Multi-culture label-free quantitative cell migration sensing with single-cell precision
Bibliographic record
Abstract
A fair comparison of multiple live cell cultures requires examining them under identical environmental conditions, which can only be done accurately if all cells are prepared simultaneously and studied at the same time and place. This contribution introduces a multiplexed lensless digital holographic microscopy system (MLS), enabling synchronous, label-free, quantitative observation of multiple live cell cultures with single-cell precision. The innovation of this setup lies in its ability to robustly compare the behaviour, i.e., migratory pathways, of cells cultured or contained in different ways (with varied stimuli applied), making it a valuable tool for dynamic biomedical diagnostics on a cellular level. The system's design allows for potential expansion to accommodate as many samples as needed, thus broadening its application scope in future quantitative diagnostics on global multi-culture cellular behaviours via their localized single-cell spatiotemporal optical signatures. We believe that our method has the potential to empower reliable live cell multi-culture comparisons through simultaneous quantitative imaging, enhancing label-free investigations into cell cultures and the effects of biochemical or physical stimuli over large areas, and unlocking novel mechanistic understandings through high-throughput time-lapse observations.
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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) | 0.000 | 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".