Antibody-Free Detection and Quantitation of TDP-43 by Mass Spectrometry (P5.179)
Bibliographic record
Abstract
OBJECTIVE: To develop a clinical-grade antibody-free mass spectrometric (MS) method to identify and quantitate TAR DNA-binding protein 43 (TDP-43) isoforms in human cerebrospinal fluid (CSF) in order to explore the relationship with disease pathogenesis. BACKGROUND: There is evidence that TDP-43, its hyper-phosphorylated, ubiquitinated and cleaved forms lead to protein mislocalization and aggregation, contributing to frontotemporal dementia and Alzheimer’s disease pathogenesis. It has been difficult to assess the potential of TDP-43 as an in vivo prognostic and diagnostic biomarker of neurodegeneration as characterization of TDP-43 in biofluids has been largely accomplished via qualitative and semi-quantitative immunometric assays. METHODS: We have designed a quantitative multiple reaction monitoring (MRM) assay for detection and quantitation of human TDP-43 isoforms in CSF. We have developed MRM transitions for measuring TDP-43 and its C-terminal fragment TDP-25, through targeted selection of tryptic peptides with optimized declustering potentials and collision energies. In order to achieve the desired assay sensitivity without the use of an immunoprecipitation step, we evaluated various protein digestion conditions (denaturants, digestion buffers and trypsin type) to optimize signal intensity. RESULTS: The MRM method can detect endogenous TDP-43 from a human CSF pool diluted to 0.002 g/L total protein (reference range < 0.5 g/L) at a signal-to-noise > 5. The assay response is linear up to 3 g/L of total CSF protein. TDP-25 can be detected in the high salt fraction of strong cation exchange chromatography. CONCLUSIONS: We have designed a digestion protocol and MRM assay that is capable of detection of TDP-43 and TDP-25 from CSF without an immuno-enrichment step. We are currently developing the quantitation strategy, including an external calibration curve and isotopically-labelled internal standards. Analytical and clinical performance of our assay will be assessed using specimens from the biobank of the University of British Columbia, Clinic for Alzheimer’s Disease and Related Disorders.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.002 |
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".