Cell Cycle Visualization in Development

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

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Automated ROI analysis for calcium imaging

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.

Raw Data : Vesicular Release from Astrocytes, SynaptopHluorange

When I was working on my Ph.D. thesis, I was trying to find some biological question to definitively answer with GluSnFR, my glutamate sensitive fluorescent reporter. One possibility was the study of glutamate release from astrocytes. Around that time, 2003/2004, there was increasing evidence that glutamate was not just scavenged by astrocytes, but was also released from astrocytic vesicles. It released in response to calcium elevations within the cell. Existing methods for measuring this release were somewhat crude, so it seemed a great test system for GluSnFR.

Unfortunately, since there seemed to be no specialized areas on the astrocyte where the vesicles fused, and the release rate was relatively slow, we were unable to detect glutamate release with GluSnFR. I thought this might be a problem of not knowing when and where to look. So my collaborator, Yongling Zhu, and I expressed pHluorins fused to VAMP or to synaptophysin in astrocyte cultures. When we looked at them under the microscope, they just looked green, no action…

But then we left the excitation light on for a few minutes. I happened to look back into the scope after they had been bathing in bright blue light and was astonished. I could directly see, by eye, spontaneous bursts of fluorescence across the cells. It was absolutely magnificent. The long application of light had bleached all of the surface expressed, bright pHluorins. But the pH-quenched pHluorins in the vesicles were resistant to bleaching. On this dimmer background, the fusion events were plain as day.

Unfortunately, the green color overlapped with the emission of GluSnFR, so we couldn’t use it for a spatiotemporal marker of when and where to look for glutamate release. We tried using some ph-sensitive precursors to mOrange and mOrange2, developed by Nathan Shaner, but these seemed to block the release events. Since then, others have shown the functional relevance of glutamate release from astrocytes, and I turned the focus of GluSnFR measurements to synaptic spillover. This was one of the projects that was tantilizingly close, but got away. This movie of VAMP-pHluorin is almost five years old now, but it still looks cool… Enjoy!

If you are curious, this is what the Synaptophysin-mOrange looked like when we expressed it in hippocampal neuron cultures. Ammonium Chloride caused a massive fluorescence increase, by alkalizing the synaptic vesicles. Unfortunately, we never were able to see release via electrical stimulation. Details are in my thesis. Maybe someone else wants to give it a shot?

Voltage sensitive imaging powering up

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…