SM3DD with Segmented PCA: A Comprehensive Method for Interpreting 3D Spatial Transcriptomics Article Swipe
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· 2025
· Open Access
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· DOI: https://doi.org/10.1101/2025.04.17.649456
We developed Standardised Minimum 3D Distance (SM3DD), an entirely cell segmentation/annotation-free approach to the analysis of spatial RNA datasets, using it to compare lung tissue from 16 clinically normal individuals to those of 18 SARS-CoV-2 patients who died from acute respiratory distress syndrome. RNA spatial coordinates were determined using the CosMx™ Spatial Molecular Imager (Bruker Spatial Biology, US). For each individual transcript location, we calculated the three-dimensional distances to the nearest transcript of each transcript type, standardising the distances to each transcript type. Mean SM3DDs were compared between normal and SARS-CoV-2 patients. Notably, hierarchical clustering of the directional log10(P) values organized genes by functionality, making it easier to interpret biological contexts and for FKBP11, where a decrease in distance to MZT2A was the most significant difference, suggesting a role in interferon signaling. Using a segmented principal components analysis of the entire SM3DD dataset, we identified multiple pathways, including ‘SARS-CoV-2 infection’, even though the assay did not include any SARS-CoV-2 transcripts.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.04.17.649456
- https://www.biorxiv.org/content/biorxiv/early/2025/04/18/2025.04.17.649456.full.pdf
- OA Status
- green
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409640573