Optimization of preservation and storage time of sponge tissues to obtain quality mRNA for next‐generation sequencing
Bibliographic record
Abstract
Transcriptome sequencing with next-generation sequencing technologies has the potential for addressing many long-standing questions about the biology of sponges. Transcriptome sequence quality depends on good cDNA libraries, which requires high-quality mRNA. Standard protocols for preserving and isolating mRNA often require optimization for unusual tissue types. Our aim was assessing the efficiency of two preservation modes, (i) flash freezing with liquid nitrogen (LN₂) and (ii) immersion in RNAlater, for the recovery of high-quality mRNA from sponge tissues. We also tested whether the long-term storage of samples at -80 °C affects the quantity and quality of mRNA. We extracted mRNA from nine sponge species and analysed the quantity and quality (A260/230 and A260/280 ratios) of mRNA according to preservation method, storage time, and taxonomy. The quantity and quality of mRNA depended significantly on the preservation method used (LN₂) outperforming RNAlater), the sponge species, and the interaction between them. When the preservation was analysed in combination with either storage time or species, the quantity and A260/230 ratio were both significantly higher for LN₂-preserved samples. Interestingly, individual comparisons for each preservation method over time indicated that both methods performed equally efficiently during the first month, but RNAlater lost efficiency in storage times longer than 2 months compared with flash-frozen samples. In summary, we find that for long-term preservation of samples, flash freezing is the preferred method. If LN₂ is not available, RNAlater can be used, but mRNA extraction during the first month of storage is advised.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".