Mass Spectrometry-Based Assay for Targeting Fifty-Two Proteins of Brain Origin in Cerebrospinal Fluid
Bibliographic record
Abstract
Cerebrospinal fluid (CSF) is a circulatory fluid of the central nervous system and it can reflect the biochemical changes occurring in the brain. Although CSF retrieval through lumbar puncture is invasive, it remains the most commonly used fluid in exploring brain pathology as it is less complex and contains a higher concentration of brain-derived proteins than plasma (Reiber, H. Clin. Chim. Acta 2001, 310, 173−186; Macron et al. J. Proteome Res. 2018, 17, 4315−4319). We hypothesize that proteins produced by the brain will have diagnostic significance for brain pathologies. Hence, we expanded the previously in-house-developed 31-protein panel with more proteins classified as brain-specific by the Human Protein Atlas (HPA). Using the HPA, we selected 76 protein coding genes and screened CSF using liquid chromatography–mass spectrometry (LC–MS) and narrowed the protein list to candidates identified endogenously in CSF. Next, we developed a parallel reaction monitoring (PRM) assay for the 21 new proteins and merged it with the 31-protein assay developed earlier. In the process, we evaluated different screening strategies and optimized MS collision energies and ion isolation windows to achieve the highest possible analyte signal resulting in the PRM assay with an average linear dynamic range of 4.3 × 103. We also assessed the extent of Asn (N)–Gln (Q) deamidation, N-terminal pyro-Glu (E) conversion, and Met (M) oxidation and found that deamidation can be misassigned without high mass accuracy and high-resolution settings. We also assessed how many of these proteins could be reliably measured in 10 individual patient CSF samples. Our approach allows us to measure the relative levels of 52 brain-derived proteins in CSF by a single LC–MS method. This new assay may have important applications in discovering CSF biomarkers for various neurological diseases.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".