Atsushi Miyawaki’s lab has developed a series of neat tools for visualizing cell cycle progress.
For zebrafish, the zFucci system consists of two fluorescent proteins, mKO2 and mAG, that are fused to Cdt1 and geminin genes. Cell cycle- regulated proteolysis of these fusion proteins causes each cell to display orange fluorescence in G1 phase nuclei and green fluorescence in both the nucleus and cytoplasm of S/G2/M phase cells.
Video of cell cycle transitions in culture. Click for the video.
The last time I saw Atsushi give a talk, he showed an incredible time lapse video from the zebrafish cleavage stage that I haven’t been able to find online. However, here is a video from later in development of the zebrafish that is still pretty remarkable.
Development of a zebrafish visualized by zFucci. Click to see the video.
This two component system has been adapted for watching the transition from neural stem cells to differentiated neurons in living mice. The Color-Timer system uses double transgenics with the fluorescent protein KOr fused to nestin and EGFP fused to doublecortin. In this system, neural stem cells fluoresce orange, while newly differentiated neurons fluoresce green.
The cerebral cortex of an E14.5 double Tg mouse embryo of nestin/KOr was time-lapse imaged. Click for video
Sugiyama, M., Sakaue-Sawano, A., Iimura, T., Fukami, K., Kitaguchi, T., Kawakami, K., Okamoto, H., Higashijima, S., & Miyawaki, A. (2009). Illuminating cell-cycle progression in the developing zebrafish embryo Proceedings of the National Academy of Sciences, 106 (49), 20812-20817 DOI: 10.1073/pnas.0906464106
Kanki, H., Shimabukuro, M., Miyawaki, A., & Okano, H. (2010). “Color Timer” mice: visualization of neuronal differentiation with fluorescent proteins Molecular Brain, 3 (1) DOI: 10.1186/1756-6606-3-5
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.2469 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
Wired has a nice lay-person write-up of the rapidly developing field of compressed sensing. This is a technique that allows accurate reconstructions of highly undersampled sparse datasets. This field really took off in 2004 when Emmanuel J. Candès discovered that a tomography phantom image could be reconstructed exactly even with data deemed insufficient by the Nyquist-Shannon criterion. It is probably the hottest topic in imaging theory today.
Modified Shepp-Logan phantom with enhanced contrast for visual perception.
According to this review, Compressed Sensing MRI, its successful application requires three conditions to be met :
Transform Sparsity: The desired image must have a sparse representation in a known transform domain (i.e., it must be compressible by transform coding),
Incoherence of Undersampling Artifacts: The aliasing artifacts in a linear reconstruction caused by k-space undersampling must be incoherent (noise-like) in the sparsifying transform domain.
Nonlinear Reconstruction: The image must be reconstructed by a non-linear method which enforces both sparsity of the image representation and consistency of the reconstruction with the acquired samples.
These conditions are well met by MRI imaging. This decoding technique dramatically shortens the required sampling times in an MRI magnet, which reduces the impact of motion artifacts, the bane of high-resolution MRI.
Unfortunately, I don’t think it is very applicable to situations where signal/noise of the underlying source is poor, like counting action potentials in shot-noise limited in vivo calcium imaging. But it’s use is spreading into other related problems, such as mapping the functional connectivity of neural circuitry. Tao Hu and Mitya Chklovvskii apply the compressed sensing algorithms in Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME) from the latest Advances in Neural Information Processing Systems journal. They measure a post-synaptic neuron’s voltage while stimulating sequentially random subsets of multiple potentially pre-synaptic neurons. The sparseness of connectivity allows them to map connectivity much faster than by a sequential method.
Inhibition has a powerful role shaping the network dynamics of the cortex, but most studies of inhibitory circuitry are done in brain slice or anesthetized animals. In Membrane potential dynamics of GABAergic neurons in barrel cortex of behaving mice, Gentet et al use two-photon imaging to guide dual, whole-cell patch clamp of inhibitory and excitatory neurons in the mouse barrel cortex. These mice are head fixed, but awake and naturally whisking. The authors can then see how the membrane dynamics of both subthreshold and suprathreshold voltages are correlated across pairs of cells. Differences between the correlations for excitatory and inhibitory neurons shed light on how cortical circuitry processes sensory information in natural brain states.
For Journal Club #5, Mac Hooks, a post-doc here at Janelia working with Gordon Shepard and Karel Svoboda, walks us through these results. Also, there is a video introduction of the work by the lab head of the paper, Carl Petersen, provided by Cell Press.
Gentet, L., Avermann, M., Matyas, F., Staiger, J., & Petersen, C. (2010). Membrane Potential Dynamics of GABAergic Neurons in the Barrel Cortex of Behaving Mice Neuron, 65 (3), 422-435 DOI: 10.1016/j.neuron.2010.01.006
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 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.
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.
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.
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.
Most people play Quake with a computer mouse, but researchers in David Tank’s lab at Princeton have done it with a living mouse, AND they are recording the intracellular activity of individual neurons of the mouse during the gaming session. As reported in Intracellular dynamics of hippocampal place cells during virtual navigation, the virtual reality environment of the video game was sufficiently realistic to generate place cell activity in the mouse’s hippocampus.
Now where did I see that cheese power-up?
Place cells modulate their activity dependent on the location the mouse is at. They have mostly been identified with extracellular recordings in freely moving mice. Extracellular recording only permits the detection of the rates of action potential firing, rather then the subtle intracellular voltage changes that could help explain the mechanism of place cell activity generation. A few pioneers, such as Albert “my greatest strength is a tremendous capacity for boredom” Lee, have recorded intracellularly in freely moving animals, but these experiments are fiendishly difficult, as the motion of the animal’s head tends to break the seal on the recorded neuron. Only a few cells have been recorded in that manner for more than a few minutes, though the success rate has been improving recently.
Experimental setup
In Chris Harvey’s technique, they fix the head of the mouse to a bar and let the mouse walk on a floating ball, while a virtual reality screen is projected in the mouse’s field of view. The motion of the ball controls the motion on the screen. The head never moves, so intracellular recordings can be made relatively easily and held for long periods of time.
The authors find three characteristics of place cell activity that could explain their generation and function.
“An asymmetric ramp-like depolarization of the baseline membrane potential, an increase in the amplitude of intracellular theta oscillations, and a phase precession of the intracellular theta oscillation relative to the extracellularly recorded theta rhythm.”
Intracellular voltage dynamics in place cells
These could be used to explain how place cells remap their selectivity when a mouse (or a human) moves into a new environment. This also could be used to do more in depth studies of the mental replay of place locations that has been previously recorded in the activity patterns of the hippocampus. The technique itself is about as sexy as neuroscience gets. Unfortunately, this paper also provides an additional piece of evidence for Karel to use in motivating lab post-docs, “Look at Chris, he left the lab after you got here and already has a Nature article…”‘
One of the most time consuming and frustrating tasks associated with fluorescence imaging in the brain is picking out your regions of interest. Which pixels do you include in as part of the cell and which are part of the surrounding neuropil? Often, the answer is not obvious, and even with painstaking selections you can make errors. Eran Mukamel et. al, from Mark Schnitzer‘s lab just published this Neurotechnique Automated Analysis of Cellular Signals from Large-Scale Calcium Imaging Data that aims to simplify and improve the results of ROI selection.
The authors used a multistage approach to identify and quantify the calcium-dependent fluorescence changes of imaged neurons. First, they used principal component analysis to identify the components of the image that were likely calcium signal related and which were noise. The sparse nature of the calcium response (calcium transients are brief and spatially confined) helped the separation from the noise. They threw the noise away. Then they used independent component analysis to pick out which components of the calcium signal changed in a manner independent from other pieces of the signal. These likely represent individual cells. Using this output, they performed auto-segmentation of the image into numerous individual neurons or processes and measured the fluorescence change in those regions. In simulations of data, it resulted in superior data fidelity over hand drawing ROIs. They also validated it with real in vivo calcium imaging.
Automated Cell Sorting Identifies Neuronal and Glial Ca2+ Dynamics from Large-Scale Two-Photon Imaging Data
Whether its neuronal imaging, high-speed motion tracking or multielectrode recordings, tremendously large data sets are currently being generated in systems neuroscience. It is simply impossible for a single post-doc to crunch all of her data without major automated computational techniques. In calcium imaging, the resources that have been poured into the development and release of powerful new tools requires an equal effort on the data analysis end to maximize the value of this technique. The automated algorithms presented in this paper look very promising and we will definitely be checking them out in the near future.
Mark Schnitzer, who recently became an HHMI Investigtor, has a new paper out on improved optics for his group’s miniature probe microscopes. Mark has been pioneering these tiny probes to do optical imaging in deep brain structures, and is one of the only games in town if you want to look deeper then a millimeter into the brain without sucking off all the ‘irrelevant mush’ in between your microscope and the part of the brain you are interested in. A beer-lubricated former Bell Labs employee (not Karel) years ago confided to me that he wasn’t too impressed with these microprobe systems,because “its just a GRIN lens on a stick”, ignoring the painstaking engineering and minaturization of motor, supports and light paths. The limitations were quite significant though, in this new Nature Methods communication, In vivo fluorescence imaging with high-resolution microlenses, Barretto et al. note that “The best Rayleigh resolutions values achieved by two-photon fluorescence imaging with GRIN lenses are ~1.6 um lateral and ~12um axial, yielding highly elongated point spread functions that impede acquisions of high-quality, three-dimensional image stacks.” That elongated point spread function really blurs the image and dramatically reduces the power and brightness of the two-photon imaging mode. I assume this is why most of the data I’ve seen Mark present was with one-photon illumination. Now, the authors have addressed this shortcoming.
Design of the lens system. The resolution is comparable to a standard 40x microscope objective.
They coupled a plano-convex lens to a custom fabricated GRIN lens whose refractive properties were designed to compensate for the spherical aberration of the plano-convex lens. This system achieved near diffraction-limited resolution in both the lateral and axial dimensions. Rather then the dim blur of previous iterations, the new system clearly resolves synaptic spines on the dendrites of fluorescent neurons buried deeply in the hippocampus of live mice. The example system in the paper has a 1mm diameter, while the previous systems were as narrow as 0.3mm, so there is still plenty of room for further miniaturization, although I’m not sure how that would affect the light gathering capacity of the lens. Importantly, Mark’s systems are finally getting commercialized so that a much larger scientific population can start to benefit from the technology soon.
A hippocampal neuron (e) in a live mouse visualized with the new system (c) vs. the old system (d)
Here’s a group of four recent papers that are worth checking out but I don’t have the time to cover. The first provides a set of tools for neuronal circuit tracing. The second pushes super-resolution imaging into fast, live-cell imaging. The third, by a friend from graduate school, uses G-CaMP to make strong claims about olfactory coding in fruit flies. The last reports remarkable data pointing to the distributed nature of conscious perception in humans, which would have been a great data set to reference in my recent talk on free will.
We developed retrograde, transsynaptic pseudorabies viruses (PRVs) with genetically encoded activity sensors that optically report the activity of connected neurons among spatially intermingled neurons in the brain. Next we engineered PRVs to express two differentially colored fluorescent proteins in a time-shifted manner to define a time period early after infection to investigate neural activity. Finally we used multiple-colored PRVs to differentiate and dissect the complex architecture of brain regions.
Structured-illumination microscopy can double the resolution of the widefield fluorescence microscope but has previously been too slow for dynamic live imaging. Here we demonstrate a high-speed structured-illumination microscope that is capable of 100-nm resolution at frame rates up to 11 Hz for several hundred time points. We demonstrate the microscope by video imaging of tubulin and kinesin dynamics in living Drosophila melanogaster S2 cells in the total internal reflection mode.
Fruitflies show robust attraction to food odours, which usually excite several glomeruli. To understand how the representation of such odours leads to behaviour, we used genetic tools to dissect the contribution of each activated glomerulus. Apple cider vinegar triggers robust innate attraction at a relatively low concentration, which activates six glomeruli. By silencing individual glomeruli, here we show that the absence of activity in two glomeruli, DM1 and VA2, markedly reduces attraction. Conversely, when each of these two glomeruli was selectively activated, flies showed as robust an attraction to vinegar as wild-type flies. Notably, a higher concentration of vinegar excites an additional glomerulus and is less attractive to flies. We show that activation of the extra glomerulus is necessary and sufficient to mediate the behavioural switch. Together, these results indicate that individual glomeruli, rather than the entire pattern of active glomeruli, mediate innate behavioural output.
Parietal and premotor cortex regions are serious contenders for bringing motor intentions and motor responses into awareness. We used electrical stimulation in seven patients undergoing awake brain surgery. Stimulating the right inferior parietal regions triggered a strong intention and desire to move the contralateral hand, arm, or foot, whereas stimulating the left inferior parietal region provoked the intention to move the lips and to talk. When stimulation intensity was increased in parietal areas, participants believed they had really performed these movements, although no electromyographic activity was detected. Stimulation of the premotor region triggered overt mouth and contralateral limb movements. Yet, patients firmly denied that they had moved. Conscious intention and motor awareness thus arise from increased parietal activity before movement execution.
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