Abstract
Tissue functionality is determined by the characteristics of tissue-resident cells and their interactions within their microenvironment. Imaging Mass Cytometry offers the opportunity to distinguish cell types with high precision and link them to their spatial location in intact tissues at sub-cellular resolution. This technology produces large amounts of spatially-resolved high-dimensional data, which constitutes a serious challenge for the data analysis. We present an interactive visual analysis workflow for the end-to-end analysis of Imaging Mass Cytometry data that was developed in close collaboration with domain expert partners. We implemented the presented workflow in an interactive visual analysis tool; ImaCytE. Our workflow is designed to allow the user to discriminate cell types according to their protein expression profiles and analyze their cellular microenvironments, aiding in the formulation or verification of hypotheses on tissue architecture and function. Finally, we show the effectiveness of our workflow and ImaCytE through a case study performed by a collaborating specialist.
Highlights
CELLS are the structural units of life and the main orchestrators of tissue function [1]
We present an interactive visual analysis workflow for the end-to-end analysis of Imaging Mass Cytometry data that was developed in close collaboration with domain expert partners
We implemented the presented workflow in an interactive visual analysis tool; ImaCytE
Summary
CELLS are the structural units of life and the main orchestrators of tissue function [1]. Each measured protein is typically interpreted as a dimension, defining every cell as a data-point in a high-dimensional space The analysis of this highdimensional space allows for the identification of distinct cell phenotypes, not known a priori, similar to nonspatial methods. The spatial resolution of the data allows biologists to localize the identified cells in the tissue architecture and form hypotheses based on the location of cells and their microenvironment. To explore the cells in their corresponding microenvironments, we first need to identify the different cell phenotypes existing in the analyzed tissue. We extended our previous work [4] focused on the phenotype identification of non-spatial mass cytometry data to enable the exploration of cellular microenvironments and discover subsets of cell phenotypes with unique microenvironment characteristics.
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