A framework for multiplex imaging optimization and reproducible analysis

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. An approach for tissue image analysis applicable to highly multiplexed immunofluorescence imaging of the spatial distribution of multiple protein biomarkers is proposed, here applied to the analysis of multiplex IF using the multiplex imaging platform, CyCIF.

Automated summary

The researchers developed a Python software called mplexable, which is used for image processing in multiplex imaging, and shared their optimization of signal removal, antibody specificity, background correction, and batch normalization through Jupyter notebooks. This work improves the CyCIF methodology and provides a framework for easily sharing and reproducing multiplexed image analytics.