SM3DD with segmented PCA: a comprehensive method for interpreting 3D spatial transcriptomics
Year: 2026
Type: article
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.
Source: NAR Genomics and Bioinformatics
Authors: Tony Blick, Aaron Kilgallon, James Monkman, Caroline Cooper, Chin Wee Tan, Emily E Killingbeck, Liuliu Pan, Youngmi Kim, Yan Liang, Andy Nam, Michael Leon, Paulo S F Guimaraes, Seigo Nagashima, Ana P C Martins, Cleber Machado-Souza, Lucia de Noronha, John F Fraser, Gabrielle T Belz, Fernando Souza-Fonseca-Guimaraes, Arutha Kulasinghe
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,766
Awards: G4056847203, G6522182991
OpenAlex ID: W7125783313