4.8 Article

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

Journal

NATURE METHODS
Volume 19, Issue 3, Pages 311-+

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s41592-021-01308-y

Keywords

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Funding

  1. National Institutes of Health (NIH) [U54-CA225088, U2C-CA233262]
  2. Swiss National Science Foundation [P2ZHP3_181475]
  3. Damon Runyon Cancer Research Foundation [DRQ-03-20]
  4. NCI [R50-CA252138]
  5. NCI Cancer Center Support Grant [P30 CA06516]
  6. National Institutes of Health [1U01 CA224012, U2C-CA233280, R01-CA223150, R01-CA226909, R21-HD099367]
  7. Knight Cancer Institute
  8. Brenden-Colson Center for Pancreatic Care at OHSU
  9. NIH, National Cancer Institute Human Tumor Atlas Network(HTAN) Research Center [U2C-CA233280]
  10. Prospect Creek Foundation
  11. Ludwig Cancer Center at Harvard
  12. Swiss National Science Foundation (SNF) [P2ZHP3_181475] Funding Source: Swiss National Science Foundation (SNF)

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MCMICRO is a modular and open-source computational pipeline that enables the transformation of highly multiplexed tissue whole-slide images into single-cell data. It is versatile and can be used with various imaging platforms, maintaining spatial context and providing a foundation for the continued development of tissue imaging software.
MCMICRO is a modular and open-source computational pipeline for transforming highly multiplexed whole-slide images of tissues into single-cell data. MCMICRO is versatile and can be used with CODEX, mxIF, CyCIF, mIHC and H&E staining data. 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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