An integrated single cell and spatial omics atlas of human prenatal development

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Authors: Simone Webb, Antony Rose, Chuan Xu, Lloyd Steele, Masami A Kuri, Emily Stephenson, Kemal İnecik, Daniyal J. Jafree, April Rose Foster, Daniela Basurto-Lozada, Nana-Jane Chipampe, Anna Vathrakokoili Pournara
+82 more Marc‐Antoine Jacques, Ken To, Chloe Admane, Efpraxia Kritikaki, Marta A. Chroscik, David Horsfall, Julia Foreman, Koen Rademaker, Julia Karjalainen, Anna Laddach, Shaista Madad, John E. G. Lawrence, Vitalii Kleshchevnikov, Steven Lisgo, Jimmy Tsz Hang Lee, Joséphine Blévinal, Ahlam Alqahtani, Stanislaw Makarchuk, Jake Jackson, Ekin Ucuncu, Teresa Pereira Silva, Valentina Lorenzi, Fereshteh Torabi, Rachel Anne Botting, Kenny S. Roberts, Bayanne Olabi, Keerthi Priya Chakala, Leander Dony, Gabriele Dall'Aglio, Ana-Maria Cujba, Holly J. Whitfield, Christine Hale, Justin Engelbert, Tong Li, Elena Prigmore, Nusayhah Hudaa Gopee, Michael W. Mather, Ben Talks, Lijiang Fei, D. Pereira, Corina Maria Moldovan, Rasa Elmentaite, Krzysztof Polański, Alexander V. Predeus, Pasha Mazin, Vicky Rowe, Semih Bayraktar, Vincent R. Knight-Schrijver, Menna R Clatworthy, Kazumasa Kanemaru, James J. Cranley, Andrew Guo, Sanjay Kumar Sinha, D. Mcdonald, Andrew J. Filby, Janet M. Kerwin, Peng He, Inês Sequeira, Roser Vento-Tormo, Malte D. Luecken, A. Chédotal, Deanne Taylor, Silvia Martin-Almedina, Pia Østergaard, Sam Behjati, Elizabeth Robertson, Nicola K. Wilson, John Marioni, Helen V. Firth, Caroline Fiona Wright, Olivier Pourquié, Laure Gambardella, Paula Alexandre, Berthold Göttgens, Kerstin B Meyer, MS Vijayabaskar, Mo Lotfollahi, Sarah Amalia Teichmann, Fabian J Theis, Fränze Progatzky, Omer Ali Bayraktar, Muzlifah A. Haniffa
Cited by: 6
References: 187

Abstract

Abstract Single cell genomics has enabled analysis of human prenatal development at unprecedented resolution. However, most studies have relied on dissociated tissues during restricted windows of development, limiting insights into how spatially distributed networks of cells, and multicellular niches emerge and adapt to distinct organ microenvironments in situ. Moreover, existing human developmental atlases have not yet been harmonised, and we thus lack a comprehensive catalogue of known cell types in the developing human body. Here, we introduce the Human Developmental Cell Atlas (HDCA), a unified structural, cellular and molecular resource for prenatal human development. The HDCA integrates published and unpublished single cell/nucleus RNAseq atlases across prenatal organs, and includes a newly generated, spatially resolved, multimodal cell atlas of intact human embryos. Spanning 4-22 post conceptional weeks, capturing embryonic and early to mid fetal stages, the HDCA contains ∼4.6 million cells/nuclei which resolve into ∼450 cell types, explorable with a bespoke web portal. For a global overview of the human embryo’s multicellular communities, we applied unsupervised deep learning to our intact human embryo spatial data, charting 114 tissue niches that are structural and signalling hubs for the cellular interactions of the embryo. Guided by these niches, we profiled cellular networks over space and time, not examinable using single-organ atlases. In so doing, we revealed tissue-specific fibroblast patterning from previously undescribed mesenchyme progenitors, early diversification of organ-specific blood capillaries and lymphatic vasculature, emergence of neural crest cell fates, the formation of placode-and neural crest-derived peripheral sensory neurons, and how tissue niches guide peripheral neuron maturation and axonal migration. The HDCA thus serves as a comprehensive step towards a comprehensive understanding of human prenatal development, and a template towards unravelling the biology of congenital disorders.

Fields of Study & Subjects

Single-cell and spatial transcriptomics Cell Image Analysis Techniques Pluripotent Stem Cells Research

Publication details

Journal / source
bioRxiv (Cold Spring Harbor Laboratory)
Publisher
openRxiv
Publication date
1 April 2026
Document type
Preprint