Protocol for machine-learning-based 3D image analysis of nuclear envelope tubules in cultured cells
Bibliographic record
Abstract
The nuclear envelope can form complex structures in physiological and pathological contexts. Current approaches to quantify nuclear envelope structures can be time-consuming or inaccurate. Here, we present a protocol to measure nuclear envelope tubules induced by DNA double-strand breaks using a mid-throughput approach. We describe steps for the induction of these nuclear envelope structures and 3D image analysis using machine-learning-based image segmentation. This protocol can be applied to analyze various nuclear envelope structures in contexts beyond DNA repair. For complete details on the use and execution of this protocol, please refer to Shokrollahi et al. 1 • Instructions for preparing nuclear envelope tubules • Guidance on 3D imaging enables visualization of nuclear envelope tubules • Steps for quantifying nuclear surfaces using machine-learning-based segmentation • This protocol enables mid-throughput quantification of nuclear envelope tubules Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. The nuclear envelope can form complex structures in physiological and pathological contexts. Current approaches to quantify nuclear envelope structures can be time-consuming or inaccurate. Here, we present a protocol to measure nuclear envelope tubules induced by DNA double-strand breaks using a mid-throughput approach. We describe steps for the induction of these nuclear envelope structures and 3D image analysis using machine-learning-based image segmentation. This protocol can be applied to analyze various nuclear envelope structures in contexts beyond DNA repair.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.072 | 0.038 |
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".