PT  - JOURNAL ARTICLE
AU  - Muhlich, Jeremy L.
AU  - Chen, Yu-An
AU  - Yapp, Clarence
AU  - Russell, Douglas
AU  - Santagata, Sandro
AU  - Sorger, Peter K
TI  - Stitching and registering highly multiplexed whole slide images of tissues and tumors using ASHLAR
AID  - 10.1101/2021.04.20.440625
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2021.04.20.440625
4099  - http://biorxiv.org/content/early/2022/04/25/2021.04.20.440625.short
4100  - http://biorxiv.org/content/early/2022/04/25/2021.04.20.440625.full
AB  - 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 StatementThe authors have declared no competing interest.