MainThe human maternal–fetal interface (MFI) is a transient hemi-allogeneic amalgam in which maternal decidual stromal cells (DSCs) support placental attachment, recruit immune cells and create a tolerogenic milieu for patterning fetal cytotrophoblast invasion1. During placental development, fetal villous cytotrophoblasts (VCTs) in floating villi fuse into syncytiotrophoblasts (SCTs)1, which mediate nutrient and waste exchange and hormone and growth factor secretion, and limit fetal cortisol exposure1,4. Alternatively, they form anchoring villi with cell columns that generate invasive extravillous trophoblasts (EVTs)1. EVTs invade the decidua and uterine spiral arteries. By the end of the first trimester, EVT remodelling of these vessels establishes low-resistance arteries that enable high-velocity blood flow to the placenta1. Previous single-cell studies have been confined to limited gestational windows3,5,6,7,8,9 (Supplementary Table 1); here we generate a reference of the human MFI from early gestation to term across normal pregnancies by integrating single-nucleus multiomics with submicrometre spatial mapping. This framework catalogues diverse cell types, resolves transient states and trajectories, maps intercellular communication in situ, and pinpoints vulnerable cell states in pregnancy complications.A single-cell map of the interfaceOur single-nucleus multiomics profiling targeted the human MFI from known normal (term gestation) or presumed normal (early/mid gestation) pregnancies (Methods; Supplementary Table 1). Samples encompassed the decidua basalis with embedded EVTs (early gestation) and the basal plate (mid/late gestation) from gestational week (GW) 5 to 39 (Fig. 1a). Sample collection, inspection and validation are detailed in Methods (Extended Data Fig. 1a–d). We performed paired single-nucleus RNA-sequencing (snRNA-seq) and single-nucleus assay for transposase-accessible chromatin with high-throughput sequencing (snATAC–seq) using the 10x Genomics platform, yielding high-quality datasets of 221,380 nuclei (snRNA-seq) and 210,191 nuclei (snATAC–seq) after stringent quality control (Methods). Among these, 191,735 nuclei had paired snRNA-seq and snATAC–seq profiles (Supplementary Table 1). On average, 8,336 nuclei were multiomically profiled per sample. The distribution of sequenced nuclei across gestational week intervals is shown in Fig. 1a and Extended Data Fig. 1e.Fig. 1: Overview of single-cell multiomics data from the human MFI across gestation.Full size imagea, Experimental design. Nuclei (191,735) from the human MFI were profiled spanning five intervals from early gestation to term. n represents the number of nuclei per group. Pie charts show major cell-type proportions in each group. Right, stacked area plot illustrating cell-type composition changes over gestation (cell type abbreviations defined in key below b). Image credit: YG Studio/Shutterstock.com. b, UMAP projections of snATAC–seq (left) and snRNA-seq (right) for 19 broadly identified cell types (key). c, Expression (snRNA-seq) dot plot of selected marker genes across the 19 cell types. Dot size represents the percentage of expressing cells. d, Assignment as maternal or fetal for each nucleus by genotype phasing. Bottom, pie chart illustrating the proportion of maternal or fetal fibroblasts. e, ATAC–seq footprinting identifies binding sites of cell-type-enriched transcription factors: TP63 (VCT), GATA3 (EVT), SPI1 (M) and RUNX2 (dNK). Binding sites are marked by depletion of Tn5 insertion flanking the motifs. f, Chromatin accessibility heat map showing the activity of the ten most active cell-type-specific enhancers. Regions analysed were cap analysis gene expression (CAGE)-validated enhancers from the FAMTOM5 collection13. g, Rewiring of GRNs during VCT differentiation into EVT and SCT lineages. The reconstructed EVT GRN is shown. Red and blue denote activation and repression, respectively, relative to VCTs. Inset, a FOS-mediated subnetwork. The top transcription factors with the highest ranked regulatory interactions are shown as examples. h, Venn diagram showing uniquely upregulated transcription factors (TFs) in EVTs or SCTs compared to VCTs. i, Expression patterns of transcription factors and their target genes reflect the coordinated regulation across two trophoblast lineages. EVT transcription factors activated EVT genes and repressed SCT genes in EVTs, with the reciprocal pattern in SCTs. In box plots, the centre line is the median, box represents the interquartile range (IQR; 25th to 75th percentile), and whiskers show minima and maxima (0.5× IQR) end-points; outliers are not displayed. P values, two-tailed Wilcoxon rank-sum tests after Benjamini–Hochberg correction. j, Schematic toggle switch model illustrating trophoblast fate reinforcement and suppression of the alternative programme.Source dataWe annotated cell clusters in the open-chromatin epigenome and transcriptome spaces (Fig. 1b) on the basis of known markers (Supplementary Table 2), which showed strong cell-type specificity (Fig. 1c) and overall concordance between the two modalities (Extended Data Fig. 2a). To confirm annotation accuracy, we verified batch correction (Extended Data Fig. 2b) and performed label transfer by projecting our cell-type annotations onto a prior placental atlas3, yielding concordant assignments across major cell classes (Extended Data Fig. 2c). Notably, our study identified many more novel cell subtypes and states. Figure 1a summarizes cell-type composition across developmental windows. Because microanatomy (especially spiral artery distribution) varies across biopsy sites, cross-sample cell-type proportion comparisons can be misleading without spatial context. We therefore emphasized within-cell-type molecular profiles and performed compositional analyses only when spatial information was available or required.We used Souporcell10 to assign maternal versus fetal origin for more than 95% of cells (182,773 out of 191,735; Fig. 1d), validated by Y chromosome markers (Extended Data Fig. 2d,e). Some cell types contained both origins (for example, fibroblasts, FB in Fig. 1d), whereas vascular endothelial cells formed distinct maternal and fetal clusters (Fig. 1b), indicating molecular divergence.Cell-type-specific gene regulationCell-type-specific genes aligned with cell-type-specific promoter assay for ATAC–seq peaks (Extended Data Fig. 2f). Using chromVar11, we inferred transcription factor-binding motifs enriched in ATAC–seq pseudobulk peaks in each cell type (Extended Data Fig. 3a). snATAC–seq footprinting12 further resolved transcription factor-binding sites of approximately 20 bp in size within pseudobulk peaks (Fig. 1e). We then mapped open-chromatin peaks to experimentally defined enhancers13. The top ten most enriched cis-enhancers for each cell type are shown in Fig. 1f (Supplementary Table 2). At the HLA-G locus (an EVT marker), we detected an EVT-specific promoter peak and three upstream open-chromatin peaks, including a known enhancer about 10 kb upstream14 and two putative distal enhancers (Extended Data Fig. 3b). Together, these analyses revealed extensive cell-type-specific regulatory rewiring.A toggle switch model for EVT fateWe used CellOracle15 to reconstruct gene regulatory networks (GRNs) in each cell type by integrating snATAC–seq and snRNA-seq data. We focused on the trophoblast lineage to identify regulatory rewiring that directs progenitor VCTs towards terminal fates: EVTs or SCTs. Compared to VCTs, we identified 71 and 30 transcription factors that were specifically upregulated in EVTs and SCTs, respectively (false discovery rate (FDR) ≤ 0.01; top transcription factors in Fig. 1g and Extended Data Fig. 3c; representative motifs in Extended Data Fig. 3d; EVT–SCT overlap in Fig. 1h; full lists in Supplementary Table 3; Methods). CellOracle inferred target genes for these transcription factors. In EVTs, the 20 most highly upregulated transcription factors included ASCL2, FOS, KLF6 and STAT1, known regulators of VCT-to-EVT differentiation16,17,18,19, which mediated 167 high-confidence regulatory interactions (strength coefficients >0.1) with 116 target genes (Fig. 1g, Extended Data Fig. 3e and Supplementary Table 3). Among these, FOS positively regulated canonical EVT genes such as HLA-G, KRT8 and FN1 (Fig. 1g, inset). The same transcription factors activated in EVTs also showed negative associations with genes, which, as expected, were down-regulated in EVTs, but many, in fact, were known SCT markers (for example, CGA, TFPI2 and PLAC4; Fig. 1g and Extended Data Fig. 3f). Genome-wide, genes that were positively associated with EVT transcription factors were upregulated in EVTs, whereas negatively associated targets were suppressed in EVTs but enriched in SCTs (Fig. 1i). In EVTs, these negative associations between EVT transcription factors and SCT genes, predicted as putative repressive interactions, are consistent with recent reports that EVT-specific transcription factors functionally suppress the SCT programme in EVTs16,20,21. The same analysis of SCT-specific transcription factors showed the reciprocal pattern: activating the SCT programme while negatively associating with EVT-enriched genes in SCTs (Fig. 1i). Together, these observations suggest a bistable toggle switch model that enforces commitment to either the EVT or SCT lineage, locking cells into one fate while actively suppressing the alternative (Fig. 1j). Notably, only a few activating transcription factors were shared between the two lineages (Fig. 1h), including GCM1, whose loss impairs formation of both EVTs and SCTs22. Even these shared factors engaged distinct target gene sets in the two cell types (Extended Data Fig. 3g), highlighting extensive regulatory rewiring that secures mutually exclusive trophoblast fates.From single cells to tissue architectureTo enable spatial mapping of deeply sequenced cell states in a native tissue context, we generated submicrometre (0.5-µm) spatial whole-transcriptomic maps using STOmics Stereo-seq23,24 (1 cm × 1 cm chips) for 16 wide-swath basal plate biopsy sections from normal pregnancies (after RNA quality control; Methods), each from an independent donor (Fig. 2a). Stereo-seq enables single-cell segmentation from full-tissue sections and reconstructs single-cell whole transcriptomes by aggregating transcripts captured at 0.5-µm spatial resolution (Extended Data Fig. 4a–d). Focusing on the peak phase of EVT invasion and spiral artery remodelling, we profiled only second-trimester basal plate specimens that contained a well-defined MFI with anchoring villi and abundant spiral arteries (Supplementary Table 4 and Methods). The technology permits immunostaining prior to transcriptomic profiling. Most of the samples (12 out of 16) were immunostained with anti-pan-cytokeratin (CK) to label trophoblasts, and a subset that contained numerous blood vessels was co-stained with anti-CD31 to mark the vasculature. The remaining four samples were deliberately processed without immunostaining to assess its impact on transcriptomic quality and to validate annotations using transcriptomes alone. Extensive quality control confirmed data quality and the manufacturer’s recommendation that pre-run immunostaining did not compromise Stereo-seq performance (Extended Data Fig. 4e and Methods). Cell-type assignments were concordant across technical variations, such as staining protocols and chemistry versions (Extended Data Fig. 4f–h), supporting consistency across preparation methods and justifying integrating all 16 whole-slide samples for downstream analysis (Fig. 2a).Fig. 2: Overview of submicrometre spatial whole-transcriptome profiling at the MFI.Full size imagea, STOmics Stereo-seq profiling of basal-plate sections from 16 second-trimester samples of normal pregnancy. Each dot represents a single cell, colour-coded by cell type. b, UMAP projection of around 1.1 million single-cell spatial transcriptomic profiles across 16 samples. c, Spatial cell community analysis identified six recurrent communities. D1, decidua-1; D2, decidua-2; FV, floating villus; Junction, maternal–fetal junction; VCV, villous core vessel. d, Each spatial community exhibited a characteristic cell-type composition. e, Distances (in 0.5-μm pixels) to the MFI for each cell. Positive distances are maternal; negative distances are fetal. px, pixels. f, Cell-type proportions binned across the MFI. g, Spatial distances (in 0.5-μm pixels) to the nearest blood vessel (BV) wall in the decidua. Positive, intraluminal; negative, extraluminal. h, Cell-type proportion in decidua by proximity to blood vessel walls (BVWs). i, Quantification of EVT density adjacent to blood vessel walls (n = 62) compared to randomized decidual regions of same size. In box plots, the centre line is the median, box represents the IQR, and whiskers show minima and maxima (0.5× IQR) end-points. P value, two-tailed Wilcoxon rank-sum test. j, Two representative spiral arteries at distinct phases of EVT-mediated remodelling, visualized by joint immunostaining and spatial transcriptomics. Box 1, early stage; box 2, advanced. k, Spiral artery cross-sections (n = 17) with PECAM1+ endothelial cells illustrating active EVT invasion in a representative GW20 sample. l, Single-cell transcriptomic profiling revealed endothelial state transitions: caEC → R0 → R1 → R2. m, PDE3A and VIM distinguished R0–R2 states with high accuracy (n = 10 bootstrap replications). Bars show mean area under the receiver operating characteristic curve (AUROC) and error bars represent s.d. n, R2 endothelial cells are enriched for apoptosis-related genes. o,p, R0–R2 endothelial states in samples 010-GW20 (o) and 016-GW21 (p) were independently validated via paired spatial transcriptomics and pan-CK and CD31 immunostaining. Fields from one representative sample. q, Absolute pixel distances of endothelial states to the vessel walls from 17 remodelling spiral arteries (010-GW20). In box plots, the centre line is the median, box represents the IQR, and whiskers show minima and maxima (0.5× IQR) end-points. P values, two-tailed Wilcoxon rank-sum test after Benjamini–Hochberg correction.Source dataOur spatial profiling reconstructed around 1.1 million cells across all samples; their transcriptomes were harmonized and embedded in a unified uniform manifold approximation and projection (UMAP) that recapitulated the major cell types (Fig. 2b, Extended Data Fig. 4i–l and Methods). We performed spatial co-occurrence analysis to identify significantly co-localizing cell populations in the decidua, which revealed the strongest associations between decidual stromal and immune cells (Extended Data Fig. 4m), suggesting stromal–immune crosstalk25. We next performed spatial cell community analysis to identify fundamental ‘neighbourhoods’, spatial niches of adjoining cells that recur across tissues and share characteristic cell-type compositions26,27. This analysis revealed six major communities, each corresponding to a distinct anatomical niche: two decidual niches (D1 and D2), maternal arterial and fetal villous core vascular niches, the maternal–fetal junction, and the floating villi niche (see Fig. 2c for a representative section and Extended Data Fig. 5a for all tissues). Each niche had a characteristic cell-type composition (Fig. 2d).The decidual niches D1 and D2 had distinct microenvironments. D1 was enriched for EVTs, whereas D2 had fewer EVTs and was dominated by decidual stromal and immune cells (Fig. 2d). The vascular communities also diverged. The maternal arterial niche was enriched for EVTs that were probably engaged in vessel remodelling, whereas the villous core vascular niche was enriched for ACTA2+ perivascular cells (Extended Data Fig. 5b, bottom). At the maternal–fetal junction, anchoring villus VCTs and SCTs intermingled with decidual stromal and endothelial cells, whereas the floating villi niche comprised SCTs, VCTs and fetal endothelial cells lacking a perivascular sheath (Fig. 2d), consistent with capillary networks beneath the trophoblast basement membrane. Thus, spatial community analysis delineated recurrent cellular niches that constitute fundamental structural units of the human MFI.Mosaic EVTs in maternal vessel remodellingFor each cell, we computed the pixel distance (pixel size, 0.5 µm) to two anatomic landmarks: the nearest portion of the MFI and the nearest maternal spiral artery. In each sample, we first defined the interface as the boundary formed by the termini of dense anchoring villi between the placenta and decidua (Extended Data Fig. 5b, right). Cells were then binned by distance to the interface (Fig. 2e for a representative section; Extended Data Fig. 5c for all sections), and distance-stratified cell-type composition profiles were computed across samples (Fig. 2f). Overall, EVTs were more abundant in superficial decidua than in deep decidua, consistent with only the most invasive EVTs reaching those depths. However, immune and DSCs were enriched in deep decidua. The fetal cell-type composition within chorionic villi was less variable, irrespective of their distance to the interface (Fig. 2f).Across all sections, CD31 immunostaining together with PECAM1 expression (CD31 is encoded by PECAM1) delineated 62 uterine blood vessels. Detailed review confirmed spiral artery morphology and the expected enrichment of adjacent CK+ EVTs. Setting the vessel wall as distance zero, we quantified cell-type composition in concentric 100-pixel distance bins (Fig. 2g for a representative section; Extended Data Fig. 5d for all vessel-containing sections), assigning positive distances to extraluminal cells and negative distances to intraluminal cells. Distal cells extending into chorionic villi were excluded from the analysis. Luminal and perivascular regions were enriched for maternal endothelial cells and EVTs, with only a small fraction of immune cells (Fig. 2h). Moving outward, endothelial cells were reduced and EVTs remained; decidual and immune cells were the major adjacent populations.To quantitatively assess EVT aggregation around blood vessels, we estimated their density relative to randomly sampled size-equivalent decidua areas that lacked uterine blood vessels, revealing strong enrichment of EVTs surrounding uterine vessels (Fig. 2i, Methods and Supplementary Table 4). Magnified views of two representative vessels from the same sample (Fig. 2j) highlight the power of integrated immunostaining and spatial transcriptomics to capture distinct stages of EVT-mediated vascular remodelling. The first spiral artery (Fig. 2j, box 1) displayed a mostly intact CD31+ endothelial lining, with sparse CK+ EVTs clustered on one side (Fig. 2j), indicating an early stage of remodelling. By contrast, the second vessel (Fig. 2j, box 2) had a mosaic EVT phenotype: NCAM1+ (endovascular EVT marker28; Fig. 2j) EVTs co-expressed varying levels of HLA-G (Fig. 2j) and AOC1 (interstitial EVT marker29,30) (Fig. 2j). Residual CD31+ endothelial cells were sparse and fragmented along the vessel wall, consistent with EVT-mediated displacement and advanced remodelling. Decidual stromal and scattered immune cells were also present (Extended Data Fig. 5e), suggesting multicellular coordination during vessel remodulation.Mapping arterial endothelial statesIn sample 010-GW20, from a coiled spiral artery, we identified 17 cross-sections that traversed the decidua, and each had varying densities of endothelial cells and EVTs lining the vessel walls (Fig. 2k and Extended Data Fig. 5f,g for endothelial and EVT localization, respectively). This series in a single sample enabled detailed mapping of endothelial state transitions without confounding inter-sample variability. We later replicated these transitions in additional samples and validated them at the protein level. We used PDE3A, a recently identified arterial endothelial marker31 that we independently validated by reanalysis (Extended Data Fig. 6a,b) and immunostaining of uterine spiral arteries (Extended Data Fig. 6c–e), to confirm that these vessels were lined by arterial endothelium (Extended Data Fig. 5g). Whole-transcriptome profiling of PECAM1+ arterial endothelial cells within these arteries followed by pseudotime reconstruction resolved four sequential states (Fig. 2l). Differential expression analysis between adjacent states showed that co-expression of PDE3A and VIM distinguished each cell state (Fig. 2m, Methods and Supplementary Table 5): canonical arterial endothelial cells (caECs; VIMhiPDE3Ahi), which progressed through R0 (VIMlowPDE3Ahi), R1 (VIMlowPDE3Alow) and R2 (VIMhiPDE3Alow) (Fig. 2l,m), charting the endothelial response to EVT-mediated remodelling. Functional enrichment analysis confirmed the trajectory direction: genes upregulated in R2 relative to caECs were significantly associated with apoptosis (Fig. 2n and Supplementary Table 5), confirming R2 as the terminal state. The same cell states (R0, R1 and R2) were observed in independent tissue samples (Fig. 2o,p). Of note, the strong expression of the pro-apoptotic gene GADD45G was specific to the R2 terminal state (Fig. 2o). Spatial measurements further validated this trajectory (Fig. 2l). We calculated the absolute distance of each endothelial cell from the vessel wall. caECs and R0 cells were proximate, R1 cells were modestly displaced, and R2 cells were most distant (Fig. 2q). Therefore, the loss of caECs during spiral artery remodelling begins with vessel wall displacement in the R1 state, leading to the full detachment, apoptosis and eventual clearance of R2 cells.We next performed CODEX imaging2 for protein-level validation by multiplexing nine cell-type-specific antibodies (Supplementary Table 6 and Supplementary Note), which accurately detected the major cell types at the human MFI (Fig. 3a and Extended Data Fig. 6f–i; independent validation in Extended Data Fig. 7a; Methods), including CD31 (PECAM1), PDE3A and vimentin (VIM) to label the evolving endothelial cell states. At the protein level, the cell states (R0, R1 and R2) were observed in arteries at different stages of vascular remodelling (SA-A and SA-B at GW15.2 in Fig. 3b–e and a vessel at GW19 in Fig. 3f–i). These CODEX observations were validated by immunolocalization on independent samples (Extended Data Fig. 7b).Fig. 3: CODEX validation of stepwise arterial endothelial state transitions during EVT remodelling of spiral arteries.Full size imagea, An overview of multiplexed CODEX imaging of a representative tissue section from a human basal plate sample (GW15). The selected markers (and their specificity): DAPI (nuclei), CD31 (endothelial cells), CK18 (cytotrophoblasts, particularly EVTs), CDH1 (endometrial epithelium and cytotrophoblasts), Ki67 (proliferation), CD206 (macrophages and Hofbauer cells), PDE3A (arterial endothelial and perivascular smooth muscle cells), vimentin (decidua stromal cells and a subset of vascular and lymphatic endothelial cells), CD3 (T cells) and PDPN (lymphatic endothelium and fetal fibroblasts). Marker specificities are shown in Extended Data Fig. 6g. SA, spiral artery; AV, anchoring villi. Scale bar, 400 μm. b–e, Higher-magnification CODEX imaging of spiral arteries SA-A highlighting CD31 (b) and PDE3A (c), and SA-B highlighting CD31 (d) and PDE3A (e) recapitulated the transient arterial endothelial states at the protein level: R0 (PDE3A+VIM−), R1 (PDE3A−VIM−) and R2 (PDE3A−VIM+). Biological replicates of the CODEX analysis are included in the Extended Data Fig. 7a. Scale bars, 50 μm. f, Overview of CODEX imaging near an extensively remodelled spiral artery from a GW19 basal plate sample. Scale bar, 100 μm. g–i, Magnification of the boxed region from f, showing the details of the distinct endothelial states across selected channels: CD31, PDE3A, CK18 and vimentin (g), and CD31 and CK18 (h). i, R0 (green arrows) and R2 cells (yellow arrows) are spatially segregated. Scale bars, 50 μm. j,k, Differential gene expression analysis comparing spatial transcriptomic profiles: R0 versus caECs (j) and R1 versus caECs (k). Genes involved in cell–cell junctions (for example, PLAUR, CDH5 and JUP) were down-regulated in R0 versus caECs (j), whereas genes that define arterial identity (for example, DLL4, EFNB2, HEY1 and ENG) were down-regulated in R1 versus caECs (k). l, Proposed stepwise model of endothelial (CD31+) state transitions during spiral artery remodelling. CaECs (PDE3AhiVIMhi) → R0 (PDE3AhiVIMlow) → R1 (PDE3AlowVIMlow) → R2 (PDE3AlowVIMhi).For functional characterization, we compared each endothelial state to caECs in the spatial transcriptomes (Fig. 2l). R0 cells exhibited down-regulation of junctional genes (FDR = 1.8 × 10−2) and antigen-presentation pathways (FDR = 1.9 × 10−2; Fig. 3j, Extended Data Fig. 7c and Supplementary Table 5), defining a vessel wall-adherent ‘primed’ state (Fig. 2q) with reduced structural stability and immune signalling. R1 cells down-regulated PDE3A and other arterial markers (for example, EFNB2, HEY1 and DLL4; Fig. 3k and Supplementary Table 5), indicating loss of arterial identity, coincident with early detachment from the vessel wall (Fig. 2q). R2, the terminal EVT remodelling state, showed apoptotic gene activation (Fig. 2n and Extended Data Fig. 7d) and complete detachment (Figs. 2q and 3f–i), suggesting anoikis. Notably, caECs were furthest from EVTs, which came progressively closer to R1 and R2 (Extended Data Fig. 7e), supporting a spatial gradient in which increasing EVT proximity drives sequential endothelial transitions from loss of arterial identity (R1) to apoptosis (R2) (Fig. 3l).In this model (Fig. 3l), SA-A cells (Fig. 3b) were primed for remodelling (R0). SA-B showed more advanced remodelling, with R1 cells along the upper wall and EVT-displaced R2 cells along the lower wall. The artery in Fig. 3d represented a near-terminal stage, with EVTs largely replacing the endothelium.Trophoblast developmental trajectoriesDeep-coverage snRNA-seq enabled sensitive identification of intermediate trophoblast states, which were then mapped in situ using spatial transcriptomics. We clustered 95,872 trophoblasts on the basis of snRNA-seq, identifying VCT, SCT and EVT subpopulations distinguished by canonical markers (Fig. 4a–c). Within each lineage, we uncovered multiple intermediate cell states that varied across gestational stages (Fig. 4d and Extended Data Fig. 8a). Pseudotime analysis using Palantir32 reconstructed the canonical bifurcation of VCTs into SCTs and EVTs (Fig. 4e) and resolved the timing of emerging trophoblast states along developmental continua (Fig. 4f and Extended Data Fig. 8b). First, we focused on the EVT lineage (Fig. 4e), which originated from a VCT subtype, anchoring villi VCTs (Fig. 4d,f), and was distinct from another subtype, floating villi VCTs, that primarily gave rise to SCTs (Fig. 4d,f). We identified a progenitor EVT population marked by ITGA2, ITGB6 and VIT expression33 (Fig. 4e and Extended Data Fig. 8c–e), and further resolved three terminal EVT subtypes: endovascular (eEVT), interstitial (iEVT) and perivascular (pEVT) EVTs, each defined by established markers (Fig. 4e). Additionally, we identified a population of potential trophoblast giant cells (Fig. 4d), marked by CD81 (ref. 3) (Extended Data Fig. 8f). These cells also expressed canonical iEVT markers (Extended Data Fig. 8g), suggesting their formation from iEVTs; however, their transcriptomes clustered with SCTs (Fig. 4d), probably reflecting shared cell fusion mechanisms.Fig. 4: Molecular and spatial characterization of human trophoblast development and function.Full size imagea–c, UMAPs of trophoblast subtypes by marker expression: TP63+ VCTs (a), HOPX+ SCTs (b) and HLA-G+ EVTs (c). d, Clustering of trophoblast subtypes: iEVT, eEVT, pEVT, giant cell (GC), floating villi VCT (fvVCT), anchoring villi VCT (avVCT), EVT progenitor (proEVT) and SCT progenitor (proSCT). e, Reconstructed pseudotime trajectory reveals divergent developmental paths in floating versus anchoring villi by UMAP (top). Bottom, expression of EVT subtype-specific markers on UMAP: proEVT (ITGA2 and ITGB6), eEVT (FLT4 and NCAM1), iEVT (AOC1 and PRG2) and pEVT (SLIT2). f, Stream plot mapping VCT differentiation. Major branches are shown. g,h, Proportion of EVT subtypes identified in the decidua binned as the distance from the MFI (g) or maternal blood vessel walls (h). i, Spatial transcriptomics with pan-CK and CD31 immunostaining localized EVT subtypes, which were marked by HLA-G versus ITGB6 (proEVT), PRG2 and AOC1 (iEVT), SLIT2 (pEVT) and NCAM1 (eEVT). Images highlight EVT positioning near anchoring villi or blood vessels. j, Differential expression analysis (pEVT versus iEVT and eEVT versus iEVT) identified subtype-specific signatures. DEG, differentially expressed gene. k, CellChat identified distinct outgoing (top) and incoming (bottom) interactions, reciprocal signalling (eEVTs and mVECs), and crosstalk (EVTs and decidual cells). iEVTs showed strong outgoing interactions towards NK cells. l, Validation of the EVT pseudovascularization model. Left, EVTs adjacent to vessel walls exhibited high expression of endothelial signatures. Right, positive correlation of endothelial gene scores in EVTs with proximity to vessel walls (n = 9,374 EVTs from 16 samples). In box plots, the centre line is the median, box represents the IQR, and whiskers show minima and maxima (0.5× IQR) end-points. P value, Spearman (two-tailed) correlation test. m, A sparse learning model predicts EVT invasiveness from single-cell transcriptomes. Invasiveness scores (iScores) were positively correlated with EVT decidual depth in the test set (n = 63,916 EVTs from 16 samples). In box plots, the centre line is the median, box represent the IQR, and whiskers show minima and maxima (0.5× IQR) end-points. P value, Spearman (two-tailed) correlation test. n, Benchmark iScores in independent studies. Left, reduced invasiveness of smooth chorion EVTs40. Right, EVT shows increased invasiveness in placental accreta spectrum41. In box plots, the centre line is the median, box represents the IQR, and whiskers show minima and maxima (0.5× IQR) end-points; outliers are not shown. P values, two-tailed Wilcoxon rank-sum test. o, Comparison of EVT invasiveness in superficial and deep decidual compartments (left and middle) and near blood vessels (right). p, Invasiveness (iScore; bottom) and expression of ITGA2 (progenitor), HLA-G (cell column exit) and AOC1 (advanced maturation) at EVT maturation stages along the reconstructed trajectories. q,r, Identification of a novel SCT terminal state with distinct expression of KLRD1 (encoding CD94) (q) and GPC5 (r) in SCT-A versus SCT-B. s, Immunolocalization (CD94) and RNAscope (GPC5) identifies CD94+ SCT-A in syncytium and GPC5+ SCT-B in syncytial knots of floating villi cells in a representative GW23 sample (n = 3). IF, immunofluorescence. Scale bar, 25 μm. t, Spatial transcriptomics independently confirmed the presence of the SCT-B subtype in a representative sample (016-GW24, n = 16). Immunostaining: pan-CK (green). Scale bar, 25 μm.Source dataSpatial localization of EVT subtypesWe next leveraged the aggregated spatial dataset to localize EVT subtypes as a function of distance from the MFI and vessel wall (Fig. 4g,h). EVT progenitors were confined to regions adjacent to the interface, including an ITGB6+ subpopulation33 that was restricted to cell columns (Fig. 4i). The proportion of eEVTs increased with decidual depth and iEVTs were the predominant subtype across decidual layers (Fig. 4g), initiating HLA-G expression as they exited the columns (Fig. 4i). Within iEVTs, PRG2+ and AOC1+ subpopulations29 (Fig. 4e) were spatially intermingled (Fig. 4i). AOC1 was enriched in developmentally advanced iEVTs (Fig. 4e), consistent with its role as a late-stage EVT marker34. Stratifying by vessel proximity, NCAM1+ eEVTs were restricted to intraluminal and perivascular zones with concomitant iEVT depletion, whereas SLIT2+NCAM1− pEVTs35 (Fig. 4e) were specifically enriched in the perivascular region (Fig. 4h). These observations delineate a mosaic EVT organization in which HLA-G+ iEVTs encase the vessel, SLIT2+ pEVTs occupy the perivascular zone, and NCAM1+ eEVTs populate the lumen and vessel wall (Fig. 4i).Functional specializations of EVT subtypesWe compared the transcriptomes of terminally differentiated eEVTs, iEVTs, and pEVTs (Fig. 4j and Supplementary Table 7). Gene ontology analysis highlighted their distinct molecular functions (Extended Data Fig. 8h). CellChat36 analyses, which integrates ligand–receptor co-expression with spatial proximity, revealed reciprocal signalling between eEVTs and maternal vascular endothelial cells (mVECs; Fig. 4k). Signals from endothelial cells to eEVTs are likely to recruit eEVTs to spiral arteries, whereas signals from eEVTs to endothelial cells are likely to contribute to the caEC-to-R2 transition (Fig. 3f). This analysis also revealed crosstalk between iEVTs and DSCs (Fig. 4k). Additionally, iEVTs showed strong outgoing signalling towards natural killer (NK) cells, suggesting that iEVTs have a role in modulating NK cells37.The pseudovascularization modelPreviously, we proposed a pseudovascularization model in which eEVT remodelling of spiral arteries entails their mimicry of endothelial phenotypes, supported by their CDH5 expression38. Across all spatial transcriptomic samples (Fig. 2a), we validated this model transcriptome-wide. As EVTs approached the vessel wall, they shifted from an interstitial to an endovascular phenotype, down-regulating PRG2 and up-regulating NCAM1. Meanwhile, their expression of endothelial-associated genes peaked in wall-adjacent eEVTs (Fig. 4l, left). Furthermore, an ‘endothelium-like’ score39 assigned to each EVT on the basis of the 100 genes that were most enriched in mVECs (from snRNA-seq; Fig. 1b), was significantly inversely correlated with EVT distance from the vessel wall (Fig. 4l, right), suggesting progressive acquisition of an endothelial programme as EVTs occupy the spiral artery niche. Thus, spatially resolved data bolster the pseudovascularization model, showing that eEVTs systematically adopt an endothelial-like identity. In addition, eEVTs co-clustering with other EVT subtypes (from snRNA-seq; Fig. 4d) indicate preserved trophoblast identity alongside endothelial mimicry.Machine learning to infer EVT invasivenessEVTs exhibit heterogeneous invasiveness, prompting us to test whether single-cell transcriptomes predict invasive potential. This analysis was guided by three considerations. First, by focusing on the MFI, deep decidual EVTs were spatially separated from the superficial myometrium, reducing confounding from peri-myometrial EVTs that may have ceased invading. Second, most superficial decidual EVTs (around 77.3%) expressed AOC1 (Fig. 4i), a marker of mature EVTs34, limiting confounding from developmental heterogeneity. Third, because EVT transcriptomes integrate both cell-autonomous and microenvironmental cues, transcriptome-based inference captures both sources of variation relevant to invasion.We first considered EVTs that were not associated with blood vessels (that is, eEVTs and pEVTs) and trained a sparse learning model to compare EVT transcriptomes at varying decidual depths. The model agnostically selected 54 of 3,192 EVT-enriched genes (from snRNA-seq; Fig. 4e and Supplementary Table 8) and aggregated their expression into an invasiveness score (iScore; Supplementary Note). In the randomly held-out test set (EVTs not used in training), iScores tightly tracked iEVT invasion depth (Fig. 4m), and the up-regulated genes were enriched for migration and extracellular matrix (ECM)-remodelling programmes in high- versus low-scoring cells (Extended Data Fig. 8i,j and Supplementary Note), confirming biological relevance. In independent single-cell datasets, EVTs from the smooth chorion40, known to have minimal invasion, indeed showed reduced iScores (Fig. 4n, left). Conversely, EVTs from placenta accreta spectrum pregnancies41, characterized by excessive invasion, had higher iScores regardless of whether they were sampled from decidua-adherent or non-adherent regions (Fig. 4n, right and Extended Data Fig. 8k). These independent validations confirmed biological specificity. Across iEVTs from the spatial data, high iScores generally localized to deep decidua, whereas low iScores were superficial (Fig. 4o, left). Outliers included superficial high-iScore iEVTs (ongoing invasion, pink arrows; Fig. 4o, middle) and deep low-iScore iEVTs (attenuated invasion, white arrows; Fig. 4o, middle), suggesting that iScores captured heterogeneous invasive states beyond spatial position alone. Extending to eEVTs, they showed high iScores near vessels (Fig. 4o, right and Extended Data Fig. 8l). Scoring EVTs from our snRNA-seq data, progenitors exhibited the lowest iScores, whereas eEVTs and pEVTs had the highest (Extended Data Fig. 8m). iEVTs had a stage-dependent pattern: iScores rose sharply as HLA-G+ cells entered the decidua and then declined with AOC1+ expression and maturation (Fig. 4p). Thus, iEVT invasive potential is inversely correlated with developmental maturation; this antagonism suggests a mechanistic basis for tightly controlled uterine invasion.A novel SCT subtype in syncytial knotsOur 10x snRNA-seq data confirmed the previously described SCT progenitor population (proSCT; Fig. 4d), marked by syncytin-2 (ERVFRD-1) and SLC26A2 (refs. 3,9) (Extended Data Fig. 9a–c). Immediately downstream, we identified an SCT-A subtype with increased KLRD1 expression (encoding CD94) (Fig. 4q), and a terminal SCT-B state marked by GPC5 (Fig. 4r). CD94 immunostaining localized SCT-A to the syncytium of floating villi, whereas RNAscope and spatial transcriptomics showed GPC5 enrichment in syncytial knots, a finding that was supported by immunolocalization in term villi (Fig. 4s,t, Extended Data Fig. 9d–g and Supplementary Table 9). Consistent with the accumulation of syncytial knots in late gestation42, SCT-B nuclei were enriched at later gestational stages (Extended Data Fig. 9h), supporting a trajectory from proSCT to SCT-A and finally to SCT-B in syncytial knots.Novel DSC statessnRNA-seq of 20,579 DSCs across gestation identified five distinct clusters (DSC0–DSC4; Fig. 5a and Extended Data Fig. 10a). Pseudotime analysis revealed two trajectories rooted in DSC0: path A (DSC0 → DSC1 → DSC3) and path B (DSC0 → DSC2 → DSC4; Fig. 5b). DSC0 predominated in early gestation, whereas DSC3 and DSC4 emerged later (Fig. 5c). A prior early-pregnancy atlas described only path A5 (markers below); thus, path B and its terminal DSC4 population represent a previously unrecognized decidual lineage. Notably, DSC4 was also absent from non-pregnant human endometrium (proliferative and secretory phases)43, indicating pregnancy-specific emergence.Fig. 5: Molecular and spatial characterization of DSC heterogeneity and function.Full size imagea, UMAP projection of five DSC subtypes (DSC0–DSC4) from snRNA-seq. b, Reconstructed trajectory of the major decidualization paths: path A (DSC0 → DSC1 → DSC3) and path B (DSC0 → DSC2 → DSC4). c, Distribution of DSC subtypes across gestation (GW5–39). d, Expression of DSC subtype-specific markers on UMAP: ACTA2 (undecidualized DSC0), IGFBP1 and PRL (decidualized DSCs, DSC3/DSC4), SERPINF1 (path A: DSC1/DSC3), SEMA3A and CNR1 (path B: DSC2/DSC4). e, Differential expression analysis highlighted SERPINF1 (encoding PEDF) as a key marker that distinguishes path A from path B DSCs. f, Immunolocalization of DSC subtypes: PEDF+IGFBP1+ DSC3 cells were localized in interstitial decidua (left); PEDF−IGFBP1+ DSC4 cells were enriched in superficial decidua and clustered near anchoring villi (right). Images from n = 3 representative samples. g, Spatial transcriptomics: DSC3 cells (SERPINF1+PRL+) were adjacent to blood vessels in deep decidua (top); DSC4 cells (CNR1+IGFBP1+) were localized near anchoring villi in superficial decidua (bottom). Images from n = 16 representative samples. Scale bars, 100 μm. h, Spatial distance analysis (n = 78,993 DSCs) showed that DSC4 were proximal to the MFI compared with other subtypes (16 samples). In box plots, the centre line is the median, box represents the IQR, and whiskers show observed minimum and maximum values. P value, Wilcoxon rank-sum test (two-tailed) after Benjamini–Hochberg correction. i, Comparing DSC3 and DSC4 proportions across cell communities. P value, chi-square (two-tailed) test after Benjamini–Hochberg correction. j, EVTs adjacent to DSC4 exhibited lower iScores than those near DSC3, other DSCs or EVTs at comparable decidual depths (16 samples). In box plots, the centre line is the median, box represents the IQR, and whiskers show observed minimum and maximum values. P values, Wilcoxon rank-sum test (two-tailed) after Benjamini–Hochberg correction. k, In vitro decidualized HuFs recapitulated the DSC4 transcriptomic signatures (left), manifested by CNR1 (right). l, Gene set enrichment analysis (GSEA) of DSC3 versus DSC4 responses to mAEA after in vitro decidualization. mAEA induces pro-apoptotic genes in DSC3, which were repressed in DSC4. Ctrl, control; NES, normalized enrichment score. m, Schematic of Transwell invasion assay. n, Quantification of primary cytotrophoblast invasion (n = 6). Data are mean ± s.e.m.; P values, Student’s t-test (two-tailed) after Benjamini–Hochberg correction. o, Schematic of model. mAEA exposure protects DSC4 from apoptosis and reshapes DSC4 paracrine cues to enhance local suppression on adjacent EVT invasiveness. CTB, cytotrophoblast.Source dataMarker analysis supported these trajectories. DSC0 cells expressed high levels of ACTA2 and low IGFBP1 and PRL, indicating an undecidualized state, whereas DSC3 and DSC4 showed low expression of ACTA2 and strong expression of IGFBP1, demonstrating decidualization (Fig. 5d and Extended Data Fig. 10b). SERPINF1 (encoding PEDF) was specifically up-regulated along path A (Fig. 5d,e), distinguishing DSC3 (PEDF+IGFBP1+) from DSC4 (PEDF−IGFBP1+). Immunolocalization identified ACTA2+ DSC0 cells deep in the decidua (Extended Data Fig. 10c), whereas DSC3 (PEDF+IGFBP1+) cells were broadly distributed (Fig. 5f, left), often in perivascular regions (Extended Data Fig. 10d). We also observed a small population of CK+ cytotrophoblasts that immunostained for PEDF (Fig. 5f and Extended Data Fig. 10e). By contrast, the novel DSC4 subtype (PEDF−IGFBP1+) localized to the superficial decidual surface, and many were adjacent to the termini of anchoring villi (Fig. 5f, right and Extended Data Fig. 10f,g for biological replicates), a pattern later confirmed by spatial transcriptomics. Finally, independent validation using single-cell RNA-seq from 13 additional decidual samples (28,626 cells) reproduced the DSC3 and DSC4 subtypes (Extended Data Fig. 10h).We next functionally characterized the two DSC developmental pathways (Fig. 5e). Genes that were up-regulated along path A were enriched for endothelial cell migration (FDR = 9.31 × 10−3; Supplementary Table 10), consistent with the frequent perivascular localization of DSC3 (Extended Data Fig. 10d), and many other functions (Extended Data Fig. 10i and Supplementary Table 10). At the terminus of path A (DSC3), we observed bifurcation into two subclusters, DSC3.1 and DSC3.2 (Fig. 5b and Extended Data Fig. 10j), both of which exhibited IGFBP1 expression. DSC3.2 cells, which emerged mainly after GW22, broadly down-regulated angiogenic inhibitors, MHC class I molecules, complement components and anti-apoptotic genes (Extended Data Fig. 10k). These features support a senescent-like decidual state, consistent with prior reports that senescent DSCs accumulate at later gestation and contribute to parturition44.By contrast, genes that were up-regulated along path B (Fig. 5e) were enriched for ECM organization (FDR = 3.7 × 10−2; Supplementary Table 10). DSC4 cells specifically expressed SEMA3A, WNT5A and CNR1 (Fig. 5d and Extended Data Fig. 11a). SEMA3A and WNT5A promote ECM rigidity and stabilization, restraining cell motility45,46. The marker CNR1 encodes the cannabinoid receptor CB1. Because DSC4 cells are in the superficial decidua, often near the termini of anchoring villi (Fig. 5f, right) where EVTs initiate invasion, DSC4 marker expression suggests that DSC4 mediates endocannabinoid signalling at the human MFI and likely serves as a localized brake limiting EVT invasion.Regulation of DSC4 on EVT invasionWe first spatially mapped the DSC states and then leveraged the iScore metrics (Fig. 4m) to quantify EVT invasiveness adjacent to DSC4. We used gene signature scores39 from snRNA-seq to identify DSC3 and DSC4 subtypes from IGFBP1+ DSCs on the spatial map (Extended Data Fig. 11b and Methods), whose spatial localizations were consistent with immunostaining (Fig. 5f). ACTA2+VIM+ undecidualized stromal cells (Extended Data Fig. 11c,d) and SERPINF+IGFBP1+ DSC3 cells (Fig. 5g) were enriched in the deep decidua. CNR1+IGFBP1+ DSC4 cells often localized near anchoring villi (Fig. 5g). DSC3 lay farthest from and DSC4 was closest to the MFI (Fig. 5h). When mapped onto spatial niches (Fig. 2c,d), DSC4 were enriched in the EVT-rich D1 niche (Fig. 5i), suggesting interactions between EVT and DSC4. For confirmation, we compared iScores of EVTs adjacent to DSC4 with: (1) EVTs adjacent to other DSC subtypes; and (2) depth-matched EVTs (non-significant depth difference; P = 0.85, Wilcoxon rank-sum test). EVTs adjacent to DSC4 had significantly lower iScores than both comparison groups (P = 8.4 × 10−91 and P = 3.8 × 10−142, respectively; Wilcoxon rank-sum test; Fig. 5j), revealing a local DSC4-mediated suppression of EVT invasiveness.DSC4 cells mediate cannabinoid signallingBecause the cannabinoid receptor CB1 (encoded by CNR1) was specifically expressed in DSC4 cells (Fig. 5d,g), and CB1 is activated by the endocannabinoid anandamide (AEA), whose balance at the MFI is critical for pregnancy47, we investigated how CB1 agonism affects DSC4 and their regulation of trophoblast invasion. Using our in vitro decidualization model48, human uterine fibroblasts (HuFs) were differentiated into decidualized stromal cells and exposed to 0.5 μM methanandamide (mAEA, an AEA analogue) or vehicle for 72 h. Single-cell RNA-seq of 33,088 cells (13,493 control, 19,595 mAEA-exposed; Methods) identified undecidualized ACTA2+ and decidualized IGFBP1+ clusters, as well as DSC3- and DSC4-like subtypes as defined by markers of these cell types in primary tissues (Fig. 5k, left and Extended Data Fig. 11e–h). DSC4-like cells retained robust CNR1 expression (Fig. 5k, right), indicating that the model recapitulated DSC3 and DSC4 differentiation.Given the absence of CNR1 in DSC3 and its specific expression in DSC4, we performed subtype-specific differential expression after mAEA exposure (Extended Data Fig. 11i and Supplementary Table 11). Gene set enrichment analysis (Fig. 5l) revealed divergent responses. In DSC3, mAEA tended to up-regulate pro-apoptotic genes (GSEA FDR = 9.7 × 10−2), consistent with prior reports of mAEA-induced apoptosis in DSCs49. In CB1+ DSC4 cells, the apoptosis process was significantly down-regulated (GSEA FDR = 7.0 × 10−3), including expression of pro-apoptotic genes (DAPK3, FADD and GADD45G; Extended Data Fig. 11i), suggesting CB1-mediated protection from apoptosis after mAEA exposure.We tested whether endocannabinoid signalling altered DSC regulation of primary human cytotrophoblasts invasion. Decidualized DSCs were treated for 72 h with mAEA (CB1 agonist), rimonabant (SR141716A, CB1 antagonist), both, or vehicle. After washing, conditioned medium was collected for 24 h and applied to second-trimester human cytotrophoblasts in a Transwell invasion assay (Fig. 5m, Methods and Supplementary Table 12). Conditioned medium from mAEA-treated DSCs reduced cytotrophoblast invasion (P = 3.7 × 10−5), whereas conditioned medium from rimonabant-treated DSCs increased invasion (P = 4.8 × 10−3); co-treatment restored invasion towards control (P = 5.4 × 10−3) (Fig. 5n). Together, these data suggest that endocannabinoid exposure protects DSC4 from apoptosis (Fig. 5l) and modulates DSC4 paracrine cues (Fig. 5n), which enhanced its capacity in constraining adjacent EVT invasiveness (Fig. 5o).Cell types that are most vulnerable in diseaseWe next investigated whether our single-cell atlas could explain the genetic architecture of major pregnancy complications. We used SCAVENGE50 to integrate single-cell open-chromatin architecture and large-scale (n > 10,000) maternal and fetal genome-wide association studies (GWASs) for pre-eclampsia51, spontaneous preterm birth52,53 and sporadic miscarriage54, generating cell-type-resolved maps of genetic risk for each condition (Fig. 6a and Supplementary Table 13). Conventional pre-eclampsia GWAS, despite large cohorts, yielded only a few loci with modest effect sizes, leaving most heritability unexplained51. By contrast, SCAVENGE50 aggregated GWAS signals across open-chromatin regions in each cell into a per-cell trait relevance score. Pairing maternal cells with maternal pre-eclampsia GWAS51 and fetal cells with fetal pre-eclampsia GWAS51, we identified 6,221 maternal and 8,232 fetal cells with significant pre-eclampsia risk enrichment. In the fetal compartment, only EVTs, particularly iEVTs, were enriched for pre-eclampsia risk (FDR ≤ 2.2 × 10−96) (Fig. 6b). On the maternal side, enriched populations included the DSC3 subtype, arterial endothelium (not venous), perivascular cell, fibroblasts and T cells, whereas decidual macrophages and NK cells were not enriched (Fig. 6c). A distinct POU5F1+LGR5+ endometrial epithelial population, restricted to early gestation and expressing stem cell markers (Extended Data Fig. 12a–d), also showed strong enrichment, implicating early endometrial defects in pre-eclampsia. Validation using 151 DisGeNET55-curated pre-eclampsia genes confirmed expression enrichment in these vulnerable cell types (Extended Data Fig. 12e and Supplementary Table 13). Negative control experiments paired the same GWASs with fetal brain56 or adult brain57 single-cell datasets and detected no significant enrichment (Extended Data Fig. 12f,g), demonstrating specificity. Note that this analysis focused on population genetic risk (common variants), and other cell types may contribute through rare or somatic variants or non-genetic factors.Fig. 6: Single-cell risk map of major pregnancy complications.Full size imagea, Single-cell multiomics was integrated with maternal and fetal pre-eclampsia GWASs by mapping high-risk variants onto open chromatin in single cells, revealing cell-type-specific genetic vulnerabilities and pinpointing risk-enriched populations. TRS, trait relevance scores. b,c, Maternal or fetal cell types and subtypes showing significant associations with pre-eclampsia. The proportion of risk-associated cells within each source was compared to the maternal or fetal background. Enrichment is shown as fold change; P values, Fisher’s exact test (two-tailed) with Benjamini–Hochberg correction. b, Fetal GWAS risk associations in fetal cell types. c, Maternal GWAS risk associations in maternal cell types. EM, endometrium. LEC, lymphatic endothelial cell. d,e, Parallel enrichment analyses for maternal GWAS risk in spontaneous preterm birth (d) and sporadic miscarriage (e) across maternal cell types indicate distinct maternal cellular contributors. Only subtypes with more than 200 cells were included. P values, Fisher’s exact test (two-tailed) with Benjamini–Hochberg correction.Source dataWe next applied the same strategy to maternal GWASs for spontaneous preterm birth and sporadic miscarriage, pairing analysis to maternal cell types52,54. For both conditions, only POU5F1+LGR5+ endometrial epithelial cells showed significant enrichment (Fig. 6d,e), an association that was replicated in an independent spontaneous preterm birth cohort of 233,290 women53 (Extended Data Fig. 12h). The shared vulnerability of this epithelial population across pre-eclampsia, spontaneous preterm birth and miscarriage supports the concept of ‘endometrium spectrum disorders’58.DiscussionAcross gestation, our multimodal atlas systematically delineated key cell states, spatial niches, and developmental trajectories at the human MFI and pinpointed genetically vulnerable cell types in major pregnancy complications. In trophoblasts, our study suggested a bistable regulatory circuit that directs commitment to terminal fates from progenitor VCTs (Fig. 1j). Thus, perturbing key regulators could shift this circuit across the EVT–SCT boundary, triggering an aberrant fate switch that may predispose to pathology. A key pathological manifestation of trophoblast dysfunction is dysregulated EVT invasion. This study developed a quantitative framework to quantify EVT invasiveness directly from transcriptomes, with proof-of-concept validation in placenta accreta spectrum cases. While appropriate EVT invasiveness is essential for normal spiral artery remodelling, endothelial cell–intrinsic changes within the vessel wall are also likely to be critical. Our data uncovered discrete endothelial state transitions during vascular remodelling59, suggesting that disruption of these short-lived states could impair this process and contribute to pathological changes. The same is likely to extend to other perivascular populations, particularly the decidual stromal subtype DSC3, which often surrounds vessels (Extended Data Fig. 10d) and is genetically associated with pre-eclampsia (Fig. 6c). Its potential role in regulating vascular function warrants future investigation. Together, from a single-cell perspective, coordinated perturbations of trophoblast, endothelial and stromal states are likely to collectively drive many pregnancy complications, shifting the focus from fetal versus maternal contributions to failed integration at the MFI. Although immune cells were not the primary focus of this study, their contribution was evident and will be important to dissect in future work.We also identified novel cell subtypes and cellular interactions. DSC4, first described here, modulates EVT invasion of the superficial decidua via endocannabinoid signalling. This finding raises public health concerns about cannabis use during pregnancy, as its principal ingredient, Δ9-tetrahydrocannabinol, is a CB1 agonist that is likel to perturb DSC4-mediated endocannabinoid signalling and thereby dysregulate EVT invasion. Consistent with this possibility, prenatal cannabis exposure has been epidemiologically associated with spontaneous preterm birth, stillbirth, reduced birthweight and other adverse outcomes60.Integrating GWASs with our single-cell datasets showed that fetal EVTs strongly mediate population genetic risk of pre-eclampsia. Meanwhile, the POU5F1+LGR5+ endometrial epithelial cells exhibited shared genetic risk enrichment across pre-eclampsia, spontaneous preterm birth and miscarriage, highlighting a convergent and previously underappreciated cell population for future mechanistic investigation.Despite extensive profiling in this study, many more cell states and subtypes almost certainly remain to be discovered, including populations in the deep decidua, at very early gestation and in pregnancy complications. Further work expanding spatial and temporal coverage will be essential to define these elusive populations and to clarify their roles in normal pregnancy and pathological deviations.MethodsTissue acquisition and processing for joint single-nucleus multiome profiling and spatial transcriptomicsSnap-frozen decidual and basal plate samples were obtained from the existing placenta tissue banks at Stanford University and University of California, San Francisco (Supplementary Tables 1 and 4). All samples were collected with written informed consent. Tissues were derived from women undergoing elective termination of presumed normal pregnancies (first and second-trimester samples; no known or predicted fetal chromosomal abnormalities, Extended Data Fig. 1c) or after term delivery (≥37 gestational weeks). For term samples, clinical records were reviewed to exclude placenta-associated complications (for example, chorioamnionitis); cases with NICU admission and preterm premature rupture of membranes were also excluded. Fresh placental tissues were grossly inspected and dissected under a microscope (Leica Microsystems) by pathologists. Decidua basalis was micro-dissected on ice from the MFI and distinguished from decidua parietalis and capsularis on the basis of characteristic histological and morphological features (Extended Data Fig. 1a,b). Dissected tissues were sequentially washed to remove residual blood cells in DMEM/H-21 medium, supplemented with 12.5% FBS (Hyclone), 1% L-glutamine (Atlanta Biologicals), 1% penicillin/streptomycin and 0.1% gentamicin and cold 1× PBS (Gibco, Thermofisher). Samples used for single-cell or spatial transcriptomic profiling were flash-frozen in liquid nitrogen and stored at −80 °C until processing. RNA quality was assessed from adjacent cryosections using a Bioanalyzer or Tapestation. For fresh frozen samples, only samples with RIN ≥ 7.0 were included.Isolation of single nucleus from snap-frozen tissuesSingle nuclei were isolated from snap-frozen tissues as previously described61 with modification. In brief, tissues were ground on dry ice, and 30–50 mg was homogenized into a pre-chilled 7 ml PYREX dounce homogenizer (Corning Life Science). Tissue was homogenized in 2 ml ice-cold buffer (250 mM sucrose, 0.3% NP-40, 5 mM MgCl2, 25 mM KCl, 10 mM Tris-HCl pH 7.8) supplemented with protease inhibitors (Roche, cOmplete) and 0.6 U µl−1 Ribolock RNase inhibitor (thermofisher). Debris was removed by 40 μm filtration, and nuclei were purified by OptiPrep iodixanol gradient centrifugation (25%, 30%, 40%). After centrifugation in a swinging bucket centrifuge (Eppendorf 5810R) for 30 min at 3,000g, nuclei were collected from the 30–40% interface, washed, and assessed by trypan blue staining and microscopy to ensure nuclei integrity. Approximately 15,000 nuclei per sample were processed using the Chromium Next GEM Single Cell Multiome ATAC + Gene Expression platform (10x Genomics).Tissue preparation and CODEX imagingPlacenta samples for CODEX were OCT-embedded, cryosectioned at 10 µm, mounted on poly-L-lysine-coated slides and stored at −80 °C. On the day of staining, sections were equilibrated, acetone-treated, rehydrated, fixed with 1.6% paraformaldehyde, blocked, and incubated with a barcoded antibody cocktail (200 µl/section) for 3 h at room temperature. Sections were then washed, post-fixed with 4% paraformaldehyde and cold methanol, stabilized using CODEX fixative reagent, and stored in storage buffer at 4 °C (≤2 weeks) before imaging. Multiplex imaging was performed on an Akoya CODEX microfluidic system coupled to an inverted fluorescence microscope using a 7-cycle protocol (including blank cycles for alignment) across 4 channels (DAPI, FITC, Cy3 and Cy5) with a 20×/0.75 NA objective. Images were processed using CODEX Analysis Manager. The antibody panel included Akoya-validated barcoded antibodies and custom-conjugated antibodies generated using Akoya oligo barcodes following the manufacturer’s protocol (Supplementary Table 6). Additional details are provided in the Supplementary Note.Single-nucleus multiome library construction and sequencingSingle-nucleus Multiome libraries were prepared using the Chromium Next GEM Single Cell Multiome ATAC + Gene Expression kit (10x Genomics) following the manufacturer’s protocol, using one reagent kit per sample. Around 15,000 isolated nuclei per sample were encapsulated into Gel Bead-In Emulsions (GEMs) containing unique cell barcodes, where reverse transcription and transposition occurred, followed by library amplification, and separation of gene expression and chromatin accessibility libraries. Libraries were sequenced on an Illumina NovaSeq 6000 using paired-end reads, with sequencing depth selected on the basis of recommendations from 10x Genomics. On average, each sample yielded approximately 250 million paired-end reads for ATAC and RNA libraries. Raw BCL files were demultiplexed, aligned to the GRCh38 (v.3.0.0) reference genome, and processed for barcode assignment, UMI counting, and quality control using Cell Ranger ARC v.2.0.0 (10x Genomics) (https://support.10xgenomics.com/single-cell-geneexpression/software/pipelines/latest/advanced/references).Single-nucleus multiome data processingHigh-quality nuclei with paired snRNA-seq and snATAC–seq profiles were retained using the following criteria: RNA UMI counts 1,000–50,000; detected genes >400; mitochondrial reads 1.0; and nucleosome signal