The relationship between the human placenta—the extraembryonic organ made by the fetus, and the decidua—the mucosal layer of the uterus, is essential to nurture and protect the fetus during pregnancy. Extravillous trophoblast cells (EVTs) derived from placental villi infiltrate the decidua, transforming the maternal arteries into high-conductance vessels1. Defects in trophoblast invasion and arterial transformation established during early pregnancy underlie common pregnancy disorders such as pre-eclampsia2. Here we have generated a spatially resolved multiomics single-cell atlas of the entire human maternal–fetal interface including the myometrium, which enables us to resolve the full trajectory of trophoblast differentiation. We have used this cellular map to infer the possible transcription factors mediating EVT invasion and show that they are preserved in in vitro models of EVT differentiation from primary trophoblast organoids3,4 and trophoblast stem cells5. We define the transcriptomes of the final cell states of trophoblast invasion: placental bed giant cells (fused multinucleated EVTs) and endovascular EVTs (which form plugs inside the maternal arteries). We predict the cell–cell communication events contributing to trophoblast invasion and placental bed giant cell formation, and model the dual role of interstitial EVTs and endovascular EVTs in mediating arterial transformation during early pregnancy. Together, our data provide a comprehensive analysis of postimplantation trophoblast differentiation that can be used to inform the design of experimental models of the human placenta in early pregnancy.

Spatially resolved omics technologies are transforming our understanding of biological tissues. However, handling uni- and multi-modal spatial omics datasets remains a challenge owing to large volumes of data, heterogeneous data types and the lack of unified spatially-aware data structures. Here, we introduce SpatialData, a framework that establishes a unified and extensible multi-platform file-format, lazy representation of larger-than-memory data, transformations, and alignment to common coordinate systems. SpatialData facilitates spatial annotations and cross-modal aggregation and analysis, the utility of which is illustrated via multiple vignettes, including integrative analysis on a multi-modal Xenium and Visium breast cancer study.