MétaCan
Menu
Back to cohort
Record W4290744427 · doi:10.3389/fnmol.2022.976910

Biophysical characterization of light-gated ion channels using planar automated patch clamp

2022· article· en· W4290744427 on OpenAlexafffund
Elena G. Govorunova, Oleg A. Sineshchekov, Leonid S. Brown, John L. Spudich

Bibliographic record

VenueFrontiers in Molecular Neuroscience · 2022
Typearticle
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsUniversity of Guelph
FundersNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthWelch Foundation
KeywordsOptogeneticsChannelrhodopsinIon channelPatch clampElectrophysiologyDepolarizationBiologyVoltage clampMembrane potentialHyperpolarization (physics)BiophysicsChemistryNeuroscienceGenetics

Abstract

fetched live from OpenAlex

Channelrhodopsins (ChRs) are proteins that guide phototaxis in protists and exhibit light-gated channel conductance when their genes are heterologously expressed in mammalian cells. ChRs are widely used as molecular tools to control neurons and cardiomyocytes with light (optogenetics). Cation- and anion-selective ChRs (CCRs and ACRs, respectively) enable stimulation and inhibition of neuronal activity by depolarization and hyperpolarization of the membrane, respectively. More than 400 natural ChR variants have been identified so far, and high-throughput polynucleotide sequencing projects add many more each year. However, electrophysiological characterization of new ChRs lags behind because it is mostly done by time-consuming manual patch clamp (MPC). Here we report using a high-throughput automated patch clamp (APC) platform, SyncroPatch 384i from Nanion Technologies, for ChR research. We find that this instrument can be used for determination of the light intensity dependence and current-voltage relationships in ChRs and discuss its advantages and limitations.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.025
GPT teacher head0.281
Teacher spread0.255 · 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

Citations18
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in Molecular NeuroscienceSame topicPhotoreceptor and optogenetics researchFrench-language works237,207