Quantitative proteomic profiling reveals novel region‐specific markers in the adult mouse brain
Bibliographic record
Abstract
Despite major advances in neuroscience, a comprehensive understanding of the structural and functional components of the adult brain compartments remains to be fully elucidated at a quantitative molecular level. Indeed, over half of the soluble- and membrane-annotated proteins are currently unmapped within online digital brain atlases. In this study, two complementary approaches were used to assess the unique repertoire of proteins enriched within select regions of the adult mouse CNS, including the brain stem, cerebellum, and remaining brain hemispheres. Of the 1200 proteins visualized by 2D-DIGE, approximately 150 (including cytosolic and membrane proteins) were found to exhibit statistically significant changes in relative abundance thus representing putative region-specific brain markers. In addition to using a high-precision (18) O-labeling strategy for the quantitative LC-MS/MS mapping of membrane proteins isolated from myelin-enriched fractions, we have identified over 1000 proteins that have yet to be described in any other mammalian myelin proteome. A comparison of our myelin proteome was made to an existing transcriptome database containing mRNA abundance profiles during oligodendrocyte differentiation and has confirmed statistically significant abundance changes for ∼500 of these newly mapped proteins, thus revealing new roles in oligodendrocyte and myelin biology. These data offer a resource for the neuroscience community studying the molecular basis for specialized neuronal activities in the CNS and myelin-related disorders. The MS proteomics data associated with this manuscript have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD000327 (http://proteomecentral.proteomexchange.org/dataset/PXD000327).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".