Successful Delivery of Small Non-Coding RNA Molecules into Human iPSC-Derived Lung Spheroids in 3D Culture Environment
Bibliographic record
Abstract
Background/Objectives: Spheroid cultures in Matrigel are routinely used to study cell behaviour in complex 3D settings, thereby generating preclinical models of disease. Ideally, researchers would like to modulate gene expression ‘in situ’ for testing novel gene therapies while conserving the spheroid architecture. Here, we aim to provide an efficient method to transfect small RNAs (such as microRNAs and small interfering RNAs, i.e., siRNAs) into human induced pluripotent stem cell (iPSC)-derived 3D lung spheroids, specifically alveolar type II epithelial cells (iAT2) and basal cell (iBC) spheroids. Methods: Transfection of iAT2 spheroids within 3D Matrigel ‘in situ’, whole spheroids released from Matrigel or spheroids dissociated to single cells was explored via flow cytometry using a fluorescently labelled siRNA. Validation of the transfection method was performed in iAT2 and iBC spheroids using siRNA and miRNA mimics and measurement of specific target expression post-transfection. Results: Maximal delivery of siRNA was achieved in serum-free conditions in whole spheroids released from the Matrigel, followed by whole spheroids ‘in situ’. ‘In situ’ transfection of SFTPC-siRNA led to a 50% reduction in the SFTPC mRNA levels in iAT2 spheroids. Transfection of miR-29c mimic and miR-21 pre-miR into iAT2 and iBC spheroids, respectively, led to significant miRNA overexpression, together with a significant decrease in protein levels of the miR-29 target FOXO3a. Conclusions: This study demonstrates successful transfection of iPSC-derived lung spheroids without disruption of their 3D structure using a simple and feasible approach. Further development of these methods will facilitate functional studies in iPSC-derived spheroids utilizing small RNAs.
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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 | 0.001 |
| 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 | 0.001 | 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".