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.
Highlights
MCMICRO, for performing the sequential steps needed to transform whole-slide images into single-cell data
Four primary challenges must be overcome to make computational analysis of high-plex whole-slide tissue imaging (WSI) routine and reproducible: (1) data acquired in multiple image fields must be assembled precisely into large mosaic images encompassing the whole specimen and multiple imaging cycles; (2) full-resolution images must be made available in conjunction with numerical results; (3) images must be subdivided into single cells—a difficult task when cells are densely crowded and nuclei have irregular morphologies; (4) diverse image-processing algorithms and data types must be harmonized across research groups and programming languages
We describe MCMICRO (Multiple Choice MICROscopy), a scalable, modular and open-source image-processing pipeline that leverages Docker/Singularity containers[10,12] and is implemented in Nextflow[11] and Galaxy[9]
Summary
MCMICRO, for performing the sequential steps needed to transform whole-slide images into single-cell data. To create MCMICRO, we wrote five new software packages, three of which are described here (Coreograph for subdividing tissue microarrays (TMAs); Cypository for segmenting cytoplasm and SCIMAP for spatial data analysis).
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