Toward reproducible, scalable, and robust data analysis across multiplex tissue imaging platforms

• Published in: Cell reports methods Vol. 1; no. 4; p. 100053
• Main Authors: Burlingame, Erik A., Eng, Jennifer, Thibault, Guillaume, Chin, Koei, Gray, Joe W., Chang, Young Hwan
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 23.08.2021; Elsevier
• Subjects: breast cancer; Breast Neoplasms - diagnostic imaging; Diagnostic Imaging; Female; Fluorescent Antibody Technique; GPU data science; Humans; image analytics; Microarray Analysis; multiplex tissue imaging; Phenotype; systems biology; Tumor Microenvironment; systems biology; breast cancer; image analytics; GPU data science; multiplex tissue imaging
• ISSN: 2667-2375; 2667-2375
• DOI: 10.1016/j.crmeth.2021.100053

The emergence of megascale single-cell multiplex tissue imaging (MTI) datasets necessitates reproducible, scalable, and robust tools for cell phenotyping and spatial analysis. We developed open-source, graphics processing unit (GPU)-accelerated tools for intensity normalization, phenotyping, and microenvironment characterization. We deploy the toolkit on a human breast cancer (BC) tissue microarray stained by cyclic immunofluorescence and present the first cross-validation of breast cancer cell phenotypes derived by using two different MTI platforms. Finally, we demonstrate an integrative phenotypic and spatial analysis revealing BC subtype-specific features. [Display omitted] •Multiplex tissue imaging (MTI) data require a reliable analytical toolset•GPU acceleration of single-cell analyses enables rapid iteration and interaction•Inter-MTI platform validation of breast cancer (BC) cell types indicates consensus•Spatial features of tissues discriminate between BC clinical subtypes Multiplex tissue imaging (MTI) is transforming biology by linking biomarker expression with spatial context. As the number of MTI platforms and spatial tissue atlases increases, so too must the capacity of our methods to ingest and derive insight from these data. Many of the methods used in MTI analysis are crossovers from other domains like flow cytometry and single-cell genomics, do not scale well, and might be statistically unfit for atlas-level MTI data, constituting a crucial bottleneck in the search for the next generation of clinically relevant biomarkers. Here, we address this bottleneck with a computationally efficient and platform-agnostic MTI data analysis workflow and its proof-of-concept application to human breast cancer (BC) tissues. Burlingame et al. describe a GPU-accelerated workflow for normalization, phenotyping, and spatial analysis of single-cell multiplex tissue imaging (MTI) data. This workflow is deployed on breast cancer (BC) tissues to derive a cell type dictionary, which is validated between MTI platforms. Tissue architecture is used to discriminate between BC subtypes.