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VistoSeg: processing utilities for high-resolution Visium/Visium-IF images for spatial transcriptomics data

View ORCID ProfileMadhavi Tippani, Heena R. Divecha, View ORCID ProfileJoseph L. Catallini II, View ORCID ProfileSang Ho Kwon, View ORCID ProfileLukas M. Weber, View ORCID ProfileAbby Spangler, View ORCID ProfileAndrew E. Jaffe, View ORCID ProfileStephanie C. Hicks, View ORCID ProfileKeri Martinowich, View ORCID ProfileLeonardo Collado-Torres, Stephanie C. Page, View ORCID ProfileKristen R. Maynard
doi: https://doi.org/10.1101/2021.08.04.452489
Madhavi Tippani
1Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
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  • For correspondence: kristen.maynard@libd.org
Heena R. Divecha
1Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
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Joseph L. Catallini II
1Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
2Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
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  • ORCID record for Joseph L. Catallini II
Sang Ho Kwon
1Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
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Lukas M. Weber
2Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
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  • ORCID record for Lukas M. Weber
Abby Spangler
1Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
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  • ORCID record for Abby Spangler
Andrew E. Jaffe
1Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
2Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
4Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
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Stephanie C. Hicks
2Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
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  • ORCID record for Stephanie C. Hicks
Keri Martinowich
1Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
3Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
4Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
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Leonardo Collado-Torres
1Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
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  • ORCID record for Leonardo Collado-Torres
Stephanie C. Page
1Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
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  • For correspondence: kristen.maynard@libd.org
Kristen R. Maynard
1Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
3Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
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  • ORCID record for Kristen R. Maynard
  • For correspondence: kristen.maynard@libd.org
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Abstract

Background Spatial transcriptomics is a next-generation sequencing technology that combines the strengths of transcriptome-wide RNA-sequencing with histological imaging to generate spatial maps of gene expression in intact tissue sections. The 10x Genomics Visium and Visium-Immunofluorescence (Visium-IF) platforms are widely available commercial technologies for quantifying spatially-resolved gene expression. These technologies directly couple gene expression with high resolution histological or immunofluorescence images that contain rich morphological information about the tissue section. However, extracting and integrating image features with gene expression data remains challenging.

Results Using MATLAB, we developed VistoSeg, which is a pipeline to process, analyze, and interactively visualize the high-resolution images from the 10x Genomics Visium and Visium-IF platforms. The output from VistoSeg can then be integrated with the spatial-molecular information in downstream analyses using common programming languages, such as R or Python.

Conclusion VistoSeg provides user-friendly tools for integrating image-derived metrics from histological and immunofluorescent images with spatially-resolved gene expression data. This integrated approach can advance our understanding of the transcriptional landscape within tissue architecture. VistoSeg is freely available at http://research.libd.org/VistoSeg/.

Impact Statement Technologies for measuring gene activity levels, referred to as gene expression, have been evolving over decades and are the core of the transcriptomics subfield within genomics. The first report describing individual cell gene expression is from 2009 and as a method it became commercially available in 2014. While single cell transcriptomics increased our resolution beyond homogenate tissue, the advent of spatial transcriptomics technologies and commercial availability of spatial gene expression platforms, such as Visium, has facilitated studying gene expression in anatomical context. Visium measures local gene expression within the histological organization of single 6.5 mm2 cryosection of tissue. Spatially-resolved transcriptomics provides a new challenge: integrating spatial gene expression with high resolution tissue images (brightfield histology or fluorescent antibody staining). VistoSeg image processing software is compatible with both Visium and Visium-IF from 10x Genomics, which are spatially-resolved transcriptomics assays employing histological and immunofluorescent images, respectively. From these images, the number of cells, identity of cell types, and other image-derived markers can be obtained for thousands of 2,375 µm2 spots, where genome-wide gene expression is also measured. VistoSeg provides tools that enable processing these images in the context of gene expression maps to integrate these two high dimensional data types, and thus help unlock the new frontier in transcriptomics.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • This version of the manuscript is updated to a full software article from an application note and also includes details about other functionalities added to the software.

  • http://research.libd.org/VistoSeg

  • https://github.com/LieberInstitute/VistoSeg

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted May 13, 2022.
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VistoSeg: processing utilities for high-resolution Visium/Visium-IF images for spatial transcriptomics data
Madhavi Tippani, Heena R. Divecha, Joseph L. Catallini II, Sang Ho Kwon, Lukas M. Weber, Abby Spangler, Andrew E. Jaffe, Stephanie C. Hicks, Keri Martinowich, Leonardo Collado-Torres, Stephanie C. Page, Kristen R. Maynard
bioRxiv 2021.08.04.452489; doi: https://doi.org/10.1101/2021.08.04.452489
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VistoSeg: processing utilities for high-resolution Visium/Visium-IF images for spatial transcriptomics data
Madhavi Tippani, Heena R. Divecha, Joseph L. Catallini II, Sang Ho Kwon, Lukas M. Weber, Abby Spangler, Andrew E. Jaffe, Stephanie C. Hicks, Keri Martinowich, Leonardo Collado-Torres, Stephanie C. Page, Kristen R. Maynard
bioRxiv 2021.08.04.452489; doi: https://doi.org/10.1101/2021.08.04.452489

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