RT Journal Article
SR Electronic
T1 Stitching and registering highly multiplexed whole slide images of tissues and tumors using ASHLAR
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.04.20.440625
DO 10.1101/2021.04.20.440625
A1 Muhlich, Jeremy L.
A1 Chen, Yu-An
A1 Yapp, Clarence
A1 Russell, Douglas
A1 Santagata, Sandro
A1 Sorger, Peter K
YR 2022
UL http://biorxiv.org/content/early/2022/04/25/2021.04.20.440625.abstract
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.