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.
Subject(s)
Image Processing, Computer-Assisted , Neoplasms , Diagnostic Imaging , Humans , Image Processing, Computer-Assisted/methods , Neoplasms/diagnostic imaging , Neoplasms/pathology , SoftwareABSTRACT
Advances in highly multiplexed tissue imaging are transforming our understanding of human biology by enabling detection and localization of 10-100 proteins at subcellular resolution (Bodenmiller, 2016). Efforts are now underway to create public atlases of multiplexed images of normal and diseased tissues (Rozenblatt-Rosen et al., 2020). Both research and clinical applications of tissue imaging benefit from recording data from complete specimens so that data on cell state and composition can be studied in the context of overall tissue architecture. As a practical matter, specimen size is limited by the dimensions of microscopy slides (2.5 × 7.5 cm or ~2-8 cm2 of tissue depending on shape). With current microscopy technology, specimens of this size can be imaged at sub-micron resolution across ~60 spectral channels and ~106 cells, resulting in image files of terabyte size. However, the rich detail and multiscale properties of these images pose a substantial computational challenge (Rashid et al., 2020). See Rashid et al. (2020) for an comparison of existing visualization tools targeting these multiplexed tissue images.
