Divergent lymphocyte signalling revealed by a powerful new tool for analysis of time‐lapse microscopy
Bibliographic record
Abstract
We describe a new approach for interactive analysis of time-lapse microscopy, and apply this approach to elucidating whether polarity regulation is conserved between epithelial cells and lymphocytes. A key advantage of our analysis platform, 'TACTICS', is the capacity to visualize individual data points in the context of large data sets, similar to standard approaches in flow cytometry. Scatter plots representing microscopic parameters or their derivations such as polarity ratios are linked to the original data such that clicking on each dot enables a link to images and movies of the corresponding cell. Similar to flow cytometric analysis, subsets of the data can be gated and reanalyzed to explore the relationships between different parameters. TACTICS was used to dissect the regulation of polarization of the cell fate determinant, Numb, in migrating lymphocytes. We show here that residues of Numb that are phosphorylated by atypical protein kinase C (aPKC) to mediate apicobasal polarity in epithelial cells are not required for polarization of Numb in T cells, indicating that the role of aPKC is not conserved between lymphocytes and epithelia.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".