A framework for multiplex imaging optimization and reproducible analysis

Jennifer Eng, Elmar Bucher, Zhi Hu, Ting Zheng, Summer L. Gibbs, Koei Chin, Joe W. Gray

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


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 languageEnglish (US)
Article number438
JournalCommunications Biology
Issue number1
StatePublished - Dec 2022

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • General Biochemistry, Genetics and Molecular Biology
  • General Agricultural and Biological Sciences


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