ArticleNAR genomics and bioinformatics2026
SM3DD with segmented PCA: a comprehensive method for interpreting 3D spatial transcriptomics.
Article in NAR genomics and bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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 that 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 signalling. 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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