{"id":41848,"date":"2021-09-06T17:02:38","date_gmt":"2021-09-06T15:02:38","guid":{"rendered":"https:\/\/www.embl.org\/news\/?p=41848"},"modified":"2024-03-22T11:26:26","modified_gmt":"2024-03-22T10:26:26","slug":"spatial-mouse-atlas","status":"publish","type":"post","link":"https:\/\/www.embl.org\/news\/science\/spatial-mouse-atlas\/","title":{"rendered":"The Spatial Mouse Atlas: new insights into cell fate"},"content":{"rendered":"\n

High-resolution gene expression maps have been combined with single-cell genomics data to create a new resource for studying how cells adopt different identities during mammalian development. The Spatial Mouse Atlas is the result of a collaboration between researchers at EMBL\u2019s European Bioinformatics Institute (EMBL-EBI)<\/u><\/a>, the Babraham Institute<\/u><\/a>, the Cambridge Stem Cell Institute<\/u><\/a>, the Cancer Research UK (CRUK) Cambridge Institute<\/u><\/a>, and the California Institute of Technology (Caltech)<\/u><\/a> and colleagues.<\/p>\n\n\n\n

Cell fate decisions determine how cells develop into different cell types. The development of different cell types eventually leads to the formation of all the different tissues in the body. This complex process involves many different signals from surrounding tissues, as well as mechanical constraints, epigenetic modifications and changes to gene expression. These factors create unique cell and tissue types, which eventually give rise to all major organs in a process called organogenesis.<\/p>\n\n\n\n

Studies using single-cell RNA sequencing (scRNAseq) have provided insights into how the molecular landscape of cells in the mouse embryo changes during early development. However, scRNAseq methods require the cells in the embryo to be dissociated \u2013 or separated \u2013 which means spatial information is lost.<\/p>\n\n\n\n

This study, published in Nature Biotechnology<\/em><\/a>, combines scRNAseq data with spatially-resolved expression profiles to generate an atlas of gene expression at single-cell resolution across the entire embryo.<\/p>\n\n\n\n

Combining computational and image-based data<\/h2>\n\n\n\n

\u201cMethodologically, I think this is one of the most exciting examples of integrating spatially resolved transcriptomics and single-cell sequencing to create a new resource for the scientific community,\u201d said John Marioni, Head of Research at EMBL-EBI<\/u><\/a>. \u201cIt\u2019s also work we can build upon to include more target genes across different stages of development.\u201d<\/p>\n\n\n\n

The researchers carried out this study using 8\u201312 somite stage mouse embryos. This stage of development was of particular interest because it is when the cells within the embryo start to differentiate, or become a specific cell type. The researchers applied an image-based single-cell transcriptomics method \u2013 seqFISH (developed by collaborators at Caltech) \u2013 to detect 387 target genes. They combined this information with scRNAseq data to produce an atlas of cell types found across this stage of mouse development.<\/p>\n\n\n\n\n