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Record W4417503772 · doi:10.1093/braincomms/fcaf468

Stable isotope labelling kinetics of neurofilament light

2025· article· en· W4417503772 on OpenAlexafffund
Claire A. Leckey, Tatiana A. Giovannucci, John B. Coulton, Yingxin He, Chihiro Sato, Nupur Ghoshal, Tharini Vignarajah, Zane Jaunmuktane, Nicolas R. Barthélemy, Henrik Zetterberg, Donald L. Elbert, Kevin Mills, Selina Wray, Randall J. Bateman, Ross W. Paterson

Bibliographic record

VenueBrain Communications · 2025
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsCentre for Movement Disorders
FundersHorizon 2020Olav Thon StiftelsenUK Dementia Research InstituteCure Alzheimer's FundVetenskapsrådetMedical Research Council CanadaHjärnfondenUniversity College LondonHORIZON EUROPE Framework ProgrammeEuropean Partnership on MetrologyNational Institute for Health and Care ResearchEU Joint Programme – Neurodegenerative Disease ResearchNational Institute on AgingAlzheimer's AssociationHORIZON EUROPE European Research CouncilFamiljen Erling-Perssons StiftelseStiftelsen för Gamla TjänarinnorAlzheimer's Drug Discovery Foundation
KeywordsLabellingKineticsTauopathyHuman brainExtracellularNeurofilament

Abstract

fetched live from OpenAlex

Abstract This study provides the first quantification of neurofilament light chain (NfL) kinetics in the human CNS using stable isotope labelling kinetics. NfL is elevated in CSF and blood across a range of traumatic, inflammatory and neurodegenerative diseases of the CNS, and has been increasingly included in clinical trials as a secondary or exploratory outcome measure of target engagement. Interpreting trajectories of NfL post-treatment has been challenging, prompting a greater need and focus on understanding its pathophysiology. We set out to measure NfL kinetics in the human CNS using stable isotope labelling kinetics. In human neurons derived from induced pluripotent stem cells, we show that NfL turnover is relatively slow, comparable to other long-lived proteins such as tau. We detected a delay of 3 to 6 days in the release of NfL into the media, unexpected for a passively released protein and supporting that controlled mechanisms of release could contribute to the appearance of NfL in the extracellular milieu. We optimized the kinetic NfL assay to measure the turnover of NfL in the human CNS. Participants with diagnosed primary tauopathies (n = 10) were recruited to the Human CNS Tau Kinetics in Tauopathies study and a cohort of cognitively unimpaired or with mild cognitive impairment (Clinical Dementia Rating score ≤0.5; n = 22) to the Tau Stable Isotope Labelling Kinetics study. Patients with suspected normal pressure hydrocephalus (n = 3) and primary tauopathy cases (n = 3) were examined to assess labelling in the brain parenchyma and ventricular CSF. In brain tissue, isotopically labelled in vivo and sampled ex-vivo and post-mortem, NfL is rapidly labelled but remains stable 18 months after, indicating very slow turnover and likely incorporation into very stable NfL networks. In line with a controlled mechanism of release in vivo, appearance of labelled NfL in CSF was detectable between 53 and 162 days post-labelling, during which NfL labelling did not reach its peak, suggestive of a half-life in CSF >3 months. These findings support the interpretation that acute rises in CSF NfL concentration are likely to be related to passive release or CSF clearance failure. We also show that active but delayed release of newly translated NfL can contribute to the concentration of NfL in CSF, but this would not be expected for at least 8 weeks. Clinical trials using NfL as an outcome measure will benefit from substantially longer follow-up periods and isotopic labelling to understand the NfL response to therapeutic intervention.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.845
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.054
GPT teacher head0.358
Teacher spread0.304 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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