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Record W4409723366 · doi:10.1038/s41594-026-01821-8

A single vesicle fluorescence microscopy platform to quantify phospholipid scrambling

2025· preprint· en· W4409723366 on OpenAlexafffund
Sarina Veit, Grace I. Dearden, K. Menon, Faria Noor, Oliver P. Ernst, Anant K. Menon, Thomas Günther Pomorski

Bibliographic record

VenueNature Structural & Molecular Biology · 2025
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchNational Institutes of HealthDeutsche Forschungsgemeinschaft
KeywordsVesicleScramblingFluorescence microscopePhospholipidFluorescenceMicroscopyChemistryBiophysicsNanotechnologyComputer scienceMaterials sciencePhysicsOpticsBiologyBiochemistryMembraneAlgorithm

Abstract

fetched live from OpenAlex

Scramblases are physiologically important proteins that translocate phospholipids bidirectionally across cell membranes. For example, scrambling facilitated by dimers of the voltage-dependent anion channel 1 (VDAC1) enables endoplasmic reticulum-derived phospholipids to cross the outer membrane to enter mitochondria. Here we describe a protocol to obtain lipid translocation rates at a single-protein level, allowing for mechanistic understanding of scramblases. We reconstituted vesicles with fluorescent phospholipids and VDAC1 dimers and use high-throughput imaging to quantify their size and dimer content. We measure scrambling in each vesicle using a new assay and find that individual human VDAC1 dimers scramble lipids at rates ranging from under 100 to over 10,000 per second. This kinetic heterogeneity, masked in ensemble measurements, revealed that rapid scrambling is facilitated by specific VDAC1 dimers. Extending our analyses to bovine opsin, a monomeric G-protein-coupled receptor scramblase, we demonstrate the versatility of our platform for quantifying lipid scrambling and exploring its regulation.

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 categoriesMeta-epidemiology (narrow), Research integrity
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 score0.017
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0030.002
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 head0.012
GPT teacher head0.339
Teacher spread0.327 · 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.

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
Published2025
Admission routes2
Has abstractyes

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