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

Denis Schapiro; Artem Sokolov; Clarence Yapp; Yu An Chen; Jeremy L. Muhlich; Joshua Hess; Allison L. Creason; Ajit J. Nirmal; Gregory J. Baker; Maulik K. Nariya; Jia Ren Lin; Zoltan Maliga; Connor A. Jacobson; Matthew W. Hodgman; Juha Ruokonen; Samouil L. Farhi; Domenic Abbondanza; Eliot T. McKinley; Daniel Persson; Courtney Betts; Shamilene Sivagnanam; Aviv Regev; Jeremy Goecks; Robert J. Coffey; Lisa M. Coussens; Sandro Santagata; Peter K. Sorger

doi:10.1038/s41592-021-01308-y

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

Denis Schapiro, Artem Sokolov, Clarence Yapp, Yu An Chen, Jeremy L. Muhlich, Joshua Hess, Allison L. Creason, Ajit J. Nirmal, Gregory J. Baker, Maulik K. Nariya, Jia Ren Lin, Zoltan Maliga, Connor A. Jacobson, Matthew W. Hodgman, Juha Ruokonen, Samouil L. Farhi, Domenic Abbondanza, Eliot T. McKinley, Daniel Persson, Courtney BettsShamilene Sivagnanam, Aviv Regev, Jeremy Goecks, Robert J. Coffey, Lisa M. Coussens, Sandro Santagata, Peter K. Sorger

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57 Scopus citations

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.

Original language	English (US)
Pages (from-to)	311-315
Number of pages	5
Journal	Nature Methods
Volume	19
Issue number	3
DOIs	https://doi.org/10.1038/s41592-021-01308-y
State	Published - Mar 2022

ASJC Scopus subject areas

Biotechnology
Biochemistry
Molecular Biology
Cell Biology

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Schapiro, D., Sokolov, A., Yapp, C., Chen, Y. A., Muhlich, J. L., Hess, J., Creason, A. L., Nirmal, A. J., Baker, G. J., Nariya, M. K., Lin, J. R., Maliga, Z., Jacobson, C. A., Hodgman, M. W., Ruokonen, J., Farhi, S. L., Abbondanza, D., McKinley, E. T., Persson, D., ... Sorger, P. K. (2022). MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging. Nature Methods, 19(3), 311-315. https://doi.org/10.1038/s41592-021-01308-y

Schapiro, D, Sokolov, A, Yapp, C, Chen, YA, Muhlich, JL, Hess, J, Creason, AL, Nirmal, AJ, Baker, GJ, Nariya, MK, Lin, JR, Maliga, Z, Jacobson, CA, Hodgman, MW, Ruokonen, J, Farhi, SL, Abbondanza, D, McKinley, ET, Persson, D, Betts, C, Sivagnanam, S, Regev, A, Goecks, J, Coffey, RJ, Coussens, LM, Santagata, S & Sorger, PK 2022, 'MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging', Nature Methods, vol. 19, no. 3, pp. 311-315. https://doi.org/10.1038/s41592-021-01308-y

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

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.",

author = "Denis Schapiro and Artem Sokolov and Clarence Yapp and Chen, {Yu An} and Muhlich, {Jeremy L.} and Joshua Hess and Creason, {Allison L.} and Nirmal, {Ajit J.} and Baker, {Gregory J.} and Nariya, {Maulik K.} and Lin, {Jia Ren} and Zoltan Maliga and Jacobson, {Connor A.} and Hodgman, {Matthew W.} and Juha Ruokonen and Farhi, {Samouil L.} and Domenic Abbondanza and McKinley, {Eliot T.} and Daniel Persson and Courtney Betts and Shamilene Sivagnanam and Aviv Regev and Jeremy Goecks and Coffey, {Robert J.} and Coussens, {Lisa M.} and Sandro Santagata and Sorger, {Peter K.}",

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year = "2022",

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doi = "10.1038/s41592-021-01308-y",

language = "English (US)",

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AU - Schapiro, Denis

AU - Sokolov, Artem

AU - Yapp, Clarence

AU - Chen, Yu An

AU - Muhlich, Jeremy L.

AU - Hess, Joshua

AU - Creason, Allison L.

AU - Nirmal, Ajit J.

AU - Baker, Gregory J.

AU - Nariya, Maulik K.

AU - Lin, Jia Ren

AU - Maliga, Zoltan

AU - Jacobson, Connor A.

AU - Hodgman, Matthew W.

AU - Ruokonen, Juha

AU - Farhi, Samouil L.

AU - Abbondanza, Domenic

AU - McKinley, Eliot T.

AU - Persson, Daniel

AU - Betts, Courtney

AU - Sivagnanam, Shamilene

AU - Regev, Aviv

AU - Goecks, Jeremy

AU - Coffey, Robert J.

AU - Coussens, Lisa M.

AU - Santagata, Sandro

AU - Sorger, Peter K.

PY - 2022/3

Y1 - 2022/3

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

Abstract

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