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Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data

View ORCID ProfileSimon Warchol, View ORCID ProfileRobert Krueger, View ORCID ProfileAjit Johnson Nirmal, View ORCID ProfileGiorgio Gaglia, View ORCID ProfileJared Jessup, View ORCID ProfileCecily C. Ritch, View ORCID ProfileJohn Hoffer, View ORCID ProfileJeremy Muhlich, View ORCID ProfileMegan L. Burger, View ORCID ProfileTyler Jacks, View ORCID ProfileSandro Santagata, View ORCID ProfilePeter K. Sorger, View ORCID ProfileHanspeter Pfister
doi: https://doi.org/10.1101/2022.05.09.490039
Simon Warchol
2Harvard John A. Paulson School of Engineering and Applied Sciences
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  • For correspondence: simonwarchol@g.harvard.edu
Robert Krueger
2Harvard John A. Paulson School of Engineering and Applied Sciences
3Laboratory of Systems Pharmacology, Harvard Medical School
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Ajit Johnson Nirmal
3Laboratory of Systems Pharmacology, Harvard Medical School
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Giorgio Gaglia
4Brigham and Women’s Hospital
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Jared Jessup
2Harvard John A. Paulson School of Engineering and Applied Sciences
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Cecily C. Ritch
4Brigham and Women’s Hospital
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John Hoffer
3Laboratory of Systems Pharmacology, Harvard Medical School
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Jeremy Muhlich
3Laboratory of Systems Pharmacology, Harvard Medical School
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Megan L. Burger
5David H. Koch Institute for Integrative Cancer Research, MIT
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Tyler Jacks
5David H. Koch Institute for Integrative Cancer Research, MIT
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Sandro Santagata
3Laboratory of Systems Pharmacology, Harvard Medical School
4Brigham and Women’s Hospital
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Peter K. Sorger
3Laboratory of Systems Pharmacology, Harvard Medical School
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Hanspeter Pfister
2Harvard John A. Paulson School of Engineering and Applied Sciences
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Abstract

New highly-multiplexed imaging technologies have enabled the study of tissues in unprecedented detail. These methods are increasingly being applied to understand how cancer cells and immune response change during tumor development, progression, and metastasis, as well as following treatment. Yet, existing analysis approaches focus on investigating small tissue samples on a per-cell basis, not taking into account the spatial proximity of cells, which indicates cell-cell interaction and specific biological processes in the larger cancer microenvironment. We present Visinity, a scalable visual analytics system to analyze cell interaction patterns across cohorts of whole-slide multiplexed tissue images. Our approach is based on a fast regional neighborhood computation, leveraging unsupervised learning to quantify, compare, and group cells by their surrounding cellular neighborhood. These neighborhoods can be visually analyzed in an exploratory and confirmatory workflow. Users can explore spatial patterns present across tissues through a scalable image viewer and coordinated views highlighting the neighborhood composition and spatial arrangements of cells. To verify or refine existing hypotheses, users can query for specific patterns to determine their presence and statistical significance. Findings can be interactively annotated, ranked, and compared in the form of small multiples. In two case studies with biomedical experts, we demonstrate that Visinity can identify common biological processes within a human tonsil and uncover novel white-blood cell networks and immune-tumor interactions.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Updating acknowledgements

  • https://vcg.seas.harvard.edu/publications/visinity

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-ND 4.0 International license.
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Posted August 11, 2022.
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Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data
Simon Warchol, Robert Krueger, Ajit Johnson Nirmal, Giorgio Gaglia, Jared Jessup, Cecily C. Ritch, John Hoffer, Jeremy Muhlich, Megan L. Burger, Tyler Jacks, Sandro Santagata, Peter K. Sorger, Hanspeter Pfister
bioRxiv 2022.05.09.490039; doi: https://doi.org/10.1101/2022.05.09.490039
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Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data
Simon Warchol, Robert Krueger, Ajit Johnson Nirmal, Giorgio Gaglia, Jared Jessup, Cecily C. Ritch, John Hoffer, Jeremy Muhlich, Megan L. Burger, Tyler Jacks, Sandro Santagata, Peter K. Sorger, Hanspeter Pfister
bioRxiv 2022.05.09.490039; doi: https://doi.org/10.1101/2022.05.09.490039

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