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A deep learning approach to guide acquisition region selection for imaging mass cytometry
Originally presented at AACR 2024
Description

The growth in cancer immunotherapy agents requires an understanding of the immune contexture of the tumor microenvironment (TME). This can be aided by high-plex imaging and analysis to obtain phenotypes of specific cells and study their biodistribution and interactions. Imaging Mass Cytometry (IMC) is the method of choice for single-step staining and high-plex imaging of tissues, avoiding the complications of autofluorescence and cyclic imaging.

IMC has expanded its capabilities with three distinct imaging modes: Preview, Cell, and Tissue. The Preview Mode  is a rapid scanning system that captures a comprehensive overview of the stained tissue, mapping out the distribution of over 40 markers and revealing tissue heterogeneity. This enables researchers to make informed decisions about which areas warrant closer examination on the same. Building on this, Cell Mode offers high-resolution imaging for detailed analysis of the Regions of Interest (ROIs) identified during Preview, all using the same slide. Tissue Mode complements these by providing a fast acquisition of the entire tissue at a lower resolution, which is optimal for quantitative pixel-based analysis of tissue biology. These modes support automated, continuous imaging of more than 40 large tissue samples (400 mm2) weekly. Following Preview Mode, the selection of ROIs for high-resolution imaging is a critical step, enhanced by automated AI algorithms to ensure it is informed by biomarker expression.

Authors

Fabian Schneider1, Smriti Kala2, Clinton Hupple2, James Mansfield1

  1. Visiopharm A/S
  2. Standard BioTools Canada Inc.
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