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A Bright and Fast Red Fluorescent Protein Voltage Indicator That Reports Neuronal Activity in Organotypic Brain Slices

2016· article· en· W2283116424 on OpenAlexafffund
Ahmed S. Abdelfattah, Samouil L. Farhi, Yongxin Zhao, Daan Brinks, Peng Zou, Araya Ruangkittisakul, Jelena Platiša, Vincent A. Pieribone, Klaus Ballanyi, Adam E. Cohen, Robert E. Campbell

Bibliographic record

VenueJournal of Neuroscience · 2016
Typearticle
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsUniversity of Alberta
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institute of Neurological Disorders and StrokeNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchAlberta InnovatesMinistry of Advanced Education, Government of AlbertaHoward Hughes Medical InstituteUniversity of AlbertaNational Institutes of HealthNational Science Foundation
KeywordsFluorescenceVoltage-sensitive dyeOptogeneticsTemporal resolutionChannelrhodopsinBiophysicsGreen fluorescent proteinFluorescent proteinElectrophysiologyHippocampal formationFluorescence-lifetime imaging microscopyNeuroscienceChemistryBiologyOpticsPhysicsBiochemistry

Abstract

fetched live from OpenAlex

Optical imaging of voltage indicators based on green fluorescent proteins (FPs) or archaerhodopsin has emerged as a powerful approach for detecting the activity of many individual neurons with high spatial and temporal resolution. Relative to green FP-based voltage indicators, a bright red-shifted FP-based voltage indicator has the intrinsic advantages of lower phototoxicity, lower autofluorescent background, and compatibility with blue-light-excitable channelrhodopsins. Here, we report a bright red fluorescent voltage indicator (fluorescent indicator for voltage imaging red; FlicR1) with properties that are comparable to the best available green indicators. To develop FlicR1, we used directed protein evolution and rational engineering to screen libraries of thousands of variants. FlicR1 faithfully reports single action potentials (∼3% ΔF/F) and tracks electrically driven voltage oscillations at 100 Hz in dissociated Sprague Dawley rat hippocampal neurons in single trial recordings. Furthermore, FlicR1 can be easily imaged with wide-field fluorescence microscopy. We demonstrate that FlicR1 can be used in conjunction with a blue-shifted channelrhodopsin for all-optical electrophysiology, although blue light photoactivation of the FlicR1 chromophore presents a challenge for applications that require spatially overlapping yellow and blue excitation. SIGNIFICANCE STATEMENTFluorescent-protein-based voltage indicators enable imaging of the electrical activity of many genetically targeted neurons with high spatial and temporal resolution. Here, we describe the engineering of a bright red fluorescent protein-based voltage indicator designated as FlicR1 (fluorescent indicator for voltage imaging red). FlicR1 has sufficient speed and sensitivity to report single action potentials and voltage fluctuations at frequencies up to 100 Hz in single-trial recordings with wide-field microscopy. Because it is excitable with yellow light, FlicR1 can be used in conjunction with blue-light-activated optogenetic actuators. However, spatially distinct patterns of optogenetic activation and voltage imaging are required to avoid fluorescence artifacts due to photoactivation of the FlicR1 chromophore.

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)0.000
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.037
GPT teacher head0.296
Teacher spread0.258 · 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

Citations151
Published2016
Admission routes2
Has abstractyes

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