MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging
Denis Schapiro(Broad Institute), Artem Sokolov(Harvard University), Clarence Yapp(Harvard University), Yu‐An Chen(Harvard University), Jeremy L. Muhlich(Harvard University), Joshua M. Hess(Harvard University), Allison Creason(Oregon Health & Science University), Ajit J. Nirmal(Harvard University), Gregory J. Baker(Harvard University), Maulik K. Nariya(Harvard University), Jia‐Ren Lin(Harvard University), Zoltan Maliga(Harvard University), Connor A. Jacobson(Harvard University), Matthew Hodgman(Brigham Young University), Juha Ruokonen(Harvard University), Samouil L. Farhi(Broad Institute), Domenic Abbondanza(Broad Institute), Eliot T. McKinley(Vanderbilt University), Daniel Persson(Oregon Health & Science University), Courtney B. Betts(Oregon Health & Science University), Shamilene Sivagnanam(Oregon Health & Science University), Aviv Regev(Broad Institute), Jeremy Goecks(Oregon Health & Science University), Robert J. Coffey(Vanderbilt University Medical Center), Lisa M. Coussens(Oregon Health & Science University), Sandro Santagata(Brigham and Women's Hospital), Peter K. Sorger(Harvard University)
Cited by 240Open Access
Abstract
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.
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