Regionally distinct trophoblast regulate barrier function and invasion in the human placenta

  1. Bryan Marsh
  2. Yan Zhou
  3. Mirhan Kapidzic
  4. Susan Fisher  Is a corresponding author
  5. Robert Blelloch  Is a corresponding author
  1. The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell, University of California, San Francisco, United States
  2. Department of Urology, University of California, San Francisco, United States
  3. Center for Reproductive Sciences, University of California, San Francisco, United States
  4. Developmental and Stem Cell Biology Graduate Program, University of California, San Francisco, United States
  5. Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, United States

Abstract

The human placenta contains two specialized regions: the villous chorion where gases and nutrients are exchanged between maternal and fetal blood, and the smooth chorion (SC) which surrounds more than 70% of the developing fetus but whose cellular composition and function is poorly understood. Here, we use single cell RNA-sequencing to compare the cell types and molecular programs between these two regions in the second trimester human placenta. Each region consists of progenitor cytotrophoblasts (CTBs) and extravillous trophoblasts (EVTs) with similar gene expression programs. While CTBs in the villous chorion differentiate into syncytiotrophoblasts, they take an alternative trajectory in the SC producing a previously unknown CTB population which we term SC-specific CTBs (SC-CTBs). Marked by expression of region-specific cytokeratins, the SC-CTBs form a stratified epithelium above a basal layer of progenitor CTBs. They express epidermal and metabolic transcriptional programs consistent with a primary role in defense against physical stress and pathogens. Additionally, we show that SC-CTBs closely associate with EVTs and secrete factors that inhibit the migration of the EVTs. This restriction of EVT migration is in striking contrast to the villous region where EVTs migrate away from the chorion and invade deeply into the decidua. Together, these findings greatly expand our understanding of CTB differentiation in these distinct regions of the human placenta. This knowledge has broad implications for studies of the development, functions, and diseases of the human placenta.

Editor's evaluation

By using single-cell RNA sequencing, elegant computational approaches, protein validation, and in vitro functional assays, this study characterizes the cellular composition and gene expression profiles of the human placenta in mid-gestation. In addition, this work gives new insights into our understanding of trophoblast differentiation in distinct regions of the human placenta. The findings and dataset provided by the authors represent an important resource for readers interested in human development and placenta biology.

https://doi.org/10.7554/eLife.78829.sa0

Introduction

The human placenta is the first organ to develop and forms the essential bridge between maternal and fetal tissues beginning at implantation (Knöfler et al., 2019; Turco et al., 2018). The placenta must begin development rapidly upon conception in order to perform the roles of future organ systems that have not yet developed and matured in the fetus, including nutrient and oxygen transport and protection from mechanical and pathogenic insults. The placenta also performs unique functions such as modulation of maternal tolerance and hormone production (Maltepe and Fisher, 2015; Knöfler et al., 2019; Turco et al., 2018). Placental development begins with the generation of stem villi surrounding the entire embryo and then proceeds asymmetrically to produce two distinct regions. At the human implantation site, the embryonic pole, the villi grow and branch to give rise to the region essential for the exchange of gases and nutrients, the chorion frondosum (also known as the chorionic villi or the villous chorion [VC]). This region includes the placental villi and the invasive EVTs. The villi located on the opposite side, the abembryonic pole, degenerate resulting in a smooth surface lacking villi termed the chorion leave (also known as the smooth chorion [SC]). The SC fuses with the amnion forming the chorioamniotic membranes (also known as the fetal membranes) (Hamilton and Boyd, 1960; Boyd and Hamilton, 1967; Benirschke et al., 2006).

Both the VC and SC are comprised of fetal derived cytotrophoblasts (CTBs), with CTBs in the VC differentiating to either multinucleate syncytiotrophoblast (STB) or to invasive extravillous trophoblast (EVT) (Knöfler et al., 2019). Compared to the VC, little effort has been made to analyze types and functions of the CTBs that comprise the SC (Benirschke et al., 2006; Garrido-Gomez et al., 2017). The CTBs of the SC exist in an epithelial-like structure and lack STBs and proximity to the fetal vasculature, and thus cannot function in a manner comparable to villi in VC (Benirschke et al., 2006). Furthermore, in contrast to the VC where EVTs invade and remodel the maternal arteries, the cells of the SC do not invade the adjacent decidua and the maternal blood vessels it contains (Genbačev et al., 2015). Thus, the function of these SC CTBs remains unclear.

Several pieces of evidence suggest that the SC is not simply a vestigial structure. First, the intact CTB layer contains proliferating cells and is maintained until term (Yeh et al., 1989; Benirschke et al., 2006; Garrido-Gomez et al., 2017). Second, the histological heterogeneity among SC CTBs suggests functional distinctions. Yeh et al., 1989, characterize two distinct populations of vacuolated and eosinophilic CTBs. Vacuolated CTBs were positive for placental lactogen and placental alkaline phosphatase, while the eosinophilic subpopulations was not. Both populations were rich in keratin and neither had the known characteristics of villous CTBs. Bou-Resli et al., 1981 also note high levels of variation among CTBs in the SC and the existence of a vacuolated population. A more molecular characterization was carried out by Garrido-Gomez et al., 2017, which demonstrated heterogeneity of ITGA4 and HLA-G expression, markers previously associated with stemness and invasion, respectively (Genbacev et al., 2016; McMaster et al., 1995). This study also uncovered an expansion of the SC in cases of severe pre-eclampsia, along with a disease-specific gene expression pattern. In sum, these results suggest that the SC CTBs are a heterogeneous and dynamic collection of cells with important functions in development and disease.

Single cell RNA-sequencing (scRNA-seq) has emerged as the standard for transcriptional characterization of complex organs. This methodology was previously applied to the placenta, but with a focus on the maternal-fetal interface, specifically the chorionic villi and basal plate (Liu et al., 2018; Suryawanshi et al., 2018; Vento-Tormo et al., 2018). Recently, scRNA-seq was used to profile the smooch chorion at term (Pique-Regi et al., 2019; Pique-Regi et al., 2020; Garcia-Flores et al., 2022). However, in Pique-Regi et al., 2019, only 132 CTBs were identified in the SC out of 29,921 cells (0.44%) collected from this region. A comparable number of CTBs in the SC were recovered in Garcia-Flores et al., 2022, potentially reflecting a difficulty in capturing these cells at term. To better understand the differences in the cell types and functions of the two sides of the developing placenta, we applied scRNA-seq to matching samples of cells isolated from the VC and SC regions of human samples from mid to late in the second trimester. We used scRNA-seq to compare the composition and developmental trajectories of CTBs in the VC and SC. The data were validated and extended with functional studies to gain initial insights into the basis of differential migration of trophoblasts in each region. These results identified a novel SC-specific CTB population important for establishment of a protective barrier and the suppression of trophoblast invasion. In addition, these data represent a resource of CTB types, proportions, and gene expression at mid-gestation against which age-related and pathogenic alterations can be measured.

Results

The transcriptional landscape of the VC and SC at mid-gestation

To understand the cellular composition of the SC, we isolated and profiled cells from both the VC and the SC regions of four second trimester human placentas spanning gestational weeks 18–24 (GW18–24) using scRNA-seq (Figure 1a). We chose to analyze second trimester samples because the maturation of the SC is complete but the inflammation and apoptosis associated with membrane rupture and parturition is absent (Benirschke et al., 2006; Yuan et al., 2006; Yuan et al., 2008; Yuan et al., 2009; Figure 1a). SC and VC cells were isolated from each human placental sample allowing within and across patient comparisons. VC samples included cells isolated from floating and anchoring villi and areas surrounding the cell column, while most of the decidua (including spiral arteries) were dissected away. SC samples included the chorion and underlying stroma (mesenchymal and endothelial cells), but not the amnion and little of the neighboring decidua, which were also removed during dissection. CTBs were further enriched over stromal and immune cells during cell preparation as previously described (Garrido-Gomez et al., 2017 and in Materials and methods). The transcriptomes of the resulting cells were captured using the 10× Genomics scRNA-seq platform.

Figure 1 with 8 supplements see all
The transcriptional landscape of the villous (VC) and smooth chorion (SC) at mid-gestation.

(a) Left: Schematic of the placenta at mid-gestation, highlighting the regions sampled, together with the methods used for cell isolation and characterization. Right: Schematic of the cell types and their organization in each region. (b) UMAPs of integrated samples, shown by region of origin (left – VC, right – SC), and colored according to broad cell type clusters. (c) Heatmap of the transcript expression of select cell identity markers across broad cell type clusters and regions. Values are scaled expression across the clusters of each region independently.

Figure 1—source data 1

Marker genes for the clusters of the Immune subset.

https://cdn.elifesciences.org/articles/78829/elife-78829-fig1-data1-v2.csv
Figure 1—source data 2

Marker genes for the clusters of the Stroma subset.

https://cdn.elifesciences.org/articles/78829/elife-78829-fig1-data2-v2.csv

Each of the eight datasets (GW17.6, 18.2, 23.0, 24.0; VC and SC) was captured independently, then integrated computationally (Figure 1b; Stuart et al., 2019). We classified the cells of the integrated dataset into broad cell type clusters according to functional identities and annotated each by expression of canonical markers. CTBs were annotated as KRT7+, HLA-G-; EVTs as KRT7+, HLA-G+; immune cells as CD45+, VIM-; stromal cells as VIM+, CD45-; and the uterine epithelium by expression of EPCAM+, MUC1+, and MUC16+ (Figure 1c; Lee et al., 2016; McMaster et al., 1995; Vento-Tormo et al., 2018). Cells expressing exclusive markers of disparate cell types (co-expression of KRT7, HLA-G, HLA-A, VIM, ACTA2) were labelled as doublets and excluded from further analysis. The complete dataset used for further analysis contained 50,496 cells that passed quality control (between 500 and 6500 unique genes, fewer than 15% mitochondrial reads, doublets removed) (Figure 1—figure supplement 1a; McGinnis et al., 2019). Cells originating from each region (VC – 25,367 and SC – 25,129) and each sample (7181–17,705 cells per sample) were well represented (Figure 1b; Figure 1—figure supplement 1b and c). The number of cells in each broad cell type cluster demonstrated enrichment for CTBs, which represented more than 60% of the cells in the integrated dataset, as expected given the enrichment protocol (Figure 1—figure supplement 1d). The sex of each fetus was inferred by assaying expression of XIST in trophoblast cells isolated from each sample (Figure 1—figure supplement 2a), which allowed the assignment of cell types to either fetal or maternal origin (Figure 1—figure supplement 2b).

Even though there was enrichment for CTBs, we still identified 14,805 immune cells and 3883 stromal cells. We reclustered these immune and stromal cell subsets individually and compared them to previously published single cell analyses, allowing for the annotation of subtypes within each group (Figure 1—figure supplement 3a-d; Figure 1—figure supplement 4a-d; Figure 1—figure supplement 5a-c; Figure 1—figure supplement 7a-c; Vento-Tormo et al., 2018; Pique-Regi et al., 2019). Independent clustering analysis of VC and SC cells identified the similar populations as the integrated immune and stromal subsets and confirmed the relative proportions of cell identities across each region (Figure 1—figure supplement 6a-d; Figure 1—figure supplement 8a-d). All immune clusters robustly expressed Xist in each sample, identifying them to be of maternal origin (Figure 1—figure supplement 2c). Almost twofold more immune cells were recovered from the VC than the SC, although it is possible that this change in proportion is an artifact of dissection and/or the CTB enrichment protocol (Figure 1—figure supplement 5d and e). Comparing the immune cell types identified in each region revealed a higher proportion of macrophages in the VC as compared to the SC, which contained a greater proportion of NK/T cells (Figure 1—figure supplement 5e; Figure 1—source data 1).

While few stromal cells were isolated in the preparations, subclustering still revealed a differential composition of fetal stromal cells between the VC and SC (Figure 1—figure supplement 7a-c; Figure 1—source data 2). The majority of stromal cells recovered originated from the SC (2941 compared to 942 from VC). These cells included lymphatic endothelium (Pique-Regi et al., 2019) and two largely SC-specific mesenchymal cell populations of fetal origin, Mesenchyme 1 and Mesenchyme 3 (Figure 1—figure supplement 7c-e; Figure 1—figure supplement 2b). These two clusters are marked by elevated expression of EGFL6, DLK1, and uniquely by expression of COL11A1, which is observed only in the SC (Figure 1—figure supplement 7b and f ). Interestingly, several canonical CTB support factors including HGF, WNT2, and RSPO3 were expressed in fetal stromal populations in both regions, suggesting shared requirements for WNT and MET signaling (Figure 1—figure supplement 7g). Taken together these data demonstrate the identification of broad classes of CTBs, immune, and support cells from both the VC and SC regions .

Identification of an SC-specific CTB population

CTBs are the fetal cells that perform the specialized functions of the VC, and are required for normal fetal growth and development (Maltepe and Fisher, 2015; Turco et al., 2018; Knöfler et al., 2019). To better understand the composition of CTBs in the SC versus the VC, we subclustered this population (KRT7+, VIM-, CD45-, MUC1-). The CTB subset is comprised of 29,668 cells with similar representation and cell quality control metrics across all eight samples (Figure 2—figure supplement 1a, b, and c). This analysis identified 13 clusters including several CTB, EVT, and STB subtypes (Figure 2a; Figure 2—source data 1).

Figure 2 with 4 supplements see all
Identification of a smooth chorion-specific cytotrophoblast.

(a) UMAP of subclustered trophoblasts (n=29,668). Colors correspond to the clusters at the right. (b) Dot plot showing average expression and percent of cells in each cluster as identified by the marker genes listed on the x-axis. The clusters are listed on the y-axis. (c) UMAP of subclustered trophoblasts from the villous chorion (VC) (left) or the SC (right). Clusters and colors are the same as in panel a. (d) Quantification of the number of cells in each trophoblast cluster from each region. Cells from the VC are shown in black. Cells from the SC are shown in blue. (e) Violin plot of PAGE4 transcript expression across all trophoblast clusters. (f) Immunofluorescence co-localization of PAGE4 with pan-cytokeratin (marker of all trophoblast) in the VC (left) or SC (right). (g) Violin plot of KRT6A transcript expression across all trophoblast clusters. (h) Immunofluorescence co-localization of KRT6 with pan-cytokeratin (marker of all trophoblast) in the VC (left) or SC (right). (i) Immunofluorescence co-localization of CDH1 and KRT6 in the VC (left) or SC (middle). High magnification inset is denoted by the white box (right). For all images, nuclei were visualized by DAPI stain; scale bar = 100 μm. Abbreviations: AV = anchoring villi; FV = floating villi; SC = smooth chorion epithelium; Amn. = amnion; Dec. = decidua.

Figure 2—source data 1

Marker genes for each cluster in the trophoblast subset.

https://cdn.elifesciences.org/articles/78829/elife-78829-fig2-data1-v2.csv

Broad classes of trophoblast were annotated by established markers (STBs – CGA, CYP19A1, CSH1, CSH2; EVTs – HLA-G, DIO2; CTBs – PAGE4, PEG10, and no expression of EVT and STB markers) (Figure 2b and Figure 2—figure supplement 2a; McMaster et al., 1995; Lee et al., 2016; Suryawanshi et al., 2018; Liu et al., 2018). Comparison to previously published cell types in the VC and SC confirmed the identities of most clusters (Figure 2—figure supplement 1d-g; Vento-Tormo et al., 2018; Pique-Regi et al., 2019). Two cell clusters showed high expression of canonical phasic transcripts, including MKI67, with the S-phase cluster denoted by expression of PCNA and the G2/M-phase cluster by expression of TOP2A (Figure 2—figure supplement 2b; Tirosh et al., 2016). Both populations share gene expression with all clusters of CTBs, and therefore, were identified as actively cycling CTBs (Figure 2—figure supplement 2a). No STB or EVT markers were identified in the cycling clusters as was expected due to the requirement for cell cycle exit upon terminal differentiation to these lineages (Lu et al., 2017; Genbacev et al., 1997).

CTBs separated into four clusters, CTB 1–4. CTB 1 cells highly expressed PAGE4, PEG10, and CDH1 (Figure 2b, Figure 2—figure supplement 2a, Figure 2—figure supplement 4a), which have been shown to be canonical markers of villous CTB (Lee et al., 2016; Suryawanshi et al., 2018). Overall, CTB 2–4 were more transcriptionally similar to each other than to CTB 1, indicating a transcriptional program that was distinct from canonical villous CTBs (Figure 2—figure supplement 2a and c). CTB 2–4 existed along a gradient of gene expression changes suggestive of various stages of a common differentiation pathway. However, each population expressed distinct transcripts corresponding to important proposed functions of the SC. CTB 2 cells highly expressed CLU, CFD, and IFIT3, suggesting roles in responding to bacterial or viral infection through the innate arm of the immune system (Thurman and Holers, 2006; Liu et al., 2011). CTB 3 cells upregulated EGLN3 and SLC2A3, indicating an HIF-mediated response to hypoxia and a switch toward glucose metabolism, likely as an adaptation to the decreased oxygen levels in the largely avascular SC region (del Peso et al., 2003; Maxwell et al., 1997). Finally, CTB 4 specifically expressed several cytokeratins — KRT6A, KRT17, and KRT14, found in many epithelial barrier tissues and important for maintenance of integrity in response to mechanical stressors (Karantza, 2011; Figure 2b, Figure 2—figure supplement 2a). Previous analysis of the SC region at term using scRNA-seq identified fewer than 0.5% of trophoblasts as being CTB and did not identify a separate KRT6 expressing CTB population (Pique-Regi et al., 2019). Computational integration of the data from Pique-Regi et al., 2019, with the trophoblast subset confirmed CTB 3 and CTB 4 to be unique to this study (Figure 2—figure supplement 1f and g ). These results establish a previously underappreciated transcriptional diversity of CTB subpopulations.

Quantification of cells showed a strong regional bias in the number and proportion contributing to each CTB cluster (Figure 2c and d). In the VC samples, 53.8% of CTBs clustered in CTB 1 compared to 11.1% in the SC (Supplementary file 1). In contrast, the SC had a much larger proportion of the CTB 2–4 clusters. In this region, 71.3% of CTBs were nearly equally distributed among CTB 2 (25.6%), CTB 3 (24.9%), and CTB 4 (20.8%). In the VC, only 24.1% of CTBs were found in the same clusters, with the majority in CTB 2 (16.1%). The contribution to cycling clusters was consistent across regions: 22.1% and 17.5% of CTBs in the VC and SC, respectively. The relative proportions of CTB 1–4 in the VC and SC were consistent across individual samples indicating that this difference was not driven by sample variability (Figure 2—figure supplement 1b-c). Furthermore, independent clustering analysis of VC and SC cells identified the same populations as the integrated trophoblast subset. Importantly, independent clustering of each region did not identify CTB 4 in the VC or STB in the SC, suggesting CTB 4 and STB to be specific to the SC and VC, respectively. The small number of cells in the integrated dataset identified as VC CTB 4 (173 cells) or SC STB (14 cells) may be in part an artifact of computational integration (Figure 2—figure supplement 3a-f).

Next, we immunolocalized the protein products of genes that distinguished the subpopulations. At the mRNA level, PAGE4 expression was highest in CTB 1 and decreased across CTB 2–4 (Figure 2b and e). In the VC, the CTB monolayer between the fetal stromal villous core and the overlying STB layer showed strong PAGE4 immunoreactivity, which diminished upon differentiation to EVT (Figure 2f - left). PAGE4 mRNA and protein expression matched that of known villous CTB marker CDH1 (Figure 2—figure supplement 4a and b; Genbacev et al., 1997). In the SC, the PAGE4 signal was strong in the epithelial layer directly adjacent to the fetal stroma and then decreased in cells distant from the basal layer, again matching CDH1 (Figure 2f – right, Figure 2—figure supplement 4b – right). Both RNA expression and protein localization were consistent with CTB 1 cells existing in both chorionic regions and occupying a similar niche.

Staining for KRT6, a marker highly enriched in CTB 4 cells (Figure 2g) showed a strikingly different result. Cells occupying the upper layers of SC epithelium showed a strong KRT6 signal, a pattern opposite to CDH1 (Figure 2h and i – right). KRT6 was absent from either the floating or anchoring villi of the VC (Figure 2h and i – left), although rare decidual resident KRT6 positive cells were identified in the VC region (Figure 2—figure supplement 4c, Figure 6—figure supplement 1 – top). KRT6 isoforms, KRT6B and KRT6C, were not expressed, confirming KRT6A transcript and protein as highly specific markers of a CTB population found only in the SC (Figure 2—figure supplement 4d). These data describe a novel subpopulation of CTBs unique to the SC, which going forward we term CTB 4 or SC-CTBs for SC-specific CTBs.

A common CTB progenitor gives rise to STBs in the VC and SC-CTBs in the SC

Next, we investigated the developmental origin of the SC-CTBs. We performed RNA velocity analysis to predict the relationships between cells based on the proportion of exonic and intronic reads. These predictions are shown as vectors representing both the magnitude (predicted rate) and the direction of differentiation (Bergen et al., 2020). We first asked whether RNA velocity could recapitulate the well-established differentiation trajectories of trophoblasts in the VC (Knöfler et al., 2019; Turco et al., 2018; Vento-Tormo et al., 2018). In accordance with previous results, RNA velocity projections identified CTB 1 as the root for three differentiation trajectories: self-renewal, differentiation to STBs, and differentiation to EVTs (Figure 3a). Cells at the boundary of the CTB 1 cluster showed differentiation vectors of high magnitude toward STB Precursors and upregulated canonical drivers of STB differentiation and fusion (ERVW-1 and ERVFRD-1). These cells also expressed transcription factors (GCM1 and HOPX) and hormones (CSH1) necessary for STB function (Figure 3—figure supplement 1a; Baczyk et al., 2009; Mi, 2000; Blaise et al., 2003; Yabe et al., 2016).

Figure 3 with 2 supplements see all
A common cytotrophoblast (CTB) progenitor gives rise to syncytiotrophoblasts (STBs) in the villous chorion (VC) and smooth chorion (SC)-CTBs in the SC.

RNA velocity vector projections overlaid on to UMAPs for trophoblast cells isolated from the (a) VC and (b) SC. Arrows denote direction and magnitude is represented by line thickness. (c) Pseudotime reconstruction of SC derived CTB 1–4 clusters from the scVelo dynamical model of latent time. Each column represents one cell. Cells at the left are clustered in CTB 1 and progress through CTB 2, 3, and 4 along the x-axis. Select genes that were the major drivers of the pseudotime alignment are shown on the y-axis. Expression ranged from dark blue (lowest) to yellow (highest). (d) Violin plots of select factors from (c) demonstrated shared or region-specific expression for genes associated with the CTB 4 differentiation trajectory.

In the SC, CTB 1 cells once again were identified as the root for differentiation. However, CTB 1 cells showed strong directionality and magnitude toward CTB 2–4 (Figure 3b). All cells in CTB 1–4 clusters displayed uniform directionality indicating a robust differentiation trajectory ending at CTB 4. High levels of transcriptional similarity between CTB 2 and 4 compared to CTB 1 suggested CTB 2 and CTB 3 are intermediate states between CTB 1 and CTB 4 (Figure 2—figure supplement 2c). Mitotic KRT6+ cells were identified, and while the interaction between the cell cycle and differentiation of SC-CTBs remains unclear, these data show that differentiation to SC-CTB does not require cell cycle exit, unlike STBs and EVTs (Figure 3—figure supplement 1b). In contrast to the VC, we observed no velocity vectors with directionality toward the STB lineage from the CTB clusters in the SC samples. Further, the smaller number of STB precursors (460 cells) and STBs (14 cells) exhibited reduced expression of STB canonical markers such as ERVFRD-1 and GCM1, and notably, a near absence of ERVW-1 (Figure 3—figure supplement 1a). These cells may be associated with ghost villi (Benirschke et al., 2006). In sum, these data show differential developmental trajectories for the CTB 1 cells in the SC and VC, with the former largely giving rise to SC-CTBs and the latter to STBs.

To identify the genes that were correlated with progression from CTB 1–4 in the SC, we used the velocity vector predictions to construct a pseudotemporal model of differentiation. All the cells in these clusters were plotted in one dimension from the least to the most differentiated according the pseudotime model (Figure 3c). Genes that were highly expressed at the start of the pseudotemporal differentiation included pan-trophoblast factors such as EGFR, which was expressed throughout all four CTB populations in both the VC and SC. Progression along the pseudotime trajectory identified regulators of cell fate and function, including the transcription factor KLF4 and extracellular matrix (ECM) components COL5A1 and LAMA3 (Figure 3c–d); all demonstrated SC-specific expression. Elevated expression of ECM transcripts (COL4A2, FN1) and transcription factors responsive to cell contact and mechanical stress (HES1, YAP1) were coordinately upregulated, potentially highlighting the effects of the extracellular environment on fate specification (Figure 3—figure supplement 1c). To identify differential signaling events that might regulate alternative paths of differentiation in the VC and SC, we used CellPhoneDB to predict receptor-ligand interactions between CTB clusters within each region (Figure 3—figure supplement 2a-b; Efremova et al., 2020). This analysis identified BMP, Notch, and Ephrin signaling events specific to the SC region, which may help to determine cell fate and/or cell sorting within the SC trophoblast epithelium (Figure 3—figure supplement 2b). Together, these data demonstrated that SC-CTBs originate from CTB 1 progenitors common to both the VC and SC. In the SC, instead of upregulating syncytialization factors such as GCM1 and ERVFRD-1, CTB 1 progenitors upregulate transcription factors such as KLF4, YAP1, and HES1, which drive an epithelial cell fate in other contexts (Segre et al., 1999; Harvey et al., 2013; Rock et al., 2011).

SC-CTBs express a distinct epidermal transcriptional program

Next, we sought a better understanding of the physiological functions of the SC trophoblast clusters. We performed gene ontology analysis as a summary of functional processes (Figure 4a, Figure 4—figure supplement 1; Yu et al., 2012). We focused on the progenitor CTB 1 and terminally differentiated SC-CTBs as they showed enrichment for strikingly different functional categories. In CTB 1, we identified enrichment for WNT signaling, epithelial morphogenesis, and membrane transport, categories commonly associated with progenitors (Figure 4a, Figure 4—figure supplement 1). We validated the activity of WNT signaling and the location of these cells by immunolocalization of non-phosphorylated CTNNB1 (np-CTNNB1). Staining was localized to the most basal epithelial layer nearest to the stroma in both the VC and SC regions (Figure 4b), matching expression of the CTB 1 marker CDH1 (Figure 4—figure supplement 2a). WNT signaling has an important role in the maintenance of villous CTBs in vivo and in the derivation and culture of self-renewing human trophoblast stem cells (Knöfler et al., 2019; Haider et al., 2018; Okae et al., 2018). We investigated proliferation of np-CTNNB1 expressing cells in both regions using KI67 as a mitotic marker. This revealed a similar percentage of KI67+ CTB 1 cells, suggesting similar proliferative capacity across regions (Figure 4b and c). We next analyzed regional differences within CTB 1. Gene ontology identified an enrichment for oxidative phosphorylation and epithelial signaling cues in VC CTB 1 cells (Figure 4—figure supplement 2b). This is in direct contrast to SC CTB 1 cells that displayed elevated levels of hypoxia response genes (Figure 4—figure supplement 2b-c). KLF4 was identified in the RNA velocity analysis as gaining expression from CTB 1–4, but also showed greater expression in CTB 1 in the SC compared to the VC (Figure 3d). In accordance with the mRNA expression data, KLF4 protein often co-localized to CTB 1 in the SC, but was only observed in rare cells in the VC (Figure 4d). These data further support a similar location and function for CTB 1 in the SC and VC, with transcriptional and metabolic differences that presage distinct developmental trajectories.

Figure 4 with 4 supplements see all
Smooth chorion-specific cytotrophoblasts (SC-CTBs) express a distinct epidermal transcriptional program.

(a) Heatmap of gene ontology analysis adjusted p-values. Dark red corresponds to the lowest p-values and white represents p-values greater than 0.0005. Ontology categories are organized by hierarchical clustering along the y-axis. Marker genes for each cluster were used as inputs for the analysis. A subset of genes in selected categories are listed at the right. Categories and genes enriched in CTB 1 or CTB 4 are in black or blue, respectively. (b) Representative immunofluorescence co-localization of non-phosphorylated CTNNB1 and KI67 in the VC (left) and SC (right). (c) Quantification of the percent of np-CTNNB1 cells with KI67 expression in each region in three placental samples. Each dot represents the percentage in one field of view (at least seven per region per sample) as an estimate of mitotic cells per population. Percentages for the villous chorion (VC) region are shown in black and the SC region in blue. (d) Immunofluorescence co-localization of CDH1 and KLF4 protein in the VC (left) or SC (right). Arrowheads denote CDH1+/KLF4+ cells. (e) Immunofluorescence co-localization of KRT14 and KRT6 protein in the VC (left) or SC (right). The outline of the anchoring villi (AV) is denoted by the white dashed line. For all images, nuclei were visualized by DAPI stain; scale bar = 100 μm. Abbreviations: FV = floating villi; SC = smooth chorion epithelium; Amn. = amnion; Dec. = decidua; GV = ghost villi.

CTB 4 ontological analysis strongly supported important roles for these cells in the formation of a protective barrier. The greatest enrichment was for cell junction and cell substrate adhesion genes that included numerous integrin and laminin subunits as well as junctional proteins PATJ, DSP, and JUP. An enrichment for both skin and epidermal development correlated with upregulation of the ECM and junctional transcripts. These categories included many cytokeratins (KRT6A, 7, 8, 14, 17, 18, and 19) and transcription factors (KLF4, YAP1) required for epidermal identity and maintenance (Segre et al., 1999; Schlegelmilch et al., 2011). The organization of the SC is reminiscent of stratified epidermal cells of the skin, with progenitors adherent to the basal lamina and more differentiated cells progeny forming the upper layers. In many tissues, the specific domains of cytokeratin expression correspond to stratified cell layers with different functions. We asked if this was also the case in the SC epithelium. Cytokeratins 7, 8, and 18 were expressed in all trophoblast regardless of region (Figure 4—figure supplement 3a), but cytokeratin 6A, 14, and 17 displayed SC-specific expression that increased with differentiation toward CTB 4 (Figure 4—figure supplement 3b). Immunofluorescence localization confirmed expression of KRT14 as specific to the CTBs in the SC and inclusive of all KRT6 expressing cells (Figure 4e). These data support a model of accumulated cytokeratin expression that begins with CTB 1 (KRT7, KRT8, KRT18), increases in CTB 2–3 (KRT14, KRT17), and culminates in CTB 4 (KRT6A). Together these data are consistent with a central role for the CTBs of the SC in establishing a protective epithelial barrier for the rapidly growing fetus.

Beyond forming a physical barrier, important chorionic functions include protection from bacterial and viral infections. We identified an enrichment for genes involved in antiviral response in SC-CTBs, and in SC cells more broadly. For example, IFITM3, a restriction factor preventing entry of viruses into cells, is highly expressed in CTBs from the SC compared to the VC (Figure 4—figure supplement 4; Bailey et al., 2014; Spence et al., 2019). IFITM proteins have also been reported to inhibit syncytialization (Buchrieser et al., 2019), suggesting IFITM3 may also function to block differentiation of CTBs into STBs in the SC. Taken together, these data establish SC-CTBs as the building blocks and critical regulators of the SC barrier, responsible for both protection against physical forces and pathogen infection.

EVTs of the VC and SC regions display distinct invasive activity but are transcriptionally similar

EVTs are the invasive trophoblasts of the placenta (Knöfler et al., 2019; Turco et al., 2018; Red-Horse et al., 2004). While the EVTs of the VC migrate away from the villi, invade the decidua, and replace the endothelial lining of the uterine arteries, the EVTs of the SC adhere to the decidua and do not home to the maternal vasculature (Genbacev et al., 2016). To understand the basis for these differences, we compared EVT subpopulations isolated from the VC and SC. Expression of the canonical marker of EVTs, HLA-G, was similarly abundant among the cells isolated from both chorionic regions (Figure 5a and b). A greater number (VC – 6572; SC – 5021) and larger proportion (VC – 46.83%; SC – 32.12%) of CTBs from the VC expressed HLA-G as compared to the analogous population from the SC (Figure 5c). Consistent with the transcript expression data, immunolocalization of HLA-G showed strong staining of cells in both the VC and SC. However, VC derived HLA-G positive cells were found deep in the maternal decidua. In contrast, HLA-G positive cells in the SC remained in the epithelial layer (Figure 5b).

Figure 5 with 3 supplements see all
Extravillous trophoblasts (EVTs) of the villous chorion (VC) and SC regions display distinct invasive activity but are transcriptionally similar.

(a) Expression of HLA-G transcript per cell projected in UMAP space for the VC (left) and SC (right). Expression ranged from low in light gray to high in dark red. (b) Immunofluorescence localization of HLA-G in the VC (left) and SC (right). The anchoring villi (AV) are outlined in white (left). The boundaries of the smooth chorion epithelium are denoted by the white lines (right). (c) Quantification of the number of HLA-G expressing extravillous trophoblast (EVT) (left) and the percent of total trophoblast that express HLA-G (right) for each chorionic region. (d) UMAP of all trophoblast cells including the EVT clusters (left). The percent of EVT cells in each cluster from each region (right). (e) Volcano plot of the differentially expressed genes between regions for all EVTs. All genes with a log fold change greater than an absolute value of 0.25 and a p-value of less than 0.05 were plotted. Those with greater expression in VC EVT are shown in black. Those with greater expression in SC EVT are shown in blue. (f) Immunofluorescence localization of CSH1 in the VC (top) and SC (bottom). Arrowheads denote CSH1 expressing cells. (g) Violin plots of the number of differentially expressed genes between 100 cells from each chorionic region within each cluster (100 permutations). Clusters with less than 100 cells per region were omitted due to the small sample size. For all images, nuclei were visualized by DAPI stain; scale bar = 100 μm. Abbreviations: FV = floating villi; SC = smooth chorion epithelium; Amn. = amnion; Dec. = decidua; STB = syncytiotrophoblast; CC = cell column.

Figure 5—source data 1

EVT 1 cluster differentially expressed genes between regions.

https://cdn.elifesciences.org/articles/78829/elife-78829-fig5-data1-v2.csv
Figure 5—source data 2

EVT 2 cluster differentially expressed genes between regions.

https://cdn.elifesciences.org/articles/78829/elife-78829-fig5-data2-v2.csv
Figure 5—source data 3

EVT 3 cluster differentially expressed genes between regions.

https://cdn.elifesciences.org/articles/78829/elife-78829-fig5-data3-v2.csv
Figure 5—source data 4

EVT 4 cluster differentially expressed genes between regions.

https://cdn.elifesciences.org/articles/78829/elife-78829-fig5-data4-v2.csv

EVTs were further divided into clusters 1–4. No clusters were specific to the VC or SC, except for the small number of putative EVT Precursors (VC – 99 cells; SC – 8 cells) (Figure 5d, Figure 3a). Previous work has classified EVTs in the VC as columnar, interstitial, or endovascular based on gene expression and location in the decidua (Tilburgs et al., 2015; Knöfler et al., 2019). The columnar subpopulation is believed to represent newly differentiated EVTs, which lie at the base of the columns that connect the anchoring villi to the uterine wall. Interstitial EVTs migrate through the decidua homing to maternal arteries, which they invade. The relationship between this subpopulation and endovascular EVTs that replace the maternal arterial endothelium is unclear, with evidence supporting endovascular EVTs arising either from interstitial EVTs and/or an independent origin (Harris et al., 2009; Red-Horse et al., 2004; Pijnenborg et al., 2011). We expected to capture both columnar and interstitial EVT populations, but few endovascular EVTs as the cellular preparations were largely devoid of arteries. We investigated the four clusters of EVT identified in our analysis in the context of the VC and the SC. Both regions contained cells from all clusters, although distinct regional biases were evident (Supplementary file 1). The SC region contained almost twice as many EVT 1 cells as the VC (VC – 684; SC – 1256), whereas the VC contained almost twice as many EVT 2–4 as the SC (VC – 5789; SC – 3757). We next asked which of the EVT clusters corresponded to known EVT classifications, indicating maturation state or invasive capacity. Gene ontology analysis showed an enrichment for placental development and antigen presentation categories in EVT 1 (Figure 5—figure supplement 1a). Representative marker genes for these categories in EVT 1 included several lineage-specific transcription factors including GCM1, PPARG, and CEBPB (Figure 5—figure supplement 1b; Knöfler et al., 2019; Ferreira et al., 2016). In contrast, EVT 2–4 showed enrichment for extracellular structure and matrix organization, glycosylation, and peptidase activity. EVT clusters 3–4 specifically showed increased expression for transcripts of proteases such as HTRA1, MMP2, and MMP11 (Figure 5—figure supplement 1c). Based on the GO and specific gene enrichments, EVT 1 appeared most similar to columnar EVTs, while EVT 2–4 were consistent with interstitial EVTs that gain invasive capacity. The relative enrichment for EVT 1 in the SC (VC – 10.57% of EVT; SC – 25.05% of EVT) suggested an expansion in columnar-like EVTs at the expense of interstitial EVT. Conversely, the relative enrichment for EVT 2–4 in the VC region suggested an expansion of the invasive subpopulation (Figure 5d).

As almost 75% of EVTs from the SC region were in EVT clusters 2–4, we next asked if there were differences in gene expression between interstitial EVTs isolated from each region. This analysis identified relatively few differentially expressed genes, with the vast majority identified having a log fold change less than 1 (Figure 5—figure supplement 2a; Figure 5—source data 1–4). The small number of differentially expressed genes with a fold change greater than 1 was typically identified across multiple EVT clusters, suggesting an overriding transcriptional program that might supersede subcluster designations. To identify genes associated with this program, we combined all EVT subclusters into one cluster for analysis (Figure 5e). Differential expression analysis revealed the hormone CSH1 and several proteases including HTRA1, HTRA4, and PAPPA to be expressed at significantly higher levels in the more invasive VC EVTs. However, of the most differentially expressed transcripts, only CSH1, ITM2C, and DNASE1L3 were exclusive to the VC (Figure 5—figure supplement 2b). Immunolocalization of CSH1 confirmed this result (Figure 5f). CSH1, also known as human prolactin, is a secreted hormone that signals through the receptor PRLR found on maternal cells. As such, the primary effect of CSH1 is likely non-cell autonomous, raising the possibility that the largest differences in EVTs between regions may not be in inherent invasive capacity, but rather their ability to communicate with the neighboring cells. Therefore, we asked whether differential EVT-stroma signaling interactions exist between the VC and the SC (Efremova et al., 2020). While this analysis underrepresents the contribution of the stroma due to its depletion by the trophoblast enrichment protocol, we identified enrichment for collagen-integrin interactions between EVT and stromal cells in the VC, which were not present in the SC (Figure 5—figure supplement 3a). In contrast, SC-specific EVT-stromal interactions included PDGFA and FN1 signaling (Figure 5—figure supplement 3b). As we see few transcriptional changes in EVTs between regions, our data support a model by which differential signaling may create a stromal environment more amenable to EVT invasion.

We next asked whether the gene expression differences between the VC and SC were smaller for EVT clusters than for all other trophoblast clusters. Calculating the Spearman correlation coefficient across each cluster, EVT 3, EVT 4, and CTB 1 (which we have established as common to both regions) were most similar and the only clusters with a coefficient greater than 0.75 (Figure 5—figure supplement 2c). Since this analysis does not account for cluster heterogeneity or size, we quantified the number of differentially expressed genes between 100 randomly selected cells from each region within each cluster. Across 100 permutations, the number of differentially expressed genes between regions was the lowest for all four EVT clusters (Figure 5g). Together, these results showed that the EVTs of the VC and SC are surprisingly similar even though they have distinct levels of invasion in vivo.

CTBs of the SC inhibit EVT invasion

Given the transcriptional similarity between EVT clusters originating from the VC and SC, we wondered if a non-cell autonomous program could explain their distinct migratory properties. Immunofluorescence co-localization of CTB 1, SC-CTBs, and EVTs showed striking differences in the relative positioning of the EVTs and CTBs in the two regions (Figure 6a and b). On the VC side, which lacks SC-CTBs cells, HLA-G+ EVTs were distant from the np-CTNNB1+ CTB 1 cell population as expected (Genbacev et al., 2016). By the second trimester they had migrated away from the cell column of the anchoring villi into the decidua (Figure 6a). In contrast, HLA-G+ EVTs in the SC were adjacent to and in physical contact with KRT6+ SC CTBs (Figure 6b). The interactions between SC-CTBs and EVTs were numerous and widespread throughout the SC epithelium. Contacts between CTB 1 and EVTs in the SC were not observed and the VC contain only rare and irregular KRT6+ cells (Figure 6—figure supplement 1). This close association suggested possible paracrine signaling events between SC-CTBs and EVTs, which might impact each cell type.

Figure 6 with 4 supplements see all
Cytotrophoblasts (CTBs) of the SC inhibit extravillous trophoblast (EVT) invasion.

(a) Immunofluorescence co-localization of np-CTNNB1 and HLA-G in the VC. (b) Immunofluorescence co-localization of KRT6 and HLA-G in the SC. Arrowheads denote CTB and EVT interactions. (c) Schematic of the transwell invasion assay (top). Cells from either chorionic region were plated in the upper chamber of the transwell. After 39 hr of culture, the transwell membrane was fixed and stained with a Pan-cytokeratin antibody. The projections through the membrane are denoted by black arrowheads and quantified below. Results from the VC derived cells are shown in black, and the results from the SC derived cells are shown in blue. (d) Results from three biological replicates from each chorionic region cultured with conditioned medium from either VC or SC cells. The gestational ages of the plated cells are shown adjacent to the y-axis. The gestational ages of the cells from which conditioned medium was collected are noted in the legends at the right. The results for cells cultured with VC cell conditioned medium are denoted by black dots, and the results for those cultured in SC cell conditioned medium are denoted by open squares. p-Values were determined by t-test and are listed above each comparison. (e) Predicted receptor-ligand interactions from CellPhoneDB. The strength of interaction is estimated by mean expression and is plotted in the heatmap. Receptor-ligand interactions and cell pairs are listed such that Molecule 1 is expressed by Cell 1 and Molecule 2 is expressed by Cell 2. For all images, nuclei were visualized by DAPI staining; scale bar = 100 μm. Abbreviations: AV = anchoring villi; FV = floating villi; SC = smooth chorion epithelium; Dec. = decidua.

To test this theory, we attempted to recapitulate the differential invasive properties of VC and SC EVTs in a transwell migration assay. Briefly, these cells were enriched using the same protocol as described for the scRNA-seq experiments. The cells were plated on Matrigel-coated transwell filters and cultured for 39 hr. Trophoblast projections that reached the underside of the filter, a proxy for invasion, were visualized by immunostaining with a pan-cytokeratin antibody (Figure 6c, Figure 6—figure supplement 2). Consistent with the differences observed in vivo, there was greater invasion of VC as compared to SC trophoblast (Figure 6c). Next, we asked whether CTBs from the SC region secreted soluble factors that inhibited invasion. The invasion assays were repeated with VC cells cultured with conditioned medium from SC cells and vice versa. While VC conditioned medium had no impact on either VC or SC cells, SC conditioned medium significantly reduced invasion of VC cells (Figure 6d). Neither conditioned medium impacted the density of VC or SC cells (Figure 6—figure supplement 3). Therefore, a secreted factor from SC cells inhibited the invasion of VC EVTs. Given that SC-CTBs were the only cell type unique to the SC side, it is highly likely that they produced the secreted factors that inhibit the invasion of EVTs to which they are juxtaposed. To identify which factors might be responsible for the repression of cellular invasion, we subset the predicted signaling interactions between CTB-EVT isolated from the SC (Figure 6—figure supplement 4). We then subset this analysis for only those containing secreted factors, which identified numerous interactions between CTB 4 and EVTs including both canonical cell signaling pathways (TNFa, PGF, TGFB1, FGF1, and PDGFB) and modifiers of the ECM (FN1 and THBS1) (Figure 6e). These data support a paracrine signaling mechanism by which CTB 4 cells restrict EVT invasion in the SC.

Discussion

This study profiled the developing second trimester placenta, providing a high-resolution molecular description of the trophoblast in the SC and placing them in context of the trophoblast from the VC of the same placenta. In doing so, we addressed long-standing observations concerning apparent similarities and differences between VC and SC trophoblast, deconstruct the trophoblast populations in the SC, and ascribe functions to the cells of this understudied region. Our study characterized a novel trophoblast population distinct from both extant CTB in the VC and from EVT found in either region. These trophoblasts reside only in stratified epithelium of the SC, express a specific cytokeratin (KRT6), and comprise at least 25% of all trophoblast in the region. Lineage reconstruction by RNA velocity suggested that the progenitors for these SC-specific CTBs are a resident proliferative progenitor trophoblast with a transcriptional profile that matches villous CTB. We identified several potential regulators of the transition from a common villous CTB-like cell to SC-CTB, including epidermal transcription factors such as KLF4 and YAP1. These molecules along with region-specific signaling events drive an epidermal transcriptional program creating the building blocks for a barrier against both physical and pathogen stressors. Finally, we identified CTB-EVT interactions occurring only in the SC and provide evidence that paracrine signaling between these populations restricts trophoblast invasion.

Since the SC is created by a degenerative process, consensus has been that these cells are remnants of this process, making the SC a vestigial structure (Benirschke et al., 2006). However, several previous observations suggested this might be an oversimplification. First, these cells were noted to be proliferative, suggesting either cellular expansion or turnover (Benirschke et al., 2006). We confirmed the presence of proliferative trophoblasts in the SC. Each cycling CTB cluster expressed markers of CTB 1–4 in both regions (Figure 2—figure supplement 2a) and both CTB 1 (Figure 4b–c) and SC-CTBs (Figure 3—figure supplement 1b) also expressed KI67 protein. Proliferation of both the progenitor and differentiated cells suggested a need for expansion coordinated with the growth of the developing fetus. Second, the importance of the SC was suggested by the noted heterogeneity of the trophoblasts within this region (Yeh et al., 1989; Bou-Resli et al., 1981; Garrido-Gomez et al., 2017; Benirschke et al., 2006). In agreement, our scRNA-seq results showed the SC to be a complex tissue with several trophoblast types. We identified, by differential transcription and function, at least two distinct CTB cell types, columnar and interstitial EVT, and a small number of STB Precursors. As discussed, all CTB and EVT populations either matched those found in the VC or had new region-specific functions. The only evidence of degeneration was the STB Precursor population within the SC, which lacked expression of both ERVW-1 and ERVFRD-1, the fusogens necessary to form STB (Mi, 2000; Blaise et al., 2003; Liu et al., 2018). In sum, these studies suggested that the SC is a complex and functional tissue, not simply the remnant of villous degeneration.

We focused on CTB 4, a novel population found exclusively in the SC, in the context of the unique functions of this region. CTB 4 were enriched for epidermal, skin development, and antiviral gene categories suggesting concerted roles in the creation of a barrier against external forces and pathogens. Previous studies have noted that CTBs residing in the SC have high levels of keratin expression and are associated with extensive ECM deposits rich in laminin, collagen, and fibronectin (Yeh et al., 1989; Bou-Resli et al., 1981; Benirschke et al., 2006). Among all SC CTB populations, but most notably in CTB 4, we identified the upregulation of ECM components, including LAMA3, COL5A1, and FN1. Expression of ECM molecules in the SC is essential to protect against premature rupture of the fetal membranes, but its deposition has previously been ascribed to the stromal cells of the chorion (Parry and Strauss, 1998). Our results suggest that the trophoblasts of the SC create a specific ECM environment distinct from that of the VC. This altered composition likely has wide-ranging effects on trophoblast fate, gene expression, and activity. It will be interesting to explore the impact of advancing gestational age on the SC epithelium, especially with respect to the ECM. Presumably, cellular and structural changes precede the normal process of membrane rupture prior to delivery at term. Whether premature rupture of the membranes phenocopies these events or is a unique process is an important open question.

Pathogen defense is another important function of the placenta. Infections of the chorion and amnion (chorioamnionitis) often result in adverse outcomes for mother and fetus, primarily preterm birth (Romero et al., 2014). We identified specific expression of the antiviral gene IFITM3 in CTB 4. IFITM3 expression is particularly interesting due to the recent finding that it blocks STB fusion mediated by the endogenous retroviral elements, ERVW-1 and ERVFRD-1. Therefore, IFITM3 expression in SC-CTBs may have a dual role – restricting viral entry into the cell and blocking the formation of STBs in the SC. Its expression also provides a mechanism by which the lack of STBs in the SC is maintained after degradation of the villi is complete.

A distinct feature of the CTBs of the SC was remodeling of the cytokeratin network coordinated with differentiation from CTB 1 to SC-CTBs (Figure 2g, Figure 4—figure supplement 3b). The progressive expression of KRT14, KRT17, and KRT6A is reminiscent of the cytokeratin code found in the epithelial cell layers of many tissues (Karantza, 2011). KRT14 is recognized as a marker of all stratified epithelium including subsets of basal progenitor and stem cells in several tissues, usually alongside KRT5 (Moll et al., 1982; Nelson and Sun, 1983; Rock et al., 2009). While we did not identify expression of KRT5 in any trophoblast, the KRT14 expressing proliferative cells of the SC fit the profile of stratified epithelia in other locations. The roles of KRT17 and KRT6 are less clear. Most knowledge about the function of these molecules comes from the epidermis in the context of injury and disease. KRT6 and KRT17 are upregulated rapidly upon epidermal injury and are expressed through the repair phase, each contributing specific functions (Takahashi et al., 1998; McGowan and Coulombe, 1998). KRT17 promotes proliferation and increases in cell size through Akt/mTOR and STAT3, respectively (Kim et al., 2006; Yang et al., 2018). KRT6 is a negative regulator of cell migration through the inhibition of Src kinase and associations with myosin IIA (Wong and Coulombe, 2003; Rotty and Coulombe, 2012; Wang et al., 2018). Finally, this molecule promotes expression of Desmoplakin and the maintenance of desmosomes, the latter, a long-established feature of SC trophoblasts (Bou-Resli et al., 1981; Bartels and Wang, 1983; Benirschke et al., 2006). Taken together, these observations are consistent with data on SC-CTBs. We found no evidence of their invasion, but did identify frequent interactions with the ECM, CTBs, and EVTs. The coordinated expression of these cytokeratins and the transcriptional profile writ large suggested that the SC has the properties of a highly specialized epidermis with a robust proliferative capacity and strong cohesive properties, but lacking migratory or invasive behavior.

As previous work has demonstrated or assumed, the majority of trophoblast identified are represented in both the VC and SC (Benirschke et al., 2006; Garrido-Gomez et al., 2017; Pique-Regi et al., 2019). We identified two cell types with striking transcriptional similarity between regions, CTB 1 and EVT. CTB 1 cells in the VC matched canonical villous CTB in transcriptional profile, activity, and niche. We identified a similar population in the SC. They are localized to an epithelial sheet juxtaposed to the fetal stroma, separated by a thin basal lamina. These cells are supported by similar signaling pathways in both regions, including HGF and WNT (Figure 1—figure supplement 7g; Dokras et al., 2001; Okae et al., 2018; Zhou et al., 2002). The similarities in location and growth factor requirements demonstrate that a niche similar to the villous trophoblast membrane exists in the SC. Additionally, we identified CTB 1 as the progenitor population for SC-CTB, and possibly for EVT in the SC. Thus, CTB 1 in both regions functions as multipotent progenitors. Despite the many similarities, the developmental trajectories emerging from CTB 1 differ in a region-specific manner. At present, it is unclear whether CTB 1 cells in both regions originally have the same potential or if fate restriction occurs as the fetal membranes form. Future experiments will need to functionally address whether CTB 1 from the VC can be coerced to generate SC-CTBs and if CTB 1 from the SC can efficiently generate STBs and invasive EVTs. Multipotent trophoblasts have been generated from first trimester SC cells, however, these cells have a distinct transcriptional profile from the second trimester cells we characterized (Genbacev et al., 2016).

Similarities between the EVT of each region, as defined by contact with the decidua, has been documented in observational, molecular, and most recently, transcriptomic studies (Benirschke et al., 2006; Pique-Regi et al., 2019). Our data confirmed a strong correlation between gene expression for all EVT populations, regardless of regional identity. Based on previously established EVT subtypes, we identified a reduction in the proportion of interstitial EVTs and a concomitant increase in columnar-like EVTs of the SC. However, we did not find evidence of specific EVT subpopulations or intrinsic gene expression programs that would explain the differences in the depth of invasion of EVT in each region. However, we did uncover CTB-EVT interactions specific to the SC epithelium. Rather than physically separating as they do in the VC, CTBs and EVTs co-occupy the stratified epithelium of the SC, providing a contained environment for paracrine signaling, cell contact-mediated signaling, and cell-ECM interactions that may impact trophoblast invasion. In this study we show that soluble signaling factors from SC cells restrict the invasion of their counterparts from the VC. We can speculate on potential candidates that may function across multiple biological systems to restrict invasion. CTB 4 cells express high levels of TIMP1, TIMP3, and SERPINE1 (PAI-1), which inhibit the function and/or activation of MMPs necessary for trophoblast invasion (Fisher et al., 1989; Zhu et al., 2012). Addition of recombinant TIMPs or a function blocking antibody against plasminogen (whose processing is inhibited by SERPINE1) reduced trophoblast invasion in vitro (Lala and Graham, 1990). While these molecules are expressed by decidual cells, we identified their expression within the SC epithelium directly adjacent to EVTs. Another candidate regulator of invasion expressed by CTB 4 is TNFα. Treatment of trophoblasts with TNFα increases EVT apoptosis, decreases protease expression, increases SERPINE1 expression, and decreases invasion in vitro (Otun et al., 2011; Xu et al., 2011). These data suggest that SC-CTBs secrete multiple molecules that could decrease the survival and invasion of EVTs in the SC. In this study we explored the role of secreted factors, but SC-CTBs may also influence EVTs through cell contact-mediated signaling and the deposition of an ECM distinct from the basal plate. Future experiments will be necessary to explore the contributions of these distinct signaling pathways and matrices.

In summary, this study provides a high-resolution molecular accounting of the trophoblast that occupy SC. By comparison with trophoblasts isolated from the VC of the same placentas, we identified key similarities and differences to better understand the molecular determinants of trophoblast function and activity in the SC. We characterized a novel CTB population, marked by KRT6A expression, that is central to three key functions of the SC: formation of an epidermal-like barrier, blockage of aberrant STB differentiation, and restriction of EVT invasion. These data provide a better understanding of molecular and cellular pathways that control human placental development and against which disease-related changes can be identified and therapeutic targets discovered.

Materials and methods

Key resources table
Reagent type(species) or resourceDesignationSource or referenceIdentifiersAdditional information
AntibodyRat anti E-cadherin Monoclonal Antibody (ECCD-2)Thermofisher Scientific13-1900IF (1:250)
AntibodyRabbit Anti-PAGE4 antibody (Polyclonal)Sigma-AldrichHPA023880IF (1:100)
AntibodyRabbit Recombinant Anti-Cytokeratin 6 Monoclonal antibody [EPR1603Y]Abcamab52620IF (1:100)
AntibodyRabbit anti Non-phospho (Active) β-Catenin (Ser33/37/Thr41) Monoclonal AntibodyCell Signaling8814IF (1:100)
AntibodyMouse anti Cytokeratin 14 Monoclonal Antibody (LL002)InvitrogenMA5-11599IF (1:100) with Antigen Retrieval
AntibodyRabbit Anti-KLF4 antibody (polyclonal)Sigma-AldrichHPA00292IF (1:100)
AntibodyRat anti Cytokeratin (7D3) Monoclonal AntibodySusan Fisher/University of California, San Francisco Cat# Fisher_001-clone7D3,RRID:AB_2631235AB_2631235IF (1:100)
AntibodyMouse anti HLA-G (4H84) Monoclonal AntibodySusan Fisher/University of California, San Francisco Cat# Fisher_002-clone4H84, RRID:AB_2631236AB_2631236IF (1:20) with Antigen Retrieval
Software, algorithmRhttps://www.r-project.org/
Software, algorithmImageJImageJ (http://imagej.nih.gov/ij/)
Software, algorithmSeurat (3.1.3)https://satijalab.org/seurat/
Software, algorithmcellranger (3.0.2)https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/feature-bc
Software, algorithmClusterProfilerhttps://guangchuangyu.github.io/software/clusterProfiler/
Software, algorithmscVelohttps://github.com/theislab/scvelo (Marsh, 2022b) copy archived at swh:1:rev:1805ab4a72d3f34496f0ef246500a159f619d3a2
Software, algorithmPrism 6.0https://www.graphpad.com/scientific-software/prism/
Software, algorithmDoubletFinderhttps://github.com/chris-mcginnis-ucsf/DoubletFinder (Marsh, 2022c) copy archived at swh:1:rev:67fb8b5808eb16167ead5f9b439677cc24837554

Tissue collection

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The University of California, San Francisco (UCSF) Institutional Review Board approved this study (11-05530). All donors gave informed consent. All samples are from elective terminations between gestational age of 17 weeks and 6 days and 24 weeks and 0 days.

Cellular isolation of VC trophoblast

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Trophoblast were isolated from both floating and anchoring villi dissected from second trimester human placentas. Trophoblast were isolated according to previously published protocols (Fisher et al., 1989; Kliman and Feinberg, 1990). Briefly, cuts were made at the base of each cotyledon near the chorionic plate and the entire villous tree up to and including the basal plate was taken for study. The decidua was removed from the basal plate side and the remaining villous tree was dissected and dissociated. The resulting floating and anchoring chorionic villi were washed in cold phosphate-buffered saline (PBS Ca2+ and Mg2+ free), dissected into 2–4 mm pieces, and filtered through a 1 mm mesh strainer to remove small pieces of tissue. CTBs were isolated from the tissue pieces by first removing the outer STB layer by collagenase digestion (Sigma-Aldrich; C-2674). Next, CTBs were dissociated by sequential enzymatic digestion (trypsin [Sigma-Aldrich; T8003; twice] and collagenase). Finally, CTBs were purified by Percoll density gradient centrifugation. Single cells were visually inspected for quality, counted using a hemacytometer, and immediately collected for scRNA-seq or for culture experiments.

Cellular isolation of SC trophoblast

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Trophoblast were isolated according to previously published protocols (Garrido-Gomez et al., 2017). Briefly, the fetal membranes were washed with PBS (Ca2+ and Mg2+ free) supplemented with 1% penicillin-streptomycin (10,000 units/ml penicillin; 10,000 μg/ml streptomycin), 0.003% fungizone (stock solution of 250 mg/ml), and 1% gentamicin. Next, the amnion and SC were manually separated and the amnion discarded. Next, the decidua parietalis was removed and discarded. The SC CTB layer was then minced into small pieces (2–4 mm) and dissociated by sequential enzymatic digestion. First, the tissue pieces were incubated in PBS (10 ml/g of tissue) containing 3.5 mg collagenase, 1.2 mg DNase, 6.9 mg hyaluronidase, and 10 mg bovine serum albumin for 15–30 min. The supernatant was then discarded. Next, the tissue was incubated for 20–40 min in PBS containing trypsin (6.9 mg trypsin, 20 mg EDTA, 12 mg DNase per 100 ml; tissue weight: dissociation buffer volume = 1:8). The enzyme activity was quenched by adding an equal volume of media containing 10% FBS. The cell suspension was filtered through a 70 μm sterile strainer and centrifuged at 1200 g for 7 min. A second collagenase digestion was performed by adding a 7× volume of the collagenase digestion buffer (see above), calculated on the basis of the weight of the cell pellet, followed by another incubation for 15–30 min. The cell suspension was then collected again by centrifugation. The cell pellets from the trypsin and second collagenase digestions were combined and purified over a Percoll density gradient centrifugation. Single cells were visually inspected for quality, counted using a hemacytometer, and immediately collected for scRNA-seq or for culture experiments.

scRNA-seq and analysis

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To capture the transcriptome of individual cells, we used the Chromium Single Cell 3' Reagent V3 Kit from 10× Genomics. For all samples 17,500 cells were loaded into one well of a Chip B kit for GEM generation. Library preparation including reverse transcription, barcoding, cDNA amplification, and purification was performed according to Chromium 10× V3 protocols. Each sample was sequenced on a NovaSeq 6000 S4 to a depth of approximately 20,000–30,000 reads per cell. The gene expression matrices for each dataset was generated using the CellRanger software (v3.0.2–10× Genomics). All reads were aligned to GRCh38 using STAR (https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/advanced/references). The counts matrix was thresholded and analyzed in the package Seurat (v3.1.3). Cells with fewer than 500 or greater than 6000 unique genes, as well as all cells with greater than 15% mitochondrial counts, were excluded from all subsequent analyses. Doublet detection was performed for each sample using DoubletFinder and all doublets were excluded from analysis. For each sample, counts were scaled and normalized using ScaleData and NormalizeData, respectively, with default settings FindVariableFeatures used to identify the 2000 most variable genes as input for all future analyses. Principal component analysis (PCA) was performed using RunPCA and significant principal components (PCs) assessed using ElbowPlot and DimHeatmap. Dimensionality reduction and visualization using UMAP was performed by RunUMAP.

Integration of each timepoint into one dataset was performed using FindIntegrationAnchors and IntegrateData, both using 20 dimensions (after filtering each dataset for number of genes, mitochondrial counts, and normalizing as described above). Data scaling, PCA, selection of PCs, clustering and visualization proceeded as described above using 30 PCs and a resolution of 0.6.

To generate the trophoblast, stroma, and immune cell subsets, the respective clusters were subset from the integrated dataset using the function SubsetData based upon annotations from marker genes identified by FindAllMarkers. After subsetting, counts were scaled and normalized using ScaleData and NormalizeData, respectively, with default settings FindVariableFeatures used to identify the 2000 most variable genes. Differentially expressed genes for each integrated dataset were identified using FindAllMarkers.

scVelo

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RNA velocity analysis was applied to the entire conglomerate dataset using Velocyto to generate spliced and unspliced reads for all cells. This dataset was then subset for the trophoblast dataset introduced in Figure 2. The scVelo dynamical model was run with default settings and subset by each timepoint.

Differential expression between regions

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The number of trophoblast cells in each cluster were downsampled to 100 cells from each region of origin and performed differential expression using FindMarkers function and repeated this for 100 permutations. The number of genes found to be significantly differentially expressed (adj. p-value < 0.05) from each permutation are plotted. Clusters having less that 100 cells from each region were excluded from this analysis.

Gene ontology analysis

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Gene ontology analysis was performed with ClusterProfiler enrichGO function. The simplify function within this package was used to consolidate hierarchically related terms using a cutoff of 0.5. Terms were considered significantly enriched with an adjusted p-value of less than 0.05.

Immunofluorescence staining

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Placental tissue for cryosectioning was fixed in 3% PFA at 4°C for 8 hr, washed in 1× PBS, then submerged in 30% sucrose overnight at 4°C prior to embedding in OCT medium. Placental tissue in OCT was sectioned at 5 μm for all conditions. In brief, slides were washed in 1× PBST (1× PBS, 0.05% Tween-20), blocked for 1 hr (1× PBS +5% donkey or goat serum + 0.3% Triton-X), incubated in primary antibody diluted for 3 hr at room temperature (or overnight at 4°C), washed in 1× PBST, incubated in secondary antibody (Alexa Fluor 488 or 594) for 1 hr at room temperature, incubated in DAPI for 10 min at room temperature, washed in 1× PBST, and mounted and sealed for imaging. Any antigen retrieval was performed prior to the blocking step by heating the slides in a 1× citrate buffer with 0.05% Tween-20 at 95°C for 30 min. All antibodies and the dilutions are listed in the Key resources table. All immunofluorescence staining was performed in n=3 biological replicates (3 distinct placentas) and representative image is shown.

Transwell invasion assay

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Twenty-four-well plate transwell inserts with an 8 μM polycarbonate membrane (Corning Costar 3422) were coated with 10 μl of Matrigel (growth factor-containing, Corning Corp, Corning, NY) diluted 1:1 in Serum Free Media (95% DME H-21+ Glutamine, 2% Nutridoma (mostly β-D xylopyranose), 1% Pen Strep, 1% HEPES, 0.1% Gentamycin). Cells from each region of the placenta were isolated as described above and then plated in the upper well of the transwell insert at a density of 250,000 cells in 250 μl of Serum Free Media, with 1 ml of Serum Free Media (or conditioned media) in the well below the insert. The cells were then cultured for 39 hr. The cells in the transwell were then fixed in 3% PFA for 10 min at 4°C, permeabilized in ice-cold methanol for 10 min at 4°C, then washed in 1× PBS, incubated in Pan-CK primary antibody for 3 hr at 37°C, washed in 1× PBS, incubated with secondary antibody (Alexa Fluor 594) and DAPI for 1 hr at 37°C, washed in 1× PBS, then mounted and sealed for imaging. All experiments were performed in n=3 biological replicates (3 distinct placentas). For conditioned media experiments n=3 biological replicates were analyzed (cells and conditioned medium derived from 3 distinct placentas).

Transwell invasion assay quantification

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Transwell membranes mounted on slides were imaged at 40× magnification and the number of Pan-CK-positive projections through the membrane counted. Normalization for changes in cell density across fields of view and culture conditions was performed by quantifying the DAPI-positive area of each image and the number of projections normalized to the median across comparisons. Between 7 and 10 fields of view were quantified for each transwell membrane. p-Values were determined by t-test.

Culture of placental cells and generation of conditioned media

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Each well of a 24-well plate was coated with 20 μl of Matrigel undiluted (growth factor-containing, Corning Corp, Corning, NY) prior to cell seeding. Cells from each region of the placenta were isolated as described above and then plated into wells of a 24-well plate at a density of 1 × 106 cells in 1 ml of Serum Free Media (95% DME H-21+ Glutamine, 2% Nutridoma (mostly β-D xylopyranose), 1% Pen Strep, 1% HEPES, 0.1% Gentamycin). The cells were cultured for 39 hr. After 39 hr in culture the media was removed and centrifuged at 2000 g for 10 min to remove cellular debris. The supernatant was then removed, snap frozen in LN2, and stored at –80°C. This media was then thawed and used as conditioned media.

Data availability

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All sequencing data is available at the NCBI Gene Expression Omnibus GSE198373. Processed data are available as R objects at https://figshare.com/projects/Regionally_distinct_trophoblast_regulate_barrier_function_and_invasion_in_the_human_placenta/135191. Code to process all raw data and generate the datasets analyzed are available at https://github.com/marshbp/Regionally-distinct-trophoblast-regulate-barrier-function-and-invasion-in-the-human-placenta (Marsh, 2022a) copy archived at swh:1:rev:f8fb40282cc8d6c1bfabd4d4a06902eb92fefc94 . Previously published datasets from Vento-Tormo et al., 2018 Pique-Regi et al., 2019, are publicly available.

Data availability

Sequencing data have been deposited in GEO under the accession code GSE198373 Processed data have been deposited on Figshare at https://figshare.com/projects/Regionally_distinct_trophoblast_regulate_barrier_function_and_invasion_in_the_human_placenta/135191. Code to generate the processed data have been deposited on GitHub at https://github.com/marshbp/Regionally-distinct-trophoblast-regulate-barrier-function-and-invasion-in-the-human-placenta (copy archived at swh:1:rev:f8fb40282cc8d6c1bfabd4d4a06902eb92fefc94).

The following data sets were generated
    1. Marsh BP
    2. Blelloch RH
    3. Fisher SJ
    (2022) NCBI Gene Expression Omnibus
    ID GSE198373. Regionally distinct trophoblast regulate barrier function and invasion in the human placenta.
The following previously published data sets were used
    1. Vento-Tormo R
    (2018) ArrayExpress
    ID E-MTAB-6701. Reconstructing the human first trimester fetal-maternal interface using single cell transcriptomics - 10x data.
    1. Pique-Regi R
    (2019) NIH dpGAP
    ID phs001886.v1.p1. Single Cell Transcriptional Signatures of the Human Placenta in Term and Preterm Parturition.

References

    1. Hamilton WJ
    2. Boyd JD
    (1960)
    Development of the human placenta in the first three months of gestation
    Journal of Anatomy 94:297–328.
    1. Harris LK
    2. Jones CJP
    3. Aplin JD
    (2009)
    Adhesion molecules in human trophoblast - a review
    II. Extravillous Trophoblast. Placenta 30:299–304.
    1. McMaster MT
    2. Librach CL
    3. Zhou Y
    4. Lim KH
    5. Janatpour MJ
    6. DeMars R
    7. Kovats S
    8. Damsky C
    9. Fisher SJ
    (1995)
    Human placental HLA-G expression is restricted to differentiated cytotrophoblasts
    Journal of Immunology 154:3771–3778.
    1. Pijnenborg R
    2. Vercruysse L
    3. Brosens I
    (2011) Deep placentation
    Best Practice & Research. Clinical Obstetrics & Gynaecology 25:273–285.
    https://doi.org/10.1016/j.bpobgyn.2010.10.009

Decision letter

  1. Claudia Gerri
    Reviewing Editor; Max Planck Institute of Molecular Cell Biology and Genetics, Germany
  2. Didier YR Stainier
    Senior Editor; Max Planck Institute for Heart and Lung Research, Germany

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Regionally distinct trophoblast regulate barrier function and invasion in the human placenta" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by a Reviewing Editor and Didier Stainier as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

Overall, this study is novel and addresses an important and uninvestigated question utilizing scRNA-seq and elegant computational approaches. The reviewers consider that the field will benefit from the publication of this research. Yet, there are caveats that the authors need to address before publication.

General comments:

(1) A point raised by both Reviewers #1 and #3 is about sample collection. We suggest the authors explain in detail in the method sections how the cellular isolations of VC and SC trophoblasts were performed and not just cite other papers. In addition, it would be good to know how the samples were obtained. Were they normal and voluntarily terminated? Reviewer #3 mentions that only very rarely do pregnancies fail in the second trimester which is a safe period for the mother and baby, so what is the reason for their delivery? If they were voluntary terminations of pregnancy they will have been exposed to mifepristone/misoprostol that will affect gene expression. Whichever way they were obtained they are not normal. Were the samples of the SC taken from close to the disc or near the cervix?

(2) Reviewers #1 and #2 raised a similar suggestion. The authors mention that there are differences in the EVTs isolated from the placental villi and those obtained from the chorioamniotic membranes. Other studies from the first and third trimesters have documented the presence of EVTs using scRNA-seq. I think that the study would benefit from comparing the authors' data (second trimester) with these publically available datasets. I think the authors could provide a unique roadmap of EVT transcriptomic activity throughout gestation.

(3) The findings on SC-CTB-secreted factors inhibiting EVT invasion are very intriguing. Could the authors analyze the conditioned media (via mass spec or ELISA) from SC and VC to try to identify potential candidates (e.g. SERPINE1 as mentioned in the discussion)?

(4) A major concern raised by Reviewer #2 is about the nomenclature used in the paper: villous chorion and smooth chorion. According to the schematic representation shown in Figure 1A, the authors sample the placental villous attached to the basal plate. Is this correct? It would have been more appropriate to sample the placental villous neighboring the chorionic plate or that from the inner placental mass, which is not associated with the basal or chorionic plate. In addition, the names utilized to define these placental compartments are unconventional. The readers would benefit from the authors utilizing conventional nomenclatures such as placental villi or chorioamniotic membranes. Last, the trophoblast in the chorioamniotic membranes is termed chorion laeve. The authors should consider using such terms throughout the manuscript and figures.

(5) Recent studies have suggested that stromal cells from the membranes participate in the host response against viral infection (COVID-19). Did the authors analyze the different types of stromal cell types between the placental villi and the membranes, and whether these display shared or unique functionality? Please discuss.

(6) Overall, the study would benefit from incorporating publically available data into the manuscript to strengthen the conclusions.

(7) Differences between CTBs and EVTs can also rely on cell-cell communications. Therefore, the authors are encouraged to perform Cell-Cell Chat analysis using their data.

(8) The manuscript is clearly presented and well written with a comprehensive reference to primary papers. However, many cited references are not included in the list including Maltepe 2015; Boll-Resli 1981; Pique-Regi 2020; Schlegelmilch 2011; Turco 2018; Kim 2006; Garcia-Flores et al. 2022.

(9) Reviewer #3 mentioned that identification of phenotypic markers specific to the smooth chorion is of interest and explains how this layer mediates important barrier functions for the fetus. An interesting finding is the specific expression of KRT6A in the smooth chorion. What is not clear from the report is how this layer is generated from the villi that originally surround the conceptus, which then regress probably due to the high oxygen concentrations at the periphery (Burton). This process is defective when the trophoblast transformation of vessels is abnormal in conditions, such as pre-eclampsia and preterm labour. The findings would be considerably more interesting if they could access samples earlier in pregnancy as the underlying pathogenesis of preterm labour is still unclear.

Thus, the important unanswered question is: what in these two microenvironments is driving the differential trajectory of CTB 1 towards villous SCT or to smooth KRT6A+ cells in the smooth chorion? Some TFs are different but what causes their upregulation in SC and this needs addressing? Are there any cells with the SC transcriptome in the new models of human TSC (Okae 2018; Turco 2018; Haider 2018)? These provide an in vitro model to generate SC and would considerably strengthen the paper.

(10) The methods of cell isolation rely on referring back to a previous publication and need expanding. "CTBs were enriched over stromal and immune cells – line 126" needs explanation as this will be a potential source of bias in the results. It appears that samples of the maternal side of the main villous placenta have been obtained – is this correct? Was decidua deliberately included? For the smooth chorion was the whole chorion – amnion, stroma, trophoblast and decidua included? These details are important as selecting specific areas and removing others will obviously alter the comparisons made.

(11) There are 6 macrophage populations – where are they located and which one is fetal Hofbauer cells? The relative paucity of NK cells and abundance of macrophages suggest that these are not from decidua. But why are there so few blood/decidual T cells? The best way to resolve these issues is to sequence maternal blood (and cord blood if obtainable) at the same time and remove these from the analyses.

To resolve these issues, the authors should compare their immune and fibroblast datasets in S2-S3 with previous single-cell data from the first trimester (Suryawanshi et al. 2018, Vento-Tormo et al. 2018). The correspondence between the decidual macrophages described in this manuscript is unclear compared with the ones from previous studies. Surprisingly, the 3 dNK cells previously identified are not found in this new dataset.

(12) The authors integrate the data from the villous chorion and smooth chorion computationally (eg Figure 1b). However, they should demonstrate that the integration pipeline is working correctly by defining the same cell states identified in the joint manifold when analysing the data separately. The same applies to all the analyses when specific cell subsets are zoomed in. For example, Figure 2c.

In addition, for each of the UMAPs defined in the manuscript, it is important to colour them by individual to confirm the integration.

(13) The EVT populations described are problematic and the logic of this section is hard to follow. Most trophoblast cells in the SC express the definitive EVT marker, HLA-G (Hutter 1996). So how do these EVT subsets relate to CTR2-4 and to published datasets? It is also surprising that the same EVT populations are defined in the analysis of villous and smooth chorion. An artifact due to the computational algorithm could be forcing the integration and it needs to be shown that this is not the case. This is common, as, by default, the method will assume equivalent populations in both datasets. One way to do so may be by using alternative methods for data integration (e.g., harmony, scVI), or by analysing datasets independently and comparing them afterwards (e.g. correlation analysis, machine-learning tools). It is also not made clear that the interstitial trophoblast deep in the decidua have not been sampled and only those EVT in the cell columns.

Detailed comments:

(1) Line 368 "within the arterial wall, this population (interstitial EVT) further differentiates to endovascular EVTs and replaces the maternal arterial endothelium (Harris 2009, Red-Horse 2004)". This is controversial with the majority view instead being that interstitial trophoblast mediates medial destruction and then there is the replacement of the endothelium by endovascular trophoblast moving down from the shell (Pijnenborg; Bulmer).

(2) Introduction: The second paragraph of the introduction could be rewritten and/or incorporated into the third paragraph. In its present form, it reads like a textbook. In addition, the introduction is somewhat too long and would benefit from some trimming.

(3) Introduction: The last paragraph of the introduction should be revised. The authors mention that previous studies using scRNA-seq were performed in the fetal membranes from term pregnancies (Page 3, Lines 104-105). Yet, no references were provided. In the following sentence (Page 3, Lines 105-106), the authors mention "in this study, CTBs were not identified in the smooth chorion…" and the authors cite four papers. Which of these papers are the authors referring to? In the 2019 study that is cited, the authors reported the presence of CTBs and EVTs in the fetal membranes. In addition, new studies have also reported the presence of CTBs and EVTs in the fetal membranes (PMIDs: 32662421, 35042863). The authors may consider including this information.

(4) Results, Page 3, Lines 122-124: The authors mention that they chose to analyze second-trimester samples to avoid inflammation and apoptosis associated with parturition. Couldn't the authors include samples from women who delivered at term without labor?

(5) Page 8, Lines 265-266: What is the evidence to suggest that the differentiation of SC-CTB does not require a cell cycle?

(6) Results, Page 5, Lines 157-159: The authors suggest the involvement of maternal immune cells in VC and SC; yet, these results are expected since both the placental villous and the chorioamniotic membranes (which were sampled in this study) represent the maternal-fetal interface: intervillous space and decidua parietalis, respectively.

(7) Results: The authors are congratulated for validating the mRNA results with protein expression. Specifically referring to Figure 2. The authors are also congratulated for testing functional differences between the EVTs from the placental villi and those from the membranes. I think that this is a very useful piece of information, and the functionality of the different subsets of EVTs requires further investigation.

(8) Results, Page 8, Line 245: The authors claim that the CTBs from the membranes and those from the placental villi share a common progenitor by performing an elegant RNA velocity analysis. Yet, wouldn't it be expected that all trophoblasts share a progenitor?

(9) Figure 5: What is the evidence that EVTs from the two different compartments are behaviorally distinct? In other words, what do the authors mean by "behavioral"?

(10) Immune cells Figure 1S2

How were the 4 different immune cell populations – maternal blood/decidual and fetal blood/placental cells distinguished? Both stromal and immune cells from both individuals will be present in these isolates. It is said by XIST that most of the immune cells are maternal – are they from blood or decidua? Were the fetuses all male? It would be better to look at the transcriptomes and deconvolute the genotype data by SNPs to confirm.

(11) Figure 1S3

The presence of lymphatics in decidua is disputed (Volchek 2010) and Hofbauer cells express LYVE1 (Bockle 2008) so how certain is the presence of lymphatics.

(12) In Figure 2 the authors introduce a CTB cycling population. Is this population corresponding to CTB-1 only? Or, is it a mix of CTB-1-4? If the latter, defining independent clusters by, for example, removing cell cycling genes from the highly variable genes considered to define the clusters should be performed.

(13) Line 417. The "suggesting a cell non-autonomous, rather than cell-autonomous, regulation of invasion" is speculation that should be included in discussion rather than the results. How do the authors explain the fact that trophoblast organoids invade in the absence of decidua as in tubal or abdominal pregnancy? To increase the impact the proteomes of the two conditioned media from VC and SC should be studied to identify what is inhibiting invasion into decidua.

Reviewer #1 (Recommendations for the authors):

– Line 368 "within the arterial wall, this population (interstitial EVT) further differentiates to endovascular EVTs and replaces the maternal arterial endothelium (Harris 2009, Red-Horse 2004)". This is a controversial statement. Please review well the literature on this and include additional references.

– It would be very interesting to know if there are SC-CTB present in the new in vitro models (trophoblast stem cells from Okae et al. 2018 or in the placental organoids obtained as in Turco et al. 2018 and Haider et al. 2018).

– It may be important to report the sex of the fetuses, especially in the sequencing data repository.

Reviewer #2 (Recommendations for the authors):

1. A major concern is the nomenclature utilized in this study: villous chorion and smooth chorion. According to the schematic representation shown in Figure 1A, the authors sample the placental villous attached to the basal plate. Is this correct? I think it would have been more appropriate to sample the placental villous neighboring the chorionic plate or that from the inner placental mass, which is not associated with the basal or chorionic plate. In addition, the names utilized to define these placental compartments are unconventional. The readers would benefit from the authors utilizing conventional nomenclatures such as placental villi or chorioamniotic membranes. Last, the trophoblast in the chorioamniotic membranes is termed chorion laeve. The authors should consider using such terms throughout the manuscript and figures.

2. Introduction: The second paragraph of the introduction could be rewritten and/or incorporated into the third paragraph. In its present form, it reads like a textbook. In addition, the introduction is somewhat too long and would benefit from some trimming.

3. Introduction: The last paragraph of the introduction should be revised. The authors mention that previous studies using scRNA-seq were performed in the fetal membranes from term pregnancies (Page 3, Lines 104-105). Yet, no references were provided. In the following sentence (Page 3, Lines 105-106), the authors mention "in this study, CTBs were not identified in the smooth chorion…" and the authors cite four papers. Which of these papers are the authors referring to? In the 2019 study that is cited, the authors reported the presence of CTBs and EVTs in the fetal membranes. In addition, new studies have also reported the presence of CTBs and EVTs in the fetal membranes (PMIDs: 32662421, 35042863). The authors may consider including this information.

4. Results, Page 3, Lines 122-124: The authors mention that they chose to analyze second-trimester samples to avoid inflammation and apoptosis associated with parturition. Couldn't the authors include samples from women who delivered at term without labor?

5. Results, Page 5, Lines 157-159: The authors suggest the involvement of maternal immune cells in VC and SC; yet, these results are expected since both the placental villous and the chorioamniotic membranes (which were sampled in this study) represent the maternal-fetal interface: intervillous space and decidua parietalis, respectively.

6. Results: The authors are congratulated for validating the mRNA results with protein expression. I am specifically referring to Figure 2.

7. Results, Page 8, Line 245: The authors claim that the CTBs from the membranes and those from the placental villi share a common progenitor by performing an elegant RNA velocity analysis. Yet, wouldn't it be expected that all trophoblasts share a progenitor?

8. Page 8, Lines 265-266: What is the evidence to suggest that the differentiation of SC-CTB does not require a cell cycle?

9. The authors mention that there are differences in the EVTs isolated from the placental villi and those obtained from the chorioamniotic membranes. Other studies from the first and third trimesters have documented the presence of EVTs using scRNA-seq. I think that the study would benefit from comparing the authors' data (second trimester) with these publically available datasets. I think the authors could provide a unique roadmap of EVT transcriptomic activity throughout gestation.

10. Figure 5: What is the evidence that EVTs from the two different compartments are behaviorally distinct? In other words, what do the authors mean by "behavioral"?

11. I would like to congratulate the authors for testing functional differences between the EVTs from the placental villi and those from the membranes. I think that this is a very useful piece of information, and the functionality of the different subsets of EVTs requires further investigation.

12. Recent studies have suggested that stromal cells from the membranes participate in the host response against viral infection (e.g., COVID-19, PMID: 35042863). Did the authors analyze the different types of stromal cell types between the placental villi and the membranes, and whether these display shared or unique functionality? Please discuss.

13. Overall, I think the study would benefit from incorporating publically available data into the manuscript to strengthen the conclusions.

14. Last, differences between CTBs and EVTs can also rely on cell-cell communications. Therefore, the authors are encouraged to perform CellChat analysis using their data.

Reviewer #3 (Recommendations for the authors):

This is an analysis of the smooth chorion compared to the invasive trophoblast populations in the second trimester of human pregnancy.

The manuscript is clearly presented and well written with a comprehensive reference to primary papers. However, many cited references are not included in the list including Maltepe 2015; Boll-Resli 1981; Pique-Regi 2019; Schlegelmilch 2011; Turco 2018; Kim 2006.

The identification of phenotypic markers specific to the smooth chorion is of interest and explains how this layer mediates important barrier functions for the fetus. An interesting finding is the specific expression of KRT6A in the smooth chorion. What is not clear from the report is how this layer is generated from the villi that originally surround the conceptus which then regress probably due to the high oxygen concentrations at the periphery (Burton). This process is defective when the trophoblast transformation of vessels is abnormal in conditions such as pre-eclampsia and preterm labour. The findings would be considerably more interesting if they could access samples earlier in pregnancy as the underlying pathogenesis of preterm labour is still unclear.

Thus, the important unanswered question is: what in these two microenvironments is driving the differential trajectory of CTB 1 towards villous SCT or to smooth KRT6A+ cells in the smooth chorion? Some TFs are different but what causes their upregulation in SC and this needs addressing? Are there any cells with the SC transcriptome in the new models of human TSC (Okae 2018; Turco 2018; Haider 2018)? These provide an in vitro model to generate SC and would considerably strengthen the paper.

How were the samples were obtained?

No details are given about the clinical details of these pregnancies. Were they normal and voluntarily terminated? Pregnancies only very rarely fail in the second trimester which is a safe period for the mother and baby so what is the reason for their delivery?

If they were voluntary terminations of pregnancy they will have been exposed to mifepristone/misoprostol that will affect gene expression. Whichever way they were obtained they are not normal.

Were the samples of the SC taken from close to the disc or near the cervix?

The methods of cell isolation rely on referring back to a previous publication and need expanding. "CTBs were enriched over stromal and immune cells – line 126" needs explanation as this will be a potential source of bias in the results. It appears that samples of the maternal side of the main villous placenta have been obtained – is this correct? Was decidua deliberately included? For the smooth chorion was the whole chorion – amnion, stroma, trophoblast and decidua included? These details are important as selecting specific areas and removing others will obviously alter the comparisons made.

Immune cells Figure 1S2

How were the 4 different immune cell populations – maternal blood/decidual and fetal blood/placental cells distinguished? Both stromal and immune cells from both individuals will be present in these isolates. It is said by XIST that most of the immune cells are maternal – are they from blood or decidua? Were the fetuses all male? It would be better to look at the transcriptomes and deconvolute the genotype data by SNPs to confirm.

There are 6 macrophage populations – where are they located and which one is fetal Hofbauer cells? The relative paucity of NK cells and abundance of macrophages suggests that these are not from decidua. But why are there so few blood/decidual T cells? The best way to resolve these issues is to sequence maternal blood (and cord blood if obtainable) at the same time and remove these from the analyses.

To resolve these issues, the authors should compare their immune and fibroblast datasets in S2-S3 with previous single-cell data from the first trimester (Suryawanshi et al. 2018, Vento-Tormo et al. 2018). The correspondence between the decidual macrophages described in this manuscript is unclear compared with the ones from previous studies. Surprisingly, the 3 dNK cells previously identified are not found in this new dataset.

Figure 1S3

The presence of lymphatics in decidua is disputed (Volchek 2010) and Hofbauer cells express LYVE1 (Bockle 2008) so how certain is the presence of lymphatics.

The authors integrate the data from the villous chorion and smooth chorion computationally (eg Figure 1b). However, they should demonstrate that the integration pipeline is working correctly by defining the same cell states identified in the joint manifold when analysing the data separately. The same applies to all the analyses when specific cell subsets are zoomed in. For example, Figure 2c.

In addition, for each of the UMAPs defined in the manuscript, it is important to colour them by individual to confirm the integration.

In Figure 2 the authors introduce a CTB cycling population. Is this population corresponding to CTB-1 only? Or, is it a mix of CTB-1-4? If the latter, defining independent clusters by, for example, removing cell cycling genes from the highly variable genes considered to define the clusters should be performed.

The EVT populations described are problematic and the logic of this section is hard to follow. Most trophoblast cells in the SC express the definitive EVT marker, HLA-G (Hutter 1996). So how do these EVT subsets relate to CTR2-4 and to published datasets? It is also surprising that the same EVT populations are defined in the analysis of villous and smooth chorion. An artifact due to the computational algorithm could be forcing the integration and it needs to be shown that this is not the case. This is common, as by default, the method will assume equivalent populations in both datasets. One way to do so may be by using alternative methods for data integration (e.g., harmony, scVI), or by analysing datasets independently and comparing them afterwards (e.g. correlation analysis, machine-learning tools). It is also not made clear that the interstitial trophoblast deep in the decidua have not been sampled and only those EVT in the cell columns.

Line 368 "within the arterial wall, this population (interstitial EVT) further differentiates to endovascular EVTs and replaces the maternal arterial endothelium (Harris 2009, Red-Horse 2004)". This is controversial with the majority view instead being that interstitial trophoblast mediates medial destruction and then there is the replacement of the endothelium by endovascular trophoblast moving down from the shell (Pijnenborg; Bulmer).

Line 417. The "suggesting a cell non-autonomous, rather than cell-autonomous, regulation of invasion" is speculation that should be included in discussion rather than the results. How do the authors explain the fact that trophoblast organoids invade in the absence of decidua as in tubal or abdominal pregnancy? To increase the impact the proteomes of the two conditioned media from VC and SC should be studied to identify what is inhibiting invasion into decidua.

https://doi.org/10.7554/eLife.78829.sa1

Author response

Essential revisions:

Overall, this study is novel and addresses an important and uninvestigated question utilizing scRNA-seq and elegant computational approaches. The reviewers consider that the field will benefit from the publication of this research. Yet, there are caveats that the authors need to address before publication.

General comments:

(1) A point raised by both Reviewers #1 and #3 is about sample collection. We suggest the authors explain in detail in the method sections how the cellular isolations of VC and SC trophoblasts were performed and not just cite other papers. In addition, it would be good to know how the samples were obtained. Were they normal and voluntarily terminated? Reviewer #3 mentions that only very rarely do pregnancies fail in the second trimester which is a safe period for the mother and baby, so what is the reason for their delivery? If they were voluntary terminations of pregnancy they will have been exposed to mifepristone/misoprostol that will affect gene expression. Whichever way they were obtained they are not normal. Were the samples of the SC taken from close to the disc or near the cervix?

We thank the reviewers for this suggestion. We have added more detailed protocols for dissection and cellular isolation from both the villous and smooth chorion to the methods section. All samples were healthy and voluntarily terminated in the second trimester between weeks 17 and 24. This information has been added to the methods section.

We also thank the reviewers for the consideration of the influence of the elective termination procedure on the cells we collected. Mifepristone and Misoprostol were not used in the termination of these pregnancies.

As to the location of the samples, the SC samples include cells isolated from varying distances from the disk and cervix. Briefly, the fetal membranes (often not fully intact) were washed and the amnion and decidual parietalis removed. The tissue was then minced into small pieces. These pieces contain tissue from many points along the SC. Therefore, the samples represent a sampling of cells from multiple locations in the SC, not biopsies of specific locations.

(2) Reviewers #1 and #2 raised a similar suggestion. The authors mention that there are differences in the EVTs isolated from the placental villi and those obtained from the chorioamniotic membranes. Other studies from the first and third trimesters have documented the presence of EVTs using scRNA-seq. I think that the study would benefit from comparing the authors' data (second trimester) with these publically available datasets. I think the authors could provide a unique roadmap of EVT transcriptomic activity throughout gestation.

We thank the reviewers for the suggestion of incorporating public data to strengthen our analysis of the trophoblast populations, including EVTs. We compared our trophoblast data to data from the VC of first trimester placentas from Vento-Tormo et al., 2018 and to data from the VC and SC of term placentas from Pique-Regi et al., 2019. First, we performed pairwise comparisons of the populations recovered in each of these studies, to identify which cell types were common among studies (Figure 2-S1d and f). This demonstrated the transcriptome of CTB 4 (and to a lesser extent CTB 3) not to be correlated with populations found in either Vento-Tormo et al., 2018 or Pique-Regi et al., 2019. Additionally, this analysis identified a strong correlation between the EVT populations identified in each dataset. EVT identified in Vento-Tormo et al., 2018 (first trimester) showed a stronger correlation with the less mature EVT populations in our dataset, while EVT identified in Pique-Regi et al., 2019 (term) demonstrated strong correlation with the most mature EVT clusters (2-4) in our data. To investigate this with higher resolution we integrated the data from each dataset with our own. Integration of the data from Vento-Tormo et al., 2018 show first trimester EVTs (including cells FAC sorted from HLA-G protein expression) align with our EVT 1 population. This suggests these first trimester EVTs resemble the least mature EVTs in our data and lack several features of invasive cells. Integration of the data from Pique-Regi et al., 2019 show strong similarity to those identified in our dataset. The cells isolated from the SC at term appear restricted to the less invasive EVT clusters in our dataset. Together these analyses suggest that EVTs in the SC may decrease an invasive transcriptional program between the second trimester and term. However, as there are notable differences in the populations recovered by Pique-Regi et al., 2019 and our study, including SC-CTBs (not found in Pique-Regi et al., 2019) and npi-CTBs (not found in our data), we cannot rule out that differences in EVT transcriptional signature may not be solely due to gestational age but due to differences in dissection and. Therefore, we are cautious about expanding claims about the relationship between EVTs and developmental time beyond these analyses.

(3) The findings on SC-CTB-secreted factors inhibiting EVT invasion are very intriguing. Could the authors analyze the conditioned media (via mass spec or ELISA) from SC and VC to try to identify potential candidates (e.g. SERPINE1 as mentioned in the discussion)?

We agree with the reviewers that the potential for specific secreted factors from SCCTBs suppressing EVT migration is exciting and that their discovery would be exceptionally impactful. Indeed, Dr. Blelloch is currently preparing a grant proposing to seek and study such factors including by mass spec and ELISA panels. This grant proposal is a five-year plan and we feel beyond the scope of the current manuscript.

(4) A major concern raised by Reviewer #2 is about the nomenclature used in the paper: villous chorion and smooth chorion. According to the schematic representation shown in Figure 1A, the authors sample the placental villous attached to the basal plate. Is this correct? It would have been more appropriate to sample the placental villous neighboring the chorionic plate or that from the inner placental mass, which is not associated with the basal or chorionic plate.

We apologize to the reviews for the lack of clarity around the regions that were sampled in this study. We sampled cells from both floating villi and anchoring villi. We sampled these regions to ensure capture of CTBs, STBs, and EVTs. A schematic of the anchoring villi was provided because it depicts all of these cell types, whereas a schematic of the floating villi would not depict EVTs. We also deem it necessary to highlight the villous cell types including invasive EVTs in the VC to contrast with the SC trophoblast epithelium which lacks both villi and invasive EVTs. The text has been updated to specify that both floating and anchoring villi were collected.

We have included a more detailed explanation of the method of sampling. Cuts were made at the base of each cotyledon near the chorionic plate and the entire villous tree up to and including the basal plate was taken for study. The decidua was removed from the basal plate side and the remaining villous tree was dissected and dissociated. This retained cells in the basal plate (including cells moving through the villous cell column and beginning invasion into the decidua) and the floating villi near the chorionic plate and the inner placental mass.

In addition, the names utilized to define these placental compartments are unconventional. The readers would benefit from the authors utilizing conventional nomenclatures such as placental villi or chorioamniotic membranes. Last, the trophoblast in the chorioamniotic membranes is termed chorion laeve. The authors should consider using such terms throughout the manuscript and figures.

We appreciate the reviewers concern over the nomenclature in the manuscript. The terms villous chorion and smooth chorion are accepted and widely used in publications (Benirschke et al., 2006; Genbacev et al., 2015; Genbacev et al., 2016; Yuan et al., 2006; Yuan et al., 2008; Yuan et al., 2009; King, 1981; Garrido-Gomez et al., 2017). While we appreciate that placental biologists and clinicians still use the traditional Latin names of chorionic villi and chorion laeve. Since we are submitting this study to eLife for a general audience we would like to maintain the use of common English terms for the clarity of the general readership. We have rewritten the introduction to clarify the nomenclature we use and put it in the context of the more specialized terms such as chorioamniotic membranes.

(5) Recent studies have suggested that stromal cells from the membranes participate in the host response against viral infection (COVID-19). Did the authors analyze the different types of stromal cell types between the placental villi and the membranes, and whether these display shared or unique functionality? Please discuss.

We thank the reviewers for this suggestion. We feel it would be off topic and inappropriate to comment on the application of these data to the ongoing COVID-19 pandemic for multiple reasons. Primarily, the trophoblast enrichment protocol specifically selects against stromal cells. We recover fewer than 1000 cells from VC samples, including two samples with just 1 and 13 cells. This analysis is deeply underpowered to identify differentially expressed markers with specific functions in viral infection. Future cellular isolations which do not deplete stromal cells will be necessary to answer this question. Second, there are several datasets which have better representation of the stroma which would be better suited to answer this question (Pique-Regi et al., 2019; Pique-Regi et al., 2020; Garcia-Flores et al., 2022). Lastly, our study was designed to focus on trophoblast cell fate and function and a deviation to explore anti-viral properties of the stromal cells is beyond the scope of this work.

(6) Overall, the study would benefit from incorporating publically available data into the manuscript to strengthen the conclusions.

We thank the reviewers for these suggestions. We have incorporated two publicly available datasets, Vento-Tormo et al., 2018 (an atlas of first trimester villous chorion cells including emphasis on the stroma and immune populations) and the Pique-Regi et al., 2019 (a dataset that profiles both the villous and smooth chorions at term). First, we compared the cell types we identified in each of the trophoblast, stroma, and immune subsets to the cell types identified in Vento-Tormo et al., 2018 (Figure 1-S3a and c; Figure 2-S1d) and in Pique-Regi et al., 2019 (Figure 1-S4a and c; Figure 2-S1f). We found strong correlations in average expression between our cluster and those previously identified, which allowed for the validation of our cell type annotations. This comparison did not involve any computational integration and demonstrated the recovery of similar cell types in independent clustering analyses. Second, we computationally integrated our data with Vento-Tormo et al., 2018 (Figure 1-S3b and d; Figure 2 S1e) and Pique-Regi et al., 2019 (Figure 1-S4b and d; Figure 2-S1g), which demonstrated similarity between the cell types identified in each dataset.

(7) Differences between CTBs and EVTs can also rely on cell-cell communications. Therefore, the authors are encouraged to perform Cell-Cell Chat analysis using their data.

We thank the reviewers for the suggestion to model receptor-ligand interactions between cell types in each region. We analyzed the predicted signaling interactions within cell population isolated from each region using CellPhoneDB and focused on three comparisons: CTB-CTB interactions (VC and SC), EVT-stromal interactions (VC and SC) and, CTB-EVT interactions (only in SC).

Since the four distinct CTB populations in the SC reside in close proximity to each other, we hypothesized that signaling between these populations may instruct the differentiation from CTB 1 to CTB 4 (SC-CTB). The number of predicted receptor-ligand interactions unique to the SC between CTB populations far outnumber those unique to the VC, which is largely composed of only CTB 1 cells. We plotted the predicted interactions unique to each region and the weight of the interactions estimated by mean expression of both receptor and ligand in the heatmaps in Figure 3-S2. Several pathways were unique to the SC including BMP and Notch signaling, as well as, several members of the EPH-EPHRIN family (Figure 3-S2b). These interactions may contribute to the function of the SC through influencing cell fate and/or cell sorting.

Next, we analyzed the predicted interactions between EVT and stromal cells within each region to identify differential signaling events that might contribute to reduced invasion in the SC. We plotted the predicted interactions unique to each region and the weight of the interactions estimated by mean expression of both receptor and ligand in the heatmaps in Figure 5-S3. We identified a greater number of interactions unique to the VC than the SC, and these interactions were enriched for Collagen-Integrin signaling from the EVTs to stromal cells (Figure 5-S3a). Interactions unique to EVT-stroma in the SC included PDGFA and FN1 secreted by EVTs to receptors various stromal cells

(Figure 5-S3b). Future work will test the functional consequences of these interactions.

Finally, since we observed frequent CTB-EVT interactions in the SC, we investigated signaling between these populations. We did not perform this analysis for the VC cells as these populations do not contact each other in the VC and any predicted interactions would be false-positives. We plotted the predicted interactions between CTBs and EVTs in the SC and the weight of the interactions estimated by mean expression of both receptor and ligand in the heatmap in Figure 6-S4. To attempt to identify secreted factors which might be responsible for the reduced invasion in VC cells upon treatment with SC conditioned media (Figure 6d), we analyzed the interactions between CTB 4 and EVTs which contained a secreted factor (Figure 6e). This analysis revealed numerous factors for future study, including FN1, TNFa, PGF, THBS1, TGFB1, FGF1, and PDGFB.

(8) The manuscript is clearly presented and well written with a comprehensive reference to primary papers. However, many cited references are not included in the list including Maltepe 2015; Boll-Resli 1981; Pique-Regi 2020; Schlegelmilch 2011; Turco 2018; Kim 2006; Garcia-Flores et al. 2022.

We apologize for the oversight in not providing appropriate citations. All citations are now added.

(9) Reviewer #3 mentioned that identification of phenotypic markers specific to the smooth chorion is of interest and explains how this layer mediates important barrier functions for the fetus. An interesting finding is the specific expression of KRT6A in the smooth chorion. What is not clear from the report is how this layer is generated from the villi that originally surround the conceptus, which then regress probably due to the high oxygen concentrations at the periphery (Burton). This process is defective when the trophoblast transformation of vessels is abnormal in conditions, such as pre-eclampsia and preterm labour. The findings would be considerably more interesting if they could access samples earlier in pregnancy as the underlying pathogenesis of preterm labour is still unclear.

Thus, the important unanswered question is: what in these two microenvironments is driving the differential trajectory of CTB 1 towards villous SCT or to smooth KRT6A+ cells in the smooth chorion? Some TFs are different but what causes their upregulation in SC and this needs addressing?

We thank the reviewers for their interest and this insight. Like the identification of specific factors that explain the suppression of EVT migration on SC side would be very exciting, so would be the uncovering of the basis of differential trajectories for CTB1 differentiation on the two sides. As suggested here, it very well may be differential oxygen tension, but there are countless other possibilities. It may even be an underlying cell autonomous factor such as epigenetic differences that we cannot see at the RNA level. Looking earlier, i.e. the first trimester, might be one way to get at this question, although prior to clear morphological differences of the two sides, it will be difficult to know what will become future villous vs. smooth chorion from the sort of surgical specimens we receive from the clinic. In all, these are exciting, but complex questions with likely complex underlying mechanisms. We certainly plan to pursue these questions, but again feel it is beyond the scope of the paper. We believe the discovery of the different trajectories and of a previously undescribed CTB population on the SC side is already an important advance in the field. We hope the reviewers agree.

Are there any cells with the SC transcriptome in the new models of human TSC (Okae 2018; Turco 2018; Haider 2018)? These provide an in vitro model to generate SC and would considerably strengthen the paper.

We thank the reviewers for the suggestion to analyze data from existing culture systems to identify if SC-CTBs are present. The three resources for placental organoid culture (Okae 2018; Turco 2018; Haider 2018) do not perform scRNA-seq which is a limitation for detecting rare subpopulations of cells. These studies performed bulk RNA analyses and we investigated whether KRT6A transcript was expressed in any of these datasets.

Okae et al., 2018, performed bullk RNA-seq to compare the transcriptional profiles of cells isolated from chorionic villi, human trophoblast stem cells, and differentiated cells from human trophoblast stem cells. They identified very low average KRT6A transcript in cells isolated from tissue [CTB = 0.018, EVT = 0.194, STB = 0.016 log2(FPKM+1)], in accordance with the low level of expression in the VC in our study. In most cultured cells KRT6A transcript was not detected. A very low level of expression was detected in TS cell derived from blastocysts [0.016 log2(FPKM+1)] and in the 3D differentiation of STB from blastocysts [0.106 log2(FPKM+1)]. Therefore, we believe that SC-CTB are not generated by these culture methods or if they are, it must be in very small numbers.

Turco et al., 2018 performed RNA microarray comparing GW6-GW12 chorionic villi samples to cultured placental organoids. Average normalized expression of KRT6A does not change between tissue samples (2.16) and organoid samples (2.41). As we have established that KRT6A expressing cells are not found in the villous chorion, no increase in expression in organoids suggested SC-CTB were not present.

Haider et al., 2018 performed RNA-seq on CTB-organoids and compared transcriptional profiles to isolated CTB, STB, and fibroblasts. The average expression of KRT6A across all samples as calculated by DESeq2 baseMean was 69.7 compared to highly expressed CTB marker genes like KRT7 (4406) and PAGE4 (14392), ranking KRT6A expression 9740th out of 17008 transcripts identified. Additionally, KRT6A was not found to be differentially expressed in any comparison across conditions. Therefore, we concluded that SC-CTB are not generated in this organoid culture system.

We agree with the reviewers that the ability to derive SC-CTB in culture would greatly enhance our ability to study these cells. However, it seems these cells are not currently generated in either directed differentiation or heterogenous organoid culture models. We do hope in the future to be able to identify in vitro differentiation conditions that do produce SC-CTBs.

(10) The methods of cell isolation rely on referring back to a previous publication and need expanding. "CTBs were enriched over stromal and immune cells – line 126" needs explanation as this will be a potential source of bias in the results. It appears that samples of the maternal side of the main villous placenta have been obtained – is this correct? Was decidua deliberately included? For the smooth chorion was the whole chorion – amnion, stroma, trophoblast and decidua included? These details are important as selecting specific areas and removing others will obviously alter the comparisons made.

We thank the reviewers for the suggestion to expand details on the methods of dissection and cellular isolation. Please also see the response to point 1, as this raises the same question. We have greatly expanded the methods section for dissection and isolation for both VC and SC samples.

The goal of this study was to focus on the trophoblast populations. In the VC samples, floating villi and anchoring villi were carefully dissected away from the decidua. These dissections, not surprisingly, do retain some decidual cells, presumably those most closely associated with the cell columns. The cell preparation further enriches for trophoblast cells over the stroma cells (both decidua and fetal derived) as well as immune cells. The result is that stromal cells are not even found in all VC samples and the total number of stromal cells is less than 1000. Immune cells are found in greater numbers, but also likely reduced. In SC, both the decidua and amnion were dissected away. The remaining cells were once again prepared to enrich for trophoblasts over stromal and immune cells. The purposeful depletion of stromal and immune cells is now explicitly stated in the text. We only briefly mention the stromal and immune cell types we recover and make limited claims concerning their functions.

(11) There are 6 macrophage populations – where are they located and which one is fetal Hofbauer cells? The relative paucity of NK cells and abundance of macrophages suggest that these are not from decidua. But why are there so few blood/decidual T cells? The best way to resolve these issues is to sequence maternal blood (and cord blood if obtainable) at the same time and remove these from the analyses.

To resolve these issues, the authors should compare their immune and fibroblast datasets in S2-S3 with previous single-cell data from the first trimester (Suryawanshi et al. 2018, Vento-Tormo et al. 2018). The correspondence between the decidual macrophages described in this manuscript is unclear compared with the ones from previous studies. Surprisingly, the 3 dNK cells previously identified are not found in this new dataset.

Again, this study focuses on the trophoblast cells at the expense of the stroma and immune cells. The reviewer is correct that the best way to identify the maternal cells is to sequence maternal blood (as is performed in Vento-Tormo et al. 2018). However, we did not do this because we sought to minimize the contribution of stromal and immune cells and to focus on the trophoblast.

Previous publications, like Vento-Tormo et al. 2018, specifically enriched for immune cells by both collecting peripheral blood and decidual samples, then sorting for CD45+ cell by FACS and performing scRNA-seq. Additionally, characterization of the three dNK cells mentioned by the reviewer includes data from the Smart-Seq2 method of scRNAseq. Because this method captures full length RNA and sequences to a much greater depth, Smart-Seq2 is a much more sensitive assay than droplet based scRNA-seq methods like 10x genomics. For these reasons, we do not find it surprising that our droplet based scRNA-seq data isolated from samples by a method specifically designed to exclude the decidual regions does not recover these cell states with the same resolution.

By design our experiment targets trophoblast cells at the expense of immune cells and stromal cells. We understand and admit the strengths and caveats of this approach, namely that there are populations missing from the immune and stromal cells. We have added language to further emphasize these caveats and our logic in the text.

To better understand the immune populations in our dataset and to compare to existing cell type definitions we have compared the immune subsets from this study to published data in Vento-Tormo et al., 2018 and Pique-Regi et al., 2019. We did this both through correlation of expression between independently derived clusters and through the computation integration of these datasets. Our NK cells show strong correlation and integration with the three dNK subsets identified in Vento-Tormo et al., 2018 (Figure 1S5e and f).

Based on the comparison of our macrophage populations to those identified in Vento-

Tormo et al., 2018 we have amended the cluster annotations slightly. The new Macrophage 1 (M) cluster matches the dM1 cluster from Vento-Tormo et al., 2018 and contains the old Macrophage 1 (M), Macrophage 2.1 (M), Macrophage 2.2 (M) clusters. The new Macrophage 2 (M) cluster matches the dM2 cluster from Vento-Tormo et al.,

2018 and contains the old Macrophage 2.3 (M) and Macrophage 2.4 (M). Macrophage 3 (M) matches the dM3 cluster from Vento-Tormo et al., 2018 and remains unchanged. These changes are updated in Figure 1-S5.

(12) The authors integrate the data from the villous chorion and smooth chorion computationally (eg Figure 1b). However, they should demonstrate that the integration pipeline is working correctly by defining the same cell states identified in the joint manifold when analysing the data separately. The same applies to all the analyses when specific cell subsets are zoomed in. For example, Figure 2c.

We thank the reviewers for raising this point. To address this, we analyzed the cells from the VC and SC independently as suggested. Focusing on the trophoblast cells, clustering identified similar clusters as the integrated dataset. Independent analysis of the VC identified all clusters except for CTB 4 (the smooth chorion specific population), suggesting that the small number found in the integrated analysis were coerced by cellular integration. Analogously, independent analysis of the SC identified all clusters except for STB Precursor, STB, and EVT Precursor cells. These results are consistent the small number of STB (14 cells) and EVT precursor (8 cells) from the SC in the integrated dataset (Supplementary Table 1). We have included these analyses in an additional supplementary figure (Figure 2-S3a-f). We performed the same analyses for both the stroma and immune subsets and included these analyses in new supplementary figures (Figure 1-S6a-d and Figure 1-S8a-d).

In addition, for each of the UMAPs defined in the manuscript, it is important to colour them by individual to confirm the integration.

We thank the reviewers for this suggestion. The contributions of each individual to each UMAP colored by coarse cluster are provided in Figure 1-S1c and the contribution of each individual to the trophoblast subset colored by cluster annotation is shown in Figure 2-S1b. We have added the contribution of each individual to the immune and stroma subset UMAPs in Figure 1-S5c and Figure 1-S7c, respectively.

(13) The EVT populations described are problematic and the logic of this section is hard to follow. Most trophoblast cells in the SC express the definitive EVT marker, HLA-G (Hutter 1996).

We regret the confusion in this section. While we appreciate that prior work has suggested that most trophoblast cells in the smooth chorion express HLA-G (Hutter, 1996), we do not find this to be true. In our scRNA-seq data we identify that only 46.8% of trophoblasts in the VC and 32.1% of trophoblasts in the SC express HLA-G (Figure 5c). The mix of trophoblast population in SC was further supported by staining for HLAG protein in the SC (Figure 5b, Figure 6b, Figure 6-S1). Our findings are also supported in the literature by HLA-G staining (Garrido-Gomez et al., 2017).

We agree with the reviewer that EVTs are marked by the definitive marker HLA-G. Expression of HLA-G transcript was used to define the EVT clusters we identified (Figure 2 and Figure 5) and protein expression of HLA-G is used to define EVT cell identity in all tissue sections (Figure 5 and Figure 6).

So how do these EVT subsets relate to CTR2-4 and to published datasets?

CTB 1-4 and CTB S-phase and CTB G2/M-phase cells do not express HLA-G transcript, as shown in Figure 5a and Figure 2-S2b. Additionally, we demonstrated that the CTB 1 marker np-CTNNB1 (Figure 6a and Figure 6-S1) and the CTB 4 marker KRT6A (Figure 6b and Figure 6-S1) have mutually exclusive domains of protein expression from EVT marker HLA-G. Therefore, our data support that EVT identified in either the VC or SC are distinct from all CTB populations (including all proliferating cells).

We compared the trophoblasts populations we identified to those published in VentoTormo et al., 2018. The EVT cells we identify bear strong resemblance to both computationally annotated EVT cells and FACS sorted HLA-G+ cells identified in VentoTormo et al., 2018 (Figure 2S-1d and e). In accordance with our data, all EVT and HLAG+ cells cluster separately from all CTB (including the smooth chorion specific CTB 4 cells) [Figure 2-S1e]. This provides evidence from an external dataset that the EVT we identify match previously published EVT and are distinct from all CTB isolated from either the VC or the SC.

It is also surprising that the same EVT populations are defined in the analysis of villous and smooth chorion. An artifact due to the computational algorithm could be forcing the integration and it needs to be shown that this is not the case. This is common, as, by default, the method will assume equivalent populations in both datasets. One way to do so may be by using alternative methods for data integration (e.g., harmony, scVI), or by analysing datasets independently and comparing them afterwards (e.g. correlation analysis, machine-learning tools).

We thank the reviewers for this suggestion. To address the possibility that similarities between EVT population are an artifact of integration we performed independent analysis of the cells isolated from each region (see the response to point 12). We identify both strong correlations and overlapping computational integrations between independently identified EVT clusters in the VC and SC samples (Figure 2-S3a-f), between the EVT populations identified in this study with EVT identified by Vento-Tormo et al., 2018 (defined by both by transcript and HLA-G protein expression)[Figure 2-S1d and e], and between the EVT populations identified in this study with EVT identified by Pique-Regi et al., 2019 (Figure 2-S1f and g). Please see the response to point 2 above for more detail. Given the agreement in the findings of multiple orthogonal methods and multiple independent datasets, we do not believe the transcriptional similarity between EVT from the VC and SC is an artifact of computational integration.

It is also not made clear that the interstitial trophoblast deep in the decidua have not been sampled and only those EVT in the cell columns.

We apologize for the lack of clarity around this point. We have amended the text and methods to make this clearer.

Please see lines 115-119: “VC samples included cells isolated from floating and anchoring villi and areas surrounding the cell column, while most of the decidua (including spiral arteries) were dissected away. SC samples included the chorion and underlying stroma (mesenchymal and endothelial cells), but not the amnion and little of the neighboring decidua, which were also removed during dissection”

Detailed comments:

(1) Line 368 "within the arterial wall, this population (interstitial EVT) further differentiates to endovascular EVTs and replaces the maternal arterial endothelium (Harris 2009, Red-Horse 2004)". This is controversial with the majority view instead being that interstitial trophoblast mediates medial destruction and then there is the replacement of the endothelium by endovascular trophoblast moving down from the shell (Pijnenborg; Bulmer).

We thank the reviewers for highlighting the discussion around the source of endovascular EVTs. We have amended the text to reflect that there is not yet a definitive answer to this question and address both hypotheses. Please see lines 378381 – “The relationship between this subpopulation and endovascular EVTs that replace the maternal arterial endothelium is unclear, with evidence supporting endovascular EVTs arising from either from interstitial EVTs and/or an independent origin (Harris et al. 2009; Red-Horse et al. 2004; Pijnenborg et al., 2011).”

(2) Introduction: The second paragraph of the introduction could be rewritten and/or incorporated into the third paragraph. In its present form, it reads like a textbook. In addition, the introduction is somewhat too long and would benefit from some trimming.

We thank the reviewers for this helpful feedback on length and clarity. We have rewritten the introduction removing paragraph 2 as well as trimming other parts. However, given request made in point 4 of general comments, we also had to add text to clarify nomenclature we use throughout, so in the end the introduction is not much shorter than the original size.

(3) Introduction: The last paragraph of the introduction should be revised. The authors mention that previous studies using scRNA-seq were performed in the fetal membranes from term pregnancies (Page 3, Lines 104-105). Yet, no references were provided. In the following sentence (Page 3, Lines 105-106), the authors mention "in this study, CTBs were not identified in the smooth chorion…" and the authors cite four papers. Which of these papers are the authors referring to? In the 2019 study that is cited, the authors reported the presence of CTBs and EVTs in the fetal membranes. In addition, new studies have also reported the presence of CTBs and EVTs in the fetal membranes (PMIDs: 32662421, 35042863). The authors may consider including this information.

We apologize to the reviewers for lack of clarity in this section. Pique-Regi et al., 2019 was cited as a reference for the claim that CTBs were not identified in the smooth chorion. No claims were made about a lack of EVTs in either Pique-Regi et al., 2019 or in our manuscript. In Pique-Regi et al., 2019 the authors identify 132 CTBs (CTB and npi-CTB) out of 29,921 cells isolated from the chorioamniotic membranes, representing 0.44% of all cells recovered. We have also included reference to Garcia-Flores et al., 2022 as another dataset profiling the SC which contains a similar underrepresentation of CTBs. We have updated the text with accurate numbers and proper citations.

(4) Results, Page 3, Lines 122-124: The authors mention that they chose to analyze second-trimester samples to avoid inflammation and apoptosis associated with parturition. Couldn't the authors include samples from women who delivered at term without labor?

We thank the reviewers for mentioning these datasets. We have cited Garcia-Flores et al., 2022 in the manuscript as another scRNA-seq data set profiling the SC. Pique-Regi et al., 2020 uses the chorioamniotic Membrane data from Pique-Regi et al., 2019 and includes no new data from the chorioamniotic membranes and, while interesting and important, is not relevant to this study.

(5) Page 8, Lines 265-266: What is the evidence to suggest that the differentiation of SC-CTB does not require a cell cycle?

We thank the reviewers for this suggestion. Given the large changes observed in both the transcriptome and epigenome between the second trimester and term, we expect this to be a complete project unto itself. In the future, samples from placentas collected from delivery at term without labor will be considered to address the composition and roles of VC and SC trophoblast at term.

(6) Results, Page 5, Lines 157-159: The authors suggest the involvement of maternal immune cells in VC and SC; yet, these results are expected since both the placental villous and the chorioamniotic membranes (which were sampled in this study) represent the maternal-fetal interface: intervillous space and decidua parietalis, respectively.

We thank the reviewers for the opportunity to clarify this point. We do not claim (or have evidence) that the differentiation to CTB 4 does not require a cell cycle. We demonstrate KRT6+ CTB 4 cells express KI67, suggesting these cells have not exited the cell cycle. We cannot determine and do not comment on the intersection of cell cycle and differentiation beyond noting that CTB 4 cells can remain proliferative. Since both other differentiated cell types in the placenta (EVTs and STBs) exit the cell cycle in concert with differentiation (unlike CTB 4), we feel this is a distinction worth noting. We have updated the text to clarify this point.

Please see lines 268-270: “Mitotic KRT6+ cells were identified, and while the interaction between the cell cycle and differentiation of SC-CTBs remains unclear, these data show that differentiation to SC-CTB does not require cell cycle exit, unlike STBs and EVTs”

(7) Results: The authors are congratulated for validating the mRNA results with protein expression. Specifically referring to Figure 2. The authors are also congratulated for testing functional differences between the EVTs from the placental villi and those from the membranes. I think that this is a very useful piece of information, and the functionality of the different subsets of EVTs requires further investigation.

We thank the reviewers for these kind words. We look forward to investigating the functional heterogeneity of EVT subpopulations in the VC and SC in future studies.

(8) Results, Page 8, Line 245: The authors claim that the CTBs from the membranes and those from the placental villi share a common progenitor by performing an elegant RNA velocity analysis. Yet, wouldn't it be expected that all trophoblasts share a progenitor?

While it is not surprising that all trophoblasts derive from a common progenitor early in development (first trimester), it was not known whether a common progenitor for trophoblasts in two physically distinct regions remained well into the second trimester. The result we found surprising is that a common progenitor exists as late as GW23, this progenitor is transcriptionally similar to canonical Villous CTB (now demonstrated to exist in the SC), and that this progenitor appears fate restricted by its location.

(9) Figure 5: What is the evidence that EVTs from the two different compartments are behaviorally distinct? In other words, what do the authors mean by "behavioral"?

We thank the reviewers for pointing out the vagaries of this wording. We use the term behavioral to refer to the migratory/invasive actions of these cells in the tissue. In referencing distinct behavior, we refer to the documented differences in the depth of invasion/migration into maternal cells observed in the VC or the SC (Genbacev et al., 2015). To reduce confusion, we have changed “behavior” to “invasive activity”.

(10) Immune cells Figure 1S2

How were the 4 different immune cell populations – maternal blood/decidual and fetal blood/placental cells distinguished? Both stromal and immune cells from both individuals will be present in these isolates. It is said by XIST that most of the immune cells are maternal – are they from blood or decidua? Were the fetuses all male? It would be better to look at the transcriptomes and deconvolute the genotype data by SNPs to confirm.

We thank the reviewers for pointing out the need for better characterization of the origin of stromal and immune cells. We agree with the reviewers that the best method to assess the origin of these populations is to collect blood and decidua and deconvolute by SNPs. Deconvolution by SNP from transcripts using programs like freemuxlet work well for separating reads from two distinct (unrelated) individuals but fail in the case of parental relationships due to shared genomic material. As this study was focused entirely on the trophoblast populations (fetal only), we did not sequence and genotype maternal decidua, maternal blood, and fetal only cells to perform this analysis.

Instead, by assessing XIST expression in trophoblast cells from each sample (all of fetal origin), we assigned the sex of each sample to be GW17.6 = Male, GW18.2 = Male, GW23 = Female, and GW24 = Male (Figure 1-S2a). Therefore, in the stromal and immune populations cells of fetal origin would only express XIST in GW23 samples and no others. All immune populations, with the exception of Erythrocytes, express XIST in all samples (Figure 1-S2c). Stromal populations with XIST expression largely restricted to GW23 samples (Mesenchyme 1 (F), Mesenchyme 3 (F), and Mesenchyme 4 (F)), are annotated as fetal. All others show XIST expression across all samples and are thus annotated as maternal (Figure 1-S2b).

(11) Figure 1S3

The presence of lymphatics in decidua is disputed (Volchek 2010) and Hofbauer cells express LYVE1 (Bockle 2008) so how certain is the presence of lymphatics.

The lymphatic endothelium in the smooth chorion has been previously identified, validated, and localized in Pique-Regi et al., 2019. We identify strong transcriptomic correlations between the Endo Lymph cells we identify and those identified in PiqueRegi et al., 2019 and Vento-Tormo et al. 2018, demonstrating the lymphatics identified in three studies to be similar (Figure 1-S3a and b; Figure 1-S4a and b). While we did not perform any immunolocalization, Pique-Regi et al., 2019 did confirm the location of these cells in the SC using an antibody to LYVE1.

(12) In Figure 2 the authors introduce a CTB cycling population. Is this population corresponding to CTB-1 only? Or, is it a mix of CTB-1-4? If the latter, defining independent clusters by, for example, removing cell cycling genes from the highly variable genes considered to define the clusters should be performed.

We thank the reviewers for pointing out the lack of clarity around the identity of these populations. The S-phase and G2/M-phase CTB populations contain a mix of cells from CTB 1-4 (but not from STB, EVT, or either precursor populations). Although it can be beneficial to regress out cell cycle related genes to remove factors which obscure cell identity, we did not remove cell cycle genes from our analysis for two reasons. First, cell cycle is an important component of cell fate and identity. This is especially true in the placenta as both commitment to EVT and STB lineages is inextricably tied to cell fate (Lu et al., 2017; Genbacev et al., 1997). Therefore, we determined that excluding information relating to cell cycle state might decrease the ability to identify determinants of cell fate. The S-phase and G2/M-phase CTB containing cells expressing markers of CTB 1-4 clusters, motivated the analysis of proliferation by KI67 staining (Figure 4b and Figure 3-S1b) that demonstrates that CTB 4 cells remain proliferative. This sets this population apart from both EVT and STB. Second, the majority of CTB identified in our analysis do not contain markers of S or G2/M phase transcripts. Therefore, we are confident in the ability to define cell identity solely based on non-phasic transcripts in these 26,325 cells.

(13) Line 417. The "suggesting a cell non-autonomous, rather than cell-autonomous, regulation of invasion" is speculation that should be included in discussion rather than the results. How do the authors explain the fact that trophoblast organoids invade in the absence of decidua as in tubal or abdominal pregnancy?

We thank the reviewers for the opportunity to clarify this point. We have removed this from the Results section. We believe our data suggest that the default action of EVT is to invade/migrate and the environment (SC-CTB and decidual cells) may function to restrict this invasion. If invasion is the default, EVTs will invade in the absence of SCCTB and decidual cells, matching what is observed in organoid models. Therefore, we believe our model is consistent with what is seen in in vitro models and tubal or abdominal pregnancies.

To increase the impact the proteomes of the two conditioned media from VC and SC should be studied to identify what is inhibiting invasion into decidua.

See answer to question 3 in general comments.

References (not included in manuscript)

King, B. F. (1981). Developmental changes in the fine structure of the chorion laeve (Smooth chorion) of the rhesus monkey placenta. The Anatomical Record, 200(2), 163–175. https://doi.org/10.1002/ar.1092000206

Hutter, H., Hammer, A., Blaschitz, A., Hartmann, M., Ebbesen, P., Dohr, G., Ziegler, A., and Uchanska-Ziegler, B. (1997). Erratum: Expression of HLA class I molecules in human first trimester and term placenta trophoblast (Cell and Tissue Research (1996) 286 (439-447)). Cell and Tissue Research, 287(3), 625. https://doi.org/10.1007/s004410050786

https://doi.org/10.7554/eLife.78829.sa2

Article and author information

Author details

  1. Bryan Marsh

    1. The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell, University of California, San Francisco, San Francisco, United States
    2. Department of Urology, University of California, San Francisco, San Francisco, United States
    3. Center for Reproductive Sciences, University of California, San Francisco, San Francisco, United States
    4. Developmental and Stem Cell Biology Graduate Program, University of California, San Francisco, San Francisco, United States
    Contribution
    Conceptualization, Software, Formal analysis, Validation, Investigation, Visualization, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4979-5233
  2. Yan Zhou

    1. The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell, University of California, San Francisco, San Francisco, United States
    2. Center for Reproductive Sciences, University of California, San Francisco, San Francisco, United States
    3. Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, San Francisco, United States
    Contribution
    Validation, Investigation
    Competing interests
    No competing interests declared
  3. Mirhan Kapidzic

    1. The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell, University of California, San Francisco, San Francisco, United States
    2. Center for Reproductive Sciences, University of California, San Francisco, San Francisco, United States
    3. Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, San Francisco, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  4. Susan Fisher

    1. The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell, University of California, San Francisco, San Francisco, United States
    2. Center for Reproductive Sciences, University of California, San Francisco, San Francisco, United States
    3. Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, San Francisco, United States
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing – review and editing
    For correspondence
    susan.fisher@ucsf.edu
    Competing interests
    is a consultant for Novo Nordisk. All other authors declare no competing interests
  5. Robert Blelloch

    1. The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell, University of California, San Francisco, San Francisco, United States
    2. Department of Urology, University of California, San Francisco, San Francisco, United States
    3. Center for Reproductive Sciences, University of California, San Francisco, San Francisco, United States
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing – review and editing
    For correspondence
    robert.blelloch@ucsf.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1975-0798

Funding

National Institutes of Health (P50 HD055764)

  • Bryan Marsh
  • Robert Blelloch

National Institutes of Health (R37 HD076253)

  • Yan Zhou
  • Mirhan Kapidzic
  • Susan Fisher

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank the members of the University of California – San Francisco National Center of Translational Research in Reproduction and Infertility for helpful comments during the design, execution, and publication of this project. We would also like to thank all members of the Blelloch and Fisher Labs for their comments and support. We specifically thank Ali San and Nasim Zeighami for their research contributions. We would also like to acknowledge our funding sources, the NIH Eunice Kennedy Shriver National Institute for Child Health and Human Development P50 HD055764 and NIH R37 HD076253.

Ethics

Human subjects: The University of California, San Francisco (UCSF) Institutional Review Board approved this study (11-05530). All donors gave informed consent.

Senior Editor

  1. Didier YR Stainier, Max Planck Institute for Heart and Lung Research, Germany

Reviewing Editor

  1. Claudia Gerri, Max Planck Institute of Molecular Cell Biology and Genetics, Germany

Publication history

  1. Preprint posted: March 22, 2022 (view preprint)
  2. Received: March 22, 2022
  3. Accepted: July 6, 2022
  4. Accepted Manuscript published: July 7, 2022 (version 1)
  5. Version of Record published: July 26, 2022 (version 2)

Copyright

© 2022, Marsh et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Bryan Marsh
  2. Yan Zhou
  3. Mirhan Kapidzic
  4. Susan Fisher
  5. Robert Blelloch
(2022)
Regionally distinct trophoblast regulate barrier function and invasion in the human placenta
eLife 11:e78829.
https://doi.org/10.7554/eLife.78829
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