Enabling Coupled Quantitative Genomics and Proteomics Analyses from Rat Spinal Cord Samples
Bibliographic record
Abstract
Translational research is progressing toward combined genomics and proteomics analyses of small and precious samples. In our analyses of spinal cord material, we systematically evaluated disruption and extraction techniques to determine an optimum process for the coupled analysis of RNA and protein from a single 5-mm segment of tissue. Analyses of these distinct molecular species were performed using microarrays and high resolution two-dimensional gels, respectively. Comparison of standard homogenization with automated frozen disruption (AFD) identified negligible differences in the relative abundance of genes (44) with all genes identified by either process. Analysis on either the Affymetrix or Applied Biosystems Inc. gene array platforms provided good correlations between the extraction techniques. In contrast, the AFD technique enabled identification of more unique proteins from spinal cord tissue than did standard homogenization. Furthermore use of an optimized CHAPS/urea extraction provided better protein recovery, as shown by quantitative two-dimensional gel analyses, than did solvent precipitation during TRIzol-based RNA extraction. Thus, AFD of tissue samples followed by protein and RNA isolation from separate aliquots of the frozen powdered sample is the most effective route to ensure full, quantitative analyses of both molecular entities.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".