Semi‐Automated Analysis of Organelle Movement and Membrane Content: Understanding Rab‐Motor Complex Transport Function
Bibliographic record
Abstract
Organelle motility is an essential cellular function that is regulated by molecular motors, and their adaptors and activators. Here we established a new method that allows more direct investigation of the function of these peripheral membrane proteins in organelle motility than is possible by analysis of the organelle movement alone. This method uses multi-channel time-lapse microscopy to record the movement of organelles and associated fluorescent proteins, and automatic organelle tracking, to compare organelle movement parameters with the association of membrane proteins. This approach allowed large-scale, unbiased analysis of the contribution of organelle-associated proteins and cytoskeleton tracks in motility. Using this strategy, we addressed the role of membrane recruitment of Rab GTPases and effectors in organelle dynamics, using the melanosome as a model. We found that Rab27a and Rab32/38 were mainly recruited to sub-populations of slow-moving/static and fast-moving melanosomes, respectively. The correlation of Rab27a recruitment with slow movement/docking was dependent on the effector melanophilin. Meanwhile, using cytoskeleton-disrupting drugs, we observed that this speed:Rab content relationship corresponded to a decreased frequency of microtubule (MT)-based transport and an increased frequency of actin-dependent slow movement/docking. Overall, our data indicate the ability of Rab27a and effector recruitment to switch melanosomes from MT- to actin-based tethering and suggest that a network of Rab signalling may integrate melanosome biogenesis and transport.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".