Techniques for the Isolation of High-Quality RNA from Cells Encapsulated in Chitosan Hydrogels
Bibliographic record
Abstract
Extracting high-quality RNA from hydrogels containing polysaccharide components is challenging, as traditional RNA isolation techniques designed for cells and tissues can have limited yields and purity due to physiochemical interactions between the nucleic acids and the biomaterials. In this study, a comparative analysis of several different RNA isolation methods was performed on human adipose-derived stem cells photo-encapsulated within methacrylated glycol chitosan hydrogels. The results demonstrated that RNA isolation methods with cetyl trimethylammonium bromide (CTAB) buffer followed by purification with an RNeasy® mini kit resulted in low yields of RNA, except when the samples were preminced directly within the buffer. In addition, genomic DNA contamination during reverse transcriptase-polymerase chain reaction (RT-PCR) analysis was observed in the hydrogels processed with the CTAB-based methods. Isolation methods using TRIzol® in combination with one of a Qiaex® gel extraction kit, an RNeasy® mini kit, or an extended solvent purification method extracted RNA suitable for gene amplification, with no evidence of genomic contamination. The latter two methods yielded the best results in terms of yield and amplification efficiency. Predigestion of the scaffolds with lysozyme was investigated as a possible means of enhancing RNA extraction from the polysaccharide gels, with no improvements observed in terms of the purity, yield, or amplification efficiency. Overall, this work highlights the application of a TRIzol®+extended solvent purification method for optimizing RNA extraction that can be applied to obtain reliable and accurate gene expression data in studies investigating cells seeded in chitosan-based scaffolds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".