An ultrafast enzyme-free acoustic technique for detaching adhered cells in microchannels
Bibliographic record
Abstract
Adherent cultured cells are widely used biological tools for a variety of biochemical and biotechnology applications, including drug screening and gene expression analysis. One critical step in culturing adherent cells is the dissociation of cell monolayers into single-cell suspensions. Different enzymatic and non-enzymatic methods have been proposed for this purpose. Trypsinization, the most common enzymatic method for dislodging adhered cells, can be detrimental to cells, as it can damage cell membranes and ultimately cause cell death. Additionally, all available techniques require a prolonged treatment duration, typically on the order of minutes (5-10 min). Dissociation of cells becomes even more challenging in microfluidic devices, where, due to the nature of low Reynolds number flow and reduced mixing efficiency, multiple washing steps and prolonged trypsinization may be necessary to treat all cells. Here, we report a novel acoustofluidic method for the detachment of cells adhered onto a microchannel surface without exposing the cells to any enzymatic or non-enzymatic chemicals. This method enables a rapid (i.e., on the order of seconds), cost-effective, and easy-to-operate cell detachment strategy, yielding a detachment efficiency of similar to 99% and cellular viability similar to that of the conventional trypsinization method. Also, as opposed to biochemical-based techniques (e.g., enzymatic), in our approach, cells are exposed to the dissociating agent (i.e., substrate-mediated acoustic excitation and microstreaming flow) only for as long as they remain attached to the substrate. After dissociation, the effect of acoustic excitation is reduced to microstreaming flow, therefore, minimizing unwanted effects of the dissociating agent on the cell phenotype. Additionally, our results suggest that cell excitation at acoustic powers lower than that required for complete cell detachment can potentially be employed for probing the adhesion strength of cell-substrate attachment. This novel approach can, therefore, be used for a wide range of lab-on-a-chip applications.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".