Notes : Structural Biochemistry

26 11 2008

I TA’ed a course in structural biochemistry at UCSD.  I spent a lot of time making cool handouts for the students (at least until the last 2 weeks when my own finals sapped all my time and energy).  Found these buried online.  Nothing too mind-blowing, but I figured I’d post them to preserve them.

Handout 1 – Aqueous Solutions, Amino Acids & Secondary Protein Structure

Handout 2 – Protein, DNA & RNA Structure

Handout 3 – RNA continued, Mono- & Polysaccharides

Handout 4 – Cell Membrane Components and Structure Determination

Handout 5 – Enzyme Kinetics & Serine Proteases including Chymotrypsin

Handout 7 – Ion Channels

Handout 8 – Bacterial Recation Center & Molecular Dynamics

Raw Notes : Gyorgy Buzsaki at SFN

20 11 2008

Comparative Neuroanatomy with Buzsaki

Comparative Neuroanatomy with Buzsaki

Large Scale recording of neuronal activity in behaving rodents

Want to separate the types neurons of they record from and control.

Step 1 : extract single neurons from extracellular electrodes

Extracellular electrode arrys give best spatiotemporal resolution at depth in awake freely moving rodent.

Neurons are not point sources of action potentials. The action potential back propagates to dendrites.

Extracellular fields of many neurons strongly overlap.

Can be fooled if you look just in one layer, an action potential recorded in layer IV may actually originate in layer 5. 

Try to infer intracellular features from extracellular recording properties.

Depolarization of the cell during AP chages its intracellular waveform and is refelected in the extracellular waveform. Hence wave-shape sorting algorhitms can be fooled by changing depolarization levels.

Use multidimensional clustering to figure out which spikes come from which neurons.  

Step 2 : From single neurons to neuron types (taxonomy)

Compare the timing of spiking of each neuron relative to the theta cycle.  Also compare in the sharp wave cycle.  

After 6 years of works, they classify constellations of neurons involved in different network patterns.  

Interneurons : Timers and segreators.  Timing is locked to theta cycle.  

Looking at the assembly patterns you can predict the choice of an animal.

see Pastalkova, Science 2008.

In prefrontal cortex.  Use cross-correlation techniques across the population that is recorded.

Short latency large narrow peak indicates monosynaptic excitatory connection. Pyramidal -> Interneurons.  

Short latency spike suppression indicates monosynaptic inhibition. Inter-> Pyr

Has a database of >2500 neurons.  Comparte peak latency and waveshape to the correlation data that defines each as inhibitory or excitatory.  These two groups cluster in the peak latency vs. waveshape space.  So now they have a signature to classify excitatory or inhibitory neurons.

Interacting with circuits

Determine the neuron types

Perturb the correlations with optogenetics to test hypotheses

Simultaneous recoding and stimulation with optical fiber attached to silicon probe (Sebastian Royer @ Janelia)

Figure 8 maze.  

Can record place cells that have characteristic firing pattern during running through the maze.  Can see CHr2 stimulation in some cells.  Neurons fire in stereotyped pattern with theta cycle except when stimulated with ChR2, then it follows the ChR2 stimulation cycle.


Better probes, stimulating on every shank rather than just one shank.  

Larger density recording sites

Further volume reduction

Identification of neurons

Light activation and scilencing of neurons

Improved neurron clustering algorithims

SFN Neuroscience Picower MIT Party 2008

10 11 2008

Lots of people searching for “SFN MIT party” for this information in google… Here’s the answer you are looking for.  Right next to the convention center. Much more convenient than three years ago at Cobalt.  No excuse to be late…

Someone send me an invite to the Neuron, Nature and Emory parties, pretty please! 


Is that a man in a tuxedo wearing stilts?

Is that a man in a tuxedo wearing stilts?



The Picower Institute, The McGovern Institute, and the Department of Brain and Cognitive Sciences at MIT

Invite you to the sixth annual party at the 2008 meeting of the Society for Neuroscience

Monday, November 17th, 2008  

9pm – 2am  

Avenue Nightclub
649 New York Ave NW

Washington, DC

Brain Cell Biology – Optogenetics Probes Issue

7 11 2008

Most of the articles for the Optogenetics Probes Issue of Brain Cell Biology, edited by Ryohei Yasuda and George Augustine are out now. This journal used to be called the Journal of Neurocytology, but they have rebranded it.  Having a full issue devoted to the hot topic of optogenetic probes will doubtlessly raise the impact factor.


G-CaMP2 characteristics

G-CaMP2 characteristics



The issue include two papers on engineered Halorhodopsins with improved expression from Deisseroth and Feng. Also, there are reviews of genetically-encoded calcium, chloride, cyclic nucleotide and voltage sensors. There are a few papers reporting new findings with applications of these probes.

My own contribution is part-review, part fresh data, part modeling of genetically-encoded calcium indicators.  We examine the properties of these indicators, what things to consider when selecting them and where improvements will likely be made. If you are currently using a GECI or are planning to in the future, its probably worth a read.  Feedback welcome!

A brief history of calcium imaging

8 10 2008

A few months ago I threw together a short presentation on the history of calcium imaging for a journal club here at Janelia. It is incomplete. It lacks notes. It is technical. It focuses much attention on early genetically-encoded indicators. However, calcium imaging is so intertwined with the work of Roger Tsien, my Ph.D. thesis advisor, and since he just won the Nobel Prize, I thought it might be of interest to some of the audience of Brain Windows. It does provide a little bit of background for some of the more recent developments chronicled on this site.


Back again… + GFP mutation database

3 09 2008

Just got back from a week in the desert at Burning Man.  This was the year of Dune-like duststorms, with two 8-hour whiteouts.  Need to build a better shade structure next year…  

Some big GECI papers have come out recently, the TN-XXL paper from Griesbeck, and the “1-AP” D3cpv paper from Maz Hasan. Later this week, we will take a critical look at these two papers, and hopefully get back to that GECI shootout paper from last month.

Till then, check out this awesome spreadsheet of fluorescent protein sequences and mutations from George McNamara.

Deep & local Channelrhodopsin-2 two-photon activation

17 07 2008

An interesting paper on two-photon activation of channelrhodopsin-2 is out in Biophysical Journal. In In-depth activation of ChR2 sensitized excitable cells with high spatial resolution using two-photon excitation with near-IR laser microbeam, Mohanty et. al show cellular activation with a fast-scanning two-photon laser.

Action potential generation from Channelrhodopsin-2 with a two-photon beam has been difficult to achieve, presumably due to the small activation volume of the 2p spot. They show similar calcium transients in response to 2p stimulation as with one-photon stimulation. As depth increases, the one-photon response attenuates faster than the two-photon. Unfortunately, the supplemental info with  electrophysiology traces are not yet online.  Presumably, they are generating action potentials, but I’d like to see the raw data.  Interestingly, they also show calcium increases when the laser stays in once place.  This would imply that local depolarization causes local voltage-gated calcium channels to open, or that calcium is getting through the ChR2. I was under the impression that ChR2 has a low conductance for calcium, though this study by Caldwell et. al, in press for JBC, uses ChR2 specifically for its calcium permeability.

I’m not sure what to make of the first paper. Are they really able to fire action potentials with two-photon stimulation, at depth?  Or are the calcium traces they are seeing simply the result of localized calcium flux.  I’ll followup once the Supplemental Data becomes available.  Still worth a look if this is the sort of thing you are interested in.

Voltage sensitive imaging powering up

8 07 2008

I’m starting to come around on voltage imaging. I haven’t been a fan of it for a number of reasons.

  • The response sizes suck.  Classic dyes and genetically encoded systems get a few percent fluorescence change at best. 
  • The response speeds suck. Measuring continuous current injections from -100mV to +150mV is not very interesting.  Action potentials are interesting.  But they are fast.
  • Toxicity. The dyes kill neurons, or strongly perturb their electrical properties.

OK, voltage-sensitive imaging isn’t totally useless, for example see Carl Petersen’s recent paper on Spatiotemporal Dynamics of Cortical Sensorimotor Integration in Behaving Mice (2007). But if the above problems could be solved, then voltage sensitive imaging would be a strong competitor to calcium imaging for the non-invasive, high-resolution monitoring of patterns of network activity. There has been considerable progress ameliorating these problems in the past few years, much of it by a consortium of labs (Isacoff, Knöpfel, Bezanilla, Miesenböck, and others) focused on these issues. (Umlaut’s apparently help in this field).

First, let’s look at a minor breakthrough for the fully genetically-encoded strategy.  In Engineering and Characterization of an Enhanced Fluorescent Protein Voltag Sensor (2007), The Knöpfel group tagged the recently discovered voltage sensitive phosphotase (Ci-VSP) with CFP and YFP FRET pairs in place of the phosphotase domain. This tagged protein expressed at the membrane much more efficiently than previous genetically encoded voltage sensors based on potassium channel subunits. By injecting physiological voltage changes and averaging 50-90 traces, they were able to pull out a few percent ratio change from a brief series of action potentials. Single spikes were resolvable.  Although this sensor (VSFP2.1) was pretty slow (tau > 10ms), this new substrate looked promising for future sensor development.

They have since sped the response up.  In Engineering of a Genetically Encodable Fluorescent Voltage Sensor Exploiting Fast Ci-VSP Voltage-Sensing Movements (2008 ), they determined that the gating motion of the voltage sensing component was very fast (~1ms), while the fluorescence change was slow (~100ms). So they did what any good FRET tinkerer would do, chop away at the linkers between FP components.  The sensor response improved, and they noticed that there was a disconnect between the speed of the CFP and YFP responses. Not only was CFP decreasing from enhanced FRET, it was being directly quenched by interactions with the lipid membrane. Chopping off the YFP from the the construct then dramatically increased the speed of the CFP quench. This improved sensor, VSFP3.1 has an activation time constant of 1.3ms, though it’s response magnitude is still quite small (a few % dF/F).

A hybrid approach to measuring electrical activity in genetically specified neurons (2005) has a much greater response magnitude. Pancho Bezanilla’s group exploited the rapid, voltage-dependent translocation of the small molecule quencher dipicrylamine (DPA) through the plasma membrane to change the fluorescence of membrane-teathered GFP in a voltage-dependent manner. Responses of the hybrid voltage sensor (hVOS) were relatively large (34% per 100mV) and fast (0.5ms). Single action potentials were detectable without averaging.  However, since DPA is a charged molecule, it significantly increased the capacitance of the membrane. The levels of DPA required to see large responses inhibited action potentials and were intolerable to neurons.

Last month in Rational Optimization and Imaging In Vivo of a Genetically Encoded Optical Voltage Reporter (2008 ), Sjulson and Miesenböck reported optimized parameters for the hVOS approach. They built a quantitative model of the quenching effects of DPA on membrane-teathered GFP.  The quenching is limited by the distance the DPA can approach the chromophore of GFP.  Only the closest DPA molecule to the chromophore significantly contributes to a GFP’s quenching. After lots of pretty heat maps and graphs, the model tells them to chop off the tail of EGFP to bring the C-terminal tethering sequence closer to chromophore. I should note that an 11 amino acid C-terminal truncation of ECFP has improved the response of a tremendous number of FRET reporters and has been standard practice for the last 8 years. By shortening the linker they manage to triple the response size. I’d suggest, if they haven’t already, to lop off another six amino acids (end the EGFP with …LEFVTAA) and see if works.  EGFP and ECFP usually tolerate it.

Using this optimized reporter, they are able to reduce DPA concentrations to levels that are usable in vivo, at least for a few minutes. They record fast optical responses to electrical activity in the Drosophila antennal lobe using 2uM DPA.  But after a few minutes, the DPA loaded neurons become strongly inhibited.


The bottom line? Voltage-sensitive imaging has seen big progress in the last few years, but still has a long way to go to gently record single APs in a dish or in vivo. Or does it?  I’m hearing whispers that a different group has developed a synthetic dye technique that is getting >10% dF/F to single APs with millisecond response times. Is it the real deal? Watch this space…

Large-scale model of mammalian thalamocortical systems

28 02 2008

I’ll be the first to admit that my limited focus area in neuroscience, the levels of molecules and cells, biases me to attend to the trees, while missing the patterns in the whole forest. Many of our best recording and imaging methods, single-unit electrophysiology, fluorescence imaging and multi-unit extracellular arrays give us access to only a very tiny piece of the brain at any given moment. Yet endogenous neural activity is dependent on powerful (and subtle) interactions between geographically distant regions of the brain. Whole-brain measurement techniques, such as functional magnetic resonance imaging, magnetoencephalography, and diffusion tensor imgaging, can measure these interactions, but tend to have poor temporal-spatial resolution. Therefore, in order to understand the gestalt of the brain, it is useful to examine models that integrate our knowledge of single cell morphology and activity patterns, local circuitry, and distant connectivity.

The complexity of any brain model is limited by the computational power available. With 10^11 neurons and 10^15 synapses in the brain, each with a wide degree of possible synaptic strength, the computational power required to precisely model the entire brain is currently unavailable (though the Blue Brain project is trying). But maybe we don’t need every neuron. Maybe we can cull this 10^11 number down to something more manageable, a million, that proportionally represent a large number of the neuronal classes of the cortex. In this PNAS paper, Large-scale model of mammalian thalamocortical systems by Eugene Izhikevich and (Nobel laureate) Gerald Edelman, they demonstrate that many endogenous-like patterns of brain activity spontaneous emerge from just this sort of reduced model.

Brain Model

The model reduces the neuron count, leaves out many deep brain structures, and simplifies spike generation, but leaves intact important long-range connections, cellular morphology, dendritic spike initiation, synaptic plasticity and learning rules and dopaminergic modulation. With a sufficient ‘priming of the pump’ by allowing random miniPSPs to percolate through the network, the wiring organizes itself such that spontaneous activity patterns (delta, beta and gamma oscillations, wave propagation, anticorrelated clustering) emerge that are reminescent of in vivo patterns of activity. They also show the system is chaotic; a single spike added or subtracted evolves a totally different activity pattern after half a second, evoking the specter of determinism and the illusion of free will.

These are still early days, as they have not integrated any sensory input into the system and leave out many important brain structures. Nor do they make any testable predictions with the model. Nevertheless, the model appears to be readily extensible, and as greater understanding of brain regions and greater computing power become available, the power of the model may dramatically increase. For now, this paper is a tantalizing reminder of why many of us were originally attracted to the study of neuroscience, the quest to understand how our brain’s activity creates consciousness, and a context in which to place the little trees we struggle to understand.

Props to Neurochannels for the link.