July 30, 2010 § Leave a comment
Your Friday treat is a movie from Sean Megason’s lab of the development of a zebrafish ear. Sean has a plan to provide a complete (“in toto”) image set describing the entire development of a vertebrate, using methods described here (Megason SG (2009). In toto imaging of embryogenesis with confocal time-lapse microscopy. Methods in molecular biology (Clifton, N.J.), 546, 317-32 PMID: 19378112). When the project is complete — which will not be tomorrow — there will be a movie recording every cell division, and every morphological rearrangement, that happens as a zebrafish egg turns into a functioning fish. And then you will be able to sit at your computer and analyze vertebrate development without needing to get so much as a single finger wet.
This is from a zebrafish embryo in which both the nucleus (green) and the membrane (red) of every cell has been fluorescently labeled. If you watch carefully, you can see individual cells divide (one green blob becomes two) and move into new positions, creating (for example) the circle of cells that then opens up into the tube of the inner ear.
July 23, 2010 § Leave a comment
Neutrophils have a special place in the study of cell motility. It seems that about one in 5 talks on cell motility starts with this classic video of neutrophil crawling, from David Rogers (Vanderbilt) circa 1950 (a YouTube version set to music, just for a little variety):
(credits for original here).
I am not saying this is a bad thing. It’s a great movie, and a wonderful way to introduce a semi-naive audience to the topic. And it’s fascinating to see the neutrophil change direction in response to the movement of the bacterium. How does the neutrophil know where the bacterium has gone?
July 16, 2010 § Leave a comment
Today’s movies were generously sent to me by Markus Covert (Stanford University). This is a sampling of a very comprehensive and impressive study of the behavior of NF-κB in single cells, just published in Nature (Tay et al. 2010. Single-cell NF-kappaB dynamics reveal digital activation and analogue information processing. Nature 466 267-71. PMID: 20581820). It’s increasingly clear that a number of signaling pathways — calcium, p53, NF-κB — use spatial and temporal dynamics to deliver complex, nuanced information on the single cell level. Trying to understand how these pathways are working, or how to manipulate them using drugs, without appreciating that fact is like trying to understand what happened at the World Cup by watching a slice of the action that starts above waist level: you might be able to see when a goal or a near miss happens, but you’d have no idea about what led up to it.
What we’re looking at here is a fluorescent fusion protein of NF-κB being transported into the nucleus in response to the addition of TNF-α, then coming out again. NF-κB is a transcription factor, so it needs to be in the nucleus to do its job; moving the pre-made protein in and out of the nuclear compartment is a quick way to switch NF-κB-induced genes on and off. NF-κB is usually sequestered in the cytoplasm by a protein called IκB. When an activating signal, such as TNF-α, comes along, a kinase, IKK, is turned on (I am sweeping a good deal of complexity under the rug here) that phosphorylates IκB, causing it to be degraded by the ubiquitin pathway. The degradation of IκB releases NF-κB, which then goes into the nucleus and turns on various genes including the one for IκB.
The NF-κB—IκB interaction is thus a negative feedback loop with a fast arm (protein degradation) and a slow arm (protein synthesis), which can lead to oscillations. (Think about tweaking the taps on your shower when the temperature isn’t right (fast) — and the water taking its sweet time to make its way up the pipe to the shower head and fall onto your alternately shivering and scalded body (slow). Oscillations, and much swearing, result). The behavior of these oscillations can determine which downstream genes get activated. Since NF-κB is a remarkably versatile and important transcription factor — it’s central to the immune response, and implicated in cancer, inflammation, autoimmune disease, learning and memory — the question of exactly how the activation of downstream genes is controlled is really quite important.
July 9, 2010 § 1 Comment
This amazing movie, from Niethammer P, Grabher C, Look AT, Mitchison TJ. 2009 A tissue-scale gradient of hydrogen peroxide mediates rapid wound detection in zebrafish. Nature 459 996-9 PMCID: PMC2803098, shows leukocytes (the white blobs) rushing to the site of a wound in response to a hydrogen peroxide signal (fluorescence in upper panel).
We’ve known for a while that leukocytes rapidly (within minutes) home to the sites of wounds. What hasn’t been clear is what signal attracts them. We’ve also known for a while that hydrogen peroxide is generated in wound sites: but until now, the general belief has been that it comes from the leukocytes that are attracted into the wound. Hydrogen peroxide has a role in killing bacteria, at least under some conditions, and so this all seemed to make sense. Until this movie. You can’t really tell by eye, but the quantitative analysis clearly shows that the hydrogen peroxide production starts before the first leukocyte arrives. In fact, the timing is such that it seems very plausible that it is the hydrogen peroxide that calls in the leukocytes: soon after the hydrogen peroxide reaches the nearest blood vessel, you start seeing purposeful leukocytes making tracks towards the wound.
Is the hydrogen peroxide gradient we see causal, or is it a byproduct of something else? There are five enzymes in the zebrafish genome that can produce hydrogen peroxide, directly or indirectly (four NADPH oxidases (Nox-1, -2, -4 and -5), and Dual Oxidase, abbreviated Duox). Niethammer et al. showed that small molecule inhibitors that inhibit all 5 enzymes prevented the hydrogen peroxide gradient from forming in response to a wound, and also strongly reduced leukocyte recruitment to the wound. Using antisense morpholinos and quantitative PCR, they then narrowed down which of the five possible enzymes could be responsible: none of the Nox enzymes seems to be involved, but knocking out Duox blocked both gradient formation and leukocyte recruitment. Problem solved: the leukocyte recruitment signal has been discovered!
Well — there may still be more to the story.
June 25, 2010 § Leave a comment
It’s Friday, and what better way to get into the mood for beer hour than to talk about death? To make sure that the mood doesn’t get too lugubrious, let’s stick to the death of entities that are unlikely to remind you of anyone you know, such as single cells. This video shows HeLa cells undergoing apoptosis, from work described in Spencer SL, Gaudet S, Albeck JG, Burke JM, Sorger PK. 2009. Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis. Nature 459 428-32. PMC2858974. You’ll see that the greenest cells round up and bleb and die very fast, whereas the less green cells die much more slowly. And thereby hangs a tale.
Apoptosis — programmed cell death — is one of those processes that you would think would be entirely predictable. If you’ve triggered a cell to die, you’d think that would be the end of it. But this turns out not to be true. If a clonal population of cells is exposed to TRAIL (TNF-related apoptosis-inducing ligand), typically some survive; and the time to death is also very variable. On the face of it, this variability in genetically identical cells is quite mystifying. What makes it more than a mere curiosity is that this same variable response might be important in clinical settings, where it’s typical for cancer therapeutics to kill some cancer cells and spare others (a phenomenon called “fractional kill”).
Sabrina Spencer (who has now left the Sorger lab for post-doctoral training in the Meyer lab), set out to ask what kind of mechanism might explain this variable death. The most traditional and obvious candidate, perhaps, is cell cycle phase; another obvious (though less traditional) hypothesis is that some yet-to-be-identified key factor is present at very small numbers per cell, so that chance fluctuations in its level create significant differences in the functioning of the death pathway.
But there is a third possible explanation.
June 18, 2010 § Leave a comment
It’s Friday — but this is not a movie. One must be fair to the non-imagers among us (theorists, biochemists and such). You are looking at a representation of the geometry of steady-state phosphoform distribution in a system consisting of two enzymes (a kinase and a phosphatase) and a substrate with two distinct phosphorylation sites, from the work described in Manrai, AK and Gunawardena, J. 2008. The geometry of multisite phosphorylation, Biophys. J. 95 5533-5543. PMC2599844.
Why would you want to know about this? Post-translational regulation is a hugely important mechanism for changing the behavior or localization of proteins, and phosphorylation is possibly the most important form of post-translational regulation in eukaryotes. The number of phosphorylation sites on some of the proteins we study is staggering: the EGF receptor has 10, p53 has 16, and tau (the microtubule-associated protein in the fibrillary tangles in Alzheimer’s disease) has over 40. Since a protein with n phosphorylation sites has 2(n) possible ways of being phosphorylated, the presence of different phosphoforms adds enormously to the complexity of the mixtures of proteins found inside a cell.
How does a biological system interpret this complexity? p53 has an impressive variety of biological functions, but it’s hard to believe that the 2(16) different phosphoforms of p53 (>65,000) each have specific biological activities. It seems much more likely that cells use some kind of readout of the overall distribution of phosphorylations — possibly it’s the concentration of proteins with phosphorylations above a certain level that matters, or maybe it’s something more complicated than that. It’s hard to know without the tools to analyze phosphoform distributions.
Enter Manrai and Gunawardena. I will tread very lightly over the ground they cover: I can’t reproduce the mathematical reasoning in this format (especially since I’ve just discovered that I can’t even do superscripts in this particular WordPress style), but the entire Mathematica notebook with the proof is available if you want it. This is what they show: