Spheroid-Based Approach to Assess Tissue Relevance of Analysis of Dispersed-Settled Tissue Cells by Cytometry of Reaction Rate Constant
Bibliographic record
Abstract
File main pdf file (CRRC of dispersed-settled spheroidal cells.pdf) describes experimental results and their interpretation for a study of tissue relevance of analyses of dispersed-settled tissues cells by Cytometry of Reaction Rate Constant (CRRC). CRRC uses time-lapse fluorescence microscopy to measure a rate constant of a catalytic reaction in individual cells and, thus, facilitate accurate size determination for cell subpopulations with distinct efficiencies of this reaction. Practical CRRC requires that a tissue sample be disintegrated into a suspension of dispersed cells and these cells settle on the support surface before being analyzed by CRRC. We call such cells “dispersed-settled” to distinguish them from cells cultured as a monolayer. Studies of the dispersed-settled cells can be tissue-relevant only if the cells maintain their 3D tissue state during the multi-hour CRRC procedure. Here we propose an approach for assessing tissue relevance of the CRRC-based analysis of the dispersed-settled cells. Our approach utilizes cultured multicellular spheroids as a 3D cell model and cultured cell monolayers as a 2D cell model. The CRRC results of the dispersed-settled cells derived from spheroids are compared to those of spheroids and monolayers in order to find if the dispersed-settled cells are representative of the spheroids. To demonstrate its practical use, we applied this approach to a cellular reaction of multi-drug-resistance (MRD) transport which was followed by extrusion of a fluorescent substrate from the cells. The approach proved to be reliable and revealed long-term maintenance of MDR transport in the dispersed-settled cells obtained from cultured ovarian cancer spheroids. Accordingly, CRRC can be used to determine accurately the size of a cell subpopulation with an elevated level of MDR transport in tumor samples, which makes CRRC a suitable method for the development of MDR-based predictors of chemoresistance. The proposed spheroid-based approach for validation of CRRC is applicable to other types of cellular reactions, and, thus, will be an indispensable tool for transforming CRRC from an experimental technique into practical analytical tool. Additional (zip) files contain supporting images, kinetic traces, and histograms. Their detailed descriptions are provided in the main pdf file.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".