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

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

doi:10.1017/S2633903X23000235

Biological Imaging (Jan 2023)

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

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

Affiliations

Madhavi Tippani: Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
Heena R. Divecha: Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
Joseph L. Catallini: Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
Sang H. Kwon: Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
Lukas M. Weber: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
Abby Spangler: Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
Andrew E. Jaffe: Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Thomas M. Hyde: Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
Joel E. Kleinman: Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Stephanie C. Hicks: ORCiD; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
Keri Martinowich: Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA The Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
Leonardo Collado-Torres: Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
Stephanie C. Page: Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
Kristen R. Maynard: ORCiD; Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA

DOI: https://doi.org/10.1017/S2633903X23000235
Journal volume & issue: Vol. 3

Abstract

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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/.

Published in Biological Imaging

ISSN: 2633-903X (Online)
Publisher: Cambridge University Press
Country of publisher: United Kingdom
LCC subjects: Science: Biology (General); Medicine: Medicine (General): Medical technology
Website: https://www.cambridge.org/core/journals/biological-imaging

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