Multicolor Two‐Photon Intravital Microscopy Reveals Monocyte Patrolling and Macrophage Migration in Atherosclerosis
Bibliographic record
Abstract
Atherosclerosis is an underlying cause of two of the leading causes of death in the world, heart attacks and strokes. It is a chronic inflammatory disease characterized by plaque build‐up in large and medium arteries. During disease progression, monocytes in the blood invade the arterial wall, where they can differentiate into macrophages and dendritic cells. Together, these cells phagocytose lipids, clear apoptotic cell debris, present antigen to T cells, and produce pro‐ and anti‐inflammatory cytokines. Both monocyte recruitment to the plaque and macrophage function within the plaque require cell motion. The purpose of this work is to study monocyte and macrophage movement in the context of atherosclerosis. We developed a system for imaging fluorescent leukocytes in atherosclerotic arteries in vivo . The major challenge in intravital imaging of large arteries is the motion artifacts due to the expansion of the arterial wall with the heartbeat. This system utilizes cardiac triggering and novel image post‐processing algorithms to remove these motion artifacts and allow for cell motion quantification. Apoe −/− Cx3cr1 GFP/+ Cd11c YFP mice were generated to visualize labeled monocytes and macrophages. Monocytes were observed patrolling the endothelium of atherosclerotic arteries and the motion characteristics were quantified. Macrophages were seen actively probing their local environment as well as migrating through the plaque. Finally, these macrophages were extensively phenotyped by cell surface markers to link observed motion to known subsets and function. Support or Funding Information This work was funded by NIH R01 (115232) to K.L. and AHA (#11PRE7580009), HHMI (56005681), and NHLBI (T32HL105373‐03) to S.M.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".