103 A deep-learning approach to guide acquisition region selection for Imaging Mass Cytometry
Bibliographic record
Abstract
<h3>Background</h3> 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 mm<sup>2</sup>) weekly. <h3>Methods</h3> 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. <h3>Results</h3> 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. <h3>Conclusions</h3> 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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".