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Record W1997323649 · doi:10.1039/b911967a

Rapid multicomponent optical protein patterning

2009· article· en· W1997323649 on OpenAlexafffund
Jonathan M. Bélisle, Darío Kunik, Santiago Costantino

Bibliographic record

VenueLab on a Chip · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsHôpital Maisonneuve-RosemontUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhotobleachingChemotaxisBiophysicsNanotechnologyCytoskeletonExtracellularFluorescence recovery after photobleachingDictyosteliumSubstrate (aquarium)ChemistryProtein adsorptionCell biologyMaterials scienceCellAdsorptionBiologyFluorescenceBiochemistryOpticsPhysicsMembrane

Abstract

fetched live from OpenAlex

Cells sense spatial distributions of molecules which trigger signal transduction pathways that induce the cell to migrate or extend by remodelling the cytoskeleton. However, the influence of local and small variations of extracellular protein concentration on chemotaxis is not fully understood, due in part to the lack of simple and precise methods to pattern proteins in vitro. We recently developed a new technology to fabricate such patterns which relies on photobleaching fluorophores to adsorb proteins on a cell culture substrate: laser-assisted protein adsorption by photobleaching (LAPAP). Here we report several key improvements to LAPAP: we created arbitrary patterns made of several different proteins simultaneously, we reduced the fabrication time more than one order of magnitude and we used secondary antibodies to significantly enlarge the spectrum of proteins that can be employed. As a result, multicomponent protein gradients can be produced using reagents that are typically available in life science research laboratories on a standard inverted microscope equipped with a camera port.

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 score0.076
Threshold uncertainty score0.346

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 science0.0000.000
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 head0.013
GPT teacher head0.243
Teacher spread0.230 · 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

Citations48
Published2009
Admission routes2
Has abstractyes

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