Automated integration of multi-slice spatial transcriptomics data in 2D and 3D using VR-Omics

Automated integration of multi-slice spatial transcriptomics data in 2D and 3D using VR-Omics

Abstract The field of spatial transcriptomics is rapidly evolving, with increasing sample complexity, resolution, and tissue size. Yet the field lacks comprehensive and intuitive solutions for automated integration and analysis of multi-slice data in either co-planar (2D) or stacked (3D) formation....

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Main Authors: Denis Bienroth, Natalie Charitakis, Dillon Wong, Yunhan C. Zhang, Sabrina Jaeger-Honz, Jialin Ding, Kevin I. Watt, Julian Stolper, Hazel Chambers-Smith, Duncan MacGregor, Bronwyn Christiansen, Celine Vivien, Adam T. Piers, Lisa N. Waylen, Lucas B. Hoffmann, Jessica Tang, Hue M. La, Mei R. M. Du, Monika Mohenska, Jose M. Polo, Sean Grimmond, Ethan Scott, Fernando J. Rossello, Enzo R. Porrello, Karsten Klein, Hieu T. Nim, David A. Elliott, Falk Schreiber, Mirana Ramialison
Format: Article
Language:English
Published: BMC 2025-07-01
Series:Genome Biology
Online Access:https://doi.org/10.1186/s13059-025-03630-6
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Summary:Abstract The field of spatial transcriptomics is rapidly evolving, with increasing sample complexity, resolution, and tissue size. Yet the field lacks comprehensive and intuitive solutions for automated integration and analysis of multi-slice data in either co-planar (2D) or stacked (3D) formation. To address this, we develop VR-Omics, a free, platform-agnostic software that provides end-to-end automated processing of multi-slice data through a biologist-friendly interface. Benchmarking against existing methods demonstrates VR-Omics’ unique strengths to perform comprehensive end-to-end analysis of multi-slice stacked data. Through co-planar slice analysis, VR-Omics uncovers previously undetected, dysregulated metabolic networks within rare pediatric cardiac rhabdomyomas, demonstrating its potential for biological discoveries.
ISSN:1474-760X

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