Imaging Molecular Adhesion in Cell Rolling by Adhesion Footprint Assay
Bibliographic record
Abstract
Rolling adhesion, facilitated by selectin-mediated interactions, is a highly dynamic, passive motility in recruiting leukocytes to the site of inflammation. This phenomenon occurs in postcapillary venules, where blood flow pushes leukocytes in a rolling motion on the endothelial cells. Stable rolling requires a delicate balance between adhesion bond formation and their mechanically-driven dissociation, allowing the cell to remain attached to the surface while rolling in the direction of flow. Unlike other adhesion processes occurring in relatively static environments, rolling adhesion is highly dynamic as the rolling cells travel over thousands of microns at tens of microns per second. Consequently, conventional mechanobiology methods such as traction force microscopy are unsuitable for measuring the individual adhesion events and the associated molecular forces due to the short timescale and high sensitivity required. Here, we describe our latest implementation of the adhesion footprint assay to image the P-selectin: PSGL-1 interactions in rolling adhesion at the molecular level. This method utilizes irreversible DNA-based tension gauge tethers to produce a permanent history of molecular adhesion events in the form of fluorescence tracks. These tracks can be imaged in two ways: (1) stitching together thousands of diffraction-limited images to produce a large field of view, enabling the extraction of adhesion footprint of each rolling cell over thousands of microns in length, (2) performing DNA-PAINT to reconstruct super-resolution images of the fluorescence tracks within a small field of view. In this study, the adhesion footprint assay was used to study HL-60 cells rolling at different shear stresses. In doing so, we were able to image the spatial distribution of the P-selectin: PSGL-1 interaction and gain insight into their molecular forces through fluorescence intensity. Thus, this method provides the groundwork for the quantitative investigation of the various cell-surface interactions involved in rolling adhesion at the molecular level.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".