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Stitching and registering highly multiplexed whole slide images of tissues and tumors using ASHLAR

View ORCID ProfileJeremy L. Muhlich, View ORCID ProfileYu-An Chen, View ORCID ProfileClarence Yapp, View ORCID ProfileDouglas Russell, View ORCID ProfileSandro Santagata, View ORCID ProfilePeter K Sorger
doi: https://doi.org/10.1101/2021.04.20.440625
Jeremy L. Muhlich
1Human Tumor Atlas Network, Harvard Medical School, Boston, MA 02115, USA
2Harvard Ludwig Cancer Center and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
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  • ORCID record for Jeremy L. Muhlich
Yu-An Chen
1Human Tumor Atlas Network, Harvard Medical School, Boston, MA 02115, USA
2Harvard Ludwig Cancer Center and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
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Clarence Yapp
1Human Tumor Atlas Network, Harvard Medical School, Boston, MA 02115, USA
2Harvard Ludwig Cancer Center and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
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Douglas Russell
1Human Tumor Atlas Network, Harvard Medical School, Boston, MA 02115, USA
2Harvard Ludwig Cancer Center and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
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Sandro Santagata
1Human Tumor Atlas Network, Harvard Medical School, Boston, MA 02115, USA
2Harvard Ludwig Cancer Center and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
4Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
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Peter K Sorger
1Human Tumor Atlas Network, Harvard Medical School, Boston, MA 02115, USA
2Harvard Ludwig Cancer Center and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
3Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
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  • For correspondence: peter_sorger@hms.harvard.edu lsp-papers@hms.harvard.edu
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ABSTRACT

Motivation Stitching microscope images into a mosaic is an essential step in the analysis and visualization of large biological specimens, particularly human and animal tissues. Recent approaches to highly-multiplexed imaging generate high-plex data from sequential rounds of lower-plex imaging. These multiplexed imaging methods promise to yield precise molecular single-cell data and information on cellular neighborhoods and tissue architecture. However, attaining mosaic images with single-cell accuracy requires robust image stitching and image registration capabilities that are not met by existing methods.

Results We describe the development and testing of ASHLAR, a Python tool for coordinated stitching and registration of 103 or more individual multiplexed images to generate accurate whole-slide mosaics. ASHLAR reads image formats from most commercial microscopes and slide scanners, and we show that it performs better than existing open source and commercial software. ASHLAR outputs standard OME-TIFF images that are ready for analysis by other open-source tools and recently developed image analysis pipelines.

Availability and implementation ASHLAR is written in Python and available under an MIT license at https://github.com/labsyspharm/ashlar. An informational website with user guides and test data is available at https://labsyspharm.github.io/ashlar/.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Clarified approach and results; figure 6 added; supplementary materials updated.

  • https://github.com/labsyspharm/ashlar

  • https://dx.doi.org/10.7303/syn25826362

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 April 25, 2022.
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Stitching and registering highly multiplexed whole slide images of tissues and tumors using ASHLAR
Jeremy L. Muhlich, Yu-An Chen, Clarence Yapp, Douglas Russell, Sandro Santagata, Peter K Sorger
bioRxiv 2021.04.20.440625; doi: https://doi.org/10.1101/2021.04.20.440625
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Stitching and registering highly multiplexed whole slide images of tissues and tumors using ASHLAR
Jeremy L. Muhlich, Yu-An Chen, Clarence Yapp, Douglas Russell, Sandro Santagata, Peter K Sorger
bioRxiv 2021.04.20.440625; doi: https://doi.org/10.1101/2021.04.20.440625

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