Virtual alignment of pathology image series for multi-gigapixel whole slide images

Chandler D. Gatenbee (), Ann-Marie Baker, Sandhya Prabhakaran, Ottilie Swinyard, Robbert J. C. Slebos, Gunjan Mandal, Eoghan Mulholland, Noemi Andor, Andriy Marusyk, Simon Leedham, Jose R. Conejo-Garcia, Christine H. Chung, Mark Robertson-Tessi, Trevor A. Graham and Alexander R. A. Anderson ()
Additional contact information
Chandler D. Gatenbee: H. Lee Moffitt Cancer Center & Research Institute
Ann-Marie Baker: Queen Mary University of London
Sandhya Prabhakaran: H. Lee Moffitt Cancer Center & Research Institute
Ottilie Swinyard: Queen Mary University of London
Robbert J. C. Slebos: H. Lee Moffitt Cancer Center & Research Institute
Gunjan Mandal: H. Lee Moffitt Cancer Center & Research Institute
Eoghan Mulholland: University of Oxford
Noemi Andor: H. Lee Moffitt Cancer Center & Research Institute
Andriy Marusyk: H. Lee Moffitt Cancer Center & Research Institute
Simon Leedham: University of Oxford
Jose R. Conejo-Garcia: H. Lee Moffitt Cancer Center & Research Institute
Christine H. Chung: H. Lee Moffitt Cancer Center & Research Institute
Mark Robertson-Tessi: H. Lee Moffitt Cancer Center & Research Institute
Trevor A. Graham: Queen Mary University of London
Alexander R. A. Anderson: H. Lee Moffitt Cancer Center & Research Institute

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: Abstract Interest in spatial omics is on the rise, but generation of highly multiplexed images remains challenging, due to cost, expertise, methodical constraints, and access to technology. An alternative approach is to register collections of whole slide images (WSI), generating spatially aligned datasets. WSI registration is a two-part problem, the first being the alignment itself and the second the application of transformations to huge multi-gigapixel images. To address both challenges, we developed Virtual Alignment of pathoLogy Image Series (VALIS), software which enables generation of highly multiplexed images by aligning any number of brightfield and/or immunofluorescent WSI, the results of which can be saved in the ome.tiff format. Benchmarking using publicly available datasets indicates VALIS provides state-of-the-art accuracy in WSI registration and 3D reconstruction. Leveraging existing open-source software tools, VALIS is written in Python, providing a free, fast, scalable, robust, and easy-to-use pipeline for registering multi-gigapixel WSI, facilitating downstream spatial analyses.

Date: 2023
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DOI: 10.1038/s41467-023-40218-9

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