119 Uncovering spatial biology of mouse tumor immune microenvironment using imaging mass cytometry
Bibliographic record
Abstract
<h3>Background</h3> Advances in therapies targeting immuno-oncological processes that dictate tumor growth, metastasis, and immune response require comprehensive preclinical research. Mouse models have proven to be a preferred tool for determining important factors that influence tumor development. Conducting multiparametric analysis on mouse tumor tissue has the potential to significantly expand capabilities of tumor-targeting therapies. Imaging Mass Cytometry™ (IMC™) is a proven high-plex imaging technology that enables deep characterization of the complexity of tumor tissue to capture spatial context while eliminating artifacts caused by spectral overlap and background autofluorescence. The Hyperion™ Imaging System utilizes IMC technology to simultaneously assess 40-plus individual structural and functional markers in tissues. Here, we showcase the Maxpar® OnDemand Mouse Immuno-Oncology IMC Panel Kit (PN 9100005) for application on mouse tumor tissues. <h3>Methods</h3> We compiled a 33-plex Mouse Immuno-Oncology IMC Panel Kit to evaluate immuno-oncological-related processes and applied it to a tissue microarray containing a large variety of mouse tumors including non-small-cell lung cancer, B cell lymphoma, colon adenocarcinoma, and renal carcinoma. We digitized high-plex data from mouse tissues using the Hyperion Imaging System and generated images demonstrating the detailed layout of the tumor immune microenvironment (TIME). We conducted single-cell analysis to identify specific and relevant populations of tumor and immune cells. We further applied neighborhood analysis to determine spatial relationships between selected cellular clusters distributed across the TIME. <h3>Results</h3> The Maxpar OnDemand™ Mouse Immuno-Oncology IMC Panel Kit successfully detected immune cell infiltration and activation, signaling pathway activation, biomarkers of epithelial-to-mesenchymal transition (EMT), metabolic activity, growth, and the tumor tissue architecture. Single-cell analysis of non-small-cell lung carcinoma, B cell lymphoma, colon adenocarcinoma, and renal carcinoma separated distinct cellular clusters representing tumor, immune, stromal, and vascular cells. Neighborhood analysis pinpointed spatial relationships between specific cellular clusters within the TIME. <h3>Conclusions</h3> Our quantitative analysis of tumor composition revealed critical insights regarding prognostic parameters such as metastatic and growth potential of tumor cells and activity of immune cell infiltrates. Overall, this work demonstrates the tumor spatial profiling capabilities of IMC technology and provides evidence of its successful application in mouse tumor models. <h3>Ethics Approval</h3> The samples obtained for this study were sourced from an accredited commercial provider.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".