Neuroscience, Physics, Microbiology and Chemistry all in one
We address questions in neuroscience from an imaging perspective: How to transduce information in neurons into detectable photons, resolvable in space and time.
We have a particular soft spot for nonlinear microscopy and Genetically Encoded Voltage Indicators (GEVIs), but happily use principles, tools and techniques from Physics, Chemistry, Mathematics, Molecular Biology, Nanofabrication and Photonics where applicable.
Current projects include:
We have a particular soft spot for nonlinear microscopy and Genetically Encoded Voltage Indicators (GEVIs), but happily use principles, tools and techniques from Physics, Chemistry, Mathematics, Molecular Biology, Nanofabrication and Photonics where applicable.
Current projects include:
Voltage Nanoscopy
Signal processing in neurons begins and ends at synapses. Voltage imaging, with its promise of resolving membrane voltage dynamics in space and time, is the ideal candidate to resolve fast voltage dynamics at synapses and neuromuscular junctions, but is limited by the optical diffraction limit and finite photon fluxes.
We optically investigate voltage dynamics at dendritic spines and axon terminals using plasmonically enhanced voltage imaging. The excitation and emission of molecules can be enhanced by electromagnetically coupling the molecule to a plasmonic particle, as an optical antenna. An added advantage is that this enhancement takes place in the near field of the optical antenna, leading to nanoscopic areas of excitation and emission enhancement and pushing voltage imaging beyond the boundaries of the optical diffraction limit. We apply this method to suitable voltage sensing proteins or dyes, embedded in the cell membrane at synapses or neuromuscular junctions. We are interested in whether plasticity can manifest as varying voltage dynamics in these subcellular structures.
Funded by an NWO Startup Grant.
We optically investigate voltage dynamics at dendritic spines and axon terminals using plasmonically enhanced voltage imaging. The excitation and emission of molecules can be enhanced by electromagnetically coupling the molecule to a plasmonic particle, as an optical antenna. An added advantage is that this enhancement takes place in the near field of the optical antenna, leading to nanoscopic areas of excitation and emission enhancement and pushing voltage imaging beyond the boundaries of the optical diffraction limit. We apply this method to suitable voltage sensing proteins or dyes, embedded in the cell membrane at synapses or neuromuscular junctions. We are interested in whether plasticity can manifest as varying voltage dynamics in these subcellular structures.
Funded by an NWO Startup Grant.
Absolute Voltage Imaging
In a proof of principle paper, we introduced 2-Photon Absolute Contrast Lifetime Imaging Voltage Sensing (2PACLIVS) as a method to optically obtain absolute membrane voltage information. This method relies on Genetically Encoded Voltage Indicators (GEVIs), engineered such that membrane voltage influences the fluorescence lifetime of the probe. In this project we aim to create an optimized probe and optical setup for absolute voltage imaging, and apply the technology to answer neurodevelopmental questions in Zebrafish.
Multiphoton Voltage Imaging
We explore the possibilities of genetic engineering and optimization of optical microscopy to achieve robust, deep tissue voltage imaging using multiphoton excitation. We're interested in how to record optical signals from a large number of cells, all at different 3D coordinates, in scattering tissue, in a moving background, in the limited window available to sustain the time resolution to resolve action potentials.
Funded by an ERC Starting Grant and a convergence consortium.
Funded by an ERC Starting Grant and a convergence consortium.
Image analysis, network modeling and machine learning
With optogenetics and voltage imaging, a full suite of techniques for light mediated interaction with excitable tissues is available in the lab. This opens myriad possibilities for theoretical excursions into understanding cellular dynamics; automating interactions with excitable tissues; and mutual interaction of biological learning, in the form of plasticity, and machine learning.
We create technology for automated cell recognition, stimulation and selection, and are interested in the interface between machine learning and biological learning (or Artificial intelligence and biological intelligence).
We also use voltage imaging data to create conduction-based models of neurons and neural networks; we are interested in the added value voltage imaging has to create realistic models of measured neurons and neural networks and in the predictive value of these models to assess the biological reality underlying cellular behavior we can observe on our microscopes.
Part of the TU Delft AI lab BIOlab. Funded by a convergence consortium.
We create technology for automated cell recognition, stimulation and selection, and are interested in the interface between machine learning and biological learning (or Artificial intelligence and biological intelligence).
We also use voltage imaging data to create conduction-based models of neurons and neural networks; we are interested in the added value voltage imaging has to create realistic models of measured neurons and neural networks and in the predictive value of these models to assess the biological reality underlying cellular behavior we can observe on our microscopes.
Part of the TU Delft AI lab BIOlab. Funded by a convergence consortium.
Single cell selection and analysis
We assisted our colleagues at Erasmus in the creation of a pipeline for single cell selection. We are interested in the hardware and software development needed for functional single cell selection (see also: http://www.mpchienlab.org/)