Faculty Opinions recommendation of Robust single-particle tracking in live-cell time-lapse sequences.
Bibliographic record
Abstract
Single particle tracking (SPT) is often the rate-limiting step in live cell imaging studies of subcellular dynamics.Here we present a tracking algorithm that addresses the principal challenges of SPT, namely high particle density, particle motion heterogeneity, temporary particle disappearance, and particle merging and splitting.The algorithm first links particles between consecutive frames and then links the resulting track segments into complete trajectories.Both steps are formulated as global combinatorial optimization problems whose solution identifies the overall most likely set of particle trajectories throughout the movie.Using this approach, we show that the GTPase dynamin differentially affects the kinetics of long and short-lived endocytic structures, and that the motion of CD36 receptors along cytoskeleton-mediated linear tracks increases their aggregation probability.Both applications indicate the requirement for robust and complete tracking of dense particle fields to dissect the mechanisms of receptor organization at the level of the plasma membrane.With the development of bright fluorescent probes, stable microscopes and sensitive cameras, live cell imaging has become a standard technique to study sub-cellular dynamics.The resulting images often consist of punctate features, representing small molecular assemblies or even single molecules1-3.To gain insight into the molecular mechanisms that drive the observed dynamics, such experiments must be combined with single particle tracking (SPT) that captures the full spatio-temporal complexity of sub-cellular particle behavior.SPT faces several challenges that in practice hinder such studies.Importantly, SPT goes beyond the detection and localization of particles; its key step is the establishment of correspondence between particle images in a sequence of frames.Establishing
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.039 |
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".