neurodudes

Video-Rate Far-Field Optical Nanoscopy Dissects Synaptic Vesicle Movement

Just the optical engineering alone here deserves mention: 28 frames per second at 62nm resolution (well below the diffraction limit of 260nm for light of the wavelength used)! STED (or stimulated emission depletion, developed in Stefan Hell’s group) is ideal for visualizing synaptic vesicles, whose small size (~50nm) has typically confined them to the domain of electron microscopists. The ability to get high-speed STED allowed the researchers to track individual vesicles and their path dynamics. They conclude that vesicle movement has both motor-driven and diffusive components (ie. a biased random walk). I’m sure with more time and more analysis there will be a lot of interesting applications for this kind of real-time vesicle tracking. Perhaps in the near future we will have single vesicle “minis” monitored at multiple sites through microscopy instead of just one or two sites electrophysiologically…

Here’s the resolution difference between STED and confocal for a single vesicle:

And, for those of you with ~$1.25M lying around, you can now purchase a STED setup directly from Leica!

Pascual-Marqui has posted a preprint and would like comments. Read on for details.

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This week’s Nature has quite a few additional halorhodopsin articles for photochannel fans.

Halorhodopsin article from Deisseroth’s lab:
Multimodal fast optical interrogation of neural circuitry [News & Views]

Also, there is an intriguing article on both the general excitement in the neuroscience community with this new technology and a possible intellectual property dispute over it.

Some surprising and important news from Nature Biotechnology about a common technique in cellular imagining, fluorescence resonance energy transfer, or FRET. Specifically, it looks like ATP/Mg can significantly alter the FRET signal, which has commonly been used for looking at Ca, voltage, and various other binding interactions in neurons:

Given these findings, we predict that fluctuations in free or Mg2+-bound ATP will affect the signal output of most—if not all—CFP-YFP–based FRET indicators.

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What is the right level of biological realism to model neuronal systems in order to understand their computational properties? Some recent papers may help shed some light on the subject. Models of the computational properties of local networks of neurons are starting to come into their own. This year has already seen at least two articles published in experimentalist journals based on the same core of theoretical work.

To bring you up to speed, I need to remind you what is going on in the world of experimental neuroscience.

Experimentalists are now able to record the single-cell activities of a whole population of neurons simultaneously. From Briggman, Abarbanel, Kristan (2006):

By using multi-electrode arrays or optical imaging, investigators can now record from many individual neurons in various parts of nervous systems simultaneously while an animal performs sensory, motor or cognitive tasks. Given the large multidimensional datasets that are now routinely generated, it is often not obvious how to find meaningful results within the data.

This paper goes on to provide a nice overview on mathematical methods that researchers are using to grapple with the challenge of understanding the dynamics of the neural systems they are recording from. They make the case that conceptual progress needs to be made on the interpretation of the data these results yield. How can we understand what computations these neurons are collectively performing?

(Incidentally, this topic is being explored in a conference happening this week at the Los Alamos National Laboratory, which, according to one of the conference session chairs, is intended to help shape future directions for the lab. Hopefully there will be webcasts from this conference.)

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Postdoctoral/research scientist positions are available in the inter-disciplinary group of Dmitri Chklovskii at the new Janelia Farm Research Campus of the Howard Hughes Medical Institute located in the suburbs of Washington, D.C. Candidates are expected to have a PhD in neuroscience, physics, computer science or electrical engineering. Most of the work is theoretical or computational and is done in collaboration with several experimental laboratories. Successful applicants will work on projects centered on neuronal circuits such as high-throughput reconstruction of wiring diagrams as well as combining structural and physiological data to infer circuit function. Salary will be commensurate with qualifications. For more information about research directions in the group please see: http://www.hhmi.org/research/groupleaders/chklovskii.html
Interested applicants should send their CV and a statement of research interests to mitya (at) janelia.hhmi.org, and arrange for three recommendation letters to be emailed to me.

Today MIT’s Technology Review magazine released its annual list of innovators under the age of 35 who were nominated for recognition. Interestingly, almost a full quarter are doing work relating to or impacting the field of neuroengineering — including ways to tag synapses with quantum dots, activate neurons remotely, improve machine vision, classify whole-brain states for prosthetic purposes, and make nanowire arrays.

http://www.technologyreview.com/TR35/

A FRET-Based Calcium Biosensor with Fast Signal Kinetics and High Fluorescence Change — Mank et al. 90 (5): 1790 — Biophysical Journal

Relevant details (from the discussion):

Above we reported the generation of a FRET-based calcium biosensor employing TnC as calcium-binding moiety that is fast, is stable in imaging experiments, and shows a significantly enhanced fluorescence change. Its off-rate is significantly faster than those of previous double chromophore sensors and even outmatches the fastest single fluorophore sensors to date.

Although it is faster than what was previously available, it would be nice if the off-rate was even faster:

Its off-rate was extremely fast, optimally fitted with a double exponential with a dominating {tau} of 142 ms (A1 = 0.63) and a minor {tau} of 867 ms (A2 = 0.06) (Fig. 2 D). Mutation of the N-cap residue 131 of helix G within TnC from isoleucine to threonine (35Go) yielded an indicator of higher calcium affinity with a Kd of 1.7 µM (Fig. 2 B) and shifted the Hill slope to 1.1, although at reduced maximal fluorescence change of 270%. TN-XL expressed well in primary hippocampal neurons at 37°C. Fluorescence was evenly distributed, filling all neuronal processes, with no signs of aggregation. The nucleus was devoid of fluorescence. Repeated stimulations with high potassium followed by repeated washouts demonstrated stable baselines over long recording sessions and reproducible signals after stimulation. Moreover the signals induced by high potassium were more than doubled compared to TN-L15.

From the Apr 20 issue of Neuron: Integrative Properties of Radial Oblique Dendrites in Hippocampal CA1 Pyramidal Neurons (or, for those who want just the N&V’s summary: Dendritic Enlightenment: Using Patterned Two-Photon Uncaging to Reveal the Secrets of the Brain’s Smallest Dendrites)

The technology is essentially high-speed two photon uncaging of glutamate, but the authors have used it here to create “realistic” patterns of dendritic input in an attempt to see just how dendritic arithmetic works. Although I haven’t read the paper closely, they claim to work out the spatiotemporal parameters underlying dendritic spike generation for pyramidal neurons.

A related methodology paper from a recent J. Neurophys. also uses fast acousto-optic deflectors and two-photon but for imaging purposes. It’s more descriptive about the setup and techniques for those interested in doing this type of work.

In an impressive integrative effort, a new article in this month’s issue of Neuron describes a robust object and face classification model that is consistent with both behavioral and fMRI experiments.

From a preview of the article:

“A central theme that has emerged in research on face perception therefore is whether or not faces are “special” such that the cognitive and neural mechanisms that underlie their processing are different from those underlying the processing of other visual objects. [...] In this issue of Neuron, Jiang et al. (2006) provide a compelling array of evidence supporting the idea that the processing of faces and objects do not rely on qualitatively different mechanisms. In a series of experiments, Jiang et al. present and integrate findings from neural modeling, behavior, and fMRI, showing that face classification, similarly to object classification, can be achieved by a simple-to-complex architecture, based on hierarchical shape detectors. Furthermore, variations of this model can account for both configural and feature-based processing without qualitative modification of the model’s structure.”

The Riesenhuber lab, from which this work comes, has been working on object recognition in an integrative way. The lab is particularly “at the intersection of neuroscience and AI”.