October 18, 2013
September 27, 2013
Life from single molecules to entire populations takes place in four dimensions. Three of which are spatial dimensions and last, but not least, the dimension of time. Interestingly, researchers ignored these hard realities for quite some time. During my PhD project on translational regulation within cells, we would like to master the four dimensions as good as we can. Live-cell imaging is a good method to monitor a single cell over time and to observe what is changing. However, live-cell imaging requires sharp and crisp images in order to be able to track single molecules over longer time spans. The biggest problem with conventional light microscopes are in fact the three spatial dimensions (x, y, z), because all the light from the specimen that you are observing is collected. This means not only the light of a single plane (x,y dimensions) is collected (and later observed), but also the light originating from all other planes above or below (z dimension) (see also Figure 1). Collecting a lot of this so-called “out of focus” light leads to blurred pictures, which means that fine details cannot be distinguished from each other anymore. A powerful tool to circumvent this problem is a variation of classical light microscopy called CONFOCAL MICROSCOPY. Here, I would like to give a short introduction into this extremely powerful and widely used microscope technique.
Figure 1: A cell that is observed under a microscope has three dimensions (x, y, z). However, the optics of a microscope dictate that only one z-plane can be “in focus” and not all planes at the same time. A standard microscope collects the light of all planes and therefore often produces blurred images when larger objects such as cells are observed.
In order to make sense of the confocal technique (con-focal = “having the same focus”) I would like to draw your attention to Figure 2. With the help of the steps 1 to 5 I will guide you through the figure. First of all a confocal microscope needs a strong light source. This role is often fulfilled by a short-wavelength laser (Step 1). The laser light is then reflected in a 45° angle by a so-called dichroic mirror (Step 2). This special mirror reflects short wavelengths (such as the green excitation laser), but is permissive for longer wavelengths (such as the emitted red light). The reflected green laser light is focused by the objective lens onto the specimen. Unfortunately, it is impossible to focus the light on only one single z-plane. As a consequence, a number of z-planes are excited by the green light and depending on the fluorescent molecule emit longer wavelength light, here depicted as red, orange, and purple (Step 3). Partly, this emitted light will later form the image that you can observe, but first it needs to travel to your eye: As explained above, the dichroic is permissive for the emitted longer wavelength light. Therefore, the light originating from all z-planes can pass. Since the light is originating from different planes, it also hits the so-called focal lens at different positions resulting in different focal points of this light. And now a small slit, called pinhole comes into play (Step 4): Most light (based on its origin) cannot pass this tiny opening because it is either focused in front of or behind the pinhole. The reason why a confocal microscope produces crisp images is that only light from a single z-plane is able to pass since its focal point is exactly within the pinhole (in this example the red light). Consequently, this light can reach the detector (Step 5) where it is converted into a visible image.
Figure 2: The setup of a confocal microscope can be described in five simple steps (see text). The pinhole is the central element because it blocks all “out of focus” light originating from non-desired z-planes.
Unfortunately, the seemingly simplistic confocal approach also has two important side effects. First of all, a lot of light is lost because it is shielded by the pinhole. This in turn requires a very strong light source which can damage the sample if applied for a long time. In order to prevent this from happening, the specimen is scanned point-for-point in the x,y dimension. This leads us to the second side effect: Scanning takes a lot of time and this is kind of impractical if you want to observe a live cell. But: Both problems can be (partly) resolved by a variation of confocal microscopy called “spinning-disc confocal microscopy”.
More on this technique in my next post!
September 9, 2013
Traditionally single-molecule experiments are performed in vitro and therefore in a reduced environment. Recently, it has become possible to combine this single-molecular accuracy with a living single cell and to observe what happens in real time (“live”). For biologists the combination of these three technological ideas creates a lot of possibilities to answer a number of currently unanswered questions. I am very happy to be able to be part of this adventure. In the following I would like to address some aspects of my work:
What am I doing?
Currently I am working on the intriguing and big question how cells translate their DNA into protein. Interestingly, many important sub-questions of this problem still remain unanswered, especially when focusing on the fate of mRNA molecules once they have left the nucleus and are present in the cytoplasm. The quantification of the translation process in time and space, characterization of its steps and major molecular players is our focus area. In order to elucidate what happens to mRNAs in the cellular context we mark them with fluorescent proteins and apply single- and live-cell imaging. In addition, new labeling and detecting technologies allow to study mRNAs at the single-molecular level.
Why study translation live and in single cells?
The so-called central dogma of biology, namely the conversion of information stored in the DNA into proteins, has been dissected by a large number of scientists. However, in most traditional approaches the mRNAs as the central information carriers are isolated from large numbers of cells and therefore removed from their natural cellular context. This results in functional deficits and loss of spatio-temporal information (“Why is this mRNA at this place in this cell at this time?”). In contrast, the combination of single- and live-cell imaging allows to study the fate of mRNAs during translation in their physiologic environment, over a longer period of time and with a minimum of disturbing factors. The use of only single cells also allows to detect differences between cells of the same kind (for example neurons or muscle cells). An organ represents a very heterogeneous environment, so cells have to be different in order to be able to adapt to their local environment. Even 150 years ago Charles Darwin already noted that observable traits can vary widely within a species. Why couldn’t this also be the case for individual cells?
Why single-molecular accuracy?
Next to the advantages that live single-cell analysis has to offer, it is important to keep in mind that most biological processes can be reduced to the level of molecules. When, however, a larger number of molecules is observed (even within a single cell) this automatically leads to an averaging effect. A complicated biological process, like the mRNA translation into protein, involving a number of molecules during specific stages might therefore only be recognized as single event with a “before” and “after” without knowing what really happened in between. By visualizing single molecules it becomes possible to track their role as a puzzle piece within the big picture.
Nice. And how is this done?
There are two major tools. The first one is a microscope (more specific: a light microscope called confocal spinning disc microscope) to observe the single cell with its mRNA molecules. However, the resolution of a light microscope is limited to about 220 nm (1 nm = 1 m / 1,000,000,000). Even though a RNA molecule might be longer, it is also about 1,000 times thinner and therefore not detectable. In order to be able to still detect them we label them with fluorescent proteins. The emitted light results in a so-called “diffraction limited spot” which can be detected by the cameras of our microscope. For the RNA labeling we apply the MS2 and PP7 systems which use specific bacteriophage proteins that are again fused to fluorescent proteins and can bind to specific regions within the mRNA molecule of interest. Importantly, the MS2/PP7 labeling does not harm the biological processes within the observed cell. With this system it is also possible to label a single mRNA molecule in two colors (for example red and green). During the mRNA translation process different parts of the mRNA are targeted by the translation machinery in a sequential manner which has an influence on the binding of the green and red proteins. The appearance of both colors at the same time (yellow), first green and then red, or the other way around, the speed at which this change occurs, and the location within the cell can tell us a lot about the translation process.
In case I could spark your interest for single-molecule live cell imaging please also see our website or check out the following three articles on mRNA labeling and detection:
- Hocine et al., Single-molecule analysis of gene expression using two-color RNA labeling in live yeast. Nat Methods. 2013 Feb;10(2):119-21.
- Wu et al., Fluorescence fluctuation spectroscopy enables quantitative imaging of single mRNAs in living cells. Biophys J. 2012 Jun 20;102(12):2936-44.
- Larson et al., Real-time observation of transcription initiation and elongation on an endogenous yeast gene. Science. 2011 Apr 22;332(6028):475-8.
- the Spinning-disc microscope
- the MS2 and PP7 labeling systems
- and “diffraction limited spots”
will follow later.
August 30, 2013
August 1, 2013
Presentation time. In order to attract a few people to my talk I designed this poster with the freely available GIMP software. It takes a while, but the possibilities that GIMP has to offer are astonishing. The software is great for creative outbursts. And wouldn’t it be nice if scientific posters could become more appealing to the eye in the future? The Biology Department of the University of North Carolina at Chapel Hill is already quite good at it: http://www.flickr.com/photos/biologyposters/.
April 13, 2013
Partly painted walls – Boston
Enjoying some liquid sugar – Boston
Winter swimming (no pictures) – Somewhere in the woods
The Atlantic – Rockport
Freeclimbing or something like that – Rockport
Gone fishing – The Atlantic
Mont Royal – Montréal
A house in Montréal
A factory in Montréal
More houses in Montréal (it’s a beautiful city though, photos are selective)
Montréal at night (on top of Mont Royal)
Québec and its frozen river/part of the sea
White Mountains hiking
Since form follows function, the visualization of protein structures is vital for understanding biological complexity. Several ways of producing images that are not just beautiful, but also address certain research questions and help to elucidate protein function exist. Here I briefly want to talk about the program PyMOL which was originally created by Warren Lyford DeLano in 2000. My current project deals with the single-molecule characterization of bacterial translesion DNA polymerases. The polymerase I am working with the most is Pol II and therefore it is very interesting for me to picture this molecule in a way that allows me to understand how Pol II interacts with its DNA substrate (polymerases replicate DNA). However, translesion polymerases such as Pol II are not only capable of binding regular DNA, but also damaged DNA in order to prevent a stalling of the entire replication process which would otherwise lead to cell death. But why is Pol II DNA damage tolerant?
Now we are at the point were structural protein information is required. In this case this information was created by Wang and Yang in a very elegant and interesting crystallization study of Pol II (1). Even though the authors do a great job to visualize their findings, it might be helpful to do this yourself in order to create for example a different perspective view or even a short video that shows the protein from different angles. In addition you might also want to highlight certain amino acid residues by a specific color and thereby state the importance of certain functional proteins domains. All this can elegantly be performed by PyMOL. For my purposes I created the following view of Pol II containing a DNA helix with a tetrahydrofuran (THF) lesion which can not be processed by a regular polymerase.
For protein structure visualization the Protein Data Bank is the place to go. Just search for the protein structure you are interested in (hopefully it exists) and download the so-called PDB file which contains all the 3D data that is necessary to visualize the protein. Here I used the PDB file 3K5M which contains information on Pol II bound to a THF lesion DNA.
I assume you have downloaded PyMOL by now and know how to load a PDB file into it. What I like about PyMOL is its command line which allows to rapidly change what you want to see and achieve with your protein. The downside is that you need to know the syntax of the commands and also need to memorize the important commands because looking them up all the time is time consuming. The PyMOL user guide on pages 17 to 37 is a great introduction to the most essential commands. However, I used the following command sequence to produce the picture above. This is the foundation, but it can bring you quite far.
Step 1: Know your protein domains. Pol II has five domains in total. I gave a distinctive color to each one of them. The N-terminal domain stretches form residues 1-146 and 366-388, the Exo domain from 147-365, and so on… From the literature you must figure out yourself which domains your protein has and where they are located. The following command is very important and lets you select your domains (here the example for the N-terminal domain):
PyMOL> select nterminal, resi 1-146,366-388
This command will name the indicated residues “nterminal”. Later you can use “nterminal” to address the entire domain instantaneously. Specify the residues of all your domains and give them a descriptive name. On the right side of the screen you can then see an overview of all your domains.
Step 2: Know how to hide. Proteins can be very confusing. Use the following commands to first hide everything and then only selectively display what you want to see in a style that you like.
PyMOL> hide all
PyMOL> show cartoon, nterminal
I personally like the alpha-helix and beta-sheet displaying style called “cartoon”. But please also play around with the following styles: ellipsoids, lines, ribbon, dashes, mesh, volume, sticks, and many more… In case you become confused or made a mistake just use “hide all” or “hide cartoon, nterminal” to get rid of your confusion. Now choose a fitting style for all your domains and do not worry about the colors yet.
Step 3: Colors are nice. Now it is time to further organize your residues not only by displaying style, but also by color. This command is very easy and works with all major colors like green, yellow, purple, blue, red, orange and so on…
PyMOL> color yellow, nterminal
So go ahead and color each of your domains in a clear, distinctive way.
Step 4: Size does matter. The “cartoon” view is very handy to see in which contexts residues are located without the confusion of all the side chains. However, a real protein is much bigger and the electrostatic forces and hydrophobic interactions determine which part of a protein is actually accessible by ligands or in the case of Pol II by DNA. The accessibility can be modeled in PyMOL by an algorithm displaying the surface that is available to water molecules.
PyMOL> show surface, nterminal
PyMOL> set transparency, 0.6
Use these commands for each of your domains and play around with the transparency (from 0 to 1) so that the surface depiction does not become overwhelming and the cartoon residue structure is still visible. The combination of two or more different styles at the same time such as “cartoon” and “surface” in this case is actually one of the strongest features of PyMOL!
Step 5: Make it nice. Most people’s aim is to create protein structure visualizations for presentation or publications. How to achieve a high-quality and clean file is therefore very important.
PyMOL> bg_color white
These commands set the background color to white which is most convenient for most applications. “Ray” creates a sharper (read: nicer) depiction of your protein. It is important to bear in mind that the “ray” command effects are lost once you perform other modifications to your protein. So only use “ray” at the finish line. Or just use it as many times as you want.
Step 6: Hold on to good things. In order to save your work go to the “File” menu located on the top of one of the PyMOL windows. There are a couple of options how to save your work. Most important is “Save Session as…” because it allows you to go back to your current state of the project. But you are probably also interested in just saving the current view of your protein (“Save Image as…”). If you want another view angle just turn your protein as desired and save again. A PNG image will be created that can be used in papers or presentations.
Step 7: Let’s move it. You can also make a video out of individual PNG pictures that show your protein form different angles. For Pol II the result looks like this. Luckily you do not have to turn your protein 60 times and save everything and than put it together as a movie. PyMOL does it for you.
PyMOL> mset 1 x60
PyMOL> util.mrock 1,60,180
Use these commands to make and test-view a movie of 60 frames that lets your protein structure rotate in a 180 degree space. If it gets boring after a while use “mstop” to stop. In order to save, go to the “File” menu again and choose “Save Movie as…”. Every major media player should now be able to display your moving protein.
By now you probably still have a number of unanswered questions. But there is relief. People who know much more about PyMOL have created a very convenient FAQ page which contains the answers to most questions that beginners have or that are just good to know.
And now go ahead and use structural biology research for your own purposes with PyMOL!
(1) Wang F., Yang W., Structural Insight into Translesion Synthesis by DNA Pol II, Cell 139, 1279-1289, 2009.
February 17, 2013
This little article attempts to introduce the research that professor Charlotte Hemelrijk and her group “Behavioural Ecology and Self-organization” at the University of Groningen perform on understanding the complexity of bird flocks and fish schools. In the context of the Honours College course “Leadership or Not in Animal Societies” the questions was addressed whether a single leader is necessary to organize the complex behaviour that one can observe in large and moving fish schools or bird flocks.
Is a leader required to guide the instant movement of thousands of birds or fish into one direction in order to find food or escape an enemy?
Or is some kind of intrinsic property that emerges from the school or flock sufficient to explain the observed behaviour?
In the following I will describe how the estimation of movements parameters, computer simulations, and the careful observation of fish schools in nature led to a robust model that can explain why no leader is necessary to coordinate the movement of fish schools.
For fish it is very attractive to organize in schools because spawning an area, finding food, access to mates, protection from predation and hydrodynamic effects all become more optimal for the individual fish. In order to understand how complex school behaviour evolves it first became necessary to be able to describe the formation of a school in general. It has been hypothesized that collective movements, as they occur in a school, are characterized by a directional and temporal coordination. This coordination might only become possible if individuals mutually influence each other by the distance towards other members of the school (Huth and Wissel, 1992). Fig. 1 shows which effects the distance of one to another fish has on its movement according to the formulated hypothesis. These parameters were then used in a computer simulation in order to test whether the resulting model schooling behaviour resembled the natural schooling behaviour. Interestingly, the parameters seemed to be sufficient to describe the natural behaviour (Fig. 1 (C)) that has been noted earlier (Partridge, 1981).
Fig. 1: Hypothesized effects of the presence of a second fish on the movement of the first fish depending of the distance to each other. (A) and (B) display how a first fish reacts when a second fish is present in four different proximity zones (based on Huth and Wissel, 1992). (C) shows how the modelled fish schooling behaviour (bottom) clearly resembles the observed behaviour in reality (top) (based on Partridge, 1981).
Based on the above described parameters attraction, alignment, and avoidance further studies were able to significantly link the behaviour of individual fish to distinct school shapes. However the researchers needed to introduce the factor “speed” into their model in order to being able to reproduce observations in nature (Kunz and Hemelrijk, 2003 and Hemelrijk and Hildenbrandt, 2008). The researchers found that through coordination and collision avoidance a transition to an oblong shaped (with respect to movement direction) school occurred as a function of speed. In other words, a fish school reduced its width and increases its lengths at increasing velocities because this enables the individual fish to avoid collisions. Later, Hemelrijk and colleagues were able to prove that the conclusion drawn from their model also holds true for fish schools in real-life experiments (Hemelrijk et al., 2010).
In order to narrow the gap to the experimental observations, the researchers also introduced a factor to describe the effect that the number of neighbours have on the movement decisions of the fish. They hypothesized the existence of two mechanisms that could govern the movement of individual fish when surrounded by more than one neighbouring fish. Fig. 2 schematically depicts both hypothesis and their respective outcomes in computer model when applied over a number of “decision cycles”. The first model assumes that the movement of an individual fish (3.) who has at least two neighbours (1. and 2.) in his field of view largely is the result of taking the average path between both fish. The second model was assumed to be more realistic because the fish (3.) would have a priority direction that largely depends on factors such as distance to his neighbour. A computer simulation of both models, however, resulted in a surprising outcome. After a number of cycles the priority model had led to a disturbed school pattern that is never observed in nature. On the contrary, the average direction model resulted in an accurate reproduction of field observations. The researchers therefore assumed that the priority direction effect is probably averaged out in a large school because there are many and changing neighbours. The final result is an average directional movement.
Fig. 2: Two models and their simulation results that take neighbouring fish into account during the movement decision process of an individual fish. (A) The average direction model assumes that the movement of fish 3. is the result of averaging between the direction of its neighbours. The priority direction model assumes that fish 3. decides to follow the closest neighbour. (B) Simulations of both models resulted in a dispersed fish distribution in the case of the priority model (right) and a more realistic ordered fish distribution for the average model (left).
The findings presented in Fig. 2 and the fundamental work presented in Fig. 1 therefore prove that individual fish can lead to an emergent property, such as the coordinated behaviour of a large group, without requiring a leader. It is important to note that the individual perceptions of the fish within and on the edges of the school are vital for the coordination. This means that the final direction of the fish school is probably and to a large extend based on the “decisions” that fish on the edges of the school make. These movements “decisions” might be based on knowledge and experience, but also on motivational factors such as hunger. Whether these factors can drive the behaviour of individual fish and therefore the movement of the whole school still remains to be elucidated.
The computational tools of theoretical biology therefore seem to be a good approach to describe complex behavioural patterns in animals groups. Other research projects of the Hemelrijk group used similar parameter-simulation approaches to describe the behaviour of bird flocks and their internal dynamics (Hildenbrandt et al., 2010). Also in bird flocks no real leader is necessary, but the individual movement decisions of birds in their neighbouring context seem to govern the movement of the flock as a whole. These studies can also help to improve understanding on how large groups of humans act in situations of panic and fear when rational decisions might become overruled by movement decisions that are based on the individual context.
Hemelrijk, C.K., and Hildenbrandt, H. (2008). Self-Organized Shape and Frontal Density of Fish Schools. Ethology 114, 245–254.
Hemelrijk, C.K., Hildenbrandt, H., Reinders, J., and Stamhuis, E.J. (2010). Emergence of Oblong School Shape: Models and Empirical Data of Fish. Ethology 116, 1099–1112.
Hildenbrandt, H., Carere, C., and Hemelrijk, C.K. (2010). Self-organized aerial displays of thousands of starlings: a model. Behavioral Ecology 21, 1349–1359.
Huth, A., and Wissel, C. (1992). The simulation of the movement of fish schools. Journal of Theoretical Biology 156, 365–385.
Kunz, H., and Hemelrijk, C.K. (2003). Artificial fish schools: collective effects of school size, body size, and body form. Artif. Life 9, 237–253.
Partridge, B.L. (1981). Internal dynamics and the interrelations of fish in schools. J. Comp. Physiol. 144, 313–325.