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SynPull: An advanced method for studying neurodegeneration-related aggregates in synaptosomes using super-resolution microscopy

2025· article· en· W4406798623 on OpenAlexfundno aff
Shekhar Kedia, Emre Fertan, Yunzhao Wu, Yu P. Zhang, Georg Meisl, Jeff Y. L. Lam, Frances K. Wiseman, William A. McEwan, Annelies Quaegebeur, Maria Grazia Spillantini, John S. H. Danial, David Klenerman

Bibliographic record

VenueCell chemical biology · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsnot available
FundersNIHR Cambridge Biomedical Research CentreDinosaur Research InstituteUK Dementia Research InstituteRoyal SocietyMedical Research CouncilParkinson's UK
KeywordsNeurodegenerationMicroscopyResolution (logic)Materials scienceNanotechnologyNeuroscienceComputer scienceBiologyArtificial intelligencePhysicsOpticsMedicinePathologyDisease

Abstract

fetched live from OpenAlex

Synaptic dysfunction is a primary hallmark of both Alzheimer's and Parkinson's disease, leading to cognitive and behavioral decline. While alpha-synuclein, beta-amyloid, and tau are involved in the physiological functioning of synapses, their pathological aggregation has been linked to synaptopathology. The methodology for studying the small-soluble protein aggregates formed by these proteins is limited. Here we describe SynPull, a method combining single-molecule pull-down, super-resolution microscopy, and advanced computational analyses to characterize the protein aggregates in human and mouse synaptosomes. We show that AT8-positive tau aggregates are the predominant aggregate type in synaptosomes from postmortem Alzheimer's disease brain, although the aggregate size does not change in disease. Meanwhile, the relatively smaller amount of alpha-synuclein and beta-amyloid aggregates found in the synapses are larger than the extra-synaptic ones. Collectively, these results show the utility of SynPull to study pathological aggregates in neurodegeneration, elucidating the disease mechanisms causing synaptic dysfunction.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.822

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.406
Teacher spread0.348 · 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 teacher head, 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

Citations5
Published2025
Admission routes1
Has abstractyes

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