A framework for multiplex imaging optimization and reproducible analysis
Koei Chin,Yanli Zhang +1 more
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In this article , the authors developed a python software, mplexable, for reproducible image processing and utilize Jupyter notebooks to share their optimization of signal removal, antibody specificity, background correction and batch normalization of the multiplex imaging with a focus on cyclic immunofluorescence (CyCIF).Abstract:
Multiplex imaging technologies are increasingly used for single-cell phenotyping and spatial characterization of tissues; however, transparent methods are needed for comparing the performance of platforms, protocols and analytical pipelines. We developed a python software, mplexable, for reproducible image processing and utilize Jupyter notebooks to share our optimization of signal removal, antibody specificity, background correction and batch normalization of the multiplex imaging with a focus on cyclic immunofluorescence (CyCIF). Our work both improves the CyCIF methodology and provides a framework for multiplexed image analytics that can be easily shared and reproduced. read more
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References
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TL;DR: The coupled immunohistochemical and immunocytochemical methods with high-resolution laser ablation to CyTOF mass cytometry enables the simultaneous imaging of 32 proteins and protein modifications at subcellular resolution and complements existing imaging approaches.
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