Techniques for the Isolation of High-Quality RNA from Cells Encapsulated in Chitosan Hydrogels
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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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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 it