UPDATE : DIADEM Final Results

15 09 2010

The DIADEM automated neuronal reconstruction contest has finished.  Accurate, fast, and high-resolution automated neuron reconstruction is of vital importance to cracking the mystery of how neural circuits perform. Even with perfect knowledge of the firing patterns of every cell in a circuit, our understanding of how these patterns are produced and how the information is processed would be quite limited.  True understanding requires knowledge of the precise wiring diagram.  This prize is a good first step towards bringing awareness of this tricky problem to the world’s best computer scientists.

$75,000 in prize money was to go to the group that was able to produce high-quality reconstructions of neuronal structures at least 20x faster than by-hand reconstructions.  In the finals, the fastest speed achieved was 10X the by-hand method. Some groups were hindered by slight variances in the source data formatting, which normally isn’t a big deal unless you only have 20 minutes to produce as much reconstruction as possible…

Since no group was able to beat the hard floor, but substantial progress was made, the money was distributed amongst these finalists.

Badrinath Roysam Team, $25,000
“for the better overall generality of their program in producing robust reconstructions by integration of human and machines interactions.”

Armen Stepanyants Team, $25,000
“for the better overall biological results in the spirit of pure automation.”

Eugene Myers Team, $15,000
“for the excellent quality and strength of their algorithm.”

German Gonzalez Team, $10,000
“for their deeper potential, more original approach, and ultimate scalability of their proposed solution.”

Deniz Erdogmus Team
“for elevating themselves above the current state of automated reconstructions…with a deep understanding of the technical and scientific problems.”

Congrats to the placing teams.

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Software Update : Ephus, ScanImage & Neuroptikon

20 08 2010

Three excellent pieces of neuroscience software have been recently updated or freshly released.  I have used two of them, Ephus and ScanImage, on a daily basis as primary data collection tools. The third, Neuroptikon, is quite useful for post-hoc illustration of neural circuits.

Ephus is a modular Matlab-based electrophysiology program that can control and record many channels of tools and data simultaneously.  Under control of a sophisticated internal looper or external trigger, you can initiate an ephys recording, trigger camera frames, adjust galvo positions, open/close shutters, trigger optical stimulation, punishments, rewards, etc.  It is a workhorse program for non-imaging related in vitro and in vivo electrophysiology experiments.  Ephus is named for the fabled baseball pitch, and pronounced as “EFF-ess”. As with the pitch, it may trick you at first, but eventually you’re sure to hit a home run. Of course, the name also evokes electrophysiology, which is the fundamental orientation of the project, be it optical or electrical.

Ephus 2.1.0 is a major release, and the only official version at this time.  The software is fully described in a publication in Frontiers in Neuroscience. New features include unlimited recording time, with disk streaming, for applications such as EEGs and long traces during in-vivo behavior. A number of additional scripts for in-the-loop control have been added. New configuration/start-up files have been created, with a template to help get up and running quickly. This release also includes a number of bug fixes.

ScanImage is another Matlab-related software program that is used for optical imaging and stimulation of neurons in vitro and in vivo.  It finds much use a control platform for 2-photon imaging, glutamate uncaging and laser-scanning photostimulation.  An early incarnation is described in this paper by Pologruto, et al.  It provides a lot of power right out of the box (bidirectional scanning @ 0.5ms/line, etc) and is easily extensible via custom user function plugins.

Neuroptikon is a sophisticated network visualization tool.  It can build Van Essen-like diagrams of any circuit you like, but it is so much more.  The direction of communication is animated, and subsets of regions and connections can be brought into focus, which greatly eases the clarity of the network.  The diagrams can be built in three-dimensions, to preserve relative topography, or functional grouping.  There is simple GUI-based control, while more complex tasks can use a scripting interface.  This is great software for anyone who needs to imagine information flow in a complex network.

All three tools are released for free use under the HHMI/Janelia Farm open source license.

Download Here :

Ephus 2.1.0

ScanImage 2.6.1

Neuroptikon 0.9.9





Cameleon-Nanos : High Affinity GECIs

9 08 2010

Takeharu Nagai’s lab has published in Nature Methods, Spontaneous network activity visualized by ultrasensitive Ca2+ indicators, yellow Cameleon-Nano, demonstrating a new set of calcium indicators based on yellow cameleon. Back when he was still Take-san, Take’s ability to churn out and manually screen hundreds of cameleon variants was impressive and inspiring. With high-throughput GECI pipelines now ramping up at Janelia, the idea of laboriously screening 200 variations on a theme (be it cameleons or GluSnFRs), seems a bit archaic. However, this paper is a good example of the progress that can still be made by understanding the needed sensor parameters and fiddling with the primary amino acid structure in a relatively low-throughput way. Take-sensei’s results are another example of the pramatic rule in protein design, “when in doubt, tinker with the linker.”

The cameleon-nano family achieves greater apparent calcium affinity than YC2.60-4.60, reaching levels of up to 15nM.  They did this by increasing the flexibility of the linker by extending the standard Tsien/Miyawaki/Baird Gly-Gly-Ser linker with additional glycines.  In this case, the longer the linker between the CaM and M13 segments, the greater the apparent affinity. Interestingly, improvement by increasing linker flexibility is precisely the opposite the advice Atsushi and Take gave me for achieving high ratio changes with FRET reporters.  Back at RIKEN in 2002, they suggested I use short, stiff linkers to restrict the rotational freedom of the fluorescent pairs.  Then one could find orientations where relative rotation of dipole moment gave much greater FRET changes than would be expected from changes in FP distance alone. Take and Atsushi’s big YC2.60/3.60 paper strongly supported this idea!  However, as our understanding of the ideal parameters of calcium sensor’s for in vivo imaging has grown, development directions have adjusted.

Cameleon-Nanos achieve higher signal/noise for sparse action potentials at the expense of linearity.  Like Fluo-4, the signal saturates at relatively low AP frequencies.  I think the absolute affinities measured for this family (15, 30, 50 and 140nM) should be considered very rough estimates. They extrapolated these values from stopped-flow binding experiments, because

Although we would like to measure the koff of YC2.60 and its high affinity variants such as YC-Nano15, we could not do it because it was very difficult to precisely control free Ca2+ concentration at around few tens of nM as far as we used EGTA (Kd for Ca2+ = 151 nM in 0.1 M ionic strength, pH 7.2 at 25 oC). For this purpose, much stronger Ca2+ chelator with a smaller Kd value was required. However there is no such Ca2+ chelator available now.

I’m not sure why they didn’t just use the higher affinity, Mg++ insensitive, chelator BAPTA to make the Kd measurements the right way, with a linear regression of log-log fluorescence/concentration values.  Due to instrument dead time, and the high affinity, I didn’t like stopped-flow based Kd measurements in the early GCaMP papers, and I don’t like them now.  Also, the apparent calcium Kd will be highly dependent on solution ionic strength and [Mg++] which is unreported. Despite these quibbles, which are important only inasmuch as they give insight into the mechanism of improvement and the direction of future development, the cameleon-nano family looks promising for mammalian brain imaging.  I still wonder if, assuming the reported Kd values are relevant in vivo, YC2.60 would be the best of the bunch, since cortical neurons have a resting Kd of ~50nM, which implies that a single AP transient of say 200nM free [Ca++] increase would push the calcium levels right up into the sweet-spot of YC2.60’s sensitivity.

This is all the more interesting given the recent results in YC3.60 imaging from Maz Hasan’s group.  Previously, he had shown that transgenic YC animals were pretty bad for imaging.  However, AAV-mediated gene delivery of YC3.60 has significantly improved the responses of the YC family. I’m not sure if they are really up to GCaMP3 levels under identical in vivo conditions, but they might have better long-term protein stability (or that might depend on which viral serotype is used.) What about cameleon-nanos, what about YC2.60?





CNiFERS of Acetylcholine and Attention

10 03 2010

“If you find yourself needing to reread this paragraph, perhaps it’s not that well written. Or it may be that you are low on acetylcholine.” Acetylcholine (ACh) is a major modulator of brain activity in vivo and its release strongly influences attention. If we could visualize when and where ACh is released, we could more fully understand the large trial to trial variance found in many in vivo recordings of spike activity, and perhaps correlate that to attentional and behavioral states mediated by ACh transmission.

Back in grad school, when I was desperately trying to figure out what biological question to answer with my GluSnFR glutamate sensor, I ended up in a meeting with Kleinfeld, his grad student Lee Schroder and Palmer Taylor. We plotted a strategy to make a FRET sensor for acetylcholine.  Palmer had recently solved crystal structures of an acetylcholine binding protein bound to agonists and antagonists.  Snails secrete this binding protein into their ACh synapses to modulate their potency.  The structures showed a conformational change upon agonist binding.  The hope was that by fusing CFP and YFP to the most translocated bits of the protein, they would be able to see an ACh dependent FRET change.  I was skeptical that it would work, as the translocation was much less than with calmodulin-M13 or periplasmic binding proteins used in Cameleon and GluSnFR, but thought was at least worth a shot.  FRET efficiency is highly dependent on dipole orientation, not just dipole distance, and you never know how a small conformational change might rearrange the FP dipoles…

Of course, the simple idea didn’t work.  Instead of giving up on the first dozen attempts, they kept plugging away at alternative strategies for measuring ACh release, and eventually succeeded.  In this Nature Neuroscience report, An in vivo biosensor for neurotransmitter release and in situ receptor activity, Nguyen et al demonstrate a mammalian cell based system for optically measuring ACh levels in an intact brain.  They coexpressed M1 muscarinic receptors with the genetically-encoded calcium indicator TN-XXL in HEK293 cells.  ACh binding to the M1 receptor induced IP3-mediated calcium influx.  This calcium rise was then picked up by the TN-XXL and reported as a change in CFP/YFP fluorescence.  The crazy part is that they took this cell culture assay and implanted the cells into the brains of living rats!

The CNiFER in vivo experimental paradigm

In culture, the response was highly sensitive and monotonic (for phasic response section, EC50 of 11 nM, a Hill coefficient of 1.9 and a maximum of ΔR/R = 1.1). In vivo, using two-photon imaging through a cortical window, they were able to see clear ACh responses in frontal cortex from electrical stimulation of the nucleus basalis magnocellularis, typically 200-μs current pulses of 200 μA @ 100Hz for 20-500ms.

This was essentially a in vivo proof of principal experiment, showing that one could image ACh release in spatially and temporally precise regions of the brain.  However, the imaging was done under urethane anesthesia, which is a much different brain state than an awake, behaving animal.  Are CNiFERs sensitive, powerful and stable enough to determine behavioral states via imaging in an awake animal?  Would expressing GCaMP3 (an indicator with greater fluorescence dynamic range) improve the performance of the CNiFER system? We used a very similar assay with ACh applied to HEK cells during the initial screens for better GCaMPs. Or, is the performance more limited by the properties of the M1 receptor and the adapting nature of IP3-mediated calcium dynamics?  CNiFERS provide an interesting platform for looking at ACh and potentially other G-protein mediated signaling, but it remains to be seen if labs that aren’t as technically proficient with two-photon rig will find it more useful than cyclic voltammetry for measuring acetylcholine levels.

Nature Neuroscience, 13 (1), 127-132 DOI: 10.1038/nn.2469ResearchBlogging.org
Nguyen, Q., Schroeder, L., Mank, M., Muller, A., Taylor, P., Griesbeck, O., & Kleinfeld, D. (2009). An in vivo biosensor for neurotransmitter release and in situ receptor activity





Adaptive Optics for In Vivo Microscopy

13 01 2010

Imaging fluorescence in an intact, living brain is difficult due to absorption and scattering of excitation and emission light.  Two photon microscopy uses excitation light in the narrow optical window (700-950nm) where water and hemoglobin do not significantly absorb, which allows structure determination and functional imaging down to depths of ~600nm from the surface of the brain.  However, scattering of the excitation light still occurs at these wavelengths, which distorts the excitation volume and causes a rapidly increasing fluorescent background at greater depths.

The vasculature was labeled by injecting flourescein dextran into the circulatory stream. The light source was a regenerative amplifier. ‘‘0 mm’’ corresponds to the top of the brain. Left, XZ projection. Right, examples of XY projections. Note the increase in background fluo- rescence deeper than 600 mm in the brain due to out-of-focus 2PE. (Theer et al., 2003)

In order to reduce this background and sharpen the two-photon excitation volume, Ji et al in Adaptive optics via pupil segmentation for high-resolution imaging in biological tissues adapt tricks from astronomy (see also Rueckel et al 2006). Depending on where each ray of excitation light enters the brain, the angles of scattering are different.  By sequentially illuminating with spatially restricted subsections of full illumination beam, they see how the brain warps the light from each part of the beam.  Then they use a flexible mirror and to change the angle and phase of the excitation beam so that each part of the beam comes together in the same place and phase. Since two-photon fluorescence scales as the square of the intensity of the excitation light, this dramatically improves both the resolution and the signal to noise of the fluorescence image.

Scattering in the brain warps two-photon excitation light, but adaptive optics can correct this.

Pre-warping the excitation light to cancel the scattering improves fluorescence localization and intensity.

How well will this work in the brain of a living animal?  It’s not clear. Large differences in scattering paths across a field of view may dramatically slow the determination of optimal excitation beam warping and make it difficult to scan across a field of view quickly. Motion of the brain may change the pattern of scattering faster than the system can adapt.  Still, the use of adaptive optics is one of the few promising techniques for increasing penetration depth and signal in optical brain imaging (not counting sucking off the bits of the brain that are “in the way” of the excitation beam.) There are other optical tricks in astronomy that have not yet been mined for neuroscience applications.  Hopefully these will one day allow the functional imaging of all layers of the mammalian cortex, not just layer 2/3.





Three Cheers for GCaMP : Optogenetic Brain Reading

9 11 2009

Three papers are out online in Nature Methods that show big improvements in calcium imaging with genetically encoded sensors.  They are are based on the fluorescence intensity indicator, GCaMP.   GCaMP, first developed by Junichi Nakai, consists of a GFP that has been circularly permuted so that the N and C termini are fused and new termini are made in the middle of the protein.  Fused to one terminus is calmodulin and the other is a peptide, M13, that calmodulin (CaM) binds to in the presence of calcium. The name is supposed to look like GFP with a CaM inserted into it, G-CaM-P.  Normally the GFP is dim, as there is a hole from the outside of its barrel into the chromophore.  Upon binding calcium, this hole is plugged and fluorescence increases.

Crystal structure of GCaMP2

The first paper, A genetically encoded reporter of synaptic activity in vivo, from Leon Lagnado’s group, targets GCaMP2 to the outer surface of synaptic vesicles. This localization allows the fluorescence signal to be confined to the presynaptic terminal, where calcium fluxes in response to action potentials are high.  This targeting improves the response magnitude of GCaMP2 and permits the optical recording of synaptic inputs into whatever region of the brain one looks at.  They demonstrate the technique in live zebrafish.

In the second paper, Optical interrogation of neural circuits in Caenorhabditis elegans, from Sharad Ramanathan’s group, GCaMP2 has been combined with Channelrhodopsin-2 to perform functional circuit mapping in the worm.   Since the worm’s structural wiring diagram has been essentially solved, functional data could say much about how “thick” the wires between each cell are.  Unfortunately, with GCaMP2, the responses are too slow and weak to distinguish direct from indirect connections.

Finally, we have published a paper, Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators, describing the improved GCaMP3.  This indicator has between 2-10x better signal to noise than GCaMP2, D3cpv and TN-XXL, depending on the system you are using.  It’s kinetics are faster and it is more photostable than FRET indicators, and the responses are huge.  When expressed in motor cortex of the mouse, neuronal activity is easily seen directly in the raw data.  Furthermore, the sensor can be expressed stably for months, making it a potential tool for observing how learning reshapes the patterns of activity in the cortex.

Screen shot 2009-11-09 at 7.19.27 PM

Imaging of mouse motor cortex (M1) expressing the genetically-encoded calcium indicator GCaMP3 through a cortical window. After 72 days of GCaMP3 expression, large fluorescence transients can be seen in many neurons that are highly correlated with mouse running.

GCaMP3 is not perfect. It cannot reliably detect single action potential in vivo in mammals, though I doubt that any existing GECI can. Work continues on future generations of GCaMP that may achieve 100% fidelity in optical reading of the bits in the brain. However, there is considerable evidence from a number of groups that have been beta-testing the sensor, including the Tank lab of “quake mouse” fame, that it is a significant leap forward and unlocks much of the fantastic and fantasized potential of genetically-encoded calcium indicators.

Screen shot 2009-11-09 at 7.20.12 PM

Comparison of fluorescence changes in response to trains of action potentials in acute cortical slices.

I will try to post a more complete writeup of GCaMP3 for Brain Windows soon, with an unbiased eye to its strengths and weaknesses.  We worked very hard to carefully characterize this sensor’s effects on cellular and circuit properties.  If you have any questions about GCaMP3, please post them to the comments.

For further info about strategies for GECI use and optimization, check out our previous paper, Reporting neural activity with genetically encoded calcium indicators in Brain Cell Biology.

The official press release from HHMI regarding GCaMP3 is available here.





Light-switchable protein interactions

16 09 2009

A fully genetically-encoded approach to light-activated transcription is getting closer now that a new, generalizable method of light-switchable protein interactions has been published.  In Nature’s advance online publication, Spatiotemporal control of cell signalling using a light-switchable protein interactionAnselm Levskaya of the Voigt lab at UCSF and co-authors demonstrate inducible, reversible control of protein binding, localization and signalling in mammalian cells.  

apo-PhyB covalently binds to the chromophore phycocyanobilin (PCB) to form a light-sensitive holoprotein. PhyB undergoes conformational changes between the Pr and Pfr states catalysed by red and infrared light, reversibly associating with the PIF domain only in the Pfr state. This heterodimerization interaction can be used to translocate a YFP-tagged PIF domain to PhyB tagged by mCherry and localized to the plasma membrane by the C-terminal CAAX motif of Kras.

apo-PhyB covalently binds to the chromophore phycocyanobilin (PCB) to form a light-sensitive holoprotein. PhyB undergoes conformational changes between the Pr and Pfr states catalysed by red and infrared light, reversibly associating with the PIF domain only in the Pfr state. This heterodimerization interaction can be used to translocate a YFP-tagged PIF domain to PhyB tagged by mCherry and localized to the plasma membrane by the C-terminal CAAX motif of Kras.

When asked about the possibility that this could be used in-vivo, Levskaya said

The only real caveat for in-vivo work is delivery of the non-native PCB tetrapyrrole. From the literature and my experience with cell culture I suspect it shouldn’t be hard to just administer it directly to animals to get saturating levels for holoprotein formation. It might even be possible just to feed animals Spirulina (where it comes from). There’s nutrition literature that suggests their livers are capable of freeing PCB and getting it into the blood stream.

 

Observing light-induced Cdc42 activation with a TIRF recruitment biosensor

Observing light-induced Cdc42 activation with a TIRF recruitment biosensor

Expression of genetic tools that control neural activity (Channelrhodopsins, Halorhodopsins, DREADDs) in functionally defined populations, such as neurons that are active during a particular task or thought, is the next big leap that needs to be made in systems neuroscience. This may be achieved by combining an imaging technique to identify active neurons, such as G-CaMP3, with photo-switchable transcription. The technique presented in the above paper is one promising avenue which may lead to cell-specific photo-switchable transcription.  Once robust versions of these tools are in place, scientists will begin to work out the complex and thrilling processes of reverse-engineering and manipulation of specific thoughts and memories, at least in mice and rats.