Computational Tools for the Analysis of Meiotic Prophase I Images
Bibliographic record
Abstract
Summary Prophase I is a remarkable stage of meiotic division during which homologous chromosomes pair together and exchange DNA by meiotic recombination. Fluorescence microscopy of meiotic chromosome spreads is a central tool in the study of this process, with chromosome axis proteins being visualised as extended filaments upon which recombination proteins localise in focal patterns. Chromosome pairing and recombination are dynamic processes, and hundreds of recombination foci can be present in some meiotic nuclei. As meiotic nuclei can exhibit significant variations in staining patterns within and between nuclei, particularly in mutants, manual analysis of images presents challenges for consistency, documentation, and reproducibility. Here we share a combination of complementary computational tools which can be used to partially automate the quantitative analysis of meiotic images. 1) The segmentation of axial and focal staining patterns, to automatically measure chromosome axis length and count axis-associated (and non-axis associated) recombination foci; 2) Quantification of focus position along chromosome axes to investigate spatial regulation; 3) Simulation of random distributions of foci within the nucleus or along the chromosome axes to statistically investigate observed foci-axis associations and foci-foci associations; 4) Quantification of chromosome axis proximity to investigate relationships with chromosome synapsis/asynapsis; 5) Quantification of and orientation of focus-axis distances. Together these tools provide a framework to perform routine documentation and analysis of meiotic images, as well as opening up routes to build on this initial output and perform more detailed analyses.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".