Making Cytometry of Reaction Rate Constant (CRRC) Applicable to Motile Cells
Bibliographic record
Abstract
Cytometry of Reaction Rate Constant (CRRC) is a method for studying heterogeneity of cell populations with regards to activity of cellular reactions. It is based on time-lapse fluorescence microscopy which facilitates following reaction kinetics in individual cells. The current CRRC workflow utilizes a single fluorescence image to manually identify cell contours; these contours are then used to determine fluorescence intensity of individual cells in the entire time-stack of images. This workflow can only be used reliably if the cells maintain their positions during the time-lapse measurements; if the cells move, the results of a CRRC experiment will be inaccurate. The requirement of invariant cell positions during a prolonged imaging is impossible to satisfy for motile cells. Here we report on developing an advanced workflow that makes CRRC applicable to motile cells. The new workflow combines fluorescence microscopy with brightfield (BF) microscopy and utilizes automated processing and analysis of images. A BF image is taken right after every fluorescence image and used to determine cell contours. The contours are tracked through the time-stack of BF images to account for cell movement. A set of contours, which is unique for every image, is then used to determine fluorescence intensity of cells in the associated fluorescent image. Finally, time dependencies of intracellular fluorescence intensities are used to determine the rate constant and plot a kinetic histogram “number of cells vs rate constant”. The robustness of the new workflow to cell movement was confirmed experimentally by conducting a CRRC study of cross-membrane transport in motile cells. The new workflow makes CRRC applicable to a wide range of cell types and eliminates the influence of cell motility on the accuracy of results.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".