A Novel Multiplexed, Image-Based Approach to Detect Phenotypes That Underlie Chromosome Instability in Human Cells
Bibliographic record
Abstract
Chromosome instability (CIN) is characterized by a progressive change in chromosome numbers. It is a characteristic common to virtually all tumor types, and is commonly observed in highly aggressive and drug resistant tumors. Despite this information, the majority of human CIN genes have yet to be elucidated. In this study, we developed and validated a multiplexed, image-based screen capable of detecting three different phenotypes associated with CIN. Large-scale chromosome content changes were detected by quantifying changes in nuclear volumes following RNAi-based gene silencing. Using a DsRED-LacI reporter system to fluorescently label chromosome 11 within a human fibrosarcoma cell line, we were able to detect deviations from the expected number of two foci per nucleus (one focus/labelled chromosome) that occurred following CIN gene silencing. Finally, micronucleus enumeration was performed, as an increase in micronucleus formation is a classic hallmark of CIN. To validate the ability of each assay to detect phenotypes that underlie CIN, we silenced the established CIN gene, SMC1A. Following SMC1A silencing we detected an increase in nuclear volumes, a decrease in the number of nuclei harboring two DsRED-LacI foci, and an increase in micronucleus formation relative to controls (untreated and siGAPDH). Similar results were obtained in an unrelated human fibroblast cell line. The results of this study indicate that each assay is capable of detecting CIN-associated phenotypes, and can be utilized in future experiments to uncover novel human CIN genes, which will provide novel insight into the pathogenesis of cancer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".