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103 A deep-learning approach to guide acquisition region selection for Imaging Mass Cytometry
  1. Fabian Schneider1,
  2. Smriti Kala2,
  3. Clinton Hupple3 and
  4. James Mansfield1
  1. 1Visiopharm A/S, Horsholm, Hovedstaden, Denmark
  2. 2Standard BioTools, South San Francisco, CA
  3. 3Standard BioTools, Markham, ON, Canada
  • Journal for ImmunoTherapy of Cancer (JITC) preprint. The copyright holder for this preprint are the authors/funders, who have granted JITC permission to display the preprint. All rights reserved. No reuse allowed without permission.

Abstract

Background Imaging Mass Cytometry™ (IMC™) is the method of choice for single-step staining and high-plex imaging of tissues while avoiding the complications of autofluorescence and cyclic imaging. IMC has three new imaging modes: Preview Mode (PM), Cell Mode (CM) and Tissue Mode (TM). PM rapidly scans a stained tissue to provide a comprehensive overview, 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. Following PM, regions of interest (ROIs) are selected for high-resolution imaging. This is a critical step that is informed by biomarker expression using automated AI algorithms. CM offers high-resolution imaging for detailed analysis of the ROIs identified during PM, all using the same slide. TM provides fast acquisition of the entire tissue at 5-micron resolution, optimal for quantitative pixel-based analysis. These modes support automated, continuous imaging of more than 40 large tissue samples (400 mm2) weekly.

Methods Tissue sections of colon adenocarcinoma were stained with a 30-marker IMC panel of structural, tumor, stromal, immune cell and immune activation markers. Images were acquired on the Hyperion XTi™ Imaging System (Standard BioTools™), first in PM and then in CM with automatic selection of ROIs using Phenoplex™ software (Visiopharm®). ROIs were automatically selected based on two criteria: 1) actively proliferating and non-proliferating tumor regions; 2) cold and hot tumor regions as identified by immune hotspots within stromal or epithelial tumor regions. An adjacent serial section was acquired in TM.

Single-cell analysis of the images obtained in CM was performed using Phenoplex. Tissue segmentation divided the tissue into tumor epithelial and stromal regions; cell segmentation was based on Iridium DNA channels; and phenotyping was performed using the Guided Workflow. This data was used to compare the immune contexture and spatial distributions via interactive t-SNE plots.

Results A high degree of immune infiltration was observed in the tumor, with significant levels of infiltrating myeloid cells. Hotspots of tumor-associated neutrophils expressing granzyme B were found, implicating their role in the recruitment and activation of intratumor CD4+ and cytotoxic CD8+ T cells.

Conclusions This work demonstrates that Hyperion™ XTi can greatly accelerate the ability of IMC users to gain useful insights from complex biological samples. Phenoplex enables a comprehensive workflow for the analysis of this data, providing automated ROI selection, phenotyping, and spatial analyses of high-resolution IMC images for biological assessment.

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This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See http://creativecommons.org/licenses/by-nc/4.0/.

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