Analysis of Signaling Events by Combining High-Throughput Screening Technology with Computer-Based Image Analysis
Bibliographic record
Abstract
Intracellular signaling and cell-to-cell communication depend on the coordination of numerous signaling events, and this large flow of information has to be properly organized in space and time. Common and critical to all of these processes and the ultimate cellular response is the correct spatial distribution of signaling components and their targets. This fundamental concept applies to a large number of signaling processes. It is frequently important to quantify the localization of signaling molecules within different cellular compartments to detect subtle changes or to define threshold levels of signaling molecules in a certain location that are necessary to trigger subsequent events. Of particular importance is the separation of nuclear and cytoplasmic events, and sensitive methods are required to measure their contribution to signal transduction. Procedures described here allow the quantification of fluorescence signals located in the nucleus, cytoplasm, or at the nuclear envelope. The methods rely on high-throughput imaging equipment, confocal microscopy, and software modules that measure the fluorescence intensity in the compartment of interest. We discuss the rationale for selecting the appropriate equipment for image acquisition and the proper software modules to quantify fluorescence in distinct cellular compartments. Initially, high-throughput technology for high-speed image acquisition was developed for multiwell plates. We adapted high-throughput technology for image acquisition for cells grown on cover slips. Images of higher spatial resolution along the z axis were acquired by confocal microscopy. For subsequent analyses, the choice of appropriate software modules is critical for rapid and reliable quantification of fluorescence intensities.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".