Abstract 677: Increasing plexity of imaging mass cytometry for tumor tissue analysis
Bibliographic record
Abstract
Abstract Imaging Mass Cytometry™ (IMC™) is the leading platform for high-plex tissue imaging. IMC allows for detailed assessment of cell phenotype and function using 40-plus markers simultaneously at subcellular resolution on a single slide. A comprehensive IMC panel containing structural, functional, and immune markers enables us to reveal the complex heterogeneity of tumor tissues as well as the tumor microenvironment (TME). Driven by an increase in the number of antibody markers and the addition of mRNA markers, there is an increasing demand for larger panels. In addition, increasing the number of investigated target markers on a single tissue enriches spatial characterization that may facilitate a more accurate prediction of disease progression and preclinical outcome measures in clinical research projects using tumor biopsies or tissue microarrays (TMAs). Therefore, to increase the plexity of IMC panels, it is essential to expand the number of available metal channels. Here, we demonstrate the incorporation of conjugated antibodies with yttrium (89Y) and indium (115In), two low-mass metals, for IMC application. These metal tags have been previously tested as putative channels for IMC application. We performed IMC analysis of various tissue types stained with panels of conjugated antibodies including the novel 89Y- and 115In-conjugated antibodies. At least 3 different regions of interest (ROIs) were assessed for each of the investigated tissue types. We compared images for the 89Y- and 115In-conjugated antibodies with the images generated using Maxpar® catalog antibodies of the same clones, with a focus on marker specificity and background signal. Compared with the lanthanide-conjugated catalog antibodies, the 89Y- and 115In-conjugated antibodies showed equivalent specificity and staining quality. Our results open a new avenue to assign markers to 89Y and 115In, which enables a larger list of potential targets to be investigated in any IMC study. Expanding the number of markers to 40-plus in Imaging Mass Cytometry will improve the imaging results necessary to identify novel cell signatures (phenotype and interactions) in the TME. Citation Format: Thomas D. Pfister, Shaida Ouladan, Huihui Yao, Daniel Majonis, Christina Loh, Nick Zabinyakov. Increasing plexity of imaging mass cytometry for tumor tissue analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 677.
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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 | 0.000 | 0.000 |
| 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".