Abstract
Successful pregnancy relies directly on the placenta’s complex, dynamic, gene-regulatory networks. Disruption of this vast collection of intercellular and intracellular programs leads to pregnancy complications and developmental defects. In the present study, we generated a comprehensive, spatially resolved, multimodal cell census elucidating the molecular architecture of the first trimester human placenta. We utilized paired single-nucleus (sn)ATAC (assay for transposase accessible chromatin) sequencing and RNA sequencing (RNA-seq), spatial snATAC-seq and RNA-seq, and in situ sequencing and hybridization mapping of transcriptomes at molecular resolution to spatially reconstruct the joint epigenomic and transcriptomic regulatory landscape. Paired analyses unraveled intricate tumor-like gene expression and transcription factor motif programs potentially sustaining the placenta in a hostile uterine environment; further investigation of gene-linked cis-regulatory elements revealed heightened regulatory complexity that may govern trophoblast differentiation and placental disease risk. Complementary spatial mapping techniques decoded these programs within the placental villous core and extravillous trophoblast cell column architecture while simultaneously revealing niche-establishing transcriptional elements and cell–cell communication. Finally, we computationally imputed genome-wide, multiomic single-cell profiles and spatially characterized the placental chromatin accessibility landscape. This spatially resolved, single-cell multiomic framework of the first trimester human placenta serves as a blueprint for future studies on early placental development and pregnancy.
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Data availability
All data related to the present study are available at the Broad Institute Single Cell Portal (https://singlecell.broadinstitute.org/single_cell/study/SCP2601) (snRNA-seq, snATAC-seq, Slide-tags) and via Zenodo at https://zenodo.org (ref. 97) under accession no. 10981713 (STARmap). Public datasets used in the present study include: Human GRCh38 sequences (https://www.gencodegenes.org/human/release_32.html), 1000 Genome Project (https://www.internationalgenome.org/data), cis-BP database (https://cisbp.ccbr.utoronto.ca), UKBB (http://www.ukbiobank.ac.uk/register-apply; other utilized placental GWASs can be found in refs. 78,79,80), Roadmap Epigenomics (http://www.roadmapepigenomics.org), ABC (https://www.engreitzlab.org/resources) and ENCODE (https://www.encodeproject.org/help/project-overview). Source data are provided with this paper.
Code availability
Code for comprehensive analysis as described in the present study, and figure generation as shown, can be found at https://github.com/jian-shu-lab/hPlacenta-architecture.
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Acknowledgements
This paper is part of the Human Cell Atlas: www.humancellatlas.org/publications. We are profoundly grateful to the patients for donating their tissues for research. We thank R. Jaenisch and E. Lander for early discussions and support. We thank lab members in the Chen lab, the Haider lab and the Shu lab for insightful discussions. This work was supported by funds from Massachusetts Life Science Center, Broad Institute of MIT and Harvard and Massachusetts General Hospital to J.S. and by the Austrian Science Funds (nos. P34588 and P-36159), assigned to S.H.
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Authors and Affiliations
Contributions
J.S. conceived, designed and directed the study. J.S., S.H. and F.C. co-supervised the collaborative project. S.H. established and collected human placenta tissues and performed TB-ORG/TSC experiments with contributions from T.M. and A.M.P. J.R.O. and K.Z. generated single-cell multiome data with contributions from C.C. and F.V. K.Z. generated STARmap-ISS and STARmap-ISH data with contributions from C.C. and F.V. A.J.C.R. generated Slide-tags data with contributions from N.N., R.R. and K.Z. K.J.K.K., K.J. and A.L. analyzed data with contributions from Q.G., M.L.Z., M.H. and X.L. W.M., M.K. and F.C. provided conceptual, methodological suggestions and feedback. S.H. and K.Z. assembled figures with input from all authors. J.R.O., S.H. and J.S. wrote the manuscript with contributions from all authors. All authors read and accepted the manuscript.
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Competing interests
A patent application related to this work about discovering novel immune modulators has been filed by the Massachusetts General Hospital. J.S. is a scientific advisor for Johnson & Johnson. F.C. is an academic co-founder of Curio Bioscience and Doppler Bio, and an advisor to Amber Bio. F.C., A.J.C.R. and N.M.N. are listed as inventors on a patent application related to Slide-tags. The other authors declare no competing interests.
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Nature Medicine thanks Fabian Theis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Sonia Muliyil, in collaboration with the Nature Medicine team.
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Extended data
Extended Data Fig. 1 Further characterization of multiomic data.
a, snRNA-seq quality control metrics, separated by samples. Left: number of genes detected per cell (Gex_nGenes) for each sample. Right: unique molecular identifiers per cell (Gex_nUMI) for each sample. Further QC metrics can be found in Supplementary Table 1. b, snATAC-seq quality control metrics showing TSS Enrichment scores and fragment counts per cell across all samples. c, UMAP plot showing cell origins (maternal origin or fetal origin) (Methods). d–e, UMAP plots (d) and dot plot (e) showing selected canonical marker genes expressed across main placental cell types including vCTB (PAGE4, PEG10), vCTBp (MKI67, TOP2A), EVT (HLA-G, CCNE1), STB (CYP19A1, ERVFRD-1), Endo (PECAM1, KDR), MAC (CD14, SPP1), and FIB (COL3A1, COL6A2). Further resolved clusters included vCTB1 (TBL1X), vCTB2 (SMAGP, IFI6), vCTB3 (LRP5), FIB1 (PDGFR1B, AGTR1), FIB2 (PDGFRA, CXCL14), Unknown 1 (Unk. 1) (HGF, DCN), mat. FIB (ALDH1A2, FAM155A), and Unknown 2 (Unk. 2) (PDN4, RSPO3). Manual sub-clustering revealed EVT1 (UTRN), EVT2, (HAPLN3, LY6E), EVT3 (AOC1, PAPPA2), mat. MAC (CD74, LYZ), HBC (LYVE1, ADAMTS17), and myeloid_unknown (myel. Unk) (GNA12, FGF13). f, Bar plot showing time point/donor composition per cluster. g, Myeloid cells further subclustered by Louvain clustering colored by sample. Maternal macrophages: mat. MAC, Hofbauer cells: HBC, and myeloid_unknown: myel. Unk. h, EVTs further subclustered by Louvain clustering colored by sample. i, Myeloid cells further subclustered by Louvain clustering colored by subcluster identity. j, EVTs further subclustered by louvain clustering colored by subcluster identity. k, STARmap-ISH (n = 3) characterization of trophoblast markers EGFR (pan-trophoblast), SMAGP (vCTB2), and LY6E (EVT) in sample W7-2. Stippled line demarcates the villous core from the vCTB layers. l, Gene expression patterns for novel motility and immunotolerance-promoting genes expressed in EVTs (PLXNB2, RUNX1, C12orf75, QSOX1, RASGRF2, PDCD1LG2, JAK1, MYCN, CD276, EBI3, FGFR1) and STBs (BRAF, TBX3) via UMAP visualization, with accompanying spatial visualization of FGFR1 using STARmap-ISH (n = 3). Stippled lines demarcate the villous core from vCTB layers. m, Heatmap showing novel differentially expressed genes across clusters. All differentially expressed genes from snRNA-seq can be found in Supplementary Table 2.
Extended Data Fig. 2 Chromatin accessibility dynamics across the human placenta.
a, Left: FOXP1 expression in 3D trophoblast organoids (TB-ORG, n = 4). WB analyses of TB-ORG protein lysates under stemness and EVT differentiation conditions (DIFF1= EVT differentiation under TGF-beta (TGFβ) inhibition; DIFF2=EVT differentiation under TGFβ activation) detect FOXP1 in TB-ORG under stemness conditions and its almost absence in EVTs. Right: siRNA-based downregulation of FOXP1 in trophoblast stem cells (TSCs) under stemness conditions, showing that siRNA treatment of FOXP1 reduces proliferation (CCNA2) and provokes markers for STB differentiation of TSCs (CGB, ENDOU). n = 3 independent TSC lines, two replicates each. Data are represented as mean values +/− SD. Normal distribution was tested using Kolmogorov-Smirnov test and subsequent unpaired t-tests were applied. P-values are depicted in the blots. b, Visual comparison of gene expression (RNA) and gene activity score (ATAC) UMAP plots for LY6E, with accompanying spatial visualization using STARmap-ISH (n = 3). Stippled line demarcates villous core from vCTBs. c, UMAP representation of novel trophoblast-associated microRNAs miR23B (vCTB) and miR7973-1 (STB), identified by snATAC-seq analyses. Verification of expression was performed through qPCR in isolated vCTB (TP63), STB (CGB), and EVT cells (Methods) (n = 3 donors). Per donor, qPCR was repeated twice (TP63, CGB), six times (miRNA23B), and four times (miRNA7973), respectively. Data are represented as mean values +/− SD. Kolmogorov-Smirnov test (normal distribution) was followed by either Friedman test and Dunn’s multiple comparison (non-parametric miRNA23b and CGB), or one-way ANOVA and Geisser-Greenhouse correction (miRNA7973 and TP63). P-values are depicted in the blots. d, Heatmap showing differentially accessible genes across clusters. All differentially accessible genes from snATAC-seq are listed in Supplementary Table 3. e, Corresponding gene expression (Gene Expression Matrix) and gene accessibility (Gene Score Matrix) of TF motifs from intercluster motif enrichment comparisons, ChromVAR analyses, and identified positive TF regulators. Intercluster motif enrichments can be found in Supplementary Table 4, while ChromVAR enrichments and positive TF regulators can be found in Supplementary Table 5. f, Positive TF regulators identified based on gene activity scores. Positive TF regulator motifs such as BACH2, GCM1, TFAP2C, TEAD1, and MESP2 are significantly enriched. Specific motifs and enrichment values can be found in Supplementary Table 5.
Extended Data Fig. 3 Peak-gene analysis to uncover disease risk.
a, Heatmap of representative DORCs for each cluster, revealing lineage-specific enrichment of DORCs in each cell type including FIB/MAC/Unk. 1-2 (CXCL14, COL6A3, TBX5), Endo (PECAM1), TB (TFAP2C, IRX2, VGLL3), vCTB (EGFR, KANK1, PARD6B, FOXI3), STB (CYP19A1), and EVT (HLA-G, DIO2, VGLL3). DORC scores, defined by the normalized sum of counts in all significantly correlated peaks per gene for all cells, were normalized. DORCs and DORC scores can be found in Supplementary Table 7. b, UMAPs confirming overlapping DORC scores (DORC) and gene expression (RNA) of EGFR (CTB), VGLL3 (EVT), CYP19A1 (STB), COL6A3 (FIB), CXCL14 (FIB2), and PECAM1 (Endo). DORC scores are defined by the normalized sum of counts in all significantly correlated peaks per gene for all cells. DORCs and DORC scores can be found in Supplementary Table 7. c, Transition probabilities across all trophoblasts for terminal differentiation states (vCTB2, STB, and EVT) calculated by chromatin potential and CellRank analysis. Transition probability (colorbar) is defined as the probability of a cell reaching a terminal state (Methods). d, Average heritability enrichment across 17 cell types and 9 traits (n = 8 donor placentas) explained by (i) SNPs in a 100kb window around genes specifically enriched in expression across cell types (LDSC-SEG), (ii) SNPs linked to cell type-specific genes through the union of Roadmap and Activity-By-Contact enhancer-gene maps in placenta biosamples as proposed in sc-linker (sc-linker (ABC + Roadmap)), (iii) SNPs in peaks linked to any gene in a cell type (Multiome), and (iv) SNPs linked to cell type-specific genes by the peak-gene links (sc-linker (Multiome)) (Methods). Data are represented as mean values +/− SD. Numerical results are reported in Supplementary Table 9. e, Illustration of a GWAS hit (rs117659937) associated with excessive vomiting during pregnancy linked to the TP53INP2 gene by ArchR peak-gene linkage in EVT3 cells. TP53 is a Phase 1 clinical trial drug target.
Extended Data Fig. 4 Additional spatial cell-type mapping.
a, Multiomics-derived cell types identified in space with STARmap-ISS (n = 4) via Seurat integration on samples W9, W7-1, and W11. b, Canonical marker gene spatial expression for vCTB (PAGE4), STB (CGA), EVT (NOTUM), and the stromal core (VIM) identified by STARmap-ISS (n = 4) on samples W7-1, W9, and W11. c, Canonical marker gene spatial expression for FIB (COL3A1), Endo (KDR), HBC (CD163), STB (CYP19A1), and EVT (HAPLN3, AOC1), identified by STARmap-ISS (n = 4) on samples W9, W7-1, W11. Insets depict magnified areas of villous (COL3A1, KDR, CD163, CYP19A1) and extravillous (HAPLN3, AOC1) areas.
Extended Data Fig. 5 Global overview of spatial cluster localization across all four samples.
a - d, Spatial localization of STARmap-ISS-identified placental cell clusters across the entire sections of samples (n = 4). W7-1 (a), W9 (b), W11 (c), and W8-2 (d).
Extended Data Fig. 6 Expression of tumor and immunomodulation-associated genes.
STARmap-ISH (n = 3) co-detecting multiple tumor and immunomodulation-associated genes expressed by vCTBs (KLF5, PARP1, IDH1, ABL1, SMARCA4, CEBPA, BTG2, ATM) and EVTs (KLF5, RAD50, MSH3, FGFR1, DIAPH2, CEBPA, BTG2, ATM, HRAS). Stippled lines demarcate the villous core from vCTB layers. Genes are listed and described in Supplementary Table 11.
Extended Data Fig. 7 Cell column visualization.
Spatial visualization of cell column-associated genes (overview and magnified insets) via STARmap-ISS (n = 4) on samples W8-2, W9, and W7-1. By using canonical and newly identified markers associated with various EVT maturation states (EVT1-3), we classified cell columns into three categories: cell column type 1 (associated with EVT1) contained proliferative EVT progenitors (CPS1, SPINT1, MKI67, CDK1, CDK7) and low expression of differentiated EVT markers. Cell column type 2 (associated with EVT2) demonstrated reduced areas of proliferative EVT progenitors and increasingly expressed markers for differentiated EVTs (HSPG2 and ADAM19). In cell column type 3 (associated with EVT3), proliferative markers were absent and cells highly expressed gene patterns associated with a mature EVT phenotype predominantly detected in maternal decidua (ITGB4 and PAPPA2). Images at the bottom illustrate the direction of estimated EVT differentiation (arrows) within each cell column.
Extended Data Fig. 8 Investigating shared gene expression patterns.
a–c, Spatial visualization of gene expression using STARmap-ISS (n = 4) across samples W8-2, W9, and W7-1. Insets depict magnified areas, stippled lines demarcate the villous core from vCTB layers. a, Canonical, epithelial trophoblast markers KRT7, KRT19, and KRT23 are shown. KRT7 and KRT19 are substantially enhanced in EVTs while KRT23 is restricted to vCTBs. b, Spatial exploration of gene expression shared between STBs and EVTs (ADAM12, EBI3), as well as vCTBs and EVTs (ADAMTS20, ANK2) are depicted. c, Spatial visualization of EVT-expressed COL27A1 and COL4A1 suggests an EVT-provided local source for fibrin-matrix-type fibrinoid constituting the materno-fetal border as part of the decidua basalis.
Extended Data Fig. 9 Imputation performance evaluation.
a, Cumulative curves of the imputation performance scores using different numbers of snRNA-seq nearest neighbors in samples W7-1, W8-2, W9, and W11. b, Comparison of STARmap-ISS (n = 4), STARmap-ISH (n = 3), and imputation-based (Imputed-RNA) gene expression for selected canonical markers across cell types to validate imputation results, including SMAGP (vCTB), AGTR1 (FIB1), CD163 (HBC), and KDR (Endo). Insets show magnified areas of Imputed-RNA visualization. Stippled lines in STARmap-ISH demarcate the villous core from vCTB layers. Scale bar = 50 µm c, Comparison between STARmap-ISH-detected (n = 3) and imputed (Imputed-RNA) gene expression of ATP1B3 reveals expression in selected stromal cells and vCTBs. Inset shows magnified villous area of Imputed-RNA visualization. Stippled line in STARmap-ISH demarcates the villous core from vCTB layers. Scale bar = 50 µm. d, Comparison of STARmap-ISS (n = 4) and imputed gene activity scores (Imputed-ATAC) for select genes AOC1 (EVT3), PAPPA2 (EVT3, STB), and KDR (Endo) to validate Imputed-ATAC.
Extended Data Fig. 10 Evaluation of cell-cell interactions.
a, Dotplot of identified ligand-receptor (L-R) pairs across all clusters and shared between samples. We were able to detect L-R pairs within the same cell clusters including BMP5 - BMPR1B (FIB1 - FIB1), PGF - VEGFR1 (EVT3 - EVT3), VEGFC - VEGFR2 (Endo - Endo), MDK-LRP1 (FIB2 - FIB2), and ANXA1 - FPR1 (HBC - HBC) indicating autocrine regulation. However, we also detected numerous paracrine L-R pairs such as AREG - EGFR (FIB1 - vCTB), GDF15 - TGFBR2 (EVT2 - EVT3), NAMPT - ITGA5/ITGB1 (EVT2 - EVT3), PGF - VEGFR1 (EVT2 - EVT3), VEGFA - VEGFR1/VEGFR1R2/VEGFR2 (FIB2 - Endo), MIF - CD74/CD44 (FIB2 - HBC), PDGFC - PDGFRA (HBC - FIB2), PGF - VEGFR1 (STB - vCTB2), and NAMPT - INSR/ITGA5/ITGB1 (vCTB2 - EVT2). Interaction (communication) probabilities and significance were computed by permutation test (p < 0.05). b, Spatial visualization of canonical L-R pair HGF- MET via STARmap-ISS (n = 4) across samples W7-1, W9, and W8-2 to support CellChat findings. HGF - MET has not yet been characterized in the placenta. MET expression in vCTBs and corresponding HGF expression in COL3A1-positive FIB suggests paracrine interactions between FIB1 and vCTBs. Insets depict magnified areas, stippled lines demarcate the villous core from vCTB layers.
Supplementary information
Supplementary Information
Supplementary Figs. 1–11, Tables 1–13 and References.
Supplementary Table 1
Information about the human primary samples isolated from first trimester placentas and used in the present study along with accompanying assays: Multiome (combined snRNA-seq and snATAC-seq), Slide-tags, STARmap-ISS and STARmap-ISH. Each assay is accompanied by relevant QC metrics.
Supplementary Table 2
DEGs discovered by snRNA-seq.
Supplementary Table 3
Differentially accessible genes discovered by snATAC-seq.
Supplementary Table 4
Intercluster motif enrichment comparisons. Differentially accessible peaks were identified using a two-sided Wilcoxon’s test (FDR ≤ 0.1, log2(FC) ≥ 0.5). Intercluster motif enrichments were calculated via a hypergeometric test to generate P values. For instance, ‘vCTBvsEVT’ lists motifs enriched across peaks that are more accessible in vCTB clusters compared with EVT clusters.
Supplementary Table 5
ChromVAR motif enrichment analysis and accompanying positive TF regulators. Correlation between variables (motif enrichment, gene expression, gene accessibility) was calculated using Pearson’s correlation coefficient and statistical significance was assessed using P values adjusted by the Benjamini–Hochberg method (Padj < 0.01).
Supplementary Table 6
Cluster-specific differentially expressed marker genes across all clusters identified by Slide-tags, along with spatially autocorrelated genes and TF motifs with ___location-dependent expression and enrichment as identified by Slide-tags analysis using Moran’s I (includes correlations across all cells as well as within individual cell types). P values were adjusted by the Benjamini–Hochberg method (Padj < 0.05). Cluster numbering can be found in Supplementary Fig. 5.
Supplementary Table 7
Inferred peak–gene links, DORCs and per-cluster DORC scores. SnRNA-seq-identified tumor invasion and immunomodulation-related genes overlapped with DORCs, namely RUNX1, C12orf75, QSOX1, RASGRF2, PLXNB2, JAK1 and MYCN. Overlap with snATAC-seq-identified genes included ATP11A, DIO2, ANXA1, KLF6, ASCL2, NR2F6, SNAI2, TCF21, FLI1, PITX1, GRHL1 and NFIX.
Supplementary Table 8, 12
Supplementary Table 8 Lineage drivers for chromatin potential/CellRank-derived terminal states (EVT3, STB, vCTB2) calculated by CellRank analyses. P values of the two-sided Fisher transformation tests were calculated and adjusted using the Storey–Tibshirani procedure for multiple hypothesis comparisons. Suppplementary Table 12 DEGs identified by STARmap-ISS. Statistics were derived using Wilcoxon’s test (two sided). P values were adjusted using the Benjamini–Hochberg method.
Supplementary Table 9
Description of UKBB pregnancy-related traits and accompanying heritability enrichments across cell types.
Supplementary Table 10
MAGMA GSEA of cell type-specific programs for each cell type as well as enrichment analysis of top GWAS hits for each pregnancy-related trait across cell types.
Supplementary Table 11
Genes, 1,001, for STARmap-ISS and 48 genes for STARmap-ISH and accompanying probe sequences used for ISS and ISH.
Supplementary Table 13
Ligand–receptor interactions across clusters and samples. Interaction (communication) probabilities and significance were computed by permutation test (P < 0.05).
Source data
Source Data Fig. 2a
Uncropped and unprocessed blots corresponding to Fig. 2a. Please note that. for the lower plot detecting p63, HLA-G and CGβ, an additional internal loading control has been added (TOPOIIbeta). The red stippled lines demarcate the protein bands shown in Fig. 2a.
Source Data Extended Data Fig. 2a
Uncropped and unprocessed blots corresponding to Extended Data Fig. 2a. The red stippled lines demarcate the protein bands shown in Extended Data Fig. 2a.
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Ounadjela, J.R., Zhang, K., Kobayashi-Kirschvink, K.J. et al. Spatial multiomic landscape of the human placenta at molecular resolution. Nat Med 30, 3495–3508 (2024). https://doi.org/10.1038/s41591-024-03073-9
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DOI: https://doi.org/10.1038/s41591-024-03073-9