Faculty, Staff and Student Publications
Language
English
Publication Date
10-1-2024
Journal
Nature Methods
DOI
10.1038/s41592-024-02415-2
PMID
39266749
PMCID
PMC11466694
PubMedCentral® Posted Date
10-1-2025
PubMedCentral® Full Text Version
Author MSS
Abstract
Spatial transcriptomics (ST) technologies have advanced to enable transcriptome-wide gene expression analysis at submicron resolution over large areas. However, analysis of high-resolution ST is often challenged by complex tissue structure, where existing cell segmentation methods struggle due to the irregular cell sizes and shapes, and by the absence of segmentation-free methods scalable to whole-transcriptome analysis. Here we present FICTURE (Factor Inference of Cartographic Transcriptome at Ultra-high REsolution), a segmentation-free spatial factorization method that can handle transcriptome-wide data labeled with billions of submicron-resolution spatial coordinates and is compatible with both sequencing-based and imaging-based ST data. FICTURE uses the multilayered Dirichlet model for stochastic variational inference of pixel-level spatial factors, and is orders of magnitude more efficient than existing methods. FICTURE reveals the microscopic ST architecture for challenging tissues, such as vascular, fibrotic, muscular and lipid-laden areas in real data where previous methods failed. FICTURE's cross-platform generality, scalability and precision make it a powerful tool for exploring high-resolution ST.
Keywords
Gene Expression Profiling, Transcriptome, Algorithms, Animals, Humans, Mice, Image Processing, Computer-Assisted
Published Open-Access
yes
Recommended Citation
Si, Yichen; Lee, ChangHee; Hwang, Yongha; et al., "Ficture: Scalable Segmentation-Free Analysis of Submicron-Resolution Spatial Transcriptomics" (2024). Faculty, Staff and Student Publications. 1266.
https://digitalcommons.library.tmc.edu/uthsph_docs/1266