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