Abstract 1450: Novel whole slide imaging modes for imaging mass cytometry reveal cellular and structural composition of mouse glioblastoma
Bibliographic record
Abstract
Abstract Mouse tumors have become widely used models in translational research of brain malignancies, helping to address the difficulties related to studying the human brain. Mouse brain can be regarded as a miniaturized model of the human brain, permitting visualization of the whole tissue to provide spatial cellular context. Imaging Mass Cytometry™ (IMC™) is a highly relevant tool capable of quantitative evaluation of the multiparametric protein composition in brain tumor microenvironment (TME) without the complications of autofluorescence, tissue degradation, and spectral overlap. The Hyperion XTi™ Imaging System (Standard BioTools™) utilizes IMC technology to simultaneously assess more than 40 individual structural and functional markers in tissue, providing insight into the organization and function of the TME. We demonstrate the whole slide imaging (WSI) application using a 40-marker panel composed of the Maxpar OnDemand™ Mouse Immuno-Oncology IMC Panel Kit and the Maxpar® Neuro Phenotyping IMC Panel Kit on mouse normal and glioblastoma (GBM) brain tissue. The panel was composed of mouse-specific antibodies, which highlight tumor and immune components of the mouse TME. We performed imaging using two new features of Hyperion™ XTi. Ultrafast preview mode (PM) was applied to rapidly screen entire brain sections for marker expression signatures associated with various immuno-oncology processes. This enabled biomarker-guided selection of areas in tumor tissue that were imaged using region of interest-based IMC and analyzed using single-cell analysis (SCA). In parallel, a high-throughput tissue mode (TM) was applied to perform a detailed whole-slide scan of normal and GBM mouse brain tissues that were quantified using pixel-based analysis (PBA) to unravel the composition of the TME. Using TM, we were able to successfully visualize the entire coronal and sagittal sections of normal mouse brain as well as mouse GBM tissue. PBA on TM data provided quantitative spatial expression patterns of structural and immune markers across the whole tissue. In normal tissue, we visualized the highly organized structure of the normal brain and detected neurons, oligodendrocytes, vascular-adjacent astrocytes, and axonal tracks. In GBM tissue, necrotic cores, areas with high immune infiltration, extracellular matrix deposits, and activated tumor cells were detected. SCA of GBM tissue demonstrated expansive vascularization, replicating Olig2+ cells, activation of Ras signaling, and high abundance of infiltrating immune cells. Overall, we demonstrate the successful application of two novel WSI modes and highlight the power of IMC technology to simultaneously explore dozens of relevant biological outputs to better understand the TME of GBM and other tumors. For research use only. Not for use in diagnostic procedures. Citation Format: Qanber Raza, Nick Zabinyakov, Thomas D. Pfister, Nikesh Parsotam, David Howell, Liang Lim, Christina Loh. Novel whole slide imaging modes for imaging mass cytometry reveal cellular and structural composition of mouse glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1450.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".