Whole mouse mapping

2026-05-13 · Source: Machine learning : nature.com subject feeds · Field: Science & Research — Life Sciences & Biology, Research Methodology & Innovation · Depth: Expert, quick

Summary

Researchers led by Nicolas Chevrier have developed novel spatial genomics tools for whole-body sections of adult mice, addressing a gap in technologies for measuring body-wide immune system responses and disease changes. Published in *Cell*, their method combines whole-mouse histological sectioning and spatiomolecular profiling. The team applied Array-seq on whole-body sections of 6-week-old mice, following hematoxylin and eosin (H&E) staining and imaging, to create detailed spatial transcriptomics maps. These data were then used to develop an algorithm capable of high-resolution spatial annotation for H&E-stained sections, enabling the identification of even low-abundance cell types like osteocytes in bone, which constitute only 0.2% of cells.

Key takeaway

For research scientists studying systemic diseases or immune responses, this whole-mouse mapping technique offers a powerful approach to generate comprehensive spatial transcriptomics data. You can now identify and localize even rare cell types across an entire organism, providing unprecedented detail for understanding complex biological processes. Consider integrating this spatial genomics methodology to gain a holistic view of tissue and cellular interactions in your studies.

Key insights

New spatial genomics tools enable whole-body transcriptomics and high-resolution cell type identification in mice.

Principles

• Combine historical methods with modern profiling.
• Algorithm development enhances spatial annotation.

Method

The method involves H&E staining, Array-seq on whole-body mouse sections for spatial transcriptomics, and subsequent algorithm development for high-resolution spatial annotation of images.

In practice

• Identify low-abundance cell types.
• Map body-wide immune responses.

Topics

• Whole Mouse Mapping
• Spatial Genomics
• Spatial Transcriptomics
• Array-seq
• H&E Staining

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine learning : nature.com subject feeds.