neurodudes

Berkeley researchers lay groundwork for cell version of DNA chip

This is a little off the beaten path, but I think that the Neurodudes crowd is generally interested in techniques related to neuron-to-silicon interfacing. Here’s some neat surface chemistry from Livermore Labs that facilitates binding of DNA oligos to the cell surface. Then, just like with a gene chip, you can link cells with the right (complementary) oligos to a pre-coated chip.

My first reaction to this was, Wow, another great application of the homologous base pairing machinery of nucleic acids. I’m amazed by the out-of-the-box thinking in this idea — sticking DNA to the outside of the cell. According to the article, the authors estimate that about 270,000 DNA molecules are put on the surface of each cell by their process. (Though I’m sure they’ve looked at it, one does wonder how this impacts membrane trafficking, receptor internalization processes, etc.)

Let me emphasize… This is totally cool! This allows cell-type-specific micropatterning at the level of whatever your chip printing resolution is. (Traditionally, gene chips are “spotted” using precision multi-head inkjet-like printers.) For you cell culture enthusiasts out there, you might imagine a cell culture where you have many different cell types and have full control (down to a single cell!) of where each type of cell is placed. Talk about a co-culture!

In the August PLoS Biology, there is an article showing the production of pure neural stem cells from human embryonic stem cells.

The procedure is quite simple: Add growth factors FGF-2 and EGF to the ES cells and you get pure NS cells, which overcomes several of the limitations of previous neurosphere-based assays [Nature Methods].

Jimbo, Tateno, and Robinson did a network plasticity experiment using cultured networks and a multi-electrode array.

They determine the effect of a tetanus at one electrode in a network on the network. Specifically, they look at how the tetanus potentiates or depresses the ability of a test pulse at another electrode to evoke spike trains at various neurons across the network.

They grew cultures on a MEA for a month. They stimulated each electrode in succession with a test pulse. They recorded the response at all electrodes after each test pulse. They used spike sorting to identify the reponses of individual neurons out of the electrode traces. They found that the network’s response to a given test pulse was reproducable for about 50ms after the test pulse.

Then they applied a strong stimulus (a tetanus) to a single electrode (to make it learn ). After that they re-characterized the network’s responses to test pulses at every site.

They found that some electrode sites became more potent (“potentiated response”) after the tetanus was applied. This means that, when a test pulse was applied to this electrode site, neurons in all areas of the network responded either the same, or more strongly than they had before the tetanus.

Other sites became less potent (“depressed response”) after the tetanus was applied.

Surprisingly, it was very rare for any given electrode site to become better at stimulating some neurons and worse at stimulating others as a result of the tetanus.

What determined which electrode sites became potentiated and which ones became depressed? The tetanus potentiated electrodes which evoked spike trains that tended to contain spikes which were within 40ms of the spike trains evoked by the tetanus electrode, and depressed others. That is, it potentiated sites which evoked patterns similar to the patterns evoked by the tetanus site.

However, the spike trains evoked by both potentiated and depressed neurons became more synchronized with the tetanus electrode after applying the tetanus.

See page 5 of “Distributed processing in cultured neuronal networks” for another review of this work.

See this NeuroWiki page for more details (the strange {{}} over there are because we will soon have footnotes).

Jimbo, Y., Tateno, T., and Robinson, H. P. C.,
Simultaneous Induction of Pathway-Specific Potentiation and Depression in Networks of Cortical Neurons. Biophysical Journal, 1999. 76: p. 670-678.

In the September Nature Neuroscience, we have a promising new technique: Millisecond-timescale, genetically targeted optical control of neural activity.

I think several people have suggested doing something like this before but no one has actually done it. What they’ve done is genetically modified (by lentivirus, for those curious) ordinary hippocampal neurons in culture, adding the same photo-electric transducing protein — rhodopsin – found in photoreceptors. Yup. You heard me right. They’ve expressed a cation-channel-gating rhodopsin in ordinary hippocampal neurons. With an standard fluorescence microscope (Xenon lamp + Chroma GFP cube), they can photostimulate single action potentials (and sub-threshold depolarizations) in single neurons.

Now here’s my idea for bioengineers to take this to the next level: Add a second photosensitive protein tied to an inhibitory channel. Ideally, we would want total separation between the stimulating wavelengths for the two different (excitatory, inhibitory) channels. Now, you have a system where all neurons can be directly excited or inhibited with different laser lines. In other words, a network of neurons where all voltages can be fully controlled. Sweet!

This seems like a great tool to add to the existing arsenal of photostimulation techniques (like photoelectric effect-based light-on-silicon stimulation that was pioneered by Goda lab.) Here’s a question: Is this the end of multi-electrode arrays? In slice, we already have single spike detection with Ca-sensitive dyes from Yuste’s lab. Now, we have optical single spike stimulation. Perhaps MEAs will be relegated to the domain implantable devices. Regardless, I’m proud to see several of the authors are from Stanford! Read on for the full abstract. (more…)

University of Florida has an Oct 21 press release about Thomas DeMarse’s work in hybrots, that is, cultured neurons interacting with a computer via an MEA to control an avatar in a simulated world. In this case, the neurons are flying a plane in a flight simulation program.

It looks from the press release that he has got the animat to actually learn to fly a plane (i.e. got input about the simulated environment and controlled a simulator joystick to keep the plane steady!

full article

Unfortunately he doesn’t describe what kind of learning rule/feedback was used, so I guess we’ll have to wait for the paper to see how much excitement is justified.

P.S. there’s also a lively discussion on SlashDot about this press release.