Improved drug activity in high-content screening of the microtubule network
Bibliographic record
Abstract
High-Content Screening (HCS) is a technology based on the automation of fluorescence microscopy, to screen and analyze the spatial and morphological properties of individual cells. This automation has made it possible to acquire, process, and archive tens of millions of cell images, and hundreds of compounds at a time, facilitating drug discovery. Although the large number of test compounds and cells has advantages, it removes the possibility of human inspection and so relies on quantitative analytical approaches. Much of current practice, however, does not take full advantage of the information-rich content of HCS screens and instead relies on measurement and analysis methods developed for High-Throughput Screens (HTS) which generate only one output per well (brightness) and which are unable to detect treatments which affect only a subpopulation of cells. Interest in cell subpopulations (heterogeneity) has been gaining interest lately especially in cancer cells and with the advent of single cell sequencing. This has put forward a need for evaluating a summary statistic that is sensitive to subpopulations and hence could possibly classify drug effects more accurately.Another issue in HCS is the lack of metrics for characterizing biologically-relevant phenotypes beyond changes in brightness. This is especially true in the case of the microtubule structure where texture measures are abundant but hard to interpret. Here we evaluate a family of metrics that quantify measures that are directly related to microtubule structures, such as number of branch points of fibers, potentially offering insights concerning biological mechanisms. In this study, the aim is to examine the morphology of cells treated with compounds with well-known effects on cells and in particular, on the fibrous microtubule structure, in a data set of thousands of confocal microscope images of HeLa cells. First, the performances of 14 fiber-specific metrics will be assessed in distinguishing between active and inactive compounds in both lysed and non-lysed cells. Second we present Receiver Operator Characteristic (ROC) curves as an alternative estimate of treatment effect that is potentially more sensitive to cell subpopulations than standard summary statistics. Our results show that both ROC curves and the tested fiber morphology metrics are interpretable, and provide a basis for determining active compounds under different conditions. They also outperform the standard mean fluorescence of cells in distinguishing between drug-treated and control cells, providing a relevant biological framework in which hypotheses may be developed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".