Abstract 478: Single-Cell Isolation and Analysis of Viable Proliferating Macrophages From Atherosclerotic Plaques
Bibliographic record
Abstract
INTRODUCTION: We have recently shown that the proliferation of macrophages within the atherosclerotic plaque, rather than the recruitment of new monocytes from the blood is the key driver of plaque growth in established atherosclerosis. The study of proliferating macrophages has been hampered by the lack of a technique for identifying proliferating macrophages and isolating them from the atherosclerotic plaque in a viable state. OBJECTIVE: Develop and validate a process for identifying proliferating macrophages in atherosclerotic lesions, and isolating them as single, viable cells, then perform a molecular characterization of these cells. METHODS: ApoE-/- and LDLR-/- mice were fed a diet high in fat and cholesterol. Atherosclerotic aortas were collected, and cells were isolated by mechanical and enzymatic dissociation. Cells were stained using fluorescent markers and DNA-binding dyes, then analyzed and isolated by fluorescence-activated cell sorting. RESULTS: Mincing and enzymatic digestion in collagenase I, collagenase XI, DNAse, and hyaluronidase optimally isolates macrophages from aortic explants. Cell-surface staining with fluorescent antibodies against B220, F4/80, Ly6c, MHC II, and CD11b allows identification of lesional macrophages. DNA staining with Vybrant DyeCycle Violet allows identification of proliferating cells. Combined, these processes allow for the FACS-based sorting of viable proliferating macrophages from atherosclerotic plaque, and subsequent RNA or protein analysis, or culture. CONCLUSIONS: The process described here allows the first-ever viable isolation of proliferating macrophages from atherosclerotic plaques. This will permit the investigation of molecular targets to interfere with the pathogenesis of atherosclerosis.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".