Using High-Dimensional Approaches to Probe Monocytes and Macrophages in Cardiovascular Disease
Bibliographic record
Abstract
High dimensional approaches that characterize single cells at unprecedented depth have helped uncover unappreciated heterogeneity, a better understanding of myeloid cell origins, developmental relationships and functions. These advancements are particularly important in cardiovascular disease, which remains the leading cause of death worldwide. Chronic processes, such as the development of atherosclerotic plaques, are generated by the gradual, monocyte-dependent inflammatory response within the vessel, contrasts with the acute response within the myocardium that occurs when a vessel is occluded. Monocytes and macrophages differentially contribute to tissue injury, repair and regeneration in these contexts, yet many questions remain about which myeloid cell types are involved in a coordinated, organ-level sterile inflammatory response. Single cell RNA sequencing, combined with functional techniques, have demonstrated that at least three populations of resident cardiac macrophages exist, and after tissue injury, there is significant diversification of the tissue macrophage pool driven by recruited monocytes. While these studies have provided important insights, they raise many new questions and avenues for future exploration. For example, how transcriptional defined sub-populations of cardiac macrophages relate to each other. Are they different states along a predefined program of macrophage differentiation or do local microenvironments drive a newly recruited monocytes into distinct functions? The answers to these questions will require incorporating high-dimensional approaches into biologically relevant in vivo experimental systems to ensure the predicted heterogeneity possess a functional outcome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".