A reliable Western blot workflow with improved dynamic range for the detection of myelin proteins in murine brain
Bibliographic record
Abstract
Myelin is a highly structured multilamellar sheath produced by oligodendrocytes, which insulates neuronal axons to facilitate neurotransmission. Maturation of oligodendrocytes in cortical regions of the developing murine brain occurs postnatally and corresponds to the marked upregulation of myelin-specific genes. Western blotting is a conventional technique used to study protein expression but historically has only been considered semiquantitative. This study aims to optimize a Western blot workflow for the quantification of myelin proteins in murine brain, including the examination of the following parameters: sample preparation, electrophoretic transfer conditions, detection parameters, data normalization, and linear dynamic range. As a proof of principle, the optimized protocol was employed to characterize both the absolute and relative expression of myelin oligodendrocyte glycoprotein (MOG) throughout neurodevelopment. A dynamic loading paradigm, which varied total protein load across different age groups to ensure antigen detection remained in the linear dynamic range of the assay, showed a greater relative increase in expression when compared to standard loading paradigm. This approach resulted in comparable MOG expression profiles from both absolute and relative quantification. The optimized Western blot workflow will facilitate protein quantification and will improve data reproducibility when investigating the molecular mechanisms of myelination in development, aging, and disease.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".