Keywords: Spatial Omics, Multiplexed Imaging, Oncology, Cardiology
With improvements in speed and amount of data that can be collected from tissues, data processing and analysis have become major challenges. Therefore, we developed the open-source histology topography cytometry analysis toolbox (histoCAT), which became the first software specifically tailored to analyse highly multiplexed images, such as those from IMC [1,2]. HistoCAT includes advanced machine learning approaches and basic statistical methods integrated in an interactive desktop application. Recently, we have developed a scalable and modular computational pipeline (MCMICRO) enabling the analysis of a variety of highly multiplexed spatial technologies for proteomics (antibody-based) and transcriptomics [3]. We demonstrated the use of MCMICRO on dozens of tissue and tumor images acquired using multiple imaging platforms, thereby providing a solid foundation for the continued development of tissue imaging software. In the second part of my talk, I will cover the Minimum Information about Highly Multiplexed Tissue Imaging (MITI) standard [4] as well as our spatial power analysis framework to improve experimental design strategies [5]. Lastly, I will highlight how we utilized the developed tools to process data with a focus on myocardial infarction.
[1] Giesen, C., Wang, H., Schapiro, D. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat Methods 11, 417–422 (2014). https://doi.org/10.1038/nmeth.2869
[2] Schapiro, D., Jackson, H., Raghuraman, S. et al. histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data. Nat Methods 14, 873–876 (2017). https://doi.org/10.1038/nmeth.4391
[3] Schapiro, D., Sokolov, A., Yapp, C. et al. MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging. Nat Methods (2021). https://doi.org/10.1038/s41592-021-01308-y
[4] Schapiro, D., Yapp, C., Sokolov, A. et al. MITI minimum information guidelines for highly multiplexed tissue images. Nat Methods 19, 262–267 (2022). https://doi.org/10.1038/s41592-022-01415-4
[5] Baker, E.A.G., Schapiro, D., Dumitrascu, B. et al. In silico tissue generation and power analysis for spatial omics. Nat Methods 20, 424–431 (2023). https://doi.org/10.1038/s41592-023-01766-6