A convenient analytic method for gel quantification using ImageJ paired with Python or R
Bibliographic record
Abstract
In recent years, due to the COVID-19 pandemic, there was a surge of research on mRNA therapeutics. The applications are broad and include vaccination, cancer therapy, protein replacement, and immune modulation. mRNA therapeutics have advantages over other nucleic acid therapies because of the reduced risk of mutagenesis. On the other hand, mRNA therapeutics have a large caveat due to its inherent instability, which makes it susceptible to degradation throughout all stages of production, storage, and in vivo application. Decades ago, agarose gel electrophoresis was developed to separate and resolve nucleic acids based on size. Since then, the evolution of image analysis tools, such as ImageJ, has facilitated semi-quantitative evaluation of concentration based on band intensity, and qualitative observation of RNA integrity from gel electrophoresis. Instruments utilizing capillary electrophoresis, like the Agilent 2100 Bioanalyzer, that use microchip linear acrylamide gel electrophoresis have been demonstrated to be superior to agarose gel electrophoresis in studying RNA quality. Due to the higher cost of usage, they are less accessible to the average lab than agarose electrophoresis. In this work, we review the fundamentals of mRNA assessment and propose a full-lane quantification (FLQ) method, which is a fast, simple, and inexpensive method to analyze RNA degradation from agarose gels using ImageJ paired with Python and R. This measures the area under the curve of the product peak, degradation zone, and a combined score to provide sensitive means to evaluate the degradation of mRNA. This method provides measures of the degradation profile within each lane comparable to an RNA integrity number from bioanalyzers. Using this cost-effective method, we demonstrate that the degradation index is a sensitive measure that reflects the degradation and preservation of mRNA patterns.
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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".