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Record W2980777844 · doi:10.1016/j.jalz.2019.06.661

P1‐106: A MULTIPLEX MASS SPECTROMETRY METHOD FOR CHARACTERIZATION AND QUANTIFICATION OF α‐ AND β‐SYNUCLEINS IN BRAIN TISSUE: APPLICATION TO TRANSGENIC MOUSE MODELS OF PARKINSON'S DISEASE

2019· article· en· W2980777844 on OpenAlexaff
Serena Singh, Anouar Khayachi, Austen J. Milnerwood, Mari L. DeMarco

Bibliographic record

VenueAlzheimer s & Dementia · 2019
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsSt. Paul's HospitalProvidence Health CareMcGill UniversityUniversity of British Columbia
Fundersnot available
KeywordsMultiplexSynucleinChemistryGenetically modified mouseParkinson's diseaseSynucleinopathiesPathologyBiologyTransgeneBiochemistryAlpha-synucleinMedicineDiseaseBioinformaticsGene

Abstract

fetched live from OpenAlex

α-Synuclein deposits are the primary neuropathological finding in dementia with Lewy bodies and are found as a co-pathology in approximately half of Alzheimer's disease cases. Animal models used to study synuclein pathology include those overexpressing α-synuclein and with mutations linked to familial Parkinson's disease (e.g. mutations in SNCA, LRRK2, etc.). Among these are models that display cognitive deficits, and also cortical α-synuclein aggregates as seen in dementia with Lewy bodies. Analysis of α-synuclein in brain tissue from both humans and mouse models by traditional antibody-based methods (e.g. immunoassay, western blot) has helped in the characterization of the models; however, antibody-based methods have their limitations including indirect analyte detection, lack of selectivity and challenges with multiplexing targets. As such, it would be advantageous to develop a new tool using mass spectrometry, which offers improved selectivity and multiplexing ability, for direct detection of synucleins and their modified forms in disease. A multiple reaction monitoring high-resolution liquid chromatography tandem mass spectrometry method was developed to quantify both soluble α- and β-synucleins in brain tissue homogenate. The method monitored six tryptic peptides from the α-synuclein sequence, which included three regions unique to α-synuclein and three regions shared between α- and β-synuclein. Synucleins were enriched from brain homogenate by heat treatment and quantification was achieved by addition of uniformly N-labeled human α-synuclein as an internal standard. The method was then applied to the characterization of brain tissue from wild-type and transgenic mouse models of synucleinopathies. With the multiplex mass spectrometry method, total α-synuclein and combined α- and β-synuclein were quantified in a single measurement. The tool also enabled differentiation of species-specific contributions to α-synclein burden in a transgenic model expressing both mouse and human synuclein. Finally, by monitoring multiple tryptic peptides from synuclein, peptide-specific profile differences were observed that may reflect post-translational modifications along the protein sequence. Mass spectrometry is a selective and cost-effective alternative to antibody-based methods for quantitation and characterization of synucleins. Ongoing applications of this tool include detailed characterization of α-synuclein pathology in experimental mouse models and human tissues.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.027
GPT teacher head0.308
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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