Abstract 1045: Using Multiplex Imaging For Cell Phenotyping Of Early Human Atherosclerosis
Bibliographic record
Abstract
Background: In the past few years, animal models and cultured cells have shown that smooth muscle cells (SMCs) and inflammatory cytokines are important new players in atherosclerotic disease. Validating the roles of these new players in human atherogenesis is crucial for the development of therapeutics targeting them. Multiplex imaging is a powerful tool for mapping cell phenotypes and microenvironment in tissue sections, enabling proof-of-concept studies using biobanked human specimens. However, this technology has seldom been applied to human atherosclerotic lesions. This study is a trial to benchmark the application of the multiplex imaging platform PhenoCycler to biobanked human coronary lesions. We hypothesize that this technology is a powerful tool to prove theories raised by previous studies: SMCs initiate foam cell formation and inflammation is associated with phenotypic switching in SMCs. Methods: We created an 8-plex imaging panel to map lesion cells previously identified as key players in atherogenesis, including foam cells from SMC and macrophage origins, CD68 + SMCs, apoptotic cells, and cells expressing inflammatory cytokines interleukin-1β and tumor necrosis factor α. They were imaged simultaneously on each section of early human lesions (n=9) to test theories related to foam cell formation, phenotypic transition, and inflammatory cytokines. Results: Foam cells of SMC origin greatly outnumbered those of macrophage origin and were enriched in the deep intima, where lipids accumulate in early atherogenesis, distinct from macrophage foam cells in the superficial intima. A higher presence of CD68 + SMCs was observed among lesion SMCs highly expressing interleukin-1β, but not TNF-α. Highly inflamed SMCs exhibited a trend of increased apoptosis compared to macrophages with similar cytokine levels. Conclusions: Our multiplex imaging results confirmed that SMCs form the majority of foam cells in early atherogenesis, and their phenotypic transition to macrophage-like is related to inflammation. Applying multiplex imaging to biobanked human lesions unlocks opportunities to prove that theories based on animal models and cultured cells apply to human atherogenesis.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".