The placenta serves as the lungs, gut, kidneys, and liver of the fetus (Burton and Jauniaux, 2015; Maltepe and Fisher, 2015). This fetal organ also has major biological actions that modulate maternal physiology (Burton and Jauniaux, 2015; Sasaki and Norwitz, 2011; Taglauer et al., 2014; Fitzgerald et al., 2018) and, importantly, together with the extraplacental chorioamniotic membranes, shield the fetus against microbes from hematogenous dissemination and from invading the amniotic cavity (Ander et al., 2019; Kwon et al., 2014). Indeed, most pathogens that cause hematogenous infections in the mother cannot reach the fetus, which is largely due to the potent protective mechanisms provided by placental cells (i.e. trophoblast cells: syncytiotrophoblasts and cytotrophoblasts) (Parry et al., 1997; Koi et al., 2002; Arora et al., 2017). Yet, some of these pathogens such as Toxoplasma gondii, Rubella virus, herpesvirus (HSV), cytomegalovirus (CMV), and Zika virus (ZIKV), among others, are capable of crossing the placenta and infecting the fetus, thus causing congenital disease (Stegmann and Carey, 2002; Coyne and Lazear, 2016).

In December 2019, a local outbreak of pneumonia caused by a novel coronavirus—severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)—was reported in Wuhan (Hubei, China) (Dong et al., 2020a). After exposure to SARS-COV-2, susceptible individuals can develop coronavirus disease 2019 (COVID-19) consisting of symptoms that may range from fever and cough to severe respiratory illness; in some cases, COVID-19 is life-threatening (Centers for Disease Control and Prevention, 2020a; Wadman, 2020). Since the onset of the outbreak, more than 10 million COVID-19 cases have been confirmed, accounting for more than 500,000 deaths (COVID-19, 2020). This pandemic has now spread throughout the entire world with recent epicenters in Europe (Italy and Spain) and the United States. By April 2019, the states of New York and Michigan were the most severely affected (COVID-19, 2020), given that the metropolitan areas of New York City and Detroit possess large populations subject to health disparities that include limited access to health care, chronic exposure to pollution, and pre-existing cardiovascular conditions (Centers for Disease Control and Prevention, 2020b).

Pregnant women and their fetuses represent a potential high-risk population in light of the COVID-19 outbreak (Dashraath et al., 2020; Liu et al., 2020; Rasmussen et al., 2020; Ashokka et al., 2020; Della Gatta et al., 2020; Weber LeBrun et al., 2020; Tekbali, 2020; Vintzileos, 2020; Lokken, 2020) given that viral infections such as influenza (Neuzil et al., 1998; Lindsay et al., 2006; Jamieson et al., 2006; Cervantes-Gonzalez and Launay, 2010; Pandemic H1N1 Influenza in Pregnancy Working Group et al., 2010; Mosby et al., 2011; Pazos et al., 2012; Raj et al., 2014), varicella (Triebwasser et al., 1967; Paryani and Arvin, 1986; Esmonde et al., 1989; Haake et al., 1990; Swamy and Dotters-Katz, 2019), Ebola (Olgun, 2018; Muehlenbachs et al., 2017), and measles (Christensen et al., 1954; Atmar et al., 1992) show increased severity in this physiological state. Other coronaviruses, such as SARS-CoV-1 and MERS-CoV, have severe effects in both the mother and the fetus, but vertical transmission has not been proven (Wong et al., 2004; Ng et al., 2006; Alserehi et al., 2016; Jeong et al., 2017), albeit these studies included very few cases. By contrast with the above-mentioned viral infections, only ~15% of pregnant women test positive for SARS-CoV-2 and a small fraction of them are symptomatic (Sutton et al., 2020), most of whom experience only a mild illness (Chen et al., 2020a). Consequently, the clinical characteristics of pregnant women with COVID-19 appear similar to those of non-pregnant adults (Yu et al., 2020). Yet, recent reports have shown that severe COVID-19 can lead to maternal death (Hantoushzadeh et al., 2020; Blitz, 2020). However, thus far, no conclusive evidence of vertical transmission has been generated (Weber LeBrun et al., 2020; Chen et al., 2020b; Stower, 2020; Lamouroux et al., 2020; Yan et al., 2020; Siberry et al., 2020). Consistently, infants born to mothers with COVID-19 test negative for SARS-CoV-2, do not develop serious clinical symptoms (e.g., fever, cough, diarrhea, or abnormal radiologic or hematologic evidence), and are promptly discharged from the hospital (Chen et al., 2020c). Nevertheless, new evidence has emerged suggesting that the fetus can respond to SARS-CoV-2 infection.

Case reports have shown that a small fraction of neonates born to women with COVID-19 tested positive for the virus at 1–4 days of life (Zeng et al., 2020a; Alzamora et al., 2020), yet these neonates subsequently tested negative on day 6-7 (Zeng et al., 2020a). In addition, serological studies revealed that a few neonates born to mothers with COVID-19 had increased concentrations of SARS-CoV-2 immunoglobulin (Ig)M as well as IgG (Zeng et al., 2020b; Dong et al., 2020b). The elevated concentrations of IgG are likely due to the passive transfer of this immunoglobulin from the mother to the fetus across the placenta. However, the increased levels of IgM suggest that the fetus was infected with SARS-CoV-2 given that this immunoglobulin cannot cross the placenta as a result of its large molecular weight. Nonetheless, all neonates included in the above-mentioned studies tested negative for the virus and did not present any symptoms (Zeng et al., 2020b; Dong et al., 2020b).

More recently, two case reports indicated that SARS-CoV-2 RNA has been detected in the amniotic fluid and placental tissues. In the first case report, the viral RNA was detected in amniotic fluid from a woman who was severely affected and died of COVID-19 (Zamaniyan, 2020). The premature neonate tested negative for SARS-CoV-2 after delivery but 24 hr later tested positive (Zamaniyan, 2020). In the second case report, the viral RNA was detected in the placenta and umbilical cord from a woman with severe pre-eclampsia, placental abruption, and other complications, yet none of the fetal tissues tested positive (Hosier, 2020). Therefore, whether SARS-CoV-2 can reach the fetus by crossing the placenta is still unclear.

Cell entry and the spread of SARS-CoV-2 are widely thought to depend on the angiotensin-converting enzyme 2 (ACE2) receptor (Shang et al., 2020; Wang et al., 2020a) and the serine protease TMPRSS2 (Hoffmann et al., 2020). In the study herein, we investigated whether the receptors responsible for SARS-CoV-2 infection are expressed in the human placenta (including the decidual tissues) throughout the three trimesters of pregnancy by using publicly available single-cell RNA-sequencing (scRNA-seq) data (Vento-Tormo et al., 2018; Pique-Regi et al., 2019) together with newly generated data (Supplementary file 1).

Strikingly, we found that very few cells co-express ACE2 and TMPRSS2 (Figure 1A and B). Using a very permissive threshold of expression of one transcript per cell, only four cells with co-expression were detected in any of the three trimesters, resulting in an estimated <1/10,000 cells. Our first-trimester data are in agreement with a prior report showing minimal expression of ACE2 at the human maternal-fetal interface (Zheng et al., 2020); however, the same dataset was recently used to report the opposite (Li et al., 2020). Nonetheless, the co-expression of ACE2 and TMPRSS2 was not examined by either study, and it is important to consider log-transformation and data analysis issues for low-expressed genes, focusing on the fraction of cells expressing the transcripts (Booeshaghi and Pachter, 2020). We also evaluated the expression of SARS-CoV-2 receptors in the chorioamniotic membranes (also known as the extraplacental membranes) in the third trimester; these tissues may also serve as a point of entry for microbial invasion of the amniotic cavity and potentially the fetus (Kim et al., 2015). Again, co-expression of ACE2 and TMPRSS2 was minimally detected in the chorioamniotic membranes (Figure 1A and B).

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A challenge in scRNA-seq studies is generating high-quality, single-cell suspensions containing both rare and difficult-to-dissociate (e.g., multinucleated cells) cell types. This is likely the reason why the reported scRNA-seq studies of the human placenta contain a low fraction of syncytiotrophoblast cells [STB, multinucleated cells forming the outermost fetal component of the placenta in direct contact with the maternal circulation (i.e., intervillous space)] (Vento-Tormo et al., 2018; Pique-Regi et al., 2019; Tsang et al., 2017). Therefore, we considered whether the expression of ACE2 and TMPRSS2 was minimally observed in the placental cell types as a result of the reduced fraction of STB cells (i.e., dissociation bias). To address this possibility, we prepared single-nucleus suspensions of the placental tissues (including the decidua basalis) and performed single-nuclear RNAseq (snRNA-seq), which reduces the dissociation bias against large cells (Wu et al., 2019). An important advantage of snRNA-seq is its compatibility with biobank-frozen samples; therefore, we pooled 32 placental villi/decidua samples collected in the third trimester (Supplementary file 2). This represents the first snRNA-seq study of the placental tissues. As expected, a larger fraction of STB cells/nuclei was observed using snRNA-seq compared to scRNA-seq (Figure 1A and Figure 1—figure supplement 1). Consistent with the scRNAseq analyses, the snRNAseq data demonstrated that co-expression of ACE2 and TMPRSS2 is unlikely in the placental tissues (Figure 1B). A limitation of snRNA-seq is that it has a higher background compared to scRNAseq and could capture ACE2 and TMPRSS2 transcripts from other cell types, but this should not affect the analyses reported herein because co-expression is not observed.

Finally, we explored the expression of ACE2 and TMPRSS2 in third-trimester placental tissues by mining two microarray datasets that we have previously reported (Kim et al., 2009; Toft et al., 2008). These analyses of bulk gene expression data revealed that, while ACE2 was detected above background in most of the samples, TMPRSS2 was largely undetected (Supplementary file 3). Collectively, these results consistently indicate that the human placental tissues negligibly co-express ACE2 and TMPRSS2. This reduced expression contrasts with the high expression of ACE2 and TMPRSS2 in nasal goblet and ciliated cells within the human airways, lungs, and gastrointestinal tract, which are targeted during COVID-19 (Muus, 2020; Wang, 2020b; HCA Lung Biological Network et al., 2020). Therefore, our results suggest that vertical transmission of SARS-CoV-2 is unlikely to occur unless facilitated by other concomitant pathological conditions resulting in a breach of the maternal-fetal crosstalk.

There is a possibility, however, that SARS-CoV-2 could infect the human placenta by using alternate entry routes while interacting with other proteins (Gordon et al., 2020). The expression of additional SARS-CoV-2-related receptors or proteins in the human placenta is shown in Figure 2, CoV-Alt; however, further research is required to test their participation in the pathogenesis of COVID-19. For example, in vitro studies suggest that BSG (Basigin, also called CD147 or EMMPRIN, transmembrane glycoprotein belonging to the immunoglobulin superfamily) provides an alternate entry for SARS-CoV-2 when ACE2 and TMPRSS2 are not expressed (Blanco-Melo et al., 2020; Wang, 2020c; Ulrich and Pillat, 2020). We found that the placenta and chorioamniotic membranes expressed high levels of BSG throughout pregnancy (Figure 2, CoV-Alt), yet this transcript is also widely expressed in all human tissues and cell types (Figure 2—figure supplement 1). Therefore, it is unlikely that this protein alone is a sufficient requirement for SARS-CoV-2 viral entry, and other proteins may be required to explain the cell type primarily affected by COVID-19. Moreover, cathepsin L (CSTL) and FURIN may also function as proteases priming the SARS-CoV-2 S protein (Lukassen et al., 2020). We found that these proteases are highly expressed by the placental tissues throughout gestation (Figure 2, CoV-Alt). Nevertheless, these proteases may not provide sufficient levels of priming by themselves (Shirato et al., 2017; Shirato et al., 2018; Iwata-Yoshikawa et al., 2019) when tested with SARS-CoV-1, yet this has not been verified for SARS-CoV-2. Given that the placental tissues are enriched in maternal and fetal macrophages (Pique-Regi et al., 2019), and that a subset of these immune cells expressing sialoadhesin (SIGLEC1, also known as CD169) can contribute to viral spread during SARS-CoV-2 infection (Chen, 2020; Park, 2020), we also investigated the expression of SIGLEC1 in this study. As expected, SIGLEC1 was expressed by macrophages in the placenta and chorioamniotic membranes and, to a lesser extent, in T cells (Figure 2, CoV-Alt). However, even if the virus could infect the placental/decidual macrophages expressing SIGLEC1, this is not sufficient for viral spreading. The expression of ADAM17 was also investigated in the placental tissues as this metalloproteinase competes with TMPRSS2 in ACE2 processing (Heurich et al., 2014). The placenta and chorioamniotic membranes highly expressed ADAM17 (Figure 2—figure supplement 2); however, only cleavage by TMPRSS2 results in augmented SARS-S-driven cell entry (Heurich et al., 2014). While these CoV-Alt molecules may be used for SARS-CoV-2 infection, they are likely to be less efficient than ACE2 and TMPRSS2, which are already targeted for antiviral interventions (Hoffmann et al., 2020); yet, new candidate host:viral interacting proteins and possible drugs are being investigated (Gordon et al., 2020).

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Given that the main mediators for cell entry of SARS-CoV-2 were minimally expressed by the human placenta, we also investigated whether the receptors for congenital viruses such as CMV (Stegmann and Carey, 2002; Arechavaleta-Velasco et al., 2002; Aronoff et al., 2017; Pereira et al., 2017; Al-Haddad et al., 2019; Faure Bardon et al., 2020) and ZIKV (Coyne and Lazear, 2016; Adams Waldorf et al., 2016; Aldo et al., 2016; Cao et al., 2017; Aagaard et al., 2017; Mysorekar, 2017; Valentine et al., 2018; Adams Waldorf et al., 2018; Dudley et al., 2018; Walker et al., 2019; Nelson et al., 2020), which are known to infect and cross the placenta, were detectable using our pipeline. Known receptors for CMV include NRP2 (Martinez-Martin et al., 2018), PDFGRA (Martinez-Martin et al., 2018), and CD46 (Stein et al., 2019). Notably, all of these receptors were highly expressed in several placental cell types (Figure 2, CMV and Figure 2—figure supplement 2). Next, we investigated the expression of the AXL receptor for ZIKV (Richard et al., 2017; Persaud et al., 2018) as well as other related molecules such as CD209 (Carbaugh et al., 2019) and TYRO3 (Oliveira and Peron, 2019). Consistent with vertical transmission, AXL, the preferred receptor for ZIKV, was highly expressed by the cells of the human placenta and chorioamniotic membranes throughout gestation (Figure 2, ZIKV). The expression of CD209 was mainly found in the maternal and fetal macrophage subsets, as expected (Svensson et al., 2011; Swieboda et al., 2020). Yet, the expression of TYRO3 was low (Figure 2—figure supplement 2), consistent with the view that TAM receptors are not essential for ZIKV infection (Hastings et al., 2017). The expression of other viral receptors involved in congenital disease was also documented in the placental tissues (Figure 2—figure supplement 2).

Placenta and decidua scRNA-seq data from first-trimester samples were downloaded through ArrayExpress (E-MTAB-6701). Data for third-trimester samples previously collected by our group are available through NIH dbGAP (accession number phs001886.v2.p1), and newly generated second-trimester scRNA-seq and third-trimester snRNA-seq data are deposited in the same repository.

1. 1. Pique-Regi R

   (2020) dbGaP

   ID phs001886.v2.p1. Single Cell Transcriptional Signatures of the Human Placenta.

   https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001886.v2.p1

1. 1. Vento-Tormo

   (2018) ArrayExpress

   ID E-MTAB-6701. Reconstructing the human first trimester fetal-maternal interface using single cell transcriptomics - 10x data.

   https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6701/

1. 1. Aagaard KM
   2. Lahon A
   3. Suter MA
   4. Arya RP
   5. Seferovic MD
   6. Vogt MB
   7. Hu M
   8. Stossi F
   9. Mancini MA
   10. Harris RA
   11. Kahr M
   12. Eppes C
   13. Rac M
   14. Belfort MA
   15. Park CS
   16. Lacorazza D
   17. Rico-Hesse R

   (2017) Primary human placental trophoblasts are permissive for zika virus (ZIKV) Replication

   Scientific Reports 7:41389.

   https://doi.org/10.1038/srep41389

   • PubMed
   • Google Scholar
2. 1. Adams Waldorf KM
   2. Stencel-Baerenwald JE
   3. Kapur RP
   4. Studholme C
   5. Boldenow E
   6. Vornhagen J
   7. Baldessari A
   8. Dighe MK
   9. Thiel J
   10. Merillat S
   11. Armistead B
   12. Tisoncik-Go J
   13. Green RR
   14. Davis MA
   15. Dewey EC
   16. Fairgrieve MR
   17. Gatenby JC
   18. Richards T
   19. Garden GA
   20. Diamond MS
   21. Juul SE
   22. Grant RF
   23. Kuller L
   24. Shaw DW
   25. Ogle J
   26. Gough GM
   27. Lee W
   28. English C
   29. Hevner RF
   30. Dobyns WB
   31. Gale M
   32. Rajagopal L

   (2016) Fetal brain lesions after subcutaneous inoculation of zika virus in a pregnant nonhuman primate

   Nature Medicine 22:1256–1259.

   https://doi.org/10.1038/nm.4193

   • PubMed
   • Google Scholar
3. 1. Adams Waldorf KM
   2. Nelson BR
   3. Stencel-Baerenwald JE
   4. Studholme C
   5. Kapur RP
   6. Armistead B
   7. Walker CL
   8. Merillat S
   9. Vornhagen J
   10. Tisoncik-Go J
   11. Baldessari A
   12. Coleman M
   13. Dighe MK
   14. Shaw DWW
   15. Roby JA
   16. Santana-Ufret V
   17. Boldenow E
   18. Li J
   19. Gao X
   20. Davis MA
   21. Swanstrom JA
   22. Jensen K
   23. Widman DG
   24. Baric RS
   25. Medwid JT
   26. Hanley KA
   27. Ogle J
   28. Gough GM
   29. Lee W
   30. English C
   31. Durning WM
   32. Thiel J
   33. Gatenby C
   34. Dewey EC
   35. Fairgrieve MR
   36. Hodge RD
   37. Grant RF
   38. Kuller L
   39. Dobyns WB
   40. Hevner RF
   41. Gale M
   42. Rajagopal L

   (2018) Congenital zika virus infection as a silent pathology with loss of neurogenic output in the fetal brain

   Nature Medicine 24:368–374.

   https://doi.org/10.1038/nm.4485

   • PubMed
   • Google Scholar
4. 1. Al-Haddad BJS
   2. Oler E
   3. Armistead B
   4. Elsayed NA
   5. Weinberger DR
   6. Bernier R
   7. Burd I
   8. Kapur R
   9. Jacobsson B
   10. Wang C
   11. Mysorekar I
   12. Rajagopal L
   13. Adams Waldorf KM

   (2019) The fetal origins of mental illness

   American Journal of Obstetrics and Gynecology 221:549–562.

   https://doi.org/10.1016/j.ajog.2019.06.013

   • PubMed
   • Google Scholar
5. 1. Aldo P
   2. You Y
   3. Szigeti K
   4. Horvath TL
   5. Lindenbach B
   6. Mor G

   (2016) HSV-2 enhances ZIKV infection of the placenta and induces apoptosis in first-trimester trophoblast cells

   American Journal of Reproductive Immunology 76:348–357.

   https://doi.org/10.1111/aji.12578

   • PubMed
   • Google Scholar
6. 1. Alexandre J
   2. Cracowski J-L
   3. Richard V
   4. Bouhanick B

   (2020) Renin-angiotensin-aldosterone system and COVID-19 infection

   Annales d'Endocrinologie 81:63–67.

   https://doi.org/10.1016/j.ando.2020.04.005

   • Google Scholar
7. 1. Alserehi H
   2. Wali G
   3. Alshukairi A
   4. Alraddadi B

   (2016) Impact of middle east respiratory syndrome coronavirus (MERS-CoV) on pregnancy and perinatal outcome

   BMC Infectious Diseases 16:105.

   https://doi.org/10.1186/s12879-016-1437-y

   • PubMed
   • Google Scholar
8. Preprint

   1. Alvarez M

   (2019) Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

   bioRxiv.

   https://doi.org/10.1101/786285

   • Google Scholar
9. 1. Alzamora MC
   2. Paredes T
   3. Caceres D
   4. Webb CM
   5. Valdez LM
   6. La Rosa M

   (2020) Severe COVID-19 during pregnancy and possible vertical transmission

   American Journal of Perinatology 37:861–865.

   https://doi.org/10.1055/s-0040-1710050

   • PubMed
   • Google Scholar
10. 1. Ander SE
    2. Diamond MS
    3. Coyne CB

    (2019) Immune responses at the maternal-fetal interface

    Science Immunology 4:eaat6114.

    https://doi.org/10.1126/sciimmunol.aat6114

    • PubMed
    • Google Scholar
11. 1. Arechavaleta-Velasco F
    2. Koi H
    3. Strauss JF
    4. Parry S

    (2002) Viral infection of the trophoblast: time to take a serious look at its role in abnormal implantation and placentation?

    Journal of Reproductive Immunology 55:113–121.

    https://doi.org/10.1016/S0165-0378(01)00143-7

    • PubMed
    • Google Scholar
12. 1. Aronoff DM
    2. Correa H
    3. Rogers LM
    4. Arav‐Boger R
    5. Alcendor DJ

    (2017) Placental pericytes and cytomegalovirus infectivity: Implications for HCMV placental pathology and congenital disease

    American Journal of Reproductive Immunology 78:12728.

    https://doi.org/10.1111/aji.12728

    • Google Scholar
13. 1. Arora N
    2. Sadovsky Y
    3. Dermody TS
    4. Coyne CB

    (2017) Microbial vertical transmission during human pregnancy

    Cell Host & Microbe 21:561–567.

    https://doi.org/10.1016/j.chom.2017.04.007

    • PubMed
    • Google Scholar
14. 1. Ashokka B
    2. Loh MH
    3. Tan CH
    4. Su LL
    5. Young BE
    6. Lye DC
    7. Biswas A
    8. Illanes SE
    9. Choolani M

    (2020) Care of the pregnant woman with coronavirus disease 2019 in labor and delivery: anesthesia, emergency cesarean delivery, differential diagnosis in the acutely ill parturient, care of the newborn, and protection of the healthcare personnel

    American Journal of Obstetrics and Gynecology 223:66–74.

    https://doi.org/10.1016/j.ajog.2020.04.005

    • PubMed
    • Google Scholar
15. 1. Atmar RL
    2. Englund JA
    3. Hammill H

    (1992) Complications of measles during pregnancy

    Clinical Infectious Diseases 14:217–226.

    https://doi.org/10.1093/clinids/14.1.217

    • PubMed
    • Google Scholar
16. 1. Becht E
    2. McInnes L
    3. Healy J
    4. Dutertre C-A
    5. Kwok IWH
    6. Ng LG
    7. Ginhoux F
    8. Newell EW

    (2019) Dimensionality reduction for visualizing single-cell data using UMAP

    Nature Biotechnology 37:38–44.

    https://doi.org/10.1038/nbt.4314

    • Google Scholar
17. 1. Blanco-Melo D
    2. Nilsson-Payant BE
    3. Liu WC
    4. Uhl S
    5. Hoagland D
    6. Møller R
    7. Jordan TX
    8. Oishi K
    9. Panis M
    10. Sachs D
    11. Wang TT
    12. Schwartz RE
    13. Lim JK
    14. Albrecht RA
    15. tenOever BR

    (2020) Imbalanced host response to SARS-CoV-2 drives development of COVID-19

    Cell 181:1036–1045.

    https://doi.org/10.1016/j.cell.2020.04.026

    • PubMed
    • Google Scholar
18. 1. Blitz MJ

    (2020) Maternal mortality among women with COVID-19 admitted to the intensive care unit

    American Journal of Obstetrics and Gynecology 9378:30636-0.

    https://doi.org/10.1016/j.ajog.2020.06.020

    • Google Scholar
19. Preprint

    1. Booeshaghi AS
    2. Pachter L

    (2020) Log(x+1)* and log(1+x)*

    bioRxiv.

    https://doi.org/10.1101/2020.05.19.100214

    • Google Scholar
20. 1. Bray NL
    2. Pimentel H
    3. Melsted P
    4. Pachter L

    (2016) Near-optimal probabilistic RNA-seq quantification

    Nature Biotechnology 34:525–527.

    https://doi.org/10.1038/nbt.3519

    • PubMed
    • Google Scholar
21. 1. Burton GJ
    2. Jauniaux E

    (2015) What is the placenta?

    American Journal of Obstetrics and Gynecology 213:S6.e1–S6.e4.

    https://doi.org/10.1016/j.ajog.2015.07.050

    • Google Scholar
22. 1. Cao B
    2. Parnell LA
    3. Diamond MS
    4. Mysorekar IU

    (2017) Inhibition of autophagy limits vertical transmission of zika virus in pregnant mice

    Journal of Experimental Medicine 214:2303–2313.

    https://doi.org/10.1084/jem.20170957

    • PubMed
    • Google Scholar
23. 1. Carbaugh DL
    2. Baric RS
    3. Lazear HM

    (2019) Envelope protein glycosylation mediates zika virus pathogenesis

    Journal of Virology 93:19.

    https://doi.org/10.1128/JVI.00113-19

    • Google Scholar
24. Software

    1. Centers for Disease Control and Prevention

    (2020a) Centers for Disease Control and Prevention

    Centers for Disease Control and Prevention.

    https://www.cdc.gov/coronavirus/2019-ncov/index.html
25. Software

    1. Centers for Disease Control and Prevention

    (2020b) Centers for Disease Control and Prevention

    Centers for Disease Control and Prevention.

    https://www.cdc.gov/minorityhealth/CHDIReport.html
26. 1. Cervantes-Gonzalez M
    2. Launay O

    (2010) Pandemic influenza A (H1N1) in pregnant women: impact of early diagnosis and antiviral treatment

    Expert Review of Anti-Infective Therapy 8:981–984.

    https://doi.org/10.1586/eri.10.83

    • PubMed
    • Google Scholar
27. Preprint

    1. Chen Y

    (2020) The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Directly decimates human spleens and lymph nodes

    medRxiv.

    https://doi.org/10.1101/2020.03.27.20045427v1

    • Google Scholar
28. 1. Chen L
    2. Li Q
    3. Zheng D
    4. Jiang H
    5. Wei Y
    6. Zou L
    7. Feng L
    8. Xiong G
    9. Sun G
    10. Wang H
    11. Zhao Y
    12. Qiao J

    (2020a) Clinical characteristics of pregnant women with Covid-19 in Wuhan, China

    New England Journal of Medicine 382:e100.

    https://doi.org/10.1056/NEJMc2009226

    • PubMed
    • Google Scholar
29. 1. Chen H
    2. Guo J
    3. Wang C
    4. Luo F
    5. Yu X
    6. Zhang W
    7. Li J
    8. Zhao D
    9. Xu D
    10. Gong Q
    11. Liao J
    12. Yang H
    13. Hou W
    14. Zhang Y

    (2020b) Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women: a retrospective review of medical records

    The Lancet 395:809–815.

    https://doi.org/10.1016/S0140-6736(20)30360-3

    • PubMed
    • Google Scholar
30. 1. Chen Y
    2. Peng H
    3. Wang L
    4. Zhao Y
    5. Zeng L
    6. Gao H
    7. Liu Y

    (2020c) Infants born to mothers with a new coronavirus (COVID-19)

    Frontiers in Pediatrics 8:104.

    https://doi.org/10.3389/fped.2020.00104

    • PubMed
    • Google Scholar
31. 1. Christensen PE
    2. Schmidt H
    3. Bang HO
    4. Amdersen V
    5. Jordal B
    6. Jensen O

    (1954)

    Measles in Virgin Soil, Greenland 1951

    Danish Medical Bulletin 1:2–6.

    • PubMed
    • Google Scholar
32. Software

    1. COVID-19

    (2020) COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU)

    COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU).

    https://coronavirus.jhu.edu/map.html
33. 1. Coyne CB
    2. Lazear HM

    (2016) Zika virus - reigniting the TORCH

    Nature Reviews Microbiology 14:707–715.

    https://doi.org/10.1038/nrmicro.2016.125

    • PubMed
    • Google Scholar
34. 1. Dashraath P
    2. Wong JLJ
    3. Lim MXK
    4. Lim LM
    5. Li S
    6. Biswas A
    7. Choolani M
    8. Mattar C
    9. Su LL

    (2020) Coronavirus disease 2019 (COVID-19) pandemic and pregnancy

    American Journal of Obstetrics and Gynecology 222:521–531.

    https://doi.org/10.1016/j.ajog.2020.03.021

    • PubMed
    • Google Scholar
35. 1. Della Gatta AN
    2. Rizzo R
    3. Pilu G
    4. Simonazzi G

    (2020) Coronavirus disease 2019 during pregnancy: a systematic review of reported cases

    American Journal of Obstetrics and Gynecology 223:36–41.

    https://doi.org/10.1016/j.ajog.2020.04.013

    • PubMed
    • Google Scholar
36. 1. Dong E
    2. Du H
    3. Gardner L

    (2020a) An interactive web-based dashboard to track COVID-19 in real time

    The Lancet Infectious Diseases 20:533–534.

    https://doi.org/10.1016/S1473-3099(20)30120-1

    • PubMed
    • Google Scholar
37. 1. Dong L
    2. Tian J
    3. He S
    4. Zhu C
    5. Wang J
    6. Liu C
    7. Yang J

    (2020b) Possible vertical transmission of SARS-CoV-2 from an infected mother to her newborn

    Jama 323:1846–1848.

    https://doi.org/10.1001/jama.2020.4621

    • Google Scholar
38. 1. Dudley DM
    2. Van Rompay KK
    3. Coffey LL
    4. Ardeshir A
    5. Keesler RI
    6. Bliss-Moreau E
    7. Grigsby PL
    8. Steinbach RJ
    9. Hirsch AJ
    10. MacAllister RP
    11. Pecoraro HL
    12. Colgin LM
    13. Hodge T
    14. Streblow DN
    15. Tardif S
    16. Patterson JL
    17. Tamhankar M
    18. Seferovic M
    19. Aagaard KM
    20. Martín CS
    21. Chiu CY
    22. Panganiban AT
    23. Veazey RS
    24. Wang X
    25. Maness NJ
    26. Gilbert MH
    27. Bohm RP
    28. Adams Waldorf KM
    29. Gale M
    30. Rajagopal L
    31. Hotchkiss CE
    32. Mohr EL
    33. Capuano SV
    34. Simmons HA
    35. Mejia A
    36. Friedrich TC
    37. Golos TG
    38. O'Connor DH

    (2018) Miscarriage and stillbirth following maternal zika virus infection in nonhuman primates

    Nature Medicine 24:1104–1107.

    https://doi.org/10.1038/s41591-018-0088-5

    • PubMed
    • Google Scholar
39. 1. Esmonde TF
    2. Herdman G
    3. Anderson G

    (1989) Chickenpox pneumonia: an association with pregnancy

    Thorax 44:812–815.

    https://doi.org/10.1136/thx.44.10.812

    • PubMed
    • Google Scholar
40. 1. Faure Bardon V
    2. Peytavin G
    3. Lê MP
    4. Guilleminot T
    5. Elefant E
    6. Stirnemann J
    7. Leruez-Ville M
    8. Ville Y

    (2020) Placental transfer of letermovir & maribavir in the ex vivo human Cotyledon perfusion model new perspectives for in utero treatment of congenital Cytomegalovirus infection

    PLOS ONE 15:e0232140.

    https://doi.org/10.1371/journal.pone.0232140

    • PubMed
    • Google Scholar
41. 1. Fitzgerald W
    2. Gomez-Lopez N
    3. Erez O
    4. Romero R
    5. Margolis L

    (2018) Extracellular vesicles generated by placental tissues ex vivo: a transport system for immune mediators and growth factors

    American Journal of Reproductive Immunology 80:e12860.

    https://doi.org/10.1111/aji.12860

    • PubMed
    • Google Scholar
42. 1. Gautier L
    2. Cope L
    3. Bolstad BM
    4. Irizarry RA

    (2004) Affy--analysis of affymetrix GeneChip data at the probe level

    Bioinformatics 20:307–315.

    https://doi.org/10.1093/bioinformatics/btg405

    • PubMed
    • Google Scholar
43. 1. Gordon DE
    2. Jang GM
    3. Bouhaddou M
    4. Xu J
    5. Obernier K
    6. White KM
    7. O’Meara MJ
    8. Rezelj VV
    9. Guo JZ
    10. Swaney DL
    11. Tummino TA
    12. Huettenhain R
    13. Kaake RM
    14. Richards AL
    15. Tutuncuoglu B
    16. Foussard H
    17. Batra J
    18. Haas K
    19. Modak M
    20. Kim M
    21. Haas P
    22. Polacco BJ
    23. Braberg H
    24. Fabius JM
    25. Eckhardt M
    26. Soucheray M
    27. Bennett MJ
    28. Cakir M
    29. McGregor MJ
    30. Li Q
    31. Meyer B
    32. Roesch F
    33. Vallet T
    34. Mac Kain A
    35. Miorin L
    36. Moreno E
    37. Naing ZZC
    38. Zhou Y
    39. Peng S
    40. Shi Y
    41. Zhang Z
    42. Shen W
    43. Kirby IT
    44. Melnyk JE
    45. Chorba JS
    46. Lou K
    47. Dai SA
    48. Barrio-Hernandez I
    49. Memon D
    50. Hernandez-Armenta C
    51. Lyu J
    52. Mathy CJP
    53. Perica T
    54. Pilla KB
    55. Ganesan SJ
    56. Saltzberg DJ
    57. Rakesh R
    58. Liu X
    59. Rosenthal SB
    60. Calviello L
    61. Venkataramanan S
    62. Liboy-Lugo J
    63. Lin Y
    64. Huang X-P
    65. Liu Y
    66. Wankowicz SA
    67. Bohn M
    68. Safari M
    69. Ugur FS
    70. Koh C
    71. Savar NS
    72. Tran QD
    73. Shengjuler D
    74. Fletcher SJ
    75. O’Neal MC
    76. Cai Y
    77. Chang JCJ
    78. Broadhurst DJ
    79. Klippsten S
    80. Sharp PP
    81. Wenzell NA
    82. Kuzuoglu D
    83. Wang H-Y
    84. Trenker R
    85. Young JM
    86. Cavero DA
    87. Hiatt J
    88. Roth TL
    89. Rathore U
    90. Subramanian A
    91. Noack J
    92. Hubert M
    93. Stroud RM
    94. Frankel AD
    95. Rosenberg OS
    96. Verba KA
    97. Agard DA
    98. Ott M
    99. Emerman M
    100. Jura N
    101. von Zastrow M
    102. Verdin E
    103. Ashworth A
    104. Schwartz O
    105. d’Enfert C
    106. Mukherjee S
    107. Jacobson M
    108. Malik HS
    109. Fujimori DG
    110. Ideker T
    111. Craik CS
    112. Floor SN
    113. Fraser JS
    114. Gross JD
    115. Sali A
    116. Roth BL
    117. Ruggero D
    118. Taunton J
    119. Kortemme T
    120. Beltrao P
    121. Vignuzzi M
    122. García-Sastre A
    123. Shokat KM
    124. Shoichet BK
    125. Krogan NJ

    (2020) A SARS-CoV-2 protein interaction map reveals targets for drug repurposing

    Nature 30:2286.

    https://doi.org/10.1038/s41586-020-2286-9

    • Google Scholar
44. 1. Haake DA
    2. Zakowski PC
    3. Haake DL
    4. Bryson YJ

    (1990) Early treatment with acyclovir for varicella pneumonia in otherwise healthy adults: retrospective controlled study and review

    Clinical Infectious Diseases 12:788–798.

    https://doi.org/10.1093/clinids/12.5.788

    • PubMed
    • Google Scholar
45. 1. Habib N
    2. Avraham-Davidi I
    3. Basu A
    4. Burks T
    5. Shekhar K
    6. Hofree M
    7. Choudhury SR
    8. Aguet F
    9. Gelfand E
    10. Ardlie K
    11. Weitz DA
    12. Rozenblatt-Rosen O
    13. Zhang F
    14. Regev A

    (2017) Massively parallel single-nucleus RNA-seq with DroNc-seq

    Nature Methods 14:955–958.

    https://doi.org/10.1038/nmeth.4407

    • PubMed
    • Google Scholar
46. 1. Hafemeister C
    2. Satija R

    (2019) Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression

    Genome Biology 20:296.

    https://doi.org/10.1186/s13059-019-1874-1

    • PubMed
    • Google Scholar
47. 1. Hantoushzadeh S
    2. Shamshirsaz AA
    3. Aleyasin A
    4. Seferovic MD
    5. Aski SK
    6. Arian SE
    7. Pooransari P
    8. Ghotbizadeh F
    9. Aalipour S
    10. Soleimani Z
    11. Naemi M
    12. Molaei B
    13. Ahangari R
    14. Salehi M
    15. Oskoei AD
    16. Pirozan P
    17. Darkhaneh RF
    18. Laki MG
    19. Farani AK
    20. Atrak S
    21. Miri MM
    22. Kouchek M
    23. Shojaei S
    24. Hadavand F
    25. Keikha F
    26. Hosseini MS
    27. Borna S
    28. Ariana S
    29. Shariat M
    30. Fatemi A
    31. Nouri B
    32. Nekooghadam SM
    33. Aagaard K

    (2020) Maternal death due to COVID-19

    American Journal of Obstetrics and Gynecology 223:109.

    https://doi.org/10.1016/j.ajog.2020.04.030

    • PubMed
    • Google Scholar
48. 1. Hastings AK
    2. Yockey LJ
    3. Jagger BW
    4. Hwang J
    5. Uraki R
    6. Gaitsch HF
    7. Parnell LA
    8. Cao B
    9. Mysorekar IU
    10. Rothlin CV
    11. Fikrig E
    12. Diamond MS
    13. Iwasaki A

    (2017) TAM receptors are not required for zika virus infection in mice

    Cell Reports 19:558–568.

    https://doi.org/10.1016/j.celrep.2017.03.058

    • PubMed
    • Google Scholar
49. 1. HCA Lung Biological Network
    2. Sungnak W
    3. Huang N
    4. Bécavin C
    5. Berg M
    6. Queen R
    7. Litvinukova M
    8. Talavera-López C
    9. Maatz H
    10. Reichart D
    11. Sampaziotis F
    12. Worlock KB
    13. Yoshida M
    14. Barnes JL

    (2020) SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes

    Nature Medicine 26:681–687.

    https://doi.org/10.1038/s41591-020-0868-6

    • PubMed
    • Google Scholar
50. 1. Herse F
    2. Dechend R
    3. Harsem NK
    4. Wallukat G
    5. Janke J
    6. Qadri F
    7. Hering L
    8. Muller DN
    9. Luft FC
    10. Staff AC

    (2007) Dysregulation of the circulating and tissue-based renin-angiotensin system in preeclampsia

    Hypertension 49:604–611.

    https://doi.org/10.1161/01.HYP.0000257797.49289.71

    • PubMed
    • Google Scholar
51. 1. Heurich A
    2. Hofmann-Winkler H
    3. Gierer S
    4. Liepold T
    5. Jahn O
    6. Pöhlmann S

    (2014) TMPRSS2 and ADAM17 cleave ACE2 differentially and only proteolysis by TMPRSS2 augments entry driven by the severe acute respiratory syndrome coronavirus spike protein

    Journal of Virology 88:1293–1307.

    https://doi.org/10.1128/JVI.02202-13

    • PubMed
    • Google Scholar
52. 1. Hoffmann M
    2. Kleine-Weber H
    3. Schroeder S
    4. Krüger N
    5. Herrler T
    6. Erichsen S
    7. Schiergens TS
    8. Herrler G
    9. Wu NH
    10. Nitsche A
    11. Müller MA
    12. Drosten C
    13. Pöhlmann S

    (2020) SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor

    Cell 181:271–280.

    https://doi.org/10.1016/j.cell.2020.02.052

    • PubMed
    • Google Scholar
53. 1. Hosier H

    (2020) SARS-CoV-2 infection of the placenta

    The Journal of Clinical Investigation 23:JCI139569.

    https://doi.org/10.1172/JCI139569

    • Google Scholar
54. 1. Iwata-Yoshikawa N
    2. Okamura T
    3. Shimizu Y
    4. Hasegawa H
    5. Takeda M
    6. Nagata N

    (2019) TMPRSS2 contributes to virus spread and immunopathology in the airways of murine models after coronavirus infection

    Journal of Virology 93:e01815-18.

    https://doi.org/10.1128/JVI.01815-18

    • PubMed
    • Google Scholar
55. 1. Jamieson DJ
    2. Theiler RN
    3. Rasmussen SA

    (2006) Emerging infections and pregnancy

    Emerging Infectious Diseases 12:1638–1643.

    https://doi.org/10.3201/eid1211.060152

    • PubMed
    • Google Scholar
56. 1. Jeong SY
    2. Sung SI
    3. Sung JH
    4. Ahn SY
    5. Kang ES
    6. Chang YS
    7. Park WS
    8. Kim JH

    (2017) MERS-CoV infection in a pregnant woman in Korea

    Journal of Korean Medical Science 32:1717–1720.

    https://doi.org/10.3346/jkms.2017.32.10.1717

    • PubMed
    • Google Scholar
57. 1. Kim MJ
    2. Romero R
    3. Kim CJ
    4. Tarca AL
    5. Chhauy S
    6. LaJeunesse C
    7. Lee DC
    8. Draghici S
    9. Gotsch F
    10. Kusanovic JP
    11. Hassan SS
    12. Kim JS

    (2009) 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

    The Journal of Immunology 182:3919–3927.

    https://doi.org/10.4049/jimmunol.0803834

    • PubMed
    • Google Scholar
58. 1. Kim CJ
    2. Romero R
    3. Chaemsaithong P
    4. Chaiyasit N
    5. Yoon BH
    6. Kim YM

    (2015) Acute chorioamnionitis and funisitis: definition, pathologic features, and clinical significance

    American Journal of Obstetrics and Gynecology 213:S29–S52.

    https://doi.org/10.1016/j.ajog.2015.08.040

    • PubMed
    • Google Scholar
59. 1. Koi H
    2. Zhang J
    3. Makrigiannakis A
    4. Getsios S
    5. MacCalman CD
    6. Strauss JF
    7. Parry S

    (2002) Syncytiotrophoblast is a barrier to maternal-fetal transmission of herpes simplex virus

    Biology of Reproduction 67:1572–1579.

    https://doi.org/10.1095/biolreprod.102.004325

    • PubMed
    • Google Scholar
60. 1. Korsunsky I
    2. Millard N
    3. Fan J
    4. Slowikowski K
    5. Zhang F
    6. Wei K
    7. Baglaenko Y
    8. Brenner M
    9. Loh PR
    10. Raychaudhuri S

    (2019) Fast, sensitive and accurate integration of single-cell data with harmony

    Nature Methods 16:1289–1296.

    https://doi.org/10.1038/s41592-019-0619-0

    • PubMed
    • Google Scholar
61. 1. Kwon JY
    2. Romero R
    3. Mor G

    (2014) New insights into the relationship between viral infection and pregnancy complications

    American Journal of Reproductive Immunology 71:387–390.

    https://doi.org/10.1111/aji.12243

    • PubMed
    • Google Scholar
62. 1. Lamouroux A
    2. Attie-Bitach T
    3. Martinovic J
    4. Leruez-Ville M
    5. Ville Y

    (2020) Evidence for and against vertical transmission for severe acute respiratory syndrome coronavirus 2

    American Journal of Obstetrics and Gynecology 223:91–9191.

    https://doi.org/10.1016/j.ajog.2020.04.039

    • PubMed
    • Google Scholar
63. 1. Li Y
    2. Zhao R
    3. Zheng S
    4. Chen X
    5. Wang J
    6. Sheng X
    7. Zhou J
    8. Cai H
    9. Fang Q
    10. Yu F
    11. Fan J
    12. Xu K
    13. Chen Y
    14. Sheng J

    (2020) Lack of vertical transmission of severe acute respiratory syndrome coronavirus 2, China

    Emerging Infectious Diseases 26:1335–1336.

    https://doi.org/10.3201/eid2606.200287

    • PubMed
    • Google Scholar
64. 1. Lindsay L
    2. Jackson LA
    3. Savitz DA
    4. Weber DJ
    5. Koch GG
    6. Kong L
    7. Guess HA

    (2006) Community influenza activity and risk of acute influenza-like illness episodes among healthy unvaccinated pregnant and postpartum women

    American Journal of Epidemiology 163:838–848.

    https://doi.org/10.1093/aje/kwj095

    • PubMed
    • Google Scholar
65. 1. Liu Y
    2. Chen H
    3. Tang K
    4. Guo Y

    (2020) Clinical manifestations and outcome of SARS-CoV-2 infection during pregnancy

    Journal of Infection 4:28.

    https://doi.org/10.1016/j.jinf.2020.02.028

    • Google Scholar
66. 1. Lokken EM

    (2020) Clinical characteristics of 46 pregnant women with a SARS-CoV-2 infection in Washington state

    American Journal of Obstetrics and Gynecology 19:31.

    https://doi.org/10.1016/j.ajog.2020.05.031

    • Google Scholar
67. 1. Lukassen S
    2. Chua RL
    3. Trefzer T
    4. Kahn NC
    5. Schneider MA
    6. Muley T
    7. Winter H
    8. Meister M
    9. Veith C
    10. Boots AW
    11. Hennig BP
    12. Kreuter M
    13. Conrad C
    14. Eils R

    (2020) SARS-CoV-2 receptor ACE2 and TMPRSS2 are primarily expressed in bronchial transient secretory cells

    The EMBO Journal 39:e105114.

    https://doi.org/10.15252/embj.20105114

    • PubMed
    • Google Scholar
68. 1. Maltepe E
    2. Fisher SJ

    (2015) Placenta: the forgotten organ

    Annual Review of Cell and Developmental Biology 31:523–552.

    https://doi.org/10.1146/annurev-cellbio-100814-125620

    • PubMed
    • Google Scholar
69. 1. Martinez-Martin N
    2. Marcandalli J
    3. Huang CS
    4. Arthur CP
    5. Perotti M
    6. Foglierini M
    7. Ho H
    8. Dosey AM
    9. Shriver S
    10. Payandeh J
    11. Leitner A
    12. Lanzavecchia A
    13. Perez L
    14. Ciferri C

    (2018) An unbiased screen for human Cytomegalovirus identifies Neuropilin-2 as a central viral receptor

    Cell 174:1158–1171.

    https://doi.org/10.1016/j.cell.2018.06.028

    • Google Scholar
70. Preprint

    1. McInnes L
    2. Healy J
    3. Melville J

    (2020) UMAP: uniform manifold approximation and projection for dimension reduction

    arXiv.

    https://arxiv.org/abs/1802.03426

    • Google Scholar
71. Preprint

    1. Melsted P

    (2019) Modular and efficient pre-processing of single-cell RNA-seq

    bioRxiv.

    https://doi.org/10.1101/673285

    • Google Scholar
72. 1. Mosby LG
    2. Rasmussen SA
    3. Jamieson DJ

    (2011) 2009 pandemic influenza A (H1N1) in pregnancy: a systematic review of the literature

    American Journal of Obstetrics and Gynecology 205:10–18.

    https://doi.org/10.1016/j.ajog.2010.12.033

    • Google Scholar
73. 1. Muehlenbachs A
    2. de la Rosa Vázquez O
    3. Bausch DG
    4. Schafer IJ
    5. Paddock CD
    6. Nyakio JP
    7. Lame P
    8. Bergeron E
    9. McCollum AM
    10. Goldsmith CS
    11. Bollweg BC
    12. Prieto MA
    13. Lushima RS
    14. Ilunga BK
    15. Nichol ST
    16. Shieh WJ
    17. Ströher U
    18. Rollin PE
    19. Zaki SR

    (2017) Ebola virus disease in pregnancy: clinical, Histopathologic, and immunohistochemical findings

    Journal of Infectious Diseases 215:64–69.

    https://doi.org/10.1093/infdis/jiw206

    • PubMed
    • Google Scholar
74. Preprint

    1. Muus C

    (2020) Integrated analyses of single-cell atlases reveal age, gender, and smoking status associations with cell type-specific expression of mediators of SARS-CoV-2 viral entry and highlights inflammatory programs in putative target cells

    bioRxiv.

    https://doi.org/10.1101/2020.04.19.049254C

    • Google Scholar
75. 1. Mysorekar IU

    (2017)

    Zika virus takes a transplacental route to infect fetuses: insights from an animal model

    Missouri Medicine 114:168–170.

    • PubMed
    • Google Scholar
76. 1. Nelson BR
    2. Roby JA
    3. Dobyns WB
    4. Rajagopal L
    5. Gale M
    6. Adams Waldorf KM

    (2020) Immune evasion strategies used by zika virus to infect the fetal eye and brain

    Viral Immunology 33:22–37.

    https://doi.org/10.1089/vim.2019.0082

    • PubMed
    • Google Scholar
77. 1. Neuzil KM
    2. Reed GW
    3. Mitchel EF
    4. Simonsen L
    5. Griffin MR

    (1998) Impact of influenza on acute cardiopulmonary hospitalizations in pregnant women

    American Journal of Epidemiology 148:1094–1102.

    https://doi.org/10.1093/oxfordjournals.aje.a009587

    • PubMed
    • Google Scholar
78. 1. Ng WF
    2. Wong SF
    3. Lam A
    4. Mak YF
    5. Yao H
    6. Lee KC
    7. Chow KM
    8. Yu WC
    9. Ho LC

    (2006) The placentas of patients with severe acute respiratory syndrome: a pathophysiological evaluation

    Pathology 38:210–218.

    https://doi.org/10.1080/00313020600696280

    • PubMed
    • Google Scholar
79. 1. Olgun NS

    (2018) Viral infections in pregnancy: a focus on ebola virus

    Current Pharmaceutical Design 24:993–998.

    https://doi.org/10.2174/1381612824666180130121946

    • PubMed
    • Google Scholar
80. 1. Oliveira LG
    2. Peron JPS

    (2019) Viral receptors for flaviviruses: not only gatekeepers

    Journal of Leukocyte Biology 106:695–701.

    https://doi.org/10.1002/JLB.MR1118-460R

    • PubMed
    • Google Scholar
81. 1. Pandemic H1N1 Influenza in Pregnancy Working Group
    2. Siston AM
    3. Rasmussen SA
    4. Honein MA
    5. Fry AM
    6. Seib K
    7. Callaghan WM
    8. Louie J
    9. Doyle TJ
    10. Crockett M
    11. Lynfield R
    12. Moore Z
    13. Wiedeman C
    14. Anand M
    15. Tabony L
    16. Nielsen CF
    17. Waller K
    18. Page S
    19. Thompson JM
    20. Avery C
    21. Springs CB
    22. Jones T
    23. Williams JL
    24. Newsome K
    25. Finelli L
    26. Jamieson DJ

    (2010) Pandemic 2009 influenza A(H1N1) virus illness among pregnant women in the united states

    JAMA 303:1517–1525.

    https://doi.org/10.1001/jama.2010.479

    • PubMed
    • Google Scholar
82. 1. Park MD

    (2020) Macrophages: a trojan horse in COVID-19?

    Nature Reviews Immunology 20:351.

    https://doi.org/10.1038/s41577-020-0317-2

    • PubMed
    • Google Scholar
83. 1. Parry S
    2. Holder J
    3. Strauss JF

    (1997) Mechanisms of trophoblast-virus interaction

    Journal of Reproductive Immunology 37:25–34.

    https://doi.org/10.1016/S0165-0378(97)00071-5

    • PubMed
    • Google Scholar
84. 1. Paryani SG
    2. Arvin AM

    (1986) Intrauterine infection with varicella-zoster virus after maternal varicella

    New England Journal of Medicine 314:1542–1546.

    https://doi.org/10.1056/NEJM198606123142403

    • PubMed
    • Google Scholar
85. 1. Pazos M
    2. Sperling RS
    3. Moran TM
    4. Kraus TA

    (2012) The influence of pregnancy on systemic immunity

    Immunologic Research 54:254–261.

    https://doi.org/10.1007/s12026-012-8303-9

    • PubMed
    • Google Scholar
86. 1. Pereira L
    2. Tabata T
    3. Petitt M
    4. Fang-Hoover J

    (2017) Congenital Cytomegalovirus infection undermines early development and functions of the human placenta

    Placenta 59:S8–S16.

    https://doi.org/10.1016/j.placenta.2017.04.020

    • PubMed
    • Google Scholar
87. 1. Persaud M
    2. Martinez-Lopez A
    3. Buffone C
    4. Porcelli SA
    5. Diaz-Griffero F

    (2018) Infection by zika viruses requires the transmembrane protein AXL, endocytosis and low pH

    Virology 518:301–312.

    https://doi.org/10.1016/j.virol.2018.03.009

    • PubMed
    • Google Scholar
88. 1. Pique-Regi R
    2. Romero R
    3. Tarca AL
    4. Sendler ED
    5. Xu Y
    6. Garcia-Flores V
    7. Leng Y
    8. Luca F
    9. Hassan SS
    10. Gomez-Lopez N

    (2019) Single cell transcriptional signatures of the human placenta in term and preterm parturition

    eLife 8:e52004.

    https://doi.org/10.7554/eLife.52004

    • PubMed
    • Google Scholar
89. Software

    1. Pique-Regi R

    (2020) sclabor, version cbf2f89

    GitHub.

    https://github.com/piquelab/sclabor
90. 1. Raj RS
    2. Bonney EA
    3. Phillippe M

    (2014) Influenza, immune system, and pregnancy

    Reproductive Sciences 21:1434–1451.

    https://doi.org/10.1177/1933719114537720

    • PubMed
    • Google Scholar
91. 1. Rasmussen SA
    2. Smulian JC
    3. Lednicky JA
    4. Wen TS
    5. Jamieson DJ

    (2020) Coronavirus disease 2019 (COVID-19) and pregnancy: what obstetricians need to know

    American Journal of Obstetrics and Gynecology 222:415–426.

    https://doi.org/10.1016/j.ajog.2020.02.017

    • PubMed
    • Google Scholar
92. 1. Richard AS
    2. Shim BS
    3. Kwon YC
    4. Zhang R
    5. Otsuka Y
    6. Schmitt K
    7. Berri F
    8. Diamond MS
    9. Choe H

    (2017) AXL-dependent infection of human fetal endothelial cells distinguishes zika virus from other pathogenic flaviviruses

    PNAS 114:2024–2029.

    https://doi.org/10.1073/pnas.1620558114

    • PubMed
    • Google Scholar
93. 1. Sasaki K
    2. Norwitz ER

    (2011) Gonadotropin-releasing hormone/gonadotropin-releasing hormone receptor signaling in the placenta

    Current Opinion in Endocrinology & Diabetes and Obesity 18:401–408.

    https://doi.org/10.1097/MED.0b013e32834cd3b0

    • PubMed
    • Google Scholar
94. 1. Shang J
    2. Ye G
    3. Shi K
    4. Wan Y
    5. Luo C
    6. Aihara H
    7. Geng Q
    8. Auerbach A
    9. Li F

    (2020) Structural basis of receptor recognition by SARS-CoV-2

    Nature 581:221–224.

    https://doi.org/10.1038/s41586-020-2179-y

    • Google Scholar
95. 1. Shirato K
    2. Kanou K
    3. Kawase M
    4. Matsuyama S

    (2017) Clinical isolates of human coronavirus 229E bypass the endosome for cell entry

    Journal of Virology 91:16.

    https://doi.org/10.1128/JVI.01387-16

    • Google Scholar
96. 1. Shirato K
    2. Kawase M
    3. Matsuyama S

    (2018) Wild-type human coronaviruses prefer cell-surface TMPRSS2 to endosomal cathepsins for cell entry

    Virology 517:9–15.

    https://doi.org/10.1016/j.virol.2017.11.012

    • PubMed
    • Google Scholar
97. 1. Siberry GK
    2. Reddy UM
    3. Mofenson LM

    (2020) SARS-COV-2 Maternal-Child transmission: can it occur before delivery and how do we prove it?

    The Pediatric Infectious Disease Journal 10:2820.

    https://doi.org/10.1097/INF.0000000000002820

    • Google Scholar
98. 1. Stegmann BJ
    2. Carey JC

    (2002)

    TORCH infections toxoplasmosis, other (syphilis, varicella-zoster, parvovirus B19), Rubella, Cytomegalovirus (CMV), and Herpes infections

    Current Women's Health Reports 2:253–258.

    • PubMed
    • Google Scholar
99. 1. Stein KR
    2. Gardner TJ
    3. Hernandez RE
    4. Kraus TA
    5. Duty JA
    6. Ubarretxena-Belandia I
    7. Moran TM
    8. Tortorella D

    (2019) CD46 facilitates entry and dissemination of human Cytomegalovirus

    Nature Communications 10:2699.

    https://doi.org/10.1038/s41467-019-10587-1

    • PubMed
    • Google Scholar
100. 1. Stower H

     (2020) Lack of maternal–fetal SARS-CoV-2 transmission

     Nature Medicine 26:312.

     https://doi.org/10.1038/s41591-020-0810-y

     • Google Scholar
101. 1. Stuart T
     2. Butler A
     3. Hoffman P
     4. Hafemeister C
     5. Papalexi E
     6. Mauck WM
     7. Hao Y
     8. Stoeckius M
     9. Smibert P
     10. Satija R

     (2019) Comprehensive integration of Single-Cell data

     Cell 177:1888–1902.

     https://doi.org/10.1016/j.cell.2019.05.031

     • PubMed
     • Google Scholar
102. 1. Sutton D
     2. Fuchs K
     3. D'Alton M
     4. Goffman D

     (2020) Universal screening for SARS-CoV-2 in women admitted for delivery

     New England Journal of Medicine 382:2163–2164.

     https://doi.org/10.1056/NEJMc2009316

     • PubMed
     • Google Scholar
103. 1. Svensson J
     2. Jenmalm MC
     3. Matussek A
     4. Geffers R
     5. Berg G
     6. Ernerudh J

     (2011) Macrophages at the fetal-maternal interface express markers of alternative activation and are induced by M-CSF and IL-10

     The Journal of Immunology 187:3671–3682.

     https://doi.org/10.4049/jimmunol.1100130

     • PubMed
     • Google Scholar
104. 1. Swamy GK
     2. Dotters-Katz SK

     (2019) Safety and varicella outcomes after varicella zoster immune globulin administration in pregnancy

     American Journal of Obstetrics and Gynecology 221:655–656.

     https://doi.org/10.1016/j.ajog.2019.07.003

     • PubMed
     • Google Scholar
105. 1. Swieboda D
     2. Johnson EL
     3. Beaver J
     4. Haddad L
     5. Enninga EAL
     6. Hathcock M
     7. Cordes S
     8. Jean V
     9. Lane I
     10. Skountzou I
     11. Chakraborty R

     (2020) Baby's First Macrophage: Temporal Regulation of Hofbauer Cell Phenotype Influences Ligand-Mediated Innate Immune Responses across Gestation

     The Journal of Immunology 204:2380–2391.

     https://doi.org/10.4049/jimmunol.1901185

     • PubMed
     • Google Scholar
106. 1. Taglauer ES
     2. Wilkins-Haug L
     3. Bianchi DW

     (2014) Review: cell-free fetal DNA in the maternal circulation as an indication of placental health and disease

     Placenta 35:S64–S68.

     https://doi.org/10.1016/j.placenta.2013.11.014

     • PubMed
     • Google Scholar
107. 1. Tekbali A

     (2020) Pregnant versus non-pregnant SARS-CoV-2 and COVID-19 hospital admissions: the first 4 weeks in New York

     American Journal of Obstetrics and Gynecology 223:126–127.

     https://doi.org/10.1016/j.ajog.2020.04.012

     • Google Scholar
108. 1. Toft JH
     2. Lian IA
     3. Tarca AL
     4. Erez O
     5. Espinoza J
     6. Eide IP
     7. Bjørge L
     8. Draghici S
     9. Romero R
     10. Austgulen R

     (2008) Whole-genome microarray and targeted analysis of angiogenesis-regulating gene expression (ENG, FLT1, VEGF, PlGF) in placentas from pre-eclamptic and small-for-gestational-age pregnancies

     The Journal of Maternal-Fetal & Neonatal Medicine 21:267–273.

     https://doi.org/10.1080/14767050801924118

     • PubMed
     • Google Scholar
109. 1. Triebwasser JH
     2. Harris RE
     3. Bryant RE
     4. Rhoades ER

     (1967)

     Varicella pneumonia in adults report of seven cases and a review of literature

     Medicine 46:409–423.

     • PubMed
     • Google Scholar
110. 1. Tsang JCH
     2. Vong JSL
     3. Ji L
     4. Poon LCY
     5. Jiang P
     6. Lui KO
     7. Ni YB
     8. To KF
     9. Cheng YKY
     10. Chiu RWK
     11. Lo YMD

     (2017) Integrative single-cell and cell-free plasma RNA transcriptomics elucidates placental cellular dynamics

     PNAS 114:E7786–E7795.

     https://doi.org/10.1073/pnas.1710470114

     • PubMed
     • Google Scholar
111. 1. Ulrich H
     2. Pillat MM

     (2020) CD147 as a target for COVID-19 treatment: suggested effects of azithromycin and stem cell engagement

     Stem Cell Reviews and Reports 20:1–17.

     https://doi.org/10.1007/s12015-020-09976-7

     • Google Scholar
112. 1. Valentine GC
     2. Seferovic MD
     3. Fowler SW
     4. Major AM
     5. Gorchakov R
     6. Berry R
     7. Swennes AG
     8. Murray KO
     9. Suter MA
     10. Aagaard KM

     (2018) Timing of gestational exposure to zika virus is associated with postnatal growth restriction in a murine model

     American Journal of Obstetrics and Gynecology 219:403.

     https://doi.org/10.1016/j.ajog.2018.06.005

     • PubMed
     • Google Scholar
113. 1. Vento-Tormo R
     2. Efremova M
     3. Botting RA
     4. Turco MY
     5. Vento-Tormo M
     6. Meyer KB
     7. Park JE
     8. Stephenson E
     9. Polański K
     10. Goncalves A
     11. Gardner L
     12. Holmqvist S
     13. Henriksson J
     14. Zou A
     15. Sharkey AM
     16. Millar B
     17. Innes B
     18. Wood L
     19. Wilbrey-Clark A
     20. Payne RP
     21. Ivarsson MA
     22. Lisgo S
     23. Filby A
     24. Rowitch DH
     25. Bulmer JN
     26. Wright GJ
     27. Stubbington MJT
     28. Haniffa M
     29. Moffett A
     30. Teichmann SA

     (2018) Single-cell reconstruction of the early maternal-fetal interface in humans

     Nature 563:347–353.

     https://doi.org/10.1038/s41586-018-0698-6

     • PubMed
     • Google Scholar
114. 1. Vintzileos WS

     (2020) Screening all pregnant women admitted to labor and delivery for the virus responsible for coronavirus disease

     American Journal of Obstetrics and Gynecology 2019:24.

     https://doi.org/10.1016/j.ajog.2020.04.024

     • Google Scholar
115. Software

     1. Wadman M

     (2020)

     How does coronavirus kill? Clinicians trace a ferocious rampage through the body, from brain to toes

     How does coronavirus kill? Clinicians trace a ferocious rampage through the body, from brain to toes.
116. 1. Walker CL
     2. Little ME
     3. Roby JA
     4. Armistead B
     5. Gale M
     6. Rajagopal L
     7. Nelson BR
     8. Ehinger N
     9. Mason B
     10. Nayeri U
     11. Curry CL
     12. Adams Waldorf KM

     (2019) Zika virus and the nonmicrocephalic fetus: why we should still worry

     American Journal of Obstetrics and Gynecology 220:45–56.

     https://doi.org/10.1016/j.ajog.2018.08.035

     • PubMed
     • Google Scholar
117. 1. Wang Q
     2. Zhang Y
     3. Wu L
     4. Niu S
     5. Song C
     6. Zhang Z
     7. Lu G
     8. Qiao C
     9. Hu Y
     10. Yuen KY
     11. Wang Q
     12. Zhou H
     13. Yan J
     14. Qi J

     (2020a) Structural and functional basis of SARS-CoV-2 entry by using human ACE2

     Cell 181:894–904.

     https://doi.org/10.1016/j.cell.2020.03.045

     • PubMed
     • Google Scholar
118. Preprint

     1. Wang A

     (2020b) Single nucleus multiomic profiling reveals Age-Dynamic regulation of host genes associated with SARS-CoV-2 infection

     bioRxiv.

     https://doi.org/10.1101/2020.04.12.037580

     • Google Scholar
119. Preprint

     1. Wang K

     (2020c) SARS-CoV-2 invades host cells via a novel route: cd147-spike protein

     bioRxiv.

     https://doi.org/10.1101/2020.03.14.988345

     • Google Scholar
120. 1. Weber LeBrun EE
     2. Moawad NS
     3. Rosenberg EI
     4. Morey TE
     5. Davies L
     6. Collins WO
     7. Smulian JC

     (2020) COVID-19 pandemic: staged management of surgical services for gynecology and obstetrics

     American Journal of Obstetrics and Gynecology 223:85.

     https://doi.org/10.1016/j.ajog.2020.03.038

     • PubMed
     • Google Scholar
121. 1. Wickham H

     (2011) ggplot2

     Wiley Interdisciplinary Reviews: Computational Statistics 3:180–185.

     https://doi.org/10.1002/wics.147

     • Google Scholar
122. 1. Wong SF
     2. Chow KM
     3. Leung TN
     4. Ng WF
     5. Ng TK
     6. Shek CC
     7. Ng PC
     8. Lam PW
     9. Ho LC
     10. To WW
     11. Lai ST
     12. Yan WW
     13. Tan PY

     (2004) Pregnancy and perinatal outcomes of women with severe acute respiratory syndrome

     American Journal of Obstetrics and Gynecology 191:292–297.

     https://doi.org/10.1016/j.ajog.2003.11.019

     • PubMed
     • Google Scholar
123. 1. Wu H
     2. Kirita Y
     3. Donnelly EL
     4. Humphreys BD

     (2019) Advantages of Single-Nucleus over Single-Cell RNA sequencing of adult kidney: rare cell types and novel cell states revealed in fibrosis

     Journal of the American Society of Nephrology 30:23–32.

     https://doi.org/10.1681/ASN.2018090912

     • PubMed
     • Google Scholar
124. 1. Xu Y
     2. Plazyo O
     3. Romero R
     4. Hassan SS
     5. Gomez-Lopez N

     (2015) Isolation of leukocytes from the human Maternal-fetal interface

     Journal of Visualized Experiments 99:e52863.

     https://doi.org/10.3791/52863

     • Google Scholar
125. 1. Yan J
     2. Guo J
     3. Fan C
     4. Juan J
     5. Yu X
     6. Li J
     7. Feng L
     8. Li C
     9. Chen H
     10. Qiao Y
     11. Lei D
     12. Wang C
     13. Xiong G
     14. Xiao F
     15. He W
     16. Pang Q
     17. Hu X
     18. Wang S
     19. Chen D
     20. Zhang Y
     21. Poon LC
     22. Yang H

     (2020) Coronavirus disease 2019 in pregnant women: a report based on 116 cases

     American Journal of Obstetrics and Gynecology 223:e14.

     https://doi.org/10.1016/j.ajog.2020.04.014

     • Google Scholar
126. 1. Yu N
     2. Li W
     3. Kang Q
     4. Xiong Z
     5. Wang S
     6. Lin X
     7. Liu Y
     8. Xiao J
     9. Liu H
     10. Deng D
     11. Chen S
     12. Zeng W
     13. Feng L
     14. Wu J

     (2020) Clinical features and obstetric and neonatal outcomes of pregnant patients with COVID-19 in Wuhan, China: a retrospective, single-centre, descriptive study

     The Lancet Infectious Diseases 20:559–564.

     https://doi.org/10.1016/S1473-3099(20)30176-6

     • PubMed
     • Google Scholar
127. 1. Zamaniyan M

     (2020) Preterm delivery in pregnant woman with critical COVID-19 pneumonia and vertical transmission

     Prenatal Diagnosis 7:5713.

     https://doi.org/10.1002/pd.5713

     • Google Scholar
128. 1. Zeng L
     2. Xia S
     3. Yuan W
     4. Yan K
     5. Xiao F
     6. Shao J
     7. Zhou W

     (2020a) Neonatal Early-Onset infection with SARS-CoV-2 in 33 neonates born to mothers with COVID-19 in Wuhan, China

     JAMA Pediatrics 174:722–725.

     https://doi.org/10.1001/jamapediatrics.2020.0878

     • Google Scholar
129. 1. Zeng H
     2. Xu C
     3. Fan J
     4. Tang Y
     5. Deng Q
     6. Zhang W
     7. Long X

     (2020b) Antibodies in infants born to mothers with COVID-19 pneumonia

     Jama 323:1848–1849.

     https://doi.org/10.1001/jama.2020.4861

     • Google Scholar
130. 1. Zheng Q-L
     2. Duan T
     3. Jin L-P

     (2020) Single-cell RNA expression profiling of ACE2 and AXL in the human maternal–Fetal interface

     Reproductive and Developmental Medicine 4:7–10.

     https://doi.org/10.4103/2096-2924.278679

     • Google Scholar

Author details

1. Roger Pique-Regi

   1. 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, Detroit, United States
   2. Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, United States
   3. Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, United States

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Supervision, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   rpique@wayne.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1262-2275

2. Roberto Romero

   1. 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, Detroit, United States
   2. Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, United States
   3. Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, United States
   4. Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, United States
   5. Detroit Medical Center, Detroit, United States
   6. Department of Obstetrics and Gynecology, Florida International University, Miami, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   prbchiefstaff@med.wayne.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4448-5121

3. Adi L Tarca

   1. 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, Detroit, United States
   2. Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, United States
   3. Department of Computer Science, Wayne State University College of Engineering, Detroit, United States

   Contribution

   Resources, Data curation, Formal analysis, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1712-7588

4. Francesca Luca

   1. Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, United States
   2. Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, United States

   Contribution

   Resources, Data curation, Supervision, Investigation, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8252-9052

5. Yi Xu

   1. 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, Detroit, United States
   2. Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, United States

   Contribution

   Resources, Data curation, Formal analysis, Validation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

6. Adnan Alazizi

   Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, United States

   Contribution

   Data curation, Validation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

7. Yaozhu Leng

   1. 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, Detroit, United States
   2. Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, United States

   Contribution

   Resources, Data curation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

8. Chaur-Dong Hsu

   1. 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, Detroit, United States
   2. Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, United States
   3. Department of Physiology, Wayne State University School of Medicine, Detroit, United States

   Contribution

   Resources, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

9. Nardhy Gomez-Lopez

   1. 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, Detroit, United States
   2. Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, United States
   3. Department of Biochemistry, Microbiology and Immunology, Wayne State University School of Medicine, Detroit, United States

   Contribution

   Conceptualization, Data curation, Supervision, Investigation, Writing - original draft, Writing - review and editing

   For correspondence

   ngomezlo@med.wayne.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3406-5262

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