{"doi":"10.1242/dev.204822","title":"In preprints: unfolding the spatial genomics frontier of mouse gastrulation","abstract":"Understanding how tissues and organs form, and their cellular composition and arrangement in the adult has been of interest throughout the history of biology and medicine. Even today, tissue histology is the cardinal assay for diagnostics in pathology. The first chromogenic histological stains date back to the 1700s. Over the centuries, stains highlighting distinct cellular features (e.g. nuclei and cytoplasm as in the near-ubiquitous hematoxylin and eosin, H&E, stain) were developed (Hussein and Raad, 2015). Only in the 1980s, with advent of mRNA in situ hybridization, were researchers able to marry cellular morphology and position with gene expression. Since then, technological advances have revealed details of the transcriptomes of single cells, in 2D sections and in 3D reconstructions of tissues. Spatial transcriptomics methods, which were hailed as the ‘Method of the year 2020’ (Marx, 2021), represent a revolution in the union of imaging with genomics, ushering in new ways to tackle longstanding questions. These new technologies are empowering researchers to study single cells and their neighborhoods. Such unprecedented resolution will usher in an unprecedented and multiparametric understanding of biological processes.Two recently preprinted studies offer new insights into the events of mouse gastrulation and early organogenesis by employing two distinct but equally impressive spatial genomics approaches. Harland and colleagues incorporated a light microscopic imaging multiplex mRNA in situ-based approach (Harland et al., 2024 preprint), whereas Yang and colleagues used spatial tissue capture profiling and genomics-based methods (Yang et al., 2024 preprint).In an international collaboration (centered around Berthold Gottgens', John Marioni's, Jennifer Nichols' and Wolf Reik's labs in the UK, but also involving Shila Gazanfar's lab in Australia, and Long Cai's lab in the USA), Harland and colleagues performed a detailed spatial transcriptomic analysis of 48 h in the development of the mouse embryo, beginning at embryonic day (E) 6.5, corresponding to the start of gastrulation. The authors extended their previously published spatial atlas of early organogenesis (Lohoff et al., 2022) by supplementing their original E8.5 data with spatial transcriptomic data from earlier E6.5 and E7.5 embryos, and also integrating their recently published extended mouse embryo atlas representing the transcriptomes of all cells present in embryos from gastrulation to early organogenesis (Imaz-Rosshandler et al., 2024). This resulted in a spatiotemporal atlas of the cell states present from E6.5 to E8.5. Central to their construction of a spatial transcriptomic atlas, Harland and colleagues used the seqFISH multiplex mRNA in situ hybridization method to document the expression of 351 reference genes on 20 E6.5-E8.5 embryo tissue sections, to provide positional information on cell states during this 48 h window. They also re-annotated cell populations through the inclusion of cell subtypes. Spatial information, including coordinates for the anterior-posterior or dorsal-ventral location of single cells derived from their seqFISH experiments, were computationally imputed onto their embryo scRNAseq atlas. The authors then used these integrated data to explore gene expression dynamics along the anterior-posterior axis of the developing embryo, revealing that transcriptional changes along the anterior-posterior axis occurred in migratory mesoderm cells rather than in nascent mesoderm cells exiting the primitive streak.Stem cell-derived models, such as embryoids, gastruloids and organoids represent the new vanguard for recapitulating the development of an embryo or organ at scale (Terhune et al., 2022). By projecting scRNAseq data generated from mouse 3D gastruloids (Rossi et al., 2021) onto their spatiotemporal mouse embryo atlas, Harland and colleagues sought to validate and benchmark this in vitro model, and glean ways to improve protocols for gene","journal":"Development","year":2025,"id":564206,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9588,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":255184,"name":"Anna‐Katerina Hadjantonakis","orcid":"0000-0002-7580-5124","position":1,"is_corresponding":false},{"id":285276,"name":"Sonja Nowotschin","orcid":"0000-0002-2646-8598","position":0,"is_corresponding":true}],"reference_count":18,"raw_metadata":null,"created_at":"2026-07-19T02:56:20.933088Z","pmid":"40223693","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}