Nature MethodsBioinformaticsPeer-reviewed journal article
MCMICRO: A scalable, modular image-processing pipeline for multiplexed tissue imaging
Denis SchapiroiD, Artem SokoloviD, Clarence YappiD, Yu‐An CheniD, Jeremy L. MuhlichiD, Joshua M. HessiD, Allison CreasoniD, Ajit J. NirmaliD, Gregory J. BakeriD, Maulik K. NariyaiD, Jia‐Ren LiniD, Zoltan MaligaiD, Connor A. JacobsoniD, Matthew W. HodgmaniD, Juha RuokoneniD, Samouil L. FarhiiD, Domenic AbbondanzaiD, Eliot T. McKinleyiD, Daniel PerssoniD, Courtney B. BettsiD, Shamilene SivagnanamiD, Aviv RegeviD, Jeremy GoecksiD, Robert J. CoffeyiD, Lisa M. CoussensiD, , Peter Karl SorgeriD
Abstract
Highly multiplexed tissue imaging makes molecular analysis of single cells possible in a preserved spatial context. However, reproducible analysis of the underlying data poses a substantial computational challenge. Here we describe a modular and open-source computational pipeline (MCMICRO) for performing the sequential steps needed to transform large, multi-channel whole slide images into single-cell data. We demonstrate use of MCMICRO on images of different tissues and tumors acquired using multiple imaging platforms, thereby providing a solid foundation for the continued development of tissue imaging software.
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