A roadmap for the Human Developmental Cell Atlas

Abstract

The Human Developmental Cell Atlas (HDCA) initiative, which is part of the Human Cell Atlas, aims to create a comprehensive reference map of cells during development. This will be critical to understanding normal organogenesis, the effect of mutations, environmental factors and infectious agents on human development, congenital and childhood disorders, and the cellular basis of ageing, cancer and regenerative medicine. Here we outline the HDCA initiative and the challenges of mapping and modelling human development using state-of-the-art technologies to create a reference atlas across gestation. Similar to the Human Genome Project, the HDCA will integrate the output from a growing community of scientists who are mapping human development into a unified atlas. We describe the early milestones that have been achieved and the use of human stem-cell-derived cultures, organoids and animal models to inform the HDCA, especially for prenatal tissues that are hard to acquire. Finally, we provide a roadmap towards a complete atlas of human development.

Fig. 1 |. Human embryo development and model systems.

a, Timeline of human development from fertilization to birth. CT, cytotrophoblast; ESC, embryonic stem cell; EPI, epiblast; PrE, primitive endoderm; pcw, post-conception weeks; SCT, syncytiotrophoblast; TE, trophectoderm; TSC, trophoblast stem cell; XEN, extraembryonic endoderm; YSE, yolk sac endoderm. b, Models derived from human stem cells, and associated studies. In vitro model systems to study early embryonic development. A–P, anterior– posterior. c, Experimental model systems to study development, including Drosophila melanogaster, Danio rerio, Xenopus laevis, Gallus gallus, Mus musculus, cell culture and organoids, and their amenability to facilitating various aspects of scientific study.

Fig. 2 |. Building the HDCA.

a, ‘How to build an atlas’ modules, including an interdisciplinary team (biological disciplines, clinical specialities, computational scientists and technology engineers), multimodal technologies and integration of data across platforms. b, Key features of the HDCA. Single-cell measurements across three-dimensional space (alongside a fourth dimension of time) allow for the capture of dynamic developmental processes, including cell proliferation, migration and regulation. Spatial extension captures cell proliferation (changes in organ size and shape), and the sensing and control of morphogenesis; spatio-temporal events include cell differentiation and replication, and changes in cellular composition and gene expression. Permutations and variabilities outline the spatial determinants of cell differentiation, mechanical adhesive feedback and heterocellular signalling by ligands. Time series trajectories reveal fate-biased migrating progenitors, lineage specifications and cell migration. c, Utility and applications of the HDCA: cellular and molecular biological insights into the coordination of organ development across the whole embryo (left) are applied to advance regenerative medicine, tissue engineering and therapeutic strategies (right).

Fig. 3 |. Multi-omics profiling and data integration.

a, Organ or anatomical unit profiling of a prenatal embryo derived from multiple germ layers. b, Single-cell atlas technologies by relative resolution and genome scale. CODEX, co-detection by indexing; H&E, haematoxylin and eosin staining; IF, immunofluorescence; ISS, in situ sequencing; MERFISH, multiplexed error-robust FISH; MIBI, multiplexed ion beam imaging; seqFISH, sequential FISH; smFISH, single-molecule FISH; STARmap, spatially resolved transcript annotation readout mapping. c, Integration of datasets from different technologies (for example, spatial transcriptomics, scRNA-seq and targeted ISS) to profile organs or whole embryos.

Fig. 4 |. Clinical relevance and applications of the HDCA.

a, Diseases related to brain development, across lifespan. A timeline of brain development across human life, with examples of diseases with onset at different gestational stages and ages. b, Disease state compared to developmental atlas, showing how a single-cell atlas with temporal and spatial information can be used as a reference to understand disease states.