal proteomics study. PLoS ONE 14, e0217273. https://doi.org/10.1371/journal.pone.0217273 (2019).
Google Scholar
Ghaemi, M. S. et al. Proteomic signatures predict preeclampsia in individual cohorts but not across cohorts – implications for clinical biomarker studies. J. Matern. Fetal Neonatal Med. https://doi.org/10.1080/14767058.2021.1888915 (2021).
Google Scholar
Shainker, S. A. et al. Placenta accreta spectrum: Biomarker discovery using plasma proteomics. Am. J. Obstet. Gynecol. 223(433), e431-433.e414. https://doi.org/10.1016/j.ajog.2020.03.019 (2020).
Google Scholar
Williams, S. A. et al. Plasma protein patterns as comprehensive indicators of health. Nat. Med. 25, 1851–1857. https://doi.org/10.1038/s41591-019-0665-2 (2019).
Google Scholar
Ganz, P. et al. Development and validation of a protein-based risk score for cardiovascular outcomes among patients with stable coronary heart disease. JAMA 315, 2532–2541. https://doi.org/10.1001/jama.2016.5951 (2016).
Google Scholar
Williams, S. A. et al. Improving assessment of drug safety through proteomics: Early detection and mechanistic characterization of the unforeseen harmful effects of torcetrapib. Circulation 137, 999–1010. https://doi.org/10.1161/CIRCULATIONAHA.117.028213 (2018).
Google Scholar
Tarca, A. L. et al. Amniotic fluid cell-free transcriptome: A glimpse into fetal development and placental cellular dynamics during normal pregnancy. BMC Med. Genom. 13, 25. https://doi.org/10.1186/s12920-020-0690-5 (2020).
Google Scholar
Gomez-Lopez, N. et al. The cellular transcriptome in the maternal circulation during normal pregnancy: A longitudinal study. Front. Immunol. 10, 2863. https://doi.org/10.3389/fimmu.2019.02863 (2019).
Google Scholar
Su, A. I. et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc. Natl. Acad. Sci. U S A 101, 6062–6067. https://doi.org/10.1073/pnas.0400782101 (2004).
Google Scholar
Zhao, M. et al. A comparative proteomics analysis of five body fluids: Plasma, urine, cerebrospinal fluid, amniotic fluid, and saliva. Proteom. Clin. Appl. 12, e1800008. https://doi.org/10.1002/prca.201800008 (2018).
Google Scholar
Lollo, B., Steele, F. & Gold, L. Beyond antibodies: New affinity reagents to unlock the proteome. Proteomics 14, 638–644. https://doi.org/10.1002/pmic.201300187 (2014).
Google Scholar
Gerson, C. et al. The lactoperoxidase system functions in bacterial clearance of airways. Am. J. Respir. Cell Mol. Biol. 22, 665–671. https://doi.org/10.1165/ajrcmb.22.6.3980 (2000).
Google Scholar
Munther, S. The impact of salivary lactoperoxidase and histatin-5 on early childhood caries severity in relation to nutritional status. Saudi Dent. J. 32, 410–416. https://doi.org/10.1016/j.sdentj.2020.01.010 (2020).
Google Scholar
El-Chemaly, S., Salathe, M., Baier, S., Conner, G. E. & Forteza, R. Hydrogen peroxide-scavenging properties of normal human airway secretions. Am. J. Respir. Crit. Care Med. 167, 425–430. https://doi.org/10.1164/rccm.200206-531OC (2003).
Google Scholar
Kivela, J., Parkkila, S., Parkkila, A. K., Leinonen, J. & Rajaniemi, H. Salivary carbonic anhydrase isoenzyme VI. J. Physiol. 520(Pt 2), 315–320. https://doi.org/10.1111/j.1469-7793.1999.t01-1-00315.x (1999).
Google Scholar
Uhlén, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419. https://doi.org/10.1126/science.1260419 (2015).
Google Scholar
Poggi, L., Casarosa, S. & Carl, M. An eye on the wnt inhibitory factor Wif1. Front. Cell Dev. Biol. 6, 167. https://doi.org/10.3389/fcell.2018.00167 (2018).
Google Scholar
Rodrigues, T. L. et al. Hypophosphatasia-associated deficiencies in mineralization and gene expression in cultured dental pulp cells obtained from human teeth. J. Endod. 38, 907–912. https://doi.org/10.1016/j.joen.2012.02.008 (2012).
Google Scholar
Vannahme, C. et al. Molecular cloning of testican-2: Defining a novel calcium-binding proteoglycan family expressed in brain. J. Neurochem. 73, 12–20. https://doi.org/10.1046/j.1471-4159.1999.0730012.x (1999).
Google Scholar
Hadchouel, A. et al. Identification of SPOCK2 as a susceptibility gene for bronchopulmonary dysplasia. Am. J. Respir. Crit. Care Med. 184, 1164–1170. https://doi.org/10.1164/rccm.201103-0548OC (2011).
Google Scholar
Romero, R. et al. Soluble receptor for advanced glycation end products (sRAGE) and endogenous secretory RAGE (esRAGE) in amniotic fluid: Modulation by infection and inflammation. J. Perinat. Med. 36, 388–398. https://doi.org/10.1515/JPM.2008.076 (2008).
Google Scholar
Tabbah, S. et al. Amniotic fluid hepcidin in pregnancies complicated by intraamniotic infection. Am. J. Obstet. Gynecol. 212, 1 (2015).
Google Scholar
O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: Current status, taxonomic expansion, and functional annotation. Nucl. Acids Res. 44, D733-745. https://doi.org/10.1093/nar/gkv1189 (2016).
Google Scholar
Buhimschi, I. A. et al. The receptor for advanced glycation end products (RAGE) system in women with intraamniotic infection and inflammation. Am. J. Obstet. Gynecol. 196(181), e181–e113. https://doi.org/10.1016/j.ajog.2006.09.001 (2007).
Google Scholar
Kossiva, L., Soldatou, A., Gourgiotis, D. I., Stamati, L. & Tsentidis, C. Serum hepcidin: Indication of its role as an “acute phase” marker in febrile children. Ital. J. Pediatr. 39, 25. https://doi.org/10.1186/1824-7288-39-25 (2013).
Google Scholar
Pepys, M. B. & Hirschfield, G. M. C-reactive protein: A critical update. J. Clin. Invest. 111, 1805–1812. https://doi.org/10.1172/JCI18921 (2003).
Google Scholar
Bhatti, G. et al. Compartmentalized profiling of amniotic fluid cytokines in women with preterm labor. PLoS ONE 15, e0227881. https://doi.org/10.1371/journal.pone.0227881 (2020).
Google Scholar
Maglott, D., Ostell, J., Pruitt, K. D. & Tatusova, T. Entrez Gene: Gene-centered information at NCBI. Nucl. Acids Res. 33, D54-58. https://doi.org/10.1093/nar/gki031 (2005).
Google Scholar
Gomez-Lopez, N. et al. The immunophenotype of amniotic fluid leukocytes in normal and complicated pregnancies. Am. J. Reprod. Immunol. 79, e12827. https://doi.org/10.1111/aji.12827 (2018).
Google Scholar
Gomez-Lopez, N. et al. Neutrophil extracellular traps in the amniotic cavity of women with intra-amniotic infection: A new mechanism of host defense. Reprod. Sci. 24, 1139–1153. https://doi.org/10.1177/1933719116678690 (2017).
Google Scholar
Gomez-Lopez, N. et al. Amniotic fluid neutrophils can phagocytize bacteria: A mechanism for microbial killing in the amniotic cavity. Am. J. Reprod. Immunol. https://doi.org/10.1111/aji.12723 (2017).
Google Scholar
Martinez-Varea, A. et al. Clinical chorioamnionitis at term VII: The amniotic fluid cellular immune response. J. Perinat. Med. 45, 523–538. https://doi.org/10.1515/jpm-2016-0225 (2017).
Google Scholar
Gomez-Lopez, N. et al. Are amniotic fluid neutrophils in women with intraamniotic infection and/or inflammation of fetal or maternal origin?. Am. J. Obstet. Gynecol. 217(693), e691-693.e616. https://doi.org/10.1016/j.ajog.2017.09.013 (2017).
Google Scholar
Tarca, A. L. et al. Targeted expression profiling by RNA-Seq improves detection of cellular dynamics during pregnancy and identifies a role for T cells in term parturition. Sci. Rep. 9, 848. https://doi.org/10.1038/s41598-018-36649-w (2019).
Google Scholar
Galaz, J. et al. Cellular immune responses in amniotic fluid of women with preterm clinical chorioamnionitis. Inflam. Res. Off. J. Eur. Histamine Res. So. 69, 203–216. https://doi.org/10.1007/s00011-019-01308-x (2020).
Google Scholar
Gomez-Lopez, N. et al. RNA sequencing reveals diverse functions of amniotic fluid neutrophils and monocytes/macrophages in intra-amniotic infection. J. Innate Immun. 13, 63–82. https://doi.org/10.1159/000509718 (2021).
Google Scholar
Rauch, U., Hirakawa, S., Oohashi, T., Kappler, J. & Roos, G. Cartilage link protein interacts with neurocan, which shows hyaluronan binding characteristics different from CD44 and TSG-6. Matrix Biol. 22, 629–639. https://doi.org/10.1016/j.matbio.2003.11.007 (2004).
Google Scholar
Urano, T. et al. Single-nucleotide polymorphism in the hyaluronan and proteoglycan link protein 1 (HAPLN1) gene is associated with spinal osteophyte formation and disc degeneration in Japanese women. Eur. Spine J. 20, 572–577. https://doi.org/10.1007/s00586-010-1598-0 (2011).
Google Scholar
Evanko, S. P. et al. A role for HAPLN1 during phenotypic modulation of human lung fibroblasts in vitro. J. Histochem. Cytochem. 68, 797–811. https://doi.org/10.1369/0022155420966663 (2020).
Google Scholar
Watanabe, H. & Yamada, Y. Mice lacking link protein develop dwarfism and craniofacial abnormalities. Nat. Genet. 21, 225–229. https://doi.org/10.1038/6016 (1999).
Google Scholar
Wirrig, E. E. et al. Cartilage link protein 1 (Crtl1), an extracellular matrix component playing an important role in heart development. Dev. Biol. 310, 291–303. https://doi.org/10.1016/j.ydbio.2007.07.041 (2007).
Google Scholar
Eskici, N. F., Erdem-Ozdamar, S. & Dayangac-Erden, D. The altered expression of perineuronal net elements during neural differentiation. Cell Mol. Biol. Lett. 23, 5. https://doi.org/10.1186/s11658-018-0073-5 (2018).
Google Scholar
Long, K. R. et al. Extracellular matrix components HAPLN1, lumican, and collagen I cause hyaluronic acid-dependent folding of the developing human neocortex. Neuron 99, 702-719.e706. https://doi.org/10.1016/j.neuron.2018.07.013 (2018).
Google Scholar
Consortium, U. UniProt: A worldwide hub of protein knowledge. Nucl. Acids Res. 47, D506–D515. https://doi.org/10.1093/nar/gky1049 (2019).
Google Scholar
Liu, C. X., Li, Y., Obermoeller-McCormick, L. M., Schwartz, A. L. & Bu, G. The putative tumor suppressor LRP1B, a novel member of the low density lipoprotein (LDL) receptor family, exhibits both overlapping and distinct properties with the LDL receptor-related protein. J. Biol. Chem. 276, 28889–28896. https://doi.org/10.1074/jbc.M102727200 (2001).
Google Scholar
Haas, J. et al. LRP1b shows restricted expression in human tissues and binds to several extracellular ligands, including fibrinogen and apoE-carrying lipoproteins. Atherosclerosis 216, 342–347. https://doi.org/10.1016/j.atherosclerosis.2011.02.030 (2011).
Google Scholar
Bakker, J. et al. Alpha-fetoprotein protects the developing female mouse brain from masculinization and defeminization by estrogens. Nat. Neurosci. 9, 220–226. https://doi.org/10.1038/nn1624 (2006).
Google Scholar
Pacora, P. et al. Lactoferrin in intrauterine infection, human parturition, and rupture of fetal membranes. Am. J. Obstet. Gynecol. 183, 904–910. https://doi.org/10.1067/mob.2000.108882 (2000).
Google Scholar
Romero, R. et al. Isobaric labeling and tandem mass spectrometry: a novel approach for profiling and quantifying proteins differentially expressed in amniotic fluid in preterm labor with and without intra-amniotic infection/inflammation. J. Matern. Fetal Neonatal Med. 23, 261–280. https://doi.org/10.3109/14767050903067386 (2010).
Google Scholar
Rak, K., Kornafel, D., Mazurek, D. & Bronkowska, M. Lactoferrin level in maternal serum is related to birth anthropometry – an evidence for an indirect biomarker of intrauterine homeostasis?. J. Matern. Fetal Neonatal Med. 32, 4043–4050. https://doi.org/10.1080/14767058.2018.1481040 (2019).
Google Scholar
Canny, G. et al. Lipid mediator-induced expression of bactericidal/ permeability-increasing protein (BPI) in human mucosal epithelia. Proc. Natl. Acad. Sci. U S A 99, 3902–3907. https://doi.org/10.1073/pnas.052533799 (2002).
Google Scholar
Son, G. H. et al. Whole blood RNA sequencing reveals a differential transcriptomic profile associated with cervical insufficiency: A pilot study. Reprod. Biol. Endocrinol. 19, 32. https://doi.org/10.1186/s12958-021-00715-2 (2021).
Google Scholar
Wu, Z. et al. LOX-1 deletion improves neutrophil responses, enhances bacterial clearance, and reduces lung injury in a murine polymicrobial sepsis model. Infect. Immun. 79, 2865–2870. https://doi.org/10.1128/IAI.01317-10 (2011).
Google Scholar
Kim, C. J. et al. Acute chorioamnionitis and funisitis: Definition, pathologic features, and clinical significance. Am. J. Obstet. Gynecol. 213, S29-52. https://doi.org/10.1016/j.ajog.2015.08.040 (2015).
Google Scholar
Brunzel, N. A. Fundamentals of urine & body fluid analysis (Elsevier, 2018).
Jasinska, A. J., Rostamian, D., Davis, A. T. & Kavanagh, K. Transcriptomic analysis of cell-free fetal RNA in the amniotic fluid of vervet monkeys (Chlorocebus sabaeus). Comp. Med. 70, 67–74. https://doi.org/10.30802/AALAS-CM-19-000037 (2020).
Google Scholar
Hui, L., Beard, S. & Hannan, N. J. Measuring fetal brain and lung transcripts in amniotic fluid supernatant: A comparison of digital PCR and RT-qPCR methods. J. Matern. Fetal Neonatal Med. 31, 3191–3196. https://doi.org/10.1080/14767058.2017.1367378 (2018).
Google Scholar
Hui, L., Wick, H. C., Edlow, A. G., Cowan, J. M. & Bianchi, D. W. Global gene expression analysis of term amniotic fluid cell-free fetal RNA. Obstet. Gynecol. 121, 1248–1254. https://doi.org/10.1097/AOG.0b013e318293d70b (2013).
Google Scholar
SomaLogic, I. SOMAscan Proteomic Assay. (Boulder, CO, 2017).
Welch, B. L. The generalization of `Student’s’ problem when several different population variances are involved. Biometrika 34, 28–35. https://doi.org/10.2307/2332510 (1947).
Google Scholar
Fisher, R. A. S. Statistical methods for research workers. Fifth edition. edn, (Oliver and Boyd, 1934).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucl. Acids Res. 43, e47. https://doi.org/10.1093/nar/gkv007 (2015).
Google Scholar
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Royal Stat. Soc. Ser. B (Methodological) 57, 289–300 (1995).
Google Scholar
Allison, D. B., Cui, X., Page, G. P. & Sabripour, M. Microarray data analysis: from disarray to consolidation and consensus. Nat. Rev. Genet. 7, 55–65. https://doi.org/10.1038/nrg1749 (2006).
Google Scholar
Mittal, P. et al. Characterization of the myometrial transcriptome and biological pathways of spontaneous human labor at term. J. Perinat. Med. 38, 617–643. https://doi.org/10.1515/jpm.2010.097 (2010).
Google Scholar
Pappas, A. et al. Transcriptomics of maternal and fetal membranes can discriminate between gestational-age matched preterm neonates with and without cognitive impairment diagnosed at 18–24 months. PLoS ONE 10, e0118573. https://doi.org/10.1371/journal.pone.0118573 (2015).
Google Scholar
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).
Google Scholar
Langfelder, P. & Horvath, S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinform. 9, 559. https://doi.org/10.1186/1471-2105-9-559 (2008).
Google Scholar
Langfelder, P., Mischel, P. S. & Horvath, S. When is hub gene selection better than standard meta-analysis?. PLoS ONE 8, e61505. https://doi.org/10.1371/journal.pone.0061505 (2013).
Google Scholar
Falcon, S. & Gentleman, R. Using GOstats to test gene lists for GO term association. Bioinformatics 23, 257–258. https://doi.org/10.1093/bioinformatics/btl567 (2007).
Google Scholar
Ashburner, M. et al. Gene ontology: Tool for the unification of biology. The gene ontology consortium. Nat. Genet. 25, 25–29. https://doi.org/10.1038/75556 (2000).
Google Scholar
PCAtools: Everything Principal Components Analysis v. 2.4.0 (R package, 2021).
R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2020).
EnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling (R package, 2021).
pheatmap: Pretty Heatmaps v. 1.0.12 (R package, 2019).

