Comparative statistical analysis of organs across genetic backgrounds requires spatial, single-cell, atlas-scale data in replicates. Here we introduce atlas-scale transcriptome localization using aggregate signatures (ATLAS), a scalable tissue mapping method that learns transcriptional signatures from single-cell RNA sequencing data, encodes them in situ with tens of thousands of oligonucleotide probes and decodes them to infer cell types and imputed transcriptomes. We validated ATLAS against direct MERFISH measurements of marker genes and through quantitative comparisons to four other technologies.
We profiled 40 million cells across more than 400 coronal sections from central brains of five male and five female C57BL/6J mice and five male BTBR T+tf/J mice, an idiopathic autism model. Our analysis revealed more than 40 significant differences in cell-type distributions and identified 16 regional composition changes across male–female and B6–BTBR comparisons. ATLAS thus enables systematic comparative studies, facilitating organ-level structure–function analysis of disease models.
This is a preview of subscription content, access via your institution Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription Receive 12 print issues and online access Prices may be subject to local taxes which are calculated during checkout All data are available via Zenodo at https://doi.org/10.5281/zenodo.13851748 (ref. 61). All code can be found via GitHub at ATLAS, https://github.com/wollmanlab/ATLAS_Mouse_Brain/; MERFISH, https://github.com/wollmanlab/PySpots; Imaging, https://github.com/wollmanlab/Scope and Fluidics, https://github.com/wollmanlab/Fluidics/. The genotype conception of heredity.
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