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.
Original language | English (US) |
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Article number | 438 |
Journal | Communications Biology |
Volume | 5 |
Issue number | 1 |
DOIs | |
State | Published - Dec 2022 |
ASJC Scopus subject areas
- Medicine (miscellaneous)
- General Biochemistry, Genetics and Molecular Biology
- General Agricultural and Biological Sciences