A facile methodology using quantum dot multiplex labels for tracking co-transfection
Bibliographic record
Abstract
Advances in the field of genome engineering demand the development of efficient non-viral transfection agents capable of delivering multiple distinct nucleic acids efficiently to cells (co-transfection). However, current delivery methods result in lower co-transfection efficiency than single plasmid transfections, and the efficiency decreases further with increasing numbers of plasmids. The development of a high-throughput methodology is required for the validation of co-transfection platforms to facilitate independent tracking of not only the multiple DNA plasmids during transfection but also the localisation of transfection agents. This is pivotal to determine the bottlenecks in achieving high transfection efficiencies at various stages of the cell internalisation and plasmid expression process. Herein we demonstrate that this can be achieved using a facile methodology in which quantum dots (QDs) are used to label two different plasmid DNA assemblies that are delivered to cells simultaneously using a dendronised polymer system. Multispectral confocal imaging can be used to separate signals from each polyplex as well as the expressed fluorescent reporter proteins to determine whether co-transfection difficulties result from poor internalisation or the inability of DNA to escape from polyplexes. The results demonstrate the utility of this facile approach to label polyplexes without interfering with gene expression, and enable high-throughput screening of transfection reagents for achieving optimal co-transfection.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".