Imaging Mass Cytometry identifies structural and cellular composition of the mouse tissue microenvironment
Bibliographic record
Abstract
Abstract Imaging Mass Cytometry™ (IMC™) is a vital tool to deeply characterize the complexity and diversity of any tissue without disrupting spatial context. The Hyperion™ Imaging System utilizes IMC, based on CyTOF® technology, to assess up to 40 individual structural and functional markers in tissues, providing unprecedented insight into the organization and function of tissue microenvironment. We have previously demonstrated the application of IMC in combination with Maxpar® panel kits on human tissues. Here, we showcase Maxpar OnDemand Antibodies for IMC application including 11 new highly relevant markers to construe cellular and molecular composition of mouse tissues. We analyzed a normal mouse tissue microarray using IMC spatial proteomic analysis. Tissues were stained with a 20-marker panel designed to highlight tissue architecture and major immune lineage markers. We generated a detailed spatial map of the diverse tissue architecture and successfully identified immune, epithelial, and stromal cell populations in various mouse tissues. Additionally, we classified the activation state of immune cell populations, adhesion state of epithelial cells, and molecular composition of the extracellular matrix. This work demonstrates the capability of IMC to identify subcellular localization of cellular and structural markers in the mouse tissue microenvironment. Future studies utilizing IMC in combination with Maxpar OnDemand Antibodies will enable in-depth phenotypic characterization of the tissue microenvironment in various mouse models of development and disease, and thus provide the basis for the use of high-multiplex imaging in preclinical investigations. For Research Use Only. Not for use in diagnostic procedures.
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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) | 0.002 | 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".