Highly efficient adenoviral transduction of pancreatic islets using a microfluidic device
Bibliographic record
Abstract
Tissues are challenging to genetically manipulate due to limited penetration of viral particles resulting in low transduction efficiency. We are particularly interested in expressing genetically-encoded sensors in ex vivo pancreatic islets to measure glucose-stimulated metabolism, however poor viral penetration biases these measurements to only a subset of cells at the periphery. To increase mass transfer of viral particles, we designed a microfluidic device that holds islets in parallel hydrodynamic traps connected by an expanding by-pass channel. We modeled viral particle flow into the tissue using fluorescently-labelled gold nanoparticles of varying sizes and showed a penetration threshold of only ∼5 nm. To increase this threshold, we used EDTA to transiently reduce cell-cell adhesion and expand intercellular space. Ultimately, a combination of media flow and ETDA treatment significantly increased adenoviral transduction to the core of the islet. As proof-of-principle, we used this protocol to transduce an ER-targeted redox sensitive sensor (eroGFP), and revealed significantly greater ER redox capacity at core islet cells. Overall, these data demonstrate a robust method to enhance transduction efficiency of islets, and potentially other tissues, by using a combination of microfluidic flow and transient tissue expansion.
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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".