Dosage-controlled intracellular delivery mediated by acoustofluidics for lab on a chip applications
Bibliographic record
Abstract
Biological research and many cell-based therapies rely on the successful delivery of cargo materials into cells. Intracellular delivery in an in vitro setting refers to a variety of physical and biochemical techniques developed for conducting rapid and efficient transport of materials across the plasma membrane. Generally, the techniques that are time-efficient (e.g., electroporation) suffer from heterogeneity and low cellular viability, and those that are precise (e.g., microinjection) suffer from low-throughput and are labor-intensive. Here, we present a novel in vitro microfluidic strategy for intracellular delivery, which is based on the acoustic excitation of adherent cells. Strong mechanical oscillations, mediated by Lamb waves, inside a microfluidic channel facilitate the cellular uptake of different size (e.g., 3-500 kDa, plasmid encoding EGFP) cargo materials through endocytic pathways. We demonstrate successful delivery of 500 kDa dextran to various adherent cell lines with unprecedented efficiency in the range of 65-85% above control. We also show that actuation voltage and treatment duration can be tuned to control the dosage of delivered substances. High viability (≥91%), versatility across different cargo materials and various adherent cell lines, scalability to hundreds of thousands of cells per treatment, portability, and ease-of-operation are among the unique features of this acoustofluidic strategy. Potential applications include targeting through endocytosis-dependant pathways in cellular disorders, such as lysosomal storage diseases, which other physical methods are unable to address. This novel acoustofluidic method achieves rapid, uniform, and scalable delivery of material into cells, and may find utility in 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".