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VistoSeg: Processing utilities for high-resolution images for spatially resolved transcriptomics data

Authors :
Madhavi Tippani
Heena R. Divecha
Joseph L. Catallini
Sang H. Kwon
Lukas M. Weber
Abby Spangler
Andrew E. Jaffe
Thomas M. Hyde
Joel E. Kleinman
Stephanie C. Hicks
Keri Martinowich
Leonardo Collado-Torres
Stephanie C. Page
Kristen R. Maynard
Source :
Biological Imaging, Vol 3 (2023)
Publication Year :
2023
Publisher :
Cambridge University Press, 2023.

Abstract

Spatially resolved transcriptomics (SRT) is a growing field that links gene expression to anatomical context. SRT approaches that use next-generation sequencing (NGS) combine RNA sequencing with histological or fluorescent imaging to generate spatial maps of gene expression in intact tissue sections. These technologies directly couple gene expression measurements with high-resolution histological or immunofluorescent images that contain rich morphological information about the tissue under study. While broad access to NGS-based spatial transcriptomic technology is now commercially available through the Visium platform from the vendor 10× Genomics, computational tools for extracting image-derived metrics for integration with gene expression data remain limited. We developed VistoSeg as a MATLAB pipeline to process, analyze and interactively visualize the high-resolution images generated in the Visium platform. VistoSeg outputs can be easily integrated with accompanying transcriptomic data to facilitate downstream analyses in common programing languages including R and Python. VistoSeg provides user-friendly tools for integrating image-derived metrics from histological and immunofluorescent images with spatially resolved gene expression data. Integration of this data enhances the ability to understand the transcriptional landscape within tissue architecture. VistoSeg is freely available at http://research.libd.org/VistoSeg/.

Details

Language :
English
ISSN :
2633903X
Volume :
3
Database :
Directory of Open Access Journals
Journal :
Biological Imaging
Publication Type :
Academic Journal
Accession number :
edsdoj.7340b1102c004b1bb09838cdf6156fe8
Document Type :
article
Full Text :
https://doi.org/10.1017/S2633903X23000235