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

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 10(3) 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.

Automated summary

This study developed and tested a Python tool called ASHLAR for coordinated stitching and registration of multiplexed images to generate accurate mosaics. ASHLAR outperforms existing methods and provides standard image formats for analysis by other open-source tools and image analysis pipelines.