126 Mapping neuro-oncological cellular landscape of human and mouse brain tumors using imaging mass cytometry
Bibliographic record
Abstract
Background Brain cancer research presents challenges that require comprehensive assessment of the structural and cellular organization of the tumor microenvironment (TME). Imaging Mass Cytometry™ (IMC™) offers unprecedented insight into the TME by uncovering the spatial distribution of 40-plus distinct molecular markers without data artifacts caused by autofluorescence. We developed high-plex proteomic analysis tools to thoroughly characterize the TME of both human and mouse brain tissues using IMC. Here we present a deep phenotypic spatial analysis of various mouse and human brain tumors and identify cellular composition and activation of immuno-oncological processes within the TME. Methods The Maxpar® Neuro Phenotyping IMC Panel Kit (PN 201337) is designed for imaging application on formalin-fixed, paraffin-embedded tissues. This neural research-specific panel consists of human and mouse cross-reactive clones and is compatible with Maxpar Human and Maxpar OnDemand™ Mouse Immuno-Oncology IMC Panel Kits. It enables flexible panel design for brain-specific research goals, such as brain tumor classification, and assessment of inflammation and degeneration of specific brain resident and infiltrating cells. We assembled and applied a 39-plex antibody panel on human tissue microarrays containing a variety of brain tumors including glioblastoma, astrocytoma, gliosarcoma, and transitional meningioma. In addition, we assembled and applied a 36-plex antibody panel on mouse glioblastoma and neuroblastoma tissues. The Hyperion™ Imaging System was utilized to digitize images from the tissues followed by quantitative single-cell analysis to assess the cellular composition of cancerous brain TME. Results We identified major cell populations that make up human and mouse brain matter, such as neurons, astrocytes, microglia, and oligodendrocytes. We detected presence of extensive astrogliosis and microgliosis with a pattern of infiltration of lymphoid and myeloid immune cells such as T cells, B cells, and macrophages. We also detected antigen-presenting cells as well as tumor cells exhibiting expression of key cell differentiation markers. Additionally, we assessed vascular coverage and extracellular matrix composition within the TME. Subsequent single-cell analysis provided a comprehensive and quantitative assessment of the brain TME in our human and mouse samples. Our phenotypic analysis resolved the brain TME to the single-cell level and provided insights into the spatial complexity of neuronal neoplasms. Conclusions Empowered by high-plex neuro-oncology panels, IMC can accelerate translational brain tumor research and provide multiparametric insights into the spatial complexity of neuronal neoplasms. Ethics Approval The samples obtained for this study were sourced from an accredited commercial provider.
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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) | 0.000 | 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) | 0.004 | 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".