Single-Cell-Kinetics Approach to Discover Functionally Distinct Subpopulations within Phenotypically Uniform Populations of Cells
Bibliographic record
Abstract
Phenotypically uniform cell populations may contain subpopulations with different activities of enzymes, membrane transporters, and other functional units which can be characterized kinetically. Here we propose the first approach to the discovery of such subpopulations; we term it single-cell approach to discover subpopulations or SCADS for short. SCADS combines microscopy, single-cell kinetic analysis, and population/cluster analysis to discover a functionally distinct subpopulation of cells. In this proof-of-principle work, we used SCADS to search for subpopulations with distinct kinetic patterns of membrane transport in bulk tumor cells (BTCs) and tumor-initiating cells (TICs). We used two classical Michaelis parameters, Vmax and KM, to kinetically characterize the rate of transport. We found that the BTC population was homogeneous with respect to membrane transport. When analyzing TICs, we discovered three main functionally distinct subpopulations: (i) cells with a high rate of transport (high Vmax), (ii) cells with high-affinity transporters (low KM), and (iii) cells with activity and affinity similar to those in BTCs (low Vmax and high KM).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".