Imaging mass cytometry - elemental immunohistochemistry for multiparametric imaging and quantitation
Bibliographic record
Abstract
Abstract Pathology assessment of tissue sections provides prognostic evaluation and helps select optimum treatment regimens. Digital pathology has made significant strides in the analysis of several markers. There is a growing understanding of cell heterogeneity within tumor tissue, role of microenvironment, and the impact of immune cells on cancer development. Sophisticated tools are needed to provide quantitative information for a large number of biomarkers, low-abundance small molecules and chemotherapeutic drugs, while retaining spatial resolution of cells and tissue architecture. Imaging mass cytometry (IMC) is a novel technology that can simultaneously detect and quantitatively measure more than 50 metal-containing reagents in tissue sections at 1 μm resolution. IMC combines laser ablation with the Helios CyTOF®. We will describe in detail technology, workflow, multiplexing protocols, image analysis and show representative data for human and mouse sections. We will demonstrate the use of metal-containing histological stains for tissue architecture, and endogenous element identification [iodine, platinum]. Combined detection of protein targets and transcripts within cells will be presented. Validation will be demonstrated on sequential tissue sections prepared by conventional immunohistochemistry. Precision medicine is based on access to high-density data (proteomics and genomics) which provides accurate diagnosis and information on the best therapeutic approach. IMC is a highly multiparametric, quantitative method for phenotypic, signaling pathway, and cell state protein identification together with spatial information within tissue sections.
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 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.011 |
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".