Cellular bias on the microscale: probing the effects of digital microfluidic actuation on mammalian cell health, fitness and phenotype
Bibliographic record
Abstract
The potential benefits of using new technologies such as microfluidics for life science applications are exciting, but it is critical to understand and document potential biases imposed by these technologies on the observed results. Here, we report the first study of genome-level effects on cells manipulated by digital microfluidics. These effects were evaluated using a broad suite of tools: cell-based stress sensors for heat shock activation, single-cell COMET assays to probe changes in DNA integrity, and DNA microarrays and qPCR to evaluate changes in genetic expression. The results lead to two key observations. First, most DMF operating conditions tested, including those that are commonly used in the literature, result in negligible cell-stress or genome-level effects. Second, for DMF devices operated at high driving frequency (18 kHz) and with large driving electrodes (10 mm × 10 mm), there are significant damage to DNA integrity and differential genomic regulation. We hypothesize that these effects are caused by droplet heating. We recommend that for DMF applications involving mammalian cells that driving frequencies be kept low (≤ 10 kHz) and electrode sizes be kept small (≤ 5 mm) to avoid detrimental effects.
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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 | 0.000 | 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) | 0.001 | 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 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".