New stable genetically-encoded Ca sensor

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.

Hippocampus response to KCl application


Inferring network activity on a MEA from pairwise correlations

Weak pairwise correlations imply strongly correlated network states in a neural population : Nature

Very few MEA studies make it into Nature, so this definitely got my attention.

Often in neuroscience we are confronted with a small sample measurement of a few neurons from a large population. Although many have assumed, few have actually asked: What are we missing here? What does recording a few neurons really tell you about the entire network?

Using an elegant prep (retina on a MEA viewing defined scenes/stimuli), Segev, Bialek, and students show that statistical physics models that assume pairwise correlations (but disregard any higher order phenomena) perform very well in modeling the data. This indicates a certain redundancy exists in the neural code. The results are also replicated with cultured cortical neurons on a MEA.

Some key ideas from the paper are presented after the jump. Continue reading

Optical detection via second harmonic generation

There’s been some work recently on looking at second harmonic generation for optical readout of action potentials… any opinions on this work?

First a brief primer on SHG (from Yuste’s recent Nature Methods paper on fluorescence microscopy):

In SHG, high-infrared light intensity drives the lowest-order nonlinear polarizability of molecules (or groups of molecules) in the specimen so that coherent light of exactly double frequency (or half the wavelength) is emitted. Because the process can occur away from resonance frequencies, there is no absorption of light, thus avoiding complications of photochemistry. This phenomenon is rare and requires, like two-photon excitation, a high concentration of photons at the focal point, something that also gives it optical sectioning. SHG is particularly interesting because it only occurs where chromophores are oriented in noncentrosymmetric arrays, such as chromophores adsorbed to biological membranes or other chemical interfaces. Thus, SHG is perhaps the only optical technique that is truly sensitive to biological membranes, something which makes it ideal for detecting changes in membrane potential. As many important biological processes, such as electrophysiological communication, detection and transduction of external molecules and cell-cell interactions occur at plasma membranes, SHG is likely to become a very useful tool for biologists.

Seed papers:

Cell-chip adhesion chemistry

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!

Recipe: ES cells to pure NS cells

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 et al '99: plasticity at the network level in culture

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.

Technique: Optical stimulation with single neuron resolution

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. Continue reading