Source 1primary paper2022Science Advances
Toolkit/live-cell imaging
live-cell imaging
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
Live-cell imaging is an assay method used in neurons in culture and brain slices to observe dynamic cellular processes in real time. The cited studies applied it to visualize minute-scale membrane PI(3,4,5)P3 fluctuations and microtubule retrograde flow during neuronal polarization-related dynamics.
Usefulness & Problems
Why this is useful
This method is useful for capturing transient, spatially localized biological events that are not accessible from endpoint assays. In the cited literature, it enabled observation of membrane PI(3,4,5)P3 fluctuations and neurite microtubule array movement in developing vertebrate neurons.
Source:
Our findings highlight key sources of imprecision within light-inducible dimer systems and provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
Source:
the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane
Source:
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
Problem solved
Live-cell imaging helps solve the problem of measuring dynamic cellular behavior directly in living neuronal preparations. The supplied evidence specifically supports its use for tracking minute-scale PI(3,4,5)P3 membrane changes and retrograde microtubule flow toward the soma.
Source:
Our findings highlight key sources of imprecision within light-inducible dimer systems and provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
Problem links
Live-cell imaging is directly relevant to observing biology in native or near-native states, and the summary includes use in brain slices, which is closer to intact tissue context than standard cell culture. It is a plausible baseline method for testing in situ imaging strategies.
We Can’t Take High-Resolution Movies of or Intervene in Brain Computation at the Single Neuron Level
Gap mapView gapThis is one of the few items with explicit neuron-related evidence, using live-cell imaging of neurons in culture or brain slices. It could support early testing of neuronal imaging workflows, but the supplied evidence does not establish in vivo large-network single-neuron movies.
This is a direct imaging method, so it is at least mechanistically adjacent to the problem of inadequate structural visualization. Its stronger practicality and replication metadata make it a more testable starting point than many other candidates, but the described use is clearly biological rather than materials-focused.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete measurement method used to characterize an engineered system.
Mechanisms
real-time optical observation of dynamic cellular processesreal-time optical observation of dynamic cellular processesTechniques
Functional AssayTarget processes
recombinationImplementation Constraints
cofactor dependency: cofactor requirement unknownencoding mode: genetically encodedimplementation constraint: context specific validationoperating role: sensorswitch architecture: recruitment
The evidence indicates use in neurons in culture or in brain slices, and one study combined live-cell imaging with mathematical modeling. No specific microscope platform, fluorophores, genetic constructs, or sample preparation details are provided in the supplied evidence.
The supplied evidence does not define imaging modality, spatial resolution, reporters, or quantitative performance metrics. Validation is limited to the cited neuronal and membrane-signaling contexts, and no evidence here supports the listed target process of recombination.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Source 2primary paper2021ACS Synthetic Biology
Source 3primary paper2022
Ranked Claims
PI(3,4,5)P3 membrane fluctuations are associated with local PI(3,4,5)P3 increases at sites of recycling vesicle exocytic fusion.
PI(3,4,5)P3 levels fluctuate at the membrane on a minutes time scale and that these fluctuations are associated with local PI(3,4,5)P3 increases at sites where recycling vesicles undergo exocytic fusion
timescale minutes
PI(3,4,5)P3 membrane fluctuations are associated with local PI(3,4,5)P3 increases at sites of recycling vesicle exocytic fusion.
PI(3,4,5)P3 levels fluctuate at the membrane on a minutes time scale and that these fluctuations are associated with local PI(3,4,5)P3 increases at sites where recycling vesicles undergo exocytic fusion
timescale minutes
PI(3,4,5)P3 membrane fluctuations are associated with local PI(3,4,5)P3 increases at sites of recycling vesicle exocytic fusion.
PI(3,4,5)P3 levels fluctuate at the membrane on a minutes time scale and that these fluctuations are associated with local PI(3,4,5)P3 increases at sites where recycling vesicles undergo exocytic fusion
timescale minutes
PI(3,4,5)P3 membrane fluctuations are associated with local PI(3,4,5)P3 increases at sites of recycling vesicle exocytic fusion.
PI(3,4,5)P3 levels fluctuate at the membrane on a minutes time scale and that these fluctuations are associated with local PI(3,4,5)P3 increases at sites where recycling vesicles undergo exocytic fusion
timescale minutes
PI(3,4,5)P3 membrane fluctuations are associated with local PI(3,4,5)P3 increases at sites of recycling vesicle exocytic fusion.
PI(3,4,5)P3 levels fluctuate at the membrane on a minutes time scale and that these fluctuations are associated with local PI(3,4,5)P3 increases at sites where recycling vesicles undergo exocytic fusion
timescale minutes
PI(3,4,5)P3 membrane fluctuations are associated with local PI(3,4,5)P3 increases at sites of recycling vesicle exocytic fusion.
PI(3,4,5)P3 levels fluctuate at the membrane on a minutes time scale and that these fluctuations are associated with local PI(3,4,5)P3 increases at sites where recycling vesicles undergo exocytic fusion
timescale minutes
PI(3,4,5)P3 membrane fluctuations are associated with local PI(3,4,5)P3 increases at sites of recycling vesicle exocytic fusion.
PI(3,4,5)P3 levels fluctuate at the membrane on a minutes time scale and that these fluctuations are associated with local PI(3,4,5)P3 increases at sites where recycling vesicles undergo exocytic fusion
timescale minutes
PI(3,4,5)P3 membrane fluctuations are associated with local PI(3,4,5)P3 increases at sites of recycling vesicle exocytic fusion.
PI(3,4,5)P3 levels fluctuate at the membrane on a minutes time scale and that these fluctuations are associated with local PI(3,4,5)P3 increases at sites where recycling vesicles undergo exocytic fusion
timescale minutes
PI(3,4,5)P3 membrane fluctuations are associated with local PI(3,4,5)P3 increases at sites of recycling vesicle exocytic fusion.
PI(3,4,5)P3 levels fluctuate at the membrane on a minutes time scale and that these fluctuations are associated with local PI(3,4,5)P3 increases at sites where recycling vesicles undergo exocytic fusion
timescale minutes
PI(3,4,5)P3 membrane fluctuations are associated with local PI(3,4,5)P3 increases at sites of recycling vesicle exocytic fusion.
PI(3,4,5)P3 levels fluctuate at the membrane on a minutes time scale and that these fluctuations are associated with local PI(3,4,5)P3 increases at sites where recycling vesicles undergo exocytic fusion
timescale minutes
PI(3,4,5)P3 membrane fluctuations are associated with local PI(3,4,5)P3 increases at sites of recycling vesicle exocytic fusion.
PI(3,4,5)P3 levels fluctuate at the membrane on a minutes time scale and that these fluctuations are associated with local PI(3,4,5)P3 increases at sites where recycling vesicles undergo exocytic fusion
timescale minutes
PI(3,4,5)P3 membrane fluctuations are associated with local PI(3,4,5)P3 increases at sites of recycling vesicle exocytic fusion.
PI(3,4,5)P3 levels fluctuate at the membrane on a minutes time scale and that these fluctuations are associated with local PI(3,4,5)P3 increases at sites where recycling vesicles undergo exocytic fusion
timescale minutes
PI(3,4,5)P3 membrane fluctuations are associated with local PI(3,4,5)P3 increases at sites of recycling vesicle exocytic fusion.
PI(3,4,5)P3 levels fluctuate at the membrane on a minutes time scale and that these fluctuations are associated with local PI(3,4,5)P3 increases at sites where recycling vesicles undergo exocytic fusion
timescale minutes
PI(3,4,5)P3 membrane fluctuations are associated with local PI(3,4,5)P3 increases at sites of recycling vesicle exocytic fusion.
PI(3,4,5)P3 levels fluctuate at the membrane on a minutes time scale and that these fluctuations are associated with local PI(3,4,5)P3 increases at sites where recycling vesicles undergo exocytic fusion
timescale minutes
PI(3,4,5)P3 membrane fluctuations are associated with local PI(3,4,5)P3 increases at sites of recycling vesicle exocytic fusion.
PI(3,4,5)P3 levels fluctuate at the membrane on a minutes time scale and that these fluctuations are associated with local PI(3,4,5)P3 increases at sites where recycling vesicles undergo exocytic fusion
timescale minutes
PI(3,4,5)P3 membrane fluctuations are associated with local PI(3,4,5)P3 increases at sites of recycling vesicle exocytic fusion.
PI(3,4,5)P3 levels fluctuate at the membrane on a minutes time scale and that these fluctuations are associated with local PI(3,4,5)P3 increases at sites where recycling vesicles undergo exocytic fusion
timescale minutes
PI(3,4,5)P3 membrane fluctuations are associated with local PI(3,4,5)P3 increases at sites of recycling vesicle exocytic fusion.
PI(3,4,5)P3 levels fluctuate at the membrane on a minutes time scale and that these fluctuations are associated with local PI(3,4,5)P3 increases at sites where recycling vesicles undergo exocytic fusion
timescale minutes
In developing vertebrate neurons, the microtubule array flows retrogradely within neurites toward the soma.
we uncovered that the microtubule array flows retrogradely within neurites to the soma
In developing vertebrate neurons, the microtubule array flows retrogradely within neurites toward the soma.
we uncovered that the microtubule array flows retrogradely within neurites to the soma
In developing vertebrate neurons, the microtubule array flows retrogradely within neurites toward the soma.
we uncovered that the microtubule array flows retrogradely within neurites to the soma
In developing vertebrate neurons, the microtubule array flows retrogradely within neurites toward the soma.
we uncovered that the microtubule array flows retrogradely within neurites to the soma
In developing vertebrate neurons, the microtubule array flows retrogradely within neurites toward the soma.
we uncovered that the microtubule array flows retrogradely within neurites to the soma
In developing vertebrate neurons, the microtubule array flows retrogradely within neurites toward the soma.
we uncovered that the microtubule array flows retrogradely within neurites to the soma
In developing vertebrate neurons, the microtubule array flows retrogradely within neurites toward the soma.
we uncovered that the microtubule array flows retrogradely within neurites to the soma
In developing vertebrate neurons, the microtubule array flows retrogradely within neurites toward the soma.
we uncovered that the microtubule array flows retrogradely within neurites to the soma
In developing vertebrate neurons, the microtubule array flows retrogradely within neurites toward the soma.
we uncovered that the microtubule array flows retrogradely within neurites to the soma
In developing vertebrate neurons, the microtubule array flows retrogradely within neurites toward the soma.
we uncovered that the microtubule array flows retrogradely within neurites to the soma
In developing vertebrate neurons, the microtubule array flows retrogradely within neurites toward the soma.
we uncovered that the microtubule array flows retrogradely within neurites to the soma
In developing vertebrate neurons, the microtubule array flows retrogradely within neurites toward the soma.
we uncovered that the microtubule array flows retrogradely within neurites to the soma
In developing vertebrate neurons, the microtubule array flows retrogradely within neurites toward the soma.
we uncovered that the microtubule array flows retrogradely within neurites to the soma
In developing vertebrate neurons, the microtubule array flows retrogradely within neurites toward the soma.
we uncovered that the microtubule array flows retrogradely within neurites to the soma
In developing vertebrate neurons, the microtubule array flows retrogradely within neurites toward the soma.
we uncovered that the microtubule array flows retrogradely within neurites to the soma
In developing vertebrate neurons, the microtubule array flows retrogradely within neurites toward the soma.
we uncovered that the microtubule array flows retrogradely within neurites to the soma
In developing vertebrate neurons, the microtubule array flows retrogradely within neurites toward the soma.
we uncovered that the microtubule array flows retrogradely within neurites to the soma
Dynein fuels microtubule retrograde flow.
The motor protein dynein fuels this process.
Dynein fuels microtubule retrograde flow.
The motor protein dynein fuels this process.
Dynein fuels microtubule retrograde flow.
The motor protein dynein fuels this process.
Dynein fuels microtubule retrograde flow.
The motor protein dynein fuels this process.
Dynein fuels microtubule retrograde flow.
The motor protein dynein fuels this process.
Dynein fuels microtubule retrograde flow.
The motor protein dynein fuels this process.
Dynein fuels microtubule retrograde flow.
The motor protein dynein fuels this process.
Dynein fuels microtubule retrograde flow.
The motor protein dynein fuels this process.
Dynein fuels microtubule retrograde flow.
The motor protein dynein fuels this process.
Dynein fuels microtubule retrograde flow.
The motor protein dynein fuels this process.
Dynein fuels microtubule retrograde flow.
The motor protein dynein fuels this process.
Dynein fuels microtubule retrograde flow.
The motor protein dynein fuels this process.
Dynein fuels microtubule retrograde flow.
The motor protein dynein fuels this process.
Dynein fuels microtubule retrograde flow.
The motor protein dynein fuels this process.
Dynein fuels microtubule retrograde flow.
The motor protein dynein fuels this process.
Dynein fuels microtubule retrograde flow.
The motor protein dynein fuels this process.
Dynein fuels microtubule retrograde flow.
The motor protein dynein fuels this process.
Microtubule retrograde flow drives cycles of microtubule density before axon formation and thereby inhibits neurite growth.
This flow drives cycles of microtubule density, a hallmark of the fluctuating state before axon formation, thereby inhibiting neurite growth.
Microtubule retrograde flow drives cycles of microtubule density before axon formation and thereby inhibits neurite growth.
This flow drives cycles of microtubule density, a hallmark of the fluctuating state before axon formation, thereby inhibiting neurite growth.
Microtubule retrograde flow drives cycles of microtubule density before axon formation and thereby inhibits neurite growth.
This flow drives cycles of microtubule density, a hallmark of the fluctuating state before axon formation, thereby inhibiting neurite growth.
Microtubule retrograde flow drives cycles of microtubule density before axon formation and thereby inhibits neurite growth.
This flow drives cycles of microtubule density, a hallmark of the fluctuating state before axon formation, thereby inhibiting neurite growth.
Microtubule retrograde flow drives cycles of microtubule density before axon formation and thereby inhibits neurite growth.
This flow drives cycles of microtubule density, a hallmark of the fluctuating state before axon formation, thereby inhibiting neurite growth.
Microtubule retrograde flow drives cycles of microtubule density before axon formation and thereby inhibits neurite growth.
This flow drives cycles of microtubule density, a hallmark of the fluctuating state before axon formation, thereby inhibiting neurite growth.
Microtubule retrograde flow drives cycles of microtubule density before axon formation and thereby inhibits neurite growth.
This flow drives cycles of microtubule density, a hallmark of the fluctuating state before axon formation, thereby inhibiting neurite growth.
Microtubule retrograde flow drives cycles of microtubule density before axon formation and thereby inhibits neurite growth.
This flow drives cycles of microtubule density, a hallmark of the fluctuating state before axon formation, thereby inhibiting neurite growth.
Microtubule retrograde flow drives cycles of microtubule density before axon formation and thereby inhibits neurite growth.
This flow drives cycles of microtubule density, a hallmark of the fluctuating state before axon formation, thereby inhibiting neurite growth.
Microtubule retrograde flow drives cycles of microtubule density before axon formation and thereby inhibits neurite growth.
This flow drives cycles of microtubule density, a hallmark of the fluctuating state before axon formation, thereby inhibiting neurite growth.
Microtubule retrograde flow drives cycles of microtubule density before axon formation and thereby inhibits neurite growth.
This flow drives cycles of microtubule density, a hallmark of the fluctuating state before axon formation, thereby inhibiting neurite growth.
Microtubule retrograde flow drives cycles of microtubule density before axon formation and thereby inhibits neurite growth.
This flow drives cycles of microtubule density, a hallmark of the fluctuating state before axon formation, thereby inhibiting neurite growth.
Microtubule retrograde flow drives cycles of microtubule density before axon formation and thereby inhibits neurite growth.
This flow drives cycles of microtubule density, a hallmark of the fluctuating state before axon formation, thereby inhibiting neurite growth.
Microtubule retrograde flow drives cycles of microtubule density before axon formation and thereby inhibits neurite growth.
This flow drives cycles of microtubule density, a hallmark of the fluctuating state before axon formation, thereby inhibiting neurite growth.
Microtubule retrograde flow drives cycles of microtubule density before axon formation and thereby inhibits neurite growth.
This flow drives cycles of microtubule density, a hallmark of the fluctuating state before axon formation, thereby inhibiting neurite growth.
Microtubule retrograde flow drives cycles of microtubule density before axon formation and thereby inhibits neurite growth.
This flow drives cycles of microtubule density, a hallmark of the fluctuating state before axon formation, thereby inhibiting neurite growth.
Microtubule retrograde flow drives cycles of microtubule density before axon formation and thereby inhibits neurite growth.
This flow drives cycles of microtubule density, a hallmark of the fluctuating state before axon formation, thereby inhibiting neurite growth.
Exocyst-mediated exocytosis supports EGFR-driven PI-3K/AKT pathway activation in epithelial cells by regulating plasma membrane PI(3,4,5)P3 levels.
exocyst-mediated exocytosis – by regulating PI(3,4,5)P3 levels at the plasma membrane – subserves activation of the PI-3K/AKT pathway by EGFR in epithelial cells
Exocyst-mediated exocytosis supports EGFR-driven PI-3K/AKT pathway activation in epithelial cells by regulating plasma membrane PI(3,4,5)P3 levels.
exocyst-mediated exocytosis – by regulating PI(3,4,5)P3 levels at the plasma membrane – subserves activation of the PI-3K/AKT pathway by EGFR in epithelial cells
Exocyst-mediated exocytosis supports EGFR-driven PI-3K/AKT pathway activation in epithelial cells by regulating plasma membrane PI(3,4,5)P3 levels.
exocyst-mediated exocytosis – by regulating PI(3,4,5)P3 levels at the plasma membrane – subserves activation of the PI-3K/AKT pathway by EGFR in epithelial cells
Exocyst-mediated exocytosis supports EGFR-driven PI-3K/AKT pathway activation in epithelial cells by regulating plasma membrane PI(3,4,5)P3 levels.
exocyst-mediated exocytosis – by regulating PI(3,4,5)P3 levels at the plasma membrane – subserves activation of the PI-3K/AKT pathway by EGFR in epithelial cells
Exocyst-mediated exocytosis supports EGFR-driven PI-3K/AKT pathway activation in epithelial cells by regulating plasma membrane PI(3,4,5)P3 levels.
exocyst-mediated exocytosis – by regulating PI(3,4,5)P3 levels at the plasma membrane – subserves activation of the PI-3K/AKT pathway by EGFR in epithelial cells
Exocyst-mediated exocytosis supports EGFR-driven PI-3K/AKT pathway activation in epithelial cells by regulating plasma membrane PI(3,4,5)P3 levels.
exocyst-mediated exocytosis – by regulating PI(3,4,5)P3 levels at the plasma membrane – subserves activation of the PI-3K/AKT pathway by EGFR in epithelial cells
Exocyst-mediated exocytosis supports EGFR-driven PI-3K/AKT pathway activation in epithelial cells by regulating plasma membrane PI(3,4,5)P3 levels.
exocyst-mediated exocytosis – by regulating PI(3,4,5)P3 levels at the plasma membrane – subserves activation of the PI-3K/AKT pathway by EGFR in epithelial cells
Exocyst-mediated exocytosis supports EGFR-driven PI-3K/AKT pathway activation in epithelial cells by regulating plasma membrane PI(3,4,5)P3 levels.
exocyst-mediated exocytosis – by regulating PI(3,4,5)P3 levels at the plasma membrane – subserves activation of the PI-3K/AKT pathway by EGFR in epithelial cells
Exocyst-mediated exocytosis supports EGFR-driven PI-3K/AKT pathway activation in epithelial cells by regulating plasma membrane PI(3,4,5)P3 levels.
exocyst-mediated exocytosis – by regulating PI(3,4,5)P3 levels at the plasma membrane – subserves activation of the PI-3K/AKT pathway by EGFR in epithelial cells
Exocyst-mediated exocytosis supports EGFR-driven PI-3K/AKT pathway activation in epithelial cells by regulating plasma membrane PI(3,4,5)P3 levels.
exocyst-mediated exocytosis – by regulating PI(3,4,5)P3 levels at the plasma membrane – subserves activation of the PI-3K/AKT pathway by EGFR in epithelial cells
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics relevant to neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics, which changes the hitherto axon-centric view of neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics relevant to neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics, which changes the hitherto axon-centric view of neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics relevant to neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics, which changes the hitherto axon-centric view of neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics relevant to neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics, which changes the hitherto axon-centric view of neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics relevant to neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics, which changes the hitherto axon-centric view of neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics relevant to neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics, which changes the hitherto axon-centric view of neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics relevant to neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics, which changes the hitherto axon-centric view of neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics relevant to neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics, which changes the hitherto axon-centric view of neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics relevant to neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics, which changes the hitherto axon-centric view of neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics relevant to neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics, which changes the hitherto axon-centric view of neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics relevant to neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics, which changes the hitherto axon-centric view of neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics relevant to neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics, which changes the hitherto axon-centric view of neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics relevant to neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics, which changes the hitherto axon-centric view of neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics relevant to neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics, which changes the hitherto axon-centric view of neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics relevant to neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics, which changes the hitherto axon-centric view of neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics relevant to neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics, which changes the hitherto axon-centric view of neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics relevant to neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics, which changes the hitherto axon-centric view of neuronal polarization.
In epithelial cells, the PI-3K/AKT pathway is regulated by the exocyst complex.
the PI-3K/AKT pathway in epithelial cells is regulated by the exocyst complex
In epithelial cells, the PI-3K/AKT pathway is regulated by the exocyst complex.
the PI-3K/AKT pathway in epithelial cells is regulated by the exocyst complex
In epithelial cells, the PI-3K/AKT pathway is regulated by the exocyst complex.
the PI-3K/AKT pathway in epithelial cells is regulated by the exocyst complex
In epithelial cells, the PI-3K/AKT pathway is regulated by the exocyst complex.
the PI-3K/AKT pathway in epithelial cells is regulated by the exocyst complex
In epithelial cells, the PI-3K/AKT pathway is regulated by the exocyst complex.
the PI-3K/AKT pathway in epithelial cells is regulated by the exocyst complex
In epithelial cells, the PI-3K/AKT pathway is regulated by the exocyst complex.
the PI-3K/AKT pathway in epithelial cells is regulated by the exocyst complex
In epithelial cells, the PI-3K/AKT pathway is regulated by the exocyst complex.
the PI-3K/AKT pathway in epithelial cells is regulated by the exocyst complex
In epithelial cells, the PI-3K/AKT pathway is regulated by the exocyst complex.
the PI-3K/AKT pathway in epithelial cells is regulated by the exocyst complex
In epithelial cells, the PI-3K/AKT pathway is regulated by the exocyst complex.
the PI-3K/AKT pathway in epithelial cells is regulated by the exocyst complex
In epithelial cells, the PI-3K/AKT pathway is regulated by the exocyst complex.
the PI-3K/AKT pathway in epithelial cells is regulated by the exocyst complex
Acute promotion of exocytosis by optogenetically driving exocyst-mediated vesicle tethering upregulates PI(3,4,5)P3 production and AKT activation.
acute promotion of exocytosis by optogenetically driving exocyst-mediated vesicle tethering upregulates PI(3,4,5)P3 production and AKT activation
Acute promotion of exocytosis by optogenetically driving exocyst-mediated vesicle tethering upregulates PI(3,4,5)P3 production and AKT activation.
acute promotion of exocytosis by optogenetically driving exocyst-mediated vesicle tethering upregulates PI(3,4,5)P3 production and AKT activation
Acute promotion of exocytosis by optogenetically driving exocyst-mediated vesicle tethering upregulates PI(3,4,5)P3 production and AKT activation.
acute promotion of exocytosis by optogenetically driving exocyst-mediated vesicle tethering upregulates PI(3,4,5)P3 production and AKT activation
Acute promotion of exocytosis by optogenetically driving exocyst-mediated vesicle tethering upregulates PI(3,4,5)P3 production and AKT activation.
acute promotion of exocytosis by optogenetically driving exocyst-mediated vesicle tethering upregulates PI(3,4,5)P3 production and AKT activation
Acute promotion of exocytosis by optogenetically driving exocyst-mediated vesicle tethering upregulates PI(3,4,5)P3 production and AKT activation.
acute promotion of exocytosis by optogenetically driving exocyst-mediated vesicle tethering upregulates PI(3,4,5)P3 production and AKT activation
Acute promotion of exocytosis by optogenetically driving exocyst-mediated vesicle tethering upregulates PI(3,4,5)P3 production and AKT activation.
acute promotion of exocytosis by optogenetically driving exocyst-mediated vesicle tethering upregulates PI(3,4,5)P3 production and AKT activation
Acute promotion of exocytosis by optogenetically driving exocyst-mediated vesicle tethering upregulates PI(3,4,5)P3 production and AKT activation.
acute promotion of exocytosis by optogenetically driving exocyst-mediated vesicle tethering upregulates PI(3,4,5)P3 production and AKT activation
Acute promotion of exocytosis by optogenetically driving exocyst-mediated vesicle tethering upregulates PI(3,4,5)P3 production and AKT activation.
acute promotion of exocytosis by optogenetically driving exocyst-mediated vesicle tethering upregulates PI(3,4,5)P3 production and AKT activation
Acute promotion of exocytosis by optogenetically driving exocyst-mediated vesicle tethering upregulates PI(3,4,5)P3 production and AKT activation.
acute promotion of exocytosis by optogenetically driving exocyst-mediated vesicle tethering upregulates PI(3,4,5)P3 production and AKT activation
Acute promotion of exocytosis by optogenetically driving exocyst-mediated vesicle tethering upregulates PI(3,4,5)P3 production and AKT activation.
acute promotion of exocytosis by optogenetically driving exocyst-mediated vesicle tethering upregulates PI(3,4,5)P3 production and AKT activation
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
The findings provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
Our findings highlight key sources of imprecision within light-inducible dimer systems and provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
The findings provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
Our findings highlight key sources of imprecision within light-inducible dimer systems and provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
The findings provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
Our findings highlight key sources of imprecision within light-inducible dimer systems and provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
The findings provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
Our findings highlight key sources of imprecision within light-inducible dimer systems and provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
The findings provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
Our findings highlight key sources of imprecision within light-inducible dimer systems and provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
The findings provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
Our findings highlight key sources of imprecision within light-inducible dimer systems and provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
The findings provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
Our findings highlight key sources of imprecision within light-inducible dimer systems and provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
The findings provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
Our findings highlight key sources of imprecision within light-inducible dimer systems and provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
The findings provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
Our findings highlight key sources of imprecision within light-inducible dimer systems and provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
The findings provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
Our findings highlight key sources of imprecision within light-inducible dimer systems and provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
The iLID system enables selective recruitment of components to subcellular locations, including micron-scale regions of the plasma membrane.
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
The iLID system enables selective recruitment of components to subcellular locations, including micron-scale regions of the plasma membrane.
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
The iLID system enables selective recruitment of components to subcellular locations, including micron-scale regions of the plasma membrane.
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
The iLID system enables selective recruitment of components to subcellular locations, including micron-scale regions of the plasma membrane.
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
The iLID system enables selective recruitment of components to subcellular locations, including micron-scale regions of the plasma membrane.
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
The iLID system enables selective recruitment of components to subcellular locations, including micron-scale regions of the plasma membrane.
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
The iLID system enables selective recruitment of components to subcellular locations, including micron-scale regions of the plasma membrane.
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
The iLID system enables selective recruitment of components to subcellular locations, including micron-scale regions of the plasma membrane.
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
The iLID system enables selective recruitment of components to subcellular locations, including micron-scale regions of the plasma membrane.
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
The iLID system enables selective recruitment of components to subcellular locations, including micron-scale regions of the plasma membrane.
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
The iLID system enables selective recruitment of components to subcellular locations, including micron-scale regions of the plasma membrane.
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
The iLID system enables selective recruitment of components to subcellular locations, including micron-scale regions of the plasma membrane.
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
The iLID system enables selective recruitment of components to subcellular locations, including micron-scale regions of the plasma membrane.
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
The iLID system enables selective recruitment of components to subcellular locations, including micron-scale regions of the plasma membrane.
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
The iLID system enables selective recruitment of components to subcellular locations, including micron-scale regions of the plasma membrane.
These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane.
The iLID system enables selective recruitment of components to subcellular locations such as micron-scale regions of the plasma membrane.
the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane
The iLID system enables selective recruitment of components to subcellular locations such as micron-scale regions of the plasma membrane.
the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane
The iLID system enables selective recruitment of components to subcellular locations such as micron-scale regions of the plasma membrane.
the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane
The iLID system enables selective recruitment of components to subcellular locations such as micron-scale regions of the plasma membrane.
the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane
The iLID system enables selective recruitment of components to subcellular locations such as micron-scale regions of the plasma membrane.
the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane
The iLID system enables selective recruitment of components to subcellular locations such as micron-scale regions of the plasma membrane.
the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane
The iLID system enables selective recruitment of components to subcellular locations such as micron-scale regions of the plasma membrane.
the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane
The iLID system enables selective recruitment of components to subcellular locations such as micron-scale regions of the plasma membrane.
the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane
The iLID system enables selective recruitment of components to subcellular locations such as micron-scale regions of the plasma membrane.
the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane
The iLID system enables selective recruitment of components to subcellular locations such as micron-scale regions of the plasma membrane.
the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane
Compared with the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, large N-terminal anchor fusions provide stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, large N-terminal anchor fusions provide stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, large N-terminal anchor fusions provide stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, large N-terminal anchor fusions provide stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, large N-terminal anchor fusions provide stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, large N-terminal anchor fusions provide stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, large N-terminal anchor fusions provide stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, large N-terminal anchor fusions provide stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, large N-terminal anchor fusions provide stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, large N-terminal anchor fusions provide stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, large N-terminal anchor fusions provide stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, large N-terminal anchor fusions provide stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, large N-terminal anchor fusions provide stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, large N-terminal anchor fusions provide stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with the commonly used C-terminal iLID fusion, large N-terminal anchor fusions provide stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
The study defines guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
Consistent recruitment in optogenetic dimerization systems is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Consistent recruitment in optogenetic dimerization systems is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Consistent recruitment in optogenetic dimerization systems is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Consistent recruitment in optogenetic dimerization systems is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Consistent recruitment in optogenetic dimerization systems is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Consistent recruitment in optogenetic dimerization systems is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Consistent recruitment in optogenetic dimerization systems is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Consistent recruitment in optogenetic dimerization systems is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Consistent recruitment in optogenetic dimerization systems is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Consistent recruitment in optogenetic dimerization systems is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Within iLID-based recruitment, consistent recruitment is limited by heterogeneous optogenetic component expression and spatial precision is reduced by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Within iLID-based recruitment, consistent recruitment is limited by heterogeneous optogenetic component expression and spatial precision is reduced by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Within iLID-based recruitment, consistent recruitment is limited by heterogeneous optogenetic component expression and spatial precision is reduced by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Within iLID-based recruitment, consistent recruitment is limited by heterogeneous optogenetic component expression and spatial precision is reduced by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Within iLID-based recruitment, consistent recruitment is limited by heterogeneous optogenetic component expression and spatial precision is reduced by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Within iLID-based recruitment, consistent recruitment is limited by heterogeneous optogenetic component expression and spatial precision is reduced by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Within iLID-based recruitment, consistent recruitment is limited by heterogeneous optogenetic component expression and spatial precision is reduced by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Within iLID-based recruitment, consistent recruitment is limited by heterogeneous optogenetic component expression and spatial precision is reduced by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Within iLID-based recruitment, consistent recruitment is limited by heterogeneous optogenetic component expression and spatial precision is reduced by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Within iLID-based recruitment, consistent recruitment is limited by heterogeneous optogenetic component expression and spatial precision is reduced by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Within iLID-based recruitment, consistent recruitment is limited by heterogeneous optogenetic component expression and spatial precision is reduced by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Within iLID-based recruitment, consistent recruitment is limited by heterogeneous optogenetic component expression and spatial precision is reduced by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Within iLID-based recruitment, consistent recruitment is limited by heterogeneous optogenetic component expression and spatial precision is reduced by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Within iLID-based recruitment, consistent recruitment is limited by heterogeneous optogenetic component expression and spatial precision is reduced by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Within iLID-based recruitment, consistent recruitment is limited by heterogeneous optogenetic component expression and spatial precision is reduced by protein diffusion, especially over long time scales.
Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales.
Anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics in the iLID system.
we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics
Anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics in the iLID system.
we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics
Anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics in the iLID system.
we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics
Anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics in the iLID system.
we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics
Anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics in the iLID system.
we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics
Anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics in the iLID system.
we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics
Anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics in the iLID system.
we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics
Anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics in the iLID system.
we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics
Anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics in the iLID system.
we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics
Anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics in the iLID system.
we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics
In the iLID system, anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
In the iLID system, anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
In the iLID system, anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
In the iLID system, anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
In the iLID system, anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
In the iLID system, anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
In the iLID system, anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
In the iLID system, anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
In the iLID system, anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
In the iLID system, anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
In the iLID system, anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
In the iLID system, anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
In the iLID system, anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
In the iLID system, anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
In the iLID system, anchoring strategy affects component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics.
The study defines guidelines for component expression regimes that optimize recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes that optimize recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes that optimize recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes that optimize recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes that optimize recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes that optimize recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes that optimize recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes that optimize recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes that optimize recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes that optimize recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes that optimize recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes that optimize recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes that optimize recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes that optimize recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
The study defines guidelines for component expression regimes that optimize recruitment for both cell-wide and subcellular recruitment strategies.
We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies.
Approval Evidence
3 sources6 linked approval claimsfirst-pass slug live-cell-imaging
Using live-cell imaging of neurons in culture or in brain slices
Source:
Using live-cell imaging, we demonstrate that PI(3,4,5)P3 levels fluctuate at the membrane on a minutes time scale
Source:
Using live cell imaging and mathematical modeling
Source:
associationsupports
PI(3,4,5)P3 membrane fluctuations are associated with local PI(3,4,5)P3 increases at sites of recycling vesicle exocytic fusion.
PI(3,4,5)P3 levels fluctuate at the membrane on a minutes time scale and that these fluctuations are associated with local PI(3,4,5)P3 increases at sites where recycling vesicles undergo exocytic fusion
Source:
biological observationsupports
In developing vertebrate neurons, the microtubule array flows retrogradely within neurites toward the soma.
we uncovered that the microtubule array flows retrogradely within neurites to the soma
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mechanistic claimsupports
Dynein fuels microtubule retrograde flow.
The motor protein dynein fuels this process.
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mechanistic claimsupports
Microtubule retrograde flow drives cycles of microtubule density before axon formation and thereby inhibits neurite growth.
This flow drives cycles of microtubule density, a hallmark of the fluctuating state before axon formation, thereby inhibiting neurite growth.
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novelty claimsupports
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics relevant to neuronal polarization.
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics, which changes the hitherto axon-centric view of neuronal polarization.
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state transition claimsupports
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension.
Source:
Comparisons
Source-backed strengths
A key strength is direct real-time visualization of cellular dynamics in neurons in culture or brain slices. The cited work shows that it resolved PI(3,4,5)P3 fluctuations on a minutes time scale and supported observation of retrograde microtubule flow in developing vertebrate neurons.
Source:
Microtubule retrograde flow is a previously unknown type of cytoskeletal dynamics, which changes the hitherto axon-centric view of neuronal polarization.
Source:
Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs.
Compared with high throughput screening
live-cell imaging and high throughput screening address a similar problem space because they share recombination.
Shared frame: same top-level item type; shared target processes: recombination
Strengths here: appears more independently replicated; looks easier to implement in practice.
Compared with stroke transcriptomics
live-cell imaging and stroke transcriptomics address a similar problem space because they share recombination.
Shared frame: same top-level item type; shared target processes: recombination
Strengths here: appears more independently replicated; looks easier to implement in practice.
Compared with whole genome screening of gene knockout mutants
live-cell imaging and whole genome screening of gene knockout mutants address a similar problem space because they share recombination.
Shared frame: same top-level item type; shared target processes: recombination
Strengths here: appears more independently replicated; looks easier to implement in practice.
Ranked Citations
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