Optimized gene transduction in human lung organoids: A high-efficiency method for advanced research applications
Bibliographic record
Abstract
Human induced pluripotent stem cell (iPSC)-derived lung organoids, engineered to carry targeted genes, offer a robust platform for investigating mechanistic insights in lung research. Although lentiviral vectors (LVVs) are highly effective for stable expression due to their integrative properties, achieving efficient transduction in human iPSC-derived lung organoids poses a significant technical challenge, likely due to the complex structure of these organoids. In this study, we optimized a method to enhance LVV transduction efficiency by physically disrupting the organoids to increase surface area, followed by spinoculation to apply shear force during cell dissociation. This approach, combined with the use of an optimized culture medium, significantly improved transduction efficiency. The success of this method was validated at both the gene and protein levels using single-cell RNA sequencing (scRNA-seq) and various cellular and molecular assays. Our optimized transduction protocol may provide a valuable tool for investigating specific cellular and molecular mechanisms in development and disease models using human iPSCs-derived lung organoids.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".