In two examples of imaging from a mouse ear, (above) shows the distribution of TBO, a photodynamic therapy drug, following drug administration, (below, red) shows the distribution of hemoglobin in blood vessles.
How specific is the detection of endogenous chromophores? They report that 60nm is the absolute detection limit, but this is for a pure chromophore in water. In real cells there will be many other endogenous chromophores at various concentrations. For example, endogenous background fluorescence of flavins is often easily seen when imaging at CFP wavelenghts. Watt Webb has been imaging those types of chromophores for years. The intersection of both a preferred excitation wavelengh and a preferred stimulated emission wavelength will provide some selectivity, but I suspect this will be most useful for imaging the distribution of fairly highly expressed chromophores in vivo. Distinguishing chromophores with highly overlapping spectra may not be possible. Of course, many, many proteins don’t have distinctive chromophores (tyrosine does not count!) built in to them, so GFP won’t be out of work any time soon. However, this stimulated emission imaging doesn’t require transgenic or small molecule labeling, so it could potentially allow imaging in humans.
Do the absorbance and emission spectra and the excited state lifetime provide sufficient selectivity to detect low concentrations of chromophores in vivo?
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.
Speaking of reactivating specific memories, at the 2009 Society for Neuroscience meeting, Matteo Rizzi of Michael Häusser’s lab is presenting the realization of an idea that has been floating around in some research proposals I’ve read over the last year. Express channelrhodopsin-2 under control of the immediate early gene c-fos, induce a strong memory formation via fear conditioning, and then drive the recall of that memory by stimulating the neurons that are expressing ChR2. Immediate early genes are activated shorty after high levels activity in neurons, though the precise patterns are different depending on which promoter (c-fos, Zif268, etc) you use, making precisely HOW they reflect recent neuronal activity patterns unclear. Nevertheless, the activation of the c-fos based pattern seems close enough to trigger an identical behavioral response as the conditioned stimulus.
Not yet, but getting closer...
Electrically-induced fear conditioning is probably the most blunt instrument possible, encoding a very powerful, general ‘fear’ memory, and many things can make a mouse freeze. Thus, this is definitely the low-hanging fruit on the ‘reverse-engineering’ memories tree. Understanding how the information in a memory is distributed across participating neurons is going to take a more sophisticated approach and a lot more work. This result is still incredibly cool, and I’m somewhat surprised it worked by driving ChR2 with c-fos in a hundred cells in the dentate gyrus. That has pretty powerful implications for avenues by which memories can be recalled. Surely the entire memory is not encoded by only the 100 neurons that were activated! How many other neurons participate, and how does the optical stimulation activate the entire ensemble? Is it even necessary to activate the entire ensemble to drive behavior? The poster will be MOBBED. I look forward to reading the details.
Program#/Poster#:
388.8/GG103
Title:
Memory recall driven by optical stimulation of functionally identified sub-populations of neurons
Location:
South Hall A
Presentation Time:
Monday, Oct 19, 2009, 10:00 AM -11:00 AM
Authors:
*M. RIZZI, K. POWELL, J. HEFENDEHL, A. FERNANDES, M. HAUSSER;
Wolfson Inst. for Biomed. Res., UCL, London, United Kingdom
Abstract:
The mammalian brain is capable of storing information in sparse populations of neurons encompassing several brain areas. Immediate recall of this information is possible upon presentation of a cue or context. Most aspects of this process remain unresolved: are the cells involved in information storage also responsible for its recall? What portion of this distributed circuit needs to be reactivated, in order to achieve successful recall? To answer these questions we selectively expressed a genetically encoded optogenetic probe (Boyden et al., 2005) in neurons engaged during the learning of a specific association. A plasmid encoding channelrhodopsin-2 and EGFP under an immediate early gene promoter (c-fos-ChR2-IRES-EGFP) was electroporated in vivo into granule cells (GCs) of the dorsal dentate gyrus of anaesthetized C57BL/6 mice. Mice were allowed to recover, and then underwent classical delay fear conditioning (consisting of 10-20 pairings of a 5 second auditory tone and a 2 second footshock). An optic fiber was implanted intra-cranially to allow optical stimulation of transfected neurons. Light stimulation (λ = 530 nm; 5 Hz) successfully induced recall of the fear memory, measured as freezing behaviour (n = 27 animals). Post-hoc analysis of the transfected tissue revealed that a remarkably small subpopulation of GCs (<~100 cells) was sufficient to cause this effect. We then tested whether any, comparatively sized, subset of GCs could be equally effective. We transfected neurons with a plasmid encoding ChR2 expression under a general promoter (pCAG-ChR2) to obtain ChR2 expression in a random population of cells. Interestingly, optical stimulation of this population was insufficient to induce memory recall (population data: n=30). Our results therefore suggest that recall of a learned association, sparsely stored in neuronal circuits distributed over several brain areas, can be achieved by the simple reactivation of a very small subset of neurons involved in learning this association. Furthermore, our strategy may also be useful for dissecting the complexities associated with memory storage and recall.
Support: Gatsby Charitable Foundation; Wellcome Trust
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
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.
I have been itching to post about this work since David DiGregorio presented it at a meeting at Janelia last year. His group’s results, Submillisecond Optical Reporting of Membrane Potentials In Situ Using a Neuronal Trace Dye, were published in the Journal of Neuroscience last week. Their method of optical voltage sensing is the first one that looks like its ready for “prime-time” action outside of the labs of developers of these sorts of techniques. It has sufficient speed (<1 ms resolution), sensitivity (25% dF/F per 100mV), and limited membrane perturbation to see single action potentials, without dramatically altering the shape of these currents.
Membrane depolarization causes DPA to rapidly partition to the inner membrane leaflet, quenching DiO.
Like previous methods, Bradley et al. use voltage-dependent membrane partitioning of dipicrylamine (DPA), a charged small molecule, to quench a fluorophore via FRET. Previously, high-concentrations of DPA were required to have a reasonable signal change, which caused toxicity, increased membrane capacitance and slowed voltage transients. By using DiO, a lipophilic neuronal tracer, as the fluorophore, the DPA concentration could be reduced to 1uM, while retaining sufficient optical sensitivity for action potential detection.
Annual Reviews of Neuroscience published their 2009 issue recently. These articles are usually a great way to catch up with a field, particularly when they are recently published. Here are a few that might be of interest to the Brain Windows reader.
Sensory experience and learning alter sensory representations in cerebral cortex. The synaptic mechanisms underlying sensory cortical plasticity have long been sought. Recent work indicates that long-term cortical plasticity is a complex, multicomponent process involving multiple synaptic and cellular mechanisms. Sensory use, disuse, and training drive long-term potentiation and depression (LTP and LTD), homeostatic synaptic plasticity and plasticity of intrinsic excitability, and structural changes including formation, removal, and morphological remodeling of cortical synapses and dendritic spines. Both excitatory and inhibitory circuits are strongly regulated by experience. This review summarizes these findings and proposes that these mechanisms map onto specific functional components of plasticity, which occur in common across the primary somatosensory, visual, and auditory cortices.
Diffusion imaging can be used to estimate the routes taken by fiber pathways connecting different regions of the living brain. This approach has already supplied novel insights into in vivo human brain anatomy. For example, by detecting where connection patterns change, one can define anatomical borders between cortical regions or subcortical nuclei in the living human brain for the first time. Because diffusion tractography is a relatively new technique, however, it is important to assess its validity critically. We discuss the degree to which diffusion tractography meets the requirements of a technique to assess structural connectivity and how its results compare to those from the gold-standard tract tracing methods in nonhuman animals. We conclude that although tractography offers novel opportunities it also raises significant challenges to be addressed by further validation studies to define precisely the limitations and scope of this exciting new technique.
The ultimate goal of neural interface research is to create links between the nervous system and the outside world either by stimulating or by recording from neural tissue to treat or assist people with sensory, motor, or other disabilities of neural function. Although electrical stimulation systems have already reached widespread clinical application, neural interfaces that record neural signals to decipher movement intentions are only now beginning to develop into clinically viable systems to help paralyzed people. We begin by reviewing state-of-the-art research and early-stage clinical recording systems and focus on systems that record single-unit action potentials. We then address the potential for neural interface research to enhance basic scientific understanding of brain function by offering unique insights in neural coding and representation, plasticity, brain-behavior relations, and the neurobiology of disease. Finally, we discuss technical and scientific challenges faced by these systems before they are widely adopted by severely motor-disabled patients.
Brian A. Wilt, Laurie D. Burns, Eric Tatt Wei Ho, Kunal K. Ghosh, Eran A. Mukamel, and Mark J. Schnitzer
Since the work of Golgi and Cajal, light microscopy has remained a key tool for neuroscientists to observe cellular properties. Ongoing advances have enabled new experimental capabilities using light to inspect the nervous system across multiple spatial scales, including ultrastructural scales finer than the optical diffraction limit. Other progress permits functional imaging at faster speeds, at greater depths in brain tissue, and over larger tissue volumes than previously possible. Portable, miniaturized fluorescence microscopes now allow brain imaging in freely behaving mice. Complementary progress on animal preparations has enabled imaging in head-restrained behaving animals, as well as time-lapse microscopy studies in the brains of live subjects. Mouse genetic approaches permit mosaic and inducible fluorescence-labeling strategies, whereas intrinsic contrast mechanisms allow in vivo imaging of animals and humans without use of exogenous markers. This review surveys such advances and highlights emerging capabilities of particular interest to neuroscientists.
A big advance in non-invasive neuronal remote control was published today in Neuron. Several groups have been working on expressing non-endogenous or customized receptors into neurons so that specific genetically selected neurons can be turned on or off. Channelrhodopsin, halorhodopsin and Opto-XRs do this via a light-gated membrane channels or receptors. Ligand-gated alternatives are the Drosophila allatostatin receptor, and RASSLs, GPCRs with customized binding sites. Each one of these has particular drawbacks. The opsins require coupling of a light fiber into the brain, and high expression of some opsins can cause cytotoxicity. The allatostatin ligand needs to be perfused directly into the brain. RASSLs show background activity in the absence of the applied ligand, which also can cause toxicity. Bryan Roth’s group has been pioneering RASSLs and has produced second-generation receptors which avoids these drawbacks. In these new receptors, DREADDs, the background activity is completely abolished and the ligand has no off target effects.
DREADDs have no effect on the spiking activity of the hippocampus in the absence of CNO (top). Subcutaneous injection of CNO causes bursts of action potentials in DREADD expressing hippocampus (bottom).
The DREADD, dubbed hM3Dq, in the paper, Remote Control of Neuronal Activity in Transgenic Mice Expressing Evolved G Protein-Coupled Receptors, allows selective activation of a genetically targeted population of neuron in a totally non-invasive way. Simply inject the ligand, CNO, and the activity of the expressing neurons will rise in a dose dependent manner. Onset is rather slow, starting around 10 minutes post-injection and peaking within 45 minutes. Offset takes hours, so this isn’t the right technology to explore precise temporal coding of spike trains. But, when combined with the genetic targeting information from the Allen Brain Atlas, this tool will find great use in demonstrating the function of specific brain regions and even specific cell types within a brain region.
The authors have also published an inhibiting DREADD, hM4Di, which can turn off targeted neurons. I’ve personally tested a variety of neuronal silencing technologies in the last 6 months, including the hM4Di inactivating DREADD. In in utereo electroporated cortical slices, the expression of these receptors had no discernible effect on the morphology, eletrophysiological parameters or cell health. When CNO was puffed onto the slice, the amount of current injection required to elicit a spike doubled or tripled. CNO did nothing to non-expressing neurons. The cell returned to normal within seconds of washout of the drug. I haven’t tested the hM3Dq activating DREADD, but from my experience with hM4Di, I highly recommend these tools for getting the control you want with minimal fiddling with light fibers or expression levels.
Every week or two one of the post-doc’s in our lab passes around a paper that is as much philosophy of science as experimental result. One that recently made the rounds is Can a Biologist Fix a Radio?, by Yuri Lazebnik, a spirited and humorous dissection of the flaws of the standard methods of experimentation and analysis in cell and systems biology. The primary contention is that most biologists are not sufficiently quantitative in their descriptions of the interactions of components in their system, which leads to an abundance of confusing, conflicting experimental results. If we hope to understand complex biological systems, we must describe the components in sufficient detail, with a standardized language, to allow accurate models built from numerous components. A radio can be perfectly described by its circuit diagram due to the quantitative knowledge of the component properties. Using a simplistic biological pathway diagram provides little insight.
Two schematics of a radio. The biologist's view (top), and the engineer's view (bottom).
Recently, a similar paper, On the Precarious Path of Reverse Neuro-Engineering, in Frontiers in Computations Neuroscience, highlighted a logical flaw in how much neuroscience research is done. When we find patterns or correlations between an external signal to neuronal activity, the tendency is to proclaim “Aha! This is how the circuit represents information and what it uses to make decisions.” But correlation does not equal causation. In this paper the authors build a physical Braitenberg Vehicle II, which is a component of a classic thought experiment in neuroscience. It navigates an environment by acquiring optical input, processing it through a network of cultured neurons, and then controlling a motor output through a simple rule about the firing of the neurons. Recording the activity of the neurons leads to the discovery of a ‘population rate code’ in the activity of the neurons, which describes the behavior of the robot fairly accurately. An experimental biologist would be tempted to call this finding the ‘neural code’ of the system. But, the navigation rule that robot actually follows is not based spike rates at all, but rather the order of first spikes in a handful of the neurons in the network. The correlation between population rate activity and behavior is real, but it is not what is relevant to the circuit and the behavior. This example cuts deep, and hits my unease with much current brain circuit analysis. There are huge numbers of papers that show particular stimulus features can be extracted from the firing patterns of neurons in a particular brain region, but rarely does a study conclusively demonstrate that the circuit actually uses these correlations in the computational task. Demonstrating causality is difficult, but essential if we are to gain an understanding of how brain circuits really work.
Despite the presence of many spikes (blue) and a population spike rate, the robot only cares about the delay to time of the first spike (black circle) in a subset of neurons.
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