Intensity calibration and automated cell cycle gating for high‐throughput image‐based siRNA screens of mammalian cells
Bibliographic record
Abstract
High-content microscopic screening systems are powerful tools for extracting quantitative multiparameter measures from large number of cells under numerous conditions. These systems perform well in applications that monitor the presence of objects, but lack in their ability to accurately estimate object intensities and summarize these findings due to variations in background, aberrations in illumination, and variability in staining over the image and/or sample wells. We present effective and automated methods that are applicable to analyzing intensity-based cell cycle assays under high-throughput screening conditions. We characterize the system aberration response from images of calibration beads and then enhance the detection and segmentation accuracy of traditional algorithms by preprocessing images for local background variations. We also provide a rapid, adaptive, cell-cycle partitioning algorithm to characterize each sample well based on the estimated locally and globally corrected cell intensity measures of BrdU and DAPI incorporation. We demonstrated the utility and range of our cell ploidy and probe density measurement methods in a pilot screen using a siRNA library against 779 human protein kinases. With our method, multiple image-based quantitative phenotypes can be realized from a single high-throughput image-based microtiter-plate screen.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".