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MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging

Author(s)
Schapiro, Denis; Sokolov, Artem; Yapp, Clarence; Chen, Yu-An; Muhlich, Jeremy L; Hess, Joshua; Creason, Allison L; Nirmal, Ajit J; Baker, Gregory J; Nariya, Maulik K; Lin, Jia-Ren; Maliga, Zoltan; Jacobson, Connor A; Hodgman, Matthew W; Ruokonen, Juha; Farhi, Samouil L; Abbondanza, Domenic; McKinley, Eliot T; Persson, Daniel; Betts, Courtney; Sivagnanam, Shamilene; Regev, Aviv; Goecks, Jeremy; Coffey, Robert J; Coussens, Lisa M; Santagata, Sandro; Sorger, Peter K; ... Show more Show less
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Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/
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Abstract
<jats:title>Abstract</jats:title><jats:p>Highly multiplexed tissue imaging makes detailed molecular analysis of single cells possible in a preserved spatial context. However, reproducible analysis of large multichannel images poses a substantial computational challenge. Here, we describe a modular and open-source computational pipeline, MCMICRO, for performing the sequential steps needed to transform whole-slide images into single-cell data. We demonstrate the use of MCMICRO on tissue and tumor images acquired using multiple imaging platforms, thereby providing a solid foundation for the continued development of tissue imaging software.</jats:p>
Date issued
2022
URI
https://hdl.handle.net/1721.1/147096
Department
Massachusetts Institute of Technology. Department of Biology
Journal
Nature Methods
Publisher
Springer Science and Business Media LLC
Citation
Schapiro, Denis, Sokolov, Artem, Yapp, Clarence, Chen, Yu-An, Muhlich, Jeremy L et al. 2022. "MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging." Nature Methods, 19 (3).
Version: Final published version

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