The University of Groningen where I currently study has formulated three key research areas that are meant as a strategic aim in order to distinguish itself from other universities. One of these research areas is called “Healthy Ageing”. This week the scientific journal PNAS will publish an interesting article on work by Dr. Ellen Nollen and coworkers in which they describe how the depletion of a certain gene leads to higher tryptophan levels and seems to protect C. elegans worms from protein aggregate-related disease. Since protein aggregates are hold responsible for Alzheimer and Parkinson disease in humans and not a lot is known about the related protein metabolism, this paper indicates some new potential therapeutic targets and fits perfectly into the university’s aim to promote research on “Healthy Ageing”.

As this paper indicates that high levels of thryptophan might have a protective effect against protein aggregate-related diseases I considered it interesting to look into it a little bit deeper. The gene that is central within this research is called tryptophan 2,3-dioxygenase (tdo-2) and encodes the enzyme TDO-2 which is responsible for the digestion of the essential amino acid tryptophan. During their studies the researchers from the Netherlands, Germany, and France found that genetic suppression of tdo-2 (also called knockdown) leads to inhibition of the tryptophan metabolism and thereby to higher levels of this amino acid in the tissues of the worms which were the labrats-substitutes here. Deciphering a metabolic pathway is very nice, but what makes van der Groot’s et al. results even more interesting is the fact that a longer lifespan of the worms was observed upon tdo-2 knockdown. It also proved to be effective to supply excess amounts of tryptophan and leave the tdo-2 gene “switched on”. Since in general C. elegans is regarded as a good model organisms for age-related diseases such as Parkinson or Alzheimer, in the following let us have a closer look at the study and its results.

The worms which were part of this study actually suffered from the expression of alpha-synnuclein known for its protein aggregation promoting behaviour. High levels of this compounds actually lead to less motility of the worms and expressed in the unit body bends/minute. Suppressing tdo-2 with RNAi considerably extended the time period within the worms lives with a high motility rate. According to the researchers this indicates suppressed effects of alpha-synuclein.

As metabolic networks are important in biology the next question asked as whether an up- or downstream (concerning the location of tdo-2) element was responsible for the observed effects. All the genes mentioned in Fig. 1 were knocked down in combination with or without tdo-2 expression and it was observed that knock-downs of downstream genes had no or only a very small effect on worm motility. Therefore the levels of tryptophan seemed to be the key to the observed effects.

Fig. 1: TDO-2 related metabolic pathway. 

However and as depicted in the above figure tryptophan is also a part of the neurotransmitter serotonin synthesizing pathway. Serotonin levels are currently a prime element in the understanding of Alzheimer disease and are also used in therapeutic approaches. A significant element of Nollen’s and her colleagues’ work is, however, that they prove that serotonin has nothing to do with the observed motility and lifespan effects. Knocking down the tph-1 gene in combination with tdo-2 does not significantly change the worms abilities to move. Summing up, all conducted knock-downs during this study therefore lead to the conclusion that the TDO-2 enzyme and (when it is suppressed) higher tryptophan levels are responsible for the increased mobility and decreased alpha-synuclein toxicity. This point was further stressed by the addition of tryptophan to the diet of worms which did express TDO-2 (Fig. 2). In a dose dependent manner tryptophan compensates for alpha-synuclein toxicity. 

Fig. 2: Raised tryptophan levels suppress alpha-nuclein toxicity also in the absence of a tdo-2 knockdown.

Now it starts to become interesting: What does tryptophan do? How does it prevent protein aggregation on a molecular basis? Does it regulate some yet unknown biochemical pathways? Sadly enough the authors stay very brief on these points, either because they do not know more themselves yet or because another publication in Nature or Science is waiting in the future. What they say is that they do not expect tryptophan to be directly responsible for the observed effects, but this amino acid (or its derivates) probably plays a role on other biochemical pathways or their signalling molecules. Nevertheless this work proves that a lot of knowledge about protein aggregate-related diseases still remains in the dark. It also opens up possibilities to study the observed effects in mammals since the tdo-2 gene and its enzyme product is evolutionarily extremely well conserved. TDO-2 is one of the proteins that links us with C. elegans worms. To what extend the tryptophan metabolism plays a role in human age-related diseases such as Alzheimer and Parkinson is a question that many research groups will work on in the future.

van der Groot AT et al., Delaying aging and aging-associated decline in protein homeostasis by inhibition of tryptophan degradation. Proceedings of the National Academy of Sciences of the United States of America, published online before print on August 27th 2012, accessed on August 29th 2012. 

Some things in biology can be observed best when concentrating on one molecule and its functions. System biologists will probably not agree and intervene that in biology it’s all about networks and interactions. The presence and concentration of A influences B which increases the concentration of C which consequently down regulates A again. This is all true. However, in single-molecule biology it’s about the functioning and dynamics of (you guessed it) single molecules. When looking at larger systems there is always the danger of missing out elements that might occur only under certain conditions, low concentrations, or that are masked by certain other secondary processes. The weak point of single-molecule studies has always been the fact that complex systems are drastically reduced. Again, you miss-out a lot of stuff even though now you are able to study one molecule in detail.

But: Change will come. As I described in an earlier post super resolution microscopy has been around now for a few years and it is a ready-to-use technique now. For just $ 1,000,000 you can get your own. Theoretically many fascinating research results should have been published  until now. Observe single molecule dynamics in their native environment, what more could you wish for? Indeed some spectacular footage has been produced. Stefan Hell and coworkers, for example, were able to record neurons within the cerebral cortex of a living mouse with a resolution of around 70 nm. Until 20 years ago physics books would have told you that this is impossible. So have a look yourself, right here.

Strangely enough this research at the same time also demonstrates and interesting phenomenon that can be observed when scanning through the live-cell super-resolution microscopy: Most of the time only structurally large (>200 nm) and functionally already known molecules (like neurons) are observed. Further, the temporal resolution is not great and in the order of seconds. Fast moving molecules are still hard to image due to hardware (CCD camera, scanning) limitations. Of course it is very interesting to see how dendrites in the brain expand during learning, but it does not raise any new questions and most importantly does not answer any old ones. I am sure that super-resolution microscopy has a golden  future, but it is important to improve sample preparation techniques, optimize fluorophores even further, and develop sensors that have a shorter integration time for the small amount of photons they are capturing per frame.

Classically the resolution of light microscopy has been limited by the diffraction of light. Resolution of sub 200 nm structures were thought to be impossible. Things have changed with the invention of stimulated emission depletion (STED) microscopy and other superresolution techniques. STED might be especially interesting for cell biology research in the future because cellular structures can be resolved on a molecular level. In the following I will shortly explain how STED works and I will also present a recent paper in which STED has been used to elucidate the molecular features of the centriole, a protein that is important for cell division.

The basis of STED microscopy is the on-off switching of fluorescent molecules within a probe such as a cellular structure. By turning on single or at least only a few fluorescent particles at the same time, imaging them, turning them off again and then moving to the next spot (scanning the sample) drastically increases the available resolution. As only single point-like objects are detected at one point of time at one specific location, summing up all these points in a picture allows for drastic improvements when compared to classic confocal microscopy (Fig. 1). In classic confocal microscopy all fluorescent molecules within the excited spot are detected which means that some spots are overlapping each other and thereby reduce the resolution.

Fig. 1: STED versus classic fluorescence confocal microscopy (Source: Willig et al., New J. Phys., 2006).

An essential question, however, remains: How to rapidly switch fluorophores on and off? This phenomenon is achieved by making use of stimulated emission. As described in the previous post, electrons can rise to an excited state upon absorption of photon. When they fall back fluorescence photons are emitted again. However, this process takes some time. Shining red-shifted at the previously excited spot can stimulate a faster photon emission of a different wavelengths. From now on this second beam will be called STED beam. This “forced emission” is the basic principle of STED microscopy. When the STED beam is modified to become doughnut shaped via a polarizer then only the small inner circle of the previously excited molecules will light up. All surrounding molecules will emit earlier and are therefore unavailable for imaging (Fig. 2). As described above, the size of the inner circle of the STED beam determines the resolution because it selectively allows the detection of single molecules which can be pictured by scanning whole sample and summing up all individual molecules. The diffraction barrier which has been formulated by Ernst Abbe is therefore not only shifted, but broken because theoretically the inner doughnut circle can reach extremely small diameters allowing very detailed images.

Fig. 2: Left: Diffraction limited excitation spot. Middle: Doughnut-shaped STED beam which is applied to the excitation spot and leads to stimulated emission of photons. Right: Stimulated emission of unwanted photons results in a small center containing molecules which emit at the desired wavelength. This spot is not diffraction limited anymore (Source: Marcel Lauterbach).

Now, with the above described features in mind, I shortly want to demonstrate how STED microscopy can be used in a biology related context. In a recent  article Moerner and coworkers from the Department of Chemistry at Stanford University show that the 250 nm sized centriole complex, responsible for the coordination of cell division, is surrounded by nine clusters of the Cep164 protein (Lau et al., 2012). Observations of features of protein complexes were long thought to be impossible with relatively non-invasive light microscopic techniques. However, STED can do the job. Fig. 3 shows how the nine-fold complexes around the centriole actually look like. In addition the obtained results are compared to standard confocal microscope results which of course does not make use of a STED beam. The picture in the bottom right corner also clearly illustrates the advantage of STED microscopy over confocal microscopy in a graph: When displaying the fluorescence intensity as a function of spatial location in nanometers, the observed intensity peak for confocal microscopy is much broader, but not higher.  In STED microscopy two distinct peaks become visible which allow the resolution of two individual objects. The maximum achieved resolution in this study were 60 nm which delivers about four times sharper pictures than could be achieved before Stefan Hell demonstrated the STED principle for microscopy in the 1990s.

Fig. 3: Confocal and STED microscopy of the Cep164 proteins which surround the centriole.

The researchers also note that the observed features are comparable to previously observed structures obtained by transmission electron microscopy (I explained some of its principles in an earlier post). However, electron microscopy exerts high electron forces on the samples which might distort protein conformations. In addition electron microscope pictures are assembled based on “ideal” reference pictures which inevitably leads to a modeling bias. Fig. 4 shows that it is indeed possible to compare light microscopic images to electron microscopic ones. They significantly overlap with each other and underline the strengths of both imaging techniques and especially the combination of the two.


Fig. 4: Overlap of a transmission electron microscope with a STED microscope obtained picture. The locations of the nine Cep164 complexes match each other. The scale bar has a size of 200 nm.

Here, the researches made use of fluorescent antibodies and it should be noted that antibody artifacts always distort images. Also the on-off switching right of the used fluorescence molecules determines the capabilities of this technique, next to the brightness. Still, it is very likely that in the future many practical aspects of STED microscopy will be improved which will lead to a number of interesting insights into molecular cell biology.

If you are interested actual proposed functions of the Cep164 complexes, you can find more information in the first listed article. The second article describes the above mentioned research.


Graser, S., Stierhof, Y.-D., Lavoie, S.B., Gassner, O.S., Lamla, S., Le Clech, M., and Nigg, E.A. (2007). Cep164, a novel centriole appendage protein required for primary cilium formation. J. Cell Biol. 179, 321–330.

Lau, L., Lee, Y.L., Sahl, S.J., Stearns, T., and Moerner, W.E. (2012). STED Microscopy with Optimized Labeling Density Reveals 9-Fold Arrangement of a Centriole Protein. Biophysical Journal 102, 2926–2935.

In previous posts I tried to explain why light is so important for biology and how its properties can be used in biology research. The process of absorption is especially important for understanding the role of light within the field of biology, which is obvious since light can only have an effect if it interacts with matter. Molecules can absorb light because their electrons become more “energetic” and rise form the so-called ground state to one of the excited states. These different states and their significance for biology will be explained in the following. Molecules that are able to absorb light of the biologically interesting spectrum with wavelengths form approximately 100 to 800 nm are also called chromophores. Such molecules often contain a delocalized π-electron system which means that these molecules can form bonds that occur between electron orbitals (outer electron cloud around the atomic nucleus) called π-orbitals. Many of such π-bonds result in an electron system within the molecule which is flexible once it absorbs the energy of photons which make up light. The electrons within such a flexible system can then become delocalized or spread out across the molecule. Therefore, molecules which contain a delocalized π-electron system are especially sensitive to the absorption of light. In practice a π-electron system can be found in molecules containing aromatic systems and/or a relatively high number of conjugated double bonds. Figure 1 displays how pH changes influence the protonation states of an anthocyanine molecule, a plant colorant which gives flowers and berries their distinct colors in many plants. Of course, one finally only observes the non-absorbed photons. The anthocyanine molecule is a good example for the interesting color properties of molecules that can change once the atomic make-up changes. This molecule is also a good example for a π-electron system (see the aromatic rings).

Figure 1

An electron excitation can, however, only occur if photon energy matches the energy difference between the ground and the excited state. When the electrons of single atoms fall from the excited state back to the ground state they emit the previously absorbed energy again in the form of a photon which matches the wavelength of the previously absorbed photon. Therefore, absorption and emission spectra of single atoms are identical. However, things are different for molecules which of course by definition always contain at least two atoms. Bound atoms can vibrationally interact with each other which costs energy. In a molecule every electron state can be subdivided vibrational states and each vibrational state can again be split into different rotational states. Still, the photon’s energy must match the energy difference between the ground and excited state in order to become absorbed. This results in the fact that molecules can absorb a range of wavelengths and due to vibrations emit longer wavelengths containing less energetic photons. While single atoms can only absorb and emit single spectral lines (Figure 2) molecules can absorb and emit broader band light spectra.

Figure 2

Hopefully, the basics of absorption are now a bit clearer. However, we have not discussed yet how an excited electron can lose its energy again. Depending on the excited state it is in the electrons can choose different paths to fall back into the ground state. As described, during these paths the electrons emit longer wavelengths photons which are responsible for biologically interesting processes such as fluorescence, bioluminescence, or phosphorescence. But also other processes  occur that are not directly visible by eye when the electrons fall back to the ground state. These include internal conversion, intersystem crossing, resonance energy transfer, and photochemical reactions. In the following, all of these effects will be described. A so-called Jablonski diagram (Figure 3) schematically displays the different electron states and their subdivision into vibrational and rotational states. A Jablonski diagram also helps to “visibly understand” what happens to excited and returning electrons. So let’s start.

Figure 3 (Source: Olympus)

First of all electrons become excited by photon absorptions which rises their energy level to the first or second singlet state depending on the available energy and molecular properties. This process is extremely fast is depicted by the green arrows.

Internal conversion (IC) always occurs in molecules when excited electrons fall back to the ground state. During IC the absorbed energy is converted into kinetic energy in the form of vibrations or rotations. No electromagnetic radiation occurs.  Yellow curly arrows indicate this process which logically occurs between the vibrational states, but also within one vibrational state containing more rotational states (not shown).

An observable process is fluorescence. When an electron falls back from the first singlet state into the ground state it emits electromagnetic radiation in the form of a photon. However the emission wavelength is longer because the electron has lost energy due to IC on its way from the second singlet state to the first singlet state or due to IC within just the first singlet state. Fluorescence is indicated by the red down facing arrows from S1 to S0. Bioluminescence is the process of fluorescence within biomolecules such as green fluorescent protein (GFP).

Another important feature of electron states is the process of intersystem crossing. During intersystem crossing an electron moves from the excited first singlet state into the first triplet state. A triplet state is a state in which an electron can only be found once its quantum mechanical spin reverses from -1/2 to +1/2. Quantum mechanically this very unlikely and therefore a triplet state occurs less often than the other two excited states. Intersystem crossing is indicated by the blue curly line.

The process of phosphorescence occurs one an electron has managed to enter the triplet state by intersystem crossing and falls back into the ground state. As in fluorescence a photon is emitted, but it contains less energy and it time delayed with a factor of about one million because a triplet state is not stable state. In research applications (such as the earlier described fluorescence correlation spectroscopy) where only the emitted fluorescence at a specific point in time is required, a certain percentage of phosphorescence signal therefore needs to be subtracted from the total signal. Phosphorescence is indicated by the red arrow facing to the lower left corner.

Zooming out and looking not only at one molecule, but for example two neighboring molecules, makes it possible to observe two other interesting electron effects. The first one is called resonance energy transfer. If the fluorescence spectrum of a donor molecule and the absorption spectrum of a acceptor molecule match the vibrations of the former molecule’s electron can excite the latter molecule’s electron. Then a longer wavelength fluorescent emission of the second molecule can be observed even though it has not been excited directly with photons. This process is the basis of Förster Resonance Energy Transfer (FRET) which can be used to determine whether two molecules are in close proximity. If two proteins are close two each other, the chromophore electrons of the first one therefore might vibrationally excite the electrons of the second one and fluorescence of a distinct wavelength can be observed. A second interesting photo effect between two neighboring molecules is a so-called photochemical reaction. Here, a very strong excitation removes an electron from its original orbital and the molecule therefore becomes oxidized. Another molecule which receives the electron is reduced. Oxidized and reduced molecules can then participate in “regular” chemical reactions. Photochemical reactions are the basis of photosynthesis where chlorophyll electrons power the electron transport chain in plants.

I hope that this short summary of light and biology serves its purpose of demonstrating that understanding a little bit of physics and a little bit of biology and joining both, can lead to some very interesting insights! For people who are interested more in photons and their effects on electrons I want to recommend an extremely appealing Java-animated tutorial created by Olympus.

Fluorescence Correlation Spectroscopy (FCS) in combination with a Laser-scanning confocal microscope (LSCM) is a commonly used tool to quantitatively determine properties of molecules diffusing through a defined volume and properties of the medium itself. When assuming no labelling-bias, the observed fluorescence intensity changes can yield information to determine physical properties such as the molecular diffusion coefficient and medium viscosity, but also hydrodynamic radii, average concentrations, kinetic data, and singlet-triplet dynamics. Here I want to show how you can use an LSCM-FCS setup to determine the diffusion coefficient of a fluorescent molecule (in this case Rhodamine B), the viscosity of a mixture containing Rhodamine B, and the hydrodynamic radius of fluorescent beads in water. For all measurements a 543 nm He-Ne laser was used. All calculation are based on only two values that can be determined during FCS analysis. How (A) the molecular diffusion time and (B) the number of molecules within the observed volume are derived from observed fluctuations in fluorescence intensity has briefly been described in my blog and can be found here. With just knowing (A) and (B) many interesting things can be calculated in a short amount of time. In the following for example the “exact” size of the confocal volume is determined, as well as the “diameter” of the molecule. Why the parentheses? Well, you are deriving certain values from the observation of other values and in a relatively complicated setup like this one (labeling, detecting, microscope…) there will always some kind of bias. I am still a beginner as well, so my intention for the future is to reduce these effects to a minimum by better understanding the theory behind LSCM-FCS and getting more practical experience. But now… let’s get started! All the (A) and (B) values were actually measured and give a better impression of what they actually mean in the context of the formulas. I also kindly want to thank Victor Krasnikov from the Department of Single Molecule Biophysics for supplying the microscope, the materials, and of course also the idea behind these trials!  

Diffusion coefficient of small dye molecules

The Rhodamine B diffusion time through a confocal volume of unknown size and the number of particles within this volume were measured under non-diluted and twice diluted conditions. The diffusion time (τ) was 3.3 x 10­­-5 s under both conditions and the number of particles (N) within the confocal volume was determined to be 6.8 and 3.35 under both conditions, respectively. The diffusion coefficient (D) for Rhodamine B in water is Dwater ≈ 300 µm2s-1. The observation volume (V), its radius (ω1), and the concentration (C) were calculated as describes below.






By inserting τ and D into (1) the confocal radius ω1 = 0.199 µm could be determined. Since the structural parameter S was set to 5 the half-length of the observation volume into the z-direction ω2 = 0.995 (2). In the following the volume (V) was calculated by inserting ω1 and ω2 into (3) yielding V = 0.219 µm3.





Since logically the diffusion time under both dilution conditions is the same, of course also the size of the determined confocal volume remains the same. The confocal concentration (C) for both concentrations could now be calculated by making use of the observed respective N values, and calculated V (4).



The concentration of the non-diluted solution was Cnon-diluted ≈ 31 molecules x µm-3 and Cdiluted ≈  15 molecules/µm3. Considering the 1:1 dilution a halved concentration in the confocal volume makes sense.

Viscosity of binary mixtures

Three different volume/volume mixtures of water containing Rhodamine B and ethylene glycol were made (1:2, 1:1, 2:1) and the diffusion coefficients were calculated based on the diffusion times. By inserting the measured diffusion times under the three respective conditions (8.6 x 10-5 s, 1.46 x 10-4 s, 2.65 x 10-4 s) into (5) the respective diffusion coefficients were determined as D1:2 = 115, D1:1 = 68, and D2:1 = 37 µm2s-1.




A plot of the respective diffusion coefficients, including Dwater, against the used ethylene glycol concentration results in the graph shown in Figure 1


Figure 1: The Rhodamine B diffusion coefficients as a function of ethylene glycol concentration as determined by LSCM-FCS.

An exponentially fitted curve indicates that the measured diffusion coefficients non-linearly depend on the ethylene glycol and water ratio. This is counter-intuitive, but can probably be explained by the non-linearly behaving interactions of Rhodamine B with the increasing number of ethylene glycol molecules in the solution. The volume-volume mixture alone thus does not account for changing diffusion coefficients.   

Hydrodynamic radius of fluorescent beads

For the fluorescent beads a diffusion time of τ = 1.8 x 10-5 s was observed. Inserting τ into (5) yields Dbead = 550 µm2s-1­­. Inserting Dbead and µ = 0.9 x 10-3­ Pa s-1 into (6) leads to a fluorescent bead hydrodynamic radius of r = 0.43 nm.




The hydrodynamic radius indicates the hypothetical radius of the molecular shape based on the measured diffusion time. This radius is of course distinct from the actual molecular radius because many atomic properties influence the diffusion behaviour in a given solution.

Summing up, I hope these small experiments and the six formulas help you to understand what you can do with your FCS data. Rhodamine B was just because it is an easy-to-handle fluorescent molecule. In principle, however, the steps described above remain the same for labeled proteins or other diffusing particles. Of course, a modern software package (Zen from Zeiss) can do it all for you. But isn’t it handy to understand what just two parameters can mean for the determination of physical properties?

 Electron Microscopy of Biological Macromolecules

– An Introductory Course-

Performed from 16 April to 4 May 2012 at the

Department of Biophysical Chemistry – Rijksuniversiteit Groningen



Transmission Electron Microscopes (TEM) are valuable tools for elucidating the structure of especially biological macromolecules. By making use of highly accelerated electrons and analysing their interactions with soft matter it is possible to significantly lower the diffraction limit as when compared to standard light microscopic techniques. This introductory course focused mainly on the theory behind TEM, operation in practice, and result analysis. Most important practical aspect which will be described below were finding suitable focus positions, obtaining high image qualities through accurate sample preparation, and practicing image analysis by particle overlaying.


It is a central dogma in modern biology research that the form of biologically relevant macromolecules, such as proteins, is strongly dependent on its function. However, in order to make sense out of purely functional biochemical data it is necessary to link this data to structural biophysical information to fully grasp the structure-function relationship of a macromolecule. The observation of structures with sub-wavelengths dimensions is difficult in all microscopes, due to a diffraction limit of wave-like behaving particles like photons or electrons. This relationship was first postulated by Ernst Abbe in 1873. Form. 1 indicates how the observed spot size in an optical system depends on the wavelength λ, the diffractive index n of the medium, and the angle θ by which the particles travel through the medium. The term n sin θ is also called numerical aperture (NA) and can reach up to 1.4 in a modern microscope.

(Form. 1)

Being able to distinguish two spots from each other is called resolution and is thus mainly limited by the wavelength. For a typical light microscope the resolution limit therefore lies at around 200 nm assuming λ=550 nm and NA=1.4. Structures which are interesting for modern biological research typically have dimension at or very much below this value and can therefore not be observed by standard light microscopy.

However, by using electrons instead of photons the diffraction limit can be lowered dramatically because electrons can have a much smaller wavelength. Louis de Broglie in 1923 stated that the wavelength λ of any particle depends on its momentum p which consists out of the particles mass times its speed, where h is Planck’s constant (Form. 2).

(Form. 2)

Since the mass of an electron is constant, its wavelength therefore only depends on the acceleration speed. In modern high-voltage transmission electron microscopes (TEM) therefore resolutions of down to 0.5 Ångströms (A) (0.05 nm) have been achieved 1.

TEM microscopes are therefore a valuable tool in structural research. During this introductory course several different samples such as earthworm (Lumbricida) haemoglobin, T4 phages, and tomato mosaic virus (TMV) were examined in order to demonstrate the functionality of TEM and to gain knowledge about the operation of the microscope.

Material and Methods

An electron microscope consists out many parts which are functionally analogous to parts which are used in a light microscope. However, their construction is different and based on the electron particle properties. During the course a Philips CM12 TEM operated at 120 kV was used, which was connected to a CCD-camera. The electrons were emitted and accelerated in the top-part of the TEM from a filament (Wehnelt cylinder) by applying a high charge difference between the filament and a lower mounted anode. Electromagnetic lenses are made from iron-shielded copper coils and were used to condense, focus, and project the electron beam. A magnetic field, which is generated in the iron casing in response to the current running through the copper windings, leads to deflection and focusing of the electrons. Finally the beam is spread on the screen and can be observed through the oculars or, if the screen is lifted, through the CCD-camera system. Fig. 1 gives more details about the setup of a TEM, where (A) depicts a schematic overview of the most important components of the microscope and (B) is a photo of the outside of a Philips CM12 TEM. For clarity purposes the red dotted lines divide the TEM into three functional compartments (numbers on the right). Red arrows relate some of the functional features which are schematically presented in (A) to the real microscope in (B).

Fig. 1: Overview of a Philips CM12 TEM. (A) Electrons are extracted and accelerated from the shielded filament by the large voltage difference towards the anode. The gun alignment coils adjust the electron source (gun) with regard to the first condenser lens. Both condenser lenses concentrate the electron beam, while (fixed) apertures define the cone angle of the electrons during their way through the microscope. The objective lens focuses the beam onto the specimen, while the projector lenses magnify the transmitted electron beam on the fluorescent screen. Stigmators are applied to correct for small magnetic aberrations within the lenses which can lead to deflected beams. In addition deflector coils are used to centre the beam on the region of interest. (B) Photograph of a TEM. Some features are marked by arrows. Dotted lines relate the schematic compartments to the real ones in order to demonstrate proportions. Own creation, based on 2 and 3.

Different samples (earthworm (Lumbricida) haemoglobin, T4 phages, and tomato mosaic virus (TMV)) were obtained from the Department of Biophysical Chemsitry, Rijksuniversiteit Groningen, appropriately diluted, applied to self carbon coated and glow discharged copper 400 mesh grids, stained, and observed under the microscope. The carbon coating was obtained as follows: Rectangular mica slices were placed into a carbon evaporator which applied a thin layer of carbon to the surface. This carbon was then removed in a water bath by sliding away the mica slice from the carbon. Next, through lowering the water level, the carbon layer was applied to the copper grids which were placed on a glass slide and dried afterwards. Glow discharge was used to render the carbon layer hydrophilic in order to guarantee sufficient sample molecule adherence. Samples were diluted up to 100x in 50mM HEPES buffer and 3 µL were applied for approximately 30 seconds (haemoglobin) and 1 minute (T4 phage, TMV) on the carbon grids. Then, the grids were washed with 3 µL of buffer. Uranyl acetate was used for negative staining in one fast step for less than 5 seconds and one longer step of 30 seconds. Between sample application, washing, and staining steps excess liquid was blotted off with filter paper. The ready-to-use sample containing grids were stored in glass Petri dishes at room temperature. In order to avoid excessive damage of the sample, the low-dose system setting was used for imaging.

Recorded Lumbricida haemoglobin images were processed by 2D single-particle-analysis using the programme Groningen Image Processing (GRIP). Octopus vulgaris and Sepia officinalis hemocyanine molecules containing files were supplied by the department (recorded at 45,000x magnification with a Nikon Coolscan 8000 ED camera and scan steps of 20µm) and were also analysed.


A major aim during the practical course was to learn how to operate a TEM and how to obtain good quality images of biological structures. Next to the optimal technical microscope alignments, finding the right amount of focus is a crucial step towards decent results. With the help of Fast Fourier Transformations (FFT) of the recorded TMV images (Fig. 2 (A)-(D)) it was possible to calculate whether the in-focus position chosen by the TEM operator really coincides with the physically real in-focus position. This was achieved by measuring the distance (d0) from the centre of the image derived FFT transform to the first contrast shift of this transform (first black ring, so called Thon ring). In total 11 different focal positions were chosen ranging from     -1700 nm defocus to +1700 nm overfocus (in 340 nm steps). Fig. 2 (A) and (D) display the two most diverging focus points, (B) is the defocus at which the most details are visible, and (C) is the in-focus position which was not part of the calculations. In the top left corner of every image the according FFT image is displayed. By dividing the square of d0 through the wavelength of the accelerated electrons (3.345×10-3 nm) the physically real focus position (Δf real) could be calculated.

By plotting the calculated optimal focus values Δf real against the obtained values (Δf microscope) a straight line resulted which had its y-axis intercept at approximately -137 nm (Fig. 3). This indicates that the chosen in-focus position from Fig. 2 (C) was in fact 137 nm defocused. However, in order to achieve optimal resolution with optimal contrast a certain amount of defocus, which depends on the periodicity of the sample molecule, is necessary. In order to be able to determine this value the correct starting in-focus position therefore is crucial.

Fig. 2: Results of a focal series with a TEM microscope imaging Tomato Mosaic Virus (TMV) including the Fast Fourier Transforms of the images in the top left corners. In total 11 images where taken in intervals of 340 nm. (A) displays the furthest defocus of -1700 nm, (B) has a defocus of -1020 nm and stripe patterns are clearly visible, (C) is in-focus (0 nm), and (D) is the furthest out of focus at +1700 nm. A 60K magnification was used to obtain the images.

From the results in Fig. 2 it also becomes clear how to identify the right amount of focus without making use of the calculations which led to Fig. 3. A large defocus leads to a very grainy image which has a lot of contrast, but a relatively poor resolution. Large over focus, in turn, leads to poor contrast, but good resolution. The in-focus position is marked by a strong drop of contrast. All focus positions are accompanied by typical FFT image patterns.

After having recorded high quality pictures, it was necessary to reduce the noise of the individual particles in order to be able to recognize details and separate similar but different particles from each other which might not be visible in the original dataset. Single-particle-analysis can be used to perform this task by averaging particles according to a previously defined reference consisting out of hand selected ideal particles. Averaged particles can then be split up into different classes which are defined by their bin size. During the analysis steps, the particles of the dataset are also centred and corrected for contrast differences and rotational position. For this course self-recorded worm haemoglobin (Fig. 4) and supplied Octopus vulgaris and Sepia officinalis haemocyanin molecules were analysed (Fig. 5). In the latter set it was possible to separate the molecules of the two species from each other.


Fig. 3: Plot of the calculated focus positions (Δf real) against the chosen focus positions (Δf microscope) resulting in a straight line which indicates that the chosen focus position was in fact approximately -137 nm off from the real focus position.

After the by-hand selection of a first reference set (not shown) the main Lumbricida haemoglobin containing files were aligned into nine different classes as shown in Fig. 4 (A). The classes are of varying qualities, but two molecular side- and  eight top-views are distinguishable. By further improving the reference set and decreasing the class size (i.e. more and individually improved classes) it became possible to generate more different top- and side-views of the molecule (Fig. 4 (B) and (C)). Based on these classes the highest quality top- and side-view was selected. Fig. 4 (D) portrays these particles in which previously blurred structural details become visible.

In addition single-particle-analysis can yield information which can lead to the separation of similar molecules and their spatial orientation from a mixture. As demonstrated by Fig. 5 the use of improving molecule references during particle analysis leads to a significant improvement of image quality. In Fig. 5 (A) the side-views are clearly distinguishable from the top-views, however, due to noise, it is not possible to separate the haemocyanin molecules of two species from this dataset. Improving the reference molecules more and more by aligning them to the cleaned dataset (reduction from 796 molecules to 754 molecules) yields strongly improved molecular details (Fig. 5 (B)).  After classification and selection of the original dataset against this improved reference set clearly distinguishable haemocyanin molecules become visible. It can be hypothesized that the two molecules are representatives of the two present species, respectively.

Fig. 4: Single-particle-analysis of Lumbricida haemoglobin. (A) Molecule classes based on a first hand-picked reference set. (B) and (C) Improved quality images by improving the reference and increasing the class size depicting top- and side-views of the molecules respectively. (D) Best quality views as selected form (B) and (C). 


Fig. 5: Single-particle analysis of Octopus vulgaris and Sepia officinalis haemocyanin molecules. (A) Non-analysed mix of haemocyanin molecules of both species. (B) Two distinguishable top-views and one side-view position arise from the dataset after single-particle analysis.


During the course the focus lay on the practical application of TEM and the theory behind it in order to be able to record high-quality images and resolve simple problems which can be encountered during the procedures. Since information which is not recorded can not be seen later, it is crucial that all successive steps, starting with the sample preparation, are performed with great care and accuracy in order to yield good quality pictures which can then be analyzed. As demonstrated by Fig. 2 and Fig. 3 finding the right amount of under-focus depending on the molecular characteristics (patterns) is one of the most important steps. Balancing the needed contrast with an optimum resolution is the key to good results and requires practice. In addition by-hand selection of reference molecules from the dataset to which the whole dataset will be aligned is very important, since there is a strong selection bias. Experience with the observed molecules is an advantage because it enables the correct picking of all relevant rotations from the dataset and high accuracy classification afterwards.  An experienced electron microscopist is therefore able to extract a significant amount of structural and therefore potential functional information from a sample of biological macromolecules.


1.  Erni, R., Rossell, M. D., Kisielowski, C. & Dahmen, U. Atomic-Resolution Imaging with a Sub-50-pm Electron Probe. Phys. Rev. Lett. 102, 096101 (2009).

2.  University of Iowa Central Microscopy – Transmission Electron Microscopy. at <;

3.  Universität Regensburg Zentrum für Elektronenmikroskopie. at

Quantum dots (QDots) are  a nice technique to label biological samples in a very non-invasive manner. This technique has emerged in molecular biology and biophysics during the last years since QDots offer a number of advantages such as large spectral range, high brightness, and high photostability when compared to regular fluorophores. Here, I want to talk about some of the fundamentals, possible applications, and examples of the usage of QDots. I am not a physicist, so you will not find too much of the theoretical background in this article.

In order to understand the physical principal behind QDots, it is important to be familiar with the so-called electronic band structure of atoms. QDots are nanometer-sized crystals which consist out of semiconducting  atoms such as silicon. The electrons within these atoms can have quantified energy levels which are called bands. The highest and naturally most stable band is called valence band. However, the electrons within this band can be excited by for example photons that originate from a laser, and therefore reach a higher energy-state. In order to do so, the energy-barrier between the “old” and the “new” band must be overcome first. This barrier is called band gap (see also Fig. 1). Once the energy supply to the electron has been high enough it can “tunnel” the band gap and becomes conductive, meaning it can transform its energy to lower band electrons in other atoms. Now, current is flowing.

Fig. 1: The principle behind and the build up of a QDot. Biological molecules (like antibodies) which are attached to the polymer coating of the QDot can be used to serve as a link between the QDot and a biological surface (1st picture: Wikipedia, 2nd: Jim Zuo / University of Illinois at Urbana-Champaign, 3rd: designed myself, originally from Invitrogen).

However, there is an inverse relationship between this band gap and the size of the semiconducting crystal, i.e.  the smaller the crystal, the bigger the band gap that has to be overcome. This results in the necessity to use higher energies to excite the QDot crystal in the case of a smaller crystal, which also leads to a stronger detectable photon signal when the excited electron falls back to its resting state. For biological applications an easily detectable signal is quite important, because it allows the studying of minimally labeled single molecules. Since photophysical properties of QDots strongly depend on their “custom-synthesized” structure, they are an ideal tool to stain biological samples.

Still one obstacle remains: The required excitation energy for QDots is sometimes very close to the energy of the covalent bonds which link the individual semiconducting atoms with each other. This can lead to bond-breakage and freely diffusing atoms such as the toxic cadmium. Therefore, for the use in in vivo applications most QDots still need to be optimized. Nevertheless, QDots have demonstrated that they are an important tool for the biophysicist in in vitro experiments. One example is given below. In this example QDots are used to examine, whether the enzyme DNA helicase unwinds the DNA double strand (dsDNA) by sliding along the dsDNA or the leading single strand (ssDNA) during DNA replication.

For this experiment the researchers added bulky molecules to one strand of the DNA which served as a “roadblock” for the DNA unwinding helicase. Here, this bulky molecule was a visible QDot. If helicase is attached to dsDNA to unwind it, therefore the enzyme will stop in any case since it will inevitably encounter one of the roadblocks. For the case that helicase only attaches to one strand of the DNA strands (the leading strand) in order to unwind the dsDNA, it will, of course, only stop if it encounters the bulky molecule on the leading strand, but not if this molecule is present on the lagging strand (which helicase presumably does not touch). To test this, the researchers used two different ways to visualize the situation. When DNA replicates it does so in two directions and therefore also two fork-like structures arise (Fig. 2 (A) middle). The bottom part of Fig. 2 (A) shows how this fork-situation and the replication itself can be visualized by the addition of Sytox and dig-dUTP to an isolated DNA strain which is stretched by fluid flow. Sytox is a general DNA stain, so parts of the DNA that have already been replicated occur two times as bright as the non-replicated parts. dig-dUTP is added only during the last 25 minutes of the experiment. This means it is possible to observe into which direction the replication fork extends since this region will contain dig-dUTP and antibodies can bind to it.

Fig. 2: Analysis of the helicase behaviour in the presence of a QDot roadblock on DNA. (A) Sytox staining was used to identify replicated DNA (2x stronger signal = thick red) and an antibody against dig-dUTP (blue marks) was applyed to picture directionality and potential blockade of the replication process. (B, C) Show the combination of the labels and their orientation towards each other (1).

When concentrating on Fig. 2 (B) it becomes clear that helicase can probably only translocate along ssDNA. This becomes obvious when looking at the position of the QDot (the roadblock) and relating the staining signals towards it. In addition it is important top know that helicase can only migrate from the 3′ to 5′ direction. When the QDot is positioned at the “bottom” strand, double stranded DNA is only found left from it (red). Blue signal from dig-dUTP is only found left from red Sytox signal. Therefore, the strand is only replicated into the left direction and blocked to the right. However the helicase is able to bypass QDots which are not bound to its strand (Fig. 2 (B) iv). Compare also the fitting Sytox and dig-dUTP patters to it, which indicate that replication is also found after the QDot and into both directions. The QDot can, however, also be attached to the “top” DNA strand. This situation is analogous to the situation described above. Arresting and bypassing conditions are just inverted (Fig. 2 (C)).

By making use of a visible roadblock, a.k.a. a QDot, it was possible to resolve a little puzzle piece of eukaryote DNA replication on single-molecule scale. The QDot gave the researchers some point of orientation in a complex biological process in which the identification of directionality is essential.

(1) Fu et al. (2011), Selective Bypass of a Lagging Strand Roadblock by the Eukaryotic Replicative DNA Helicase, Cell 146, 931-941.


Studying protein properties

Fluorescence cross-correlation spectroscopy (FCCS) is a powerful biophysical method to study protein-protein interactions with the help of fluorescent labels and a confocal microscope. During my work I found it a bit disappointing not to know some of the theory behind FCCS. Knowing how FCCS works and how to interpret the results is, however, essential for making sense out of the previous biochemical work. In essence FCCS measures the photons which are emitted from a fluorescent molecule after excitation through a laser which is attached to a biomolecule. Most of the time this is a protein. Another fluorescent label is attached to a second protein which is hypothesized to interact with the first one, but emits photons of a different wavelength than the first fluorophores. The confocal microscope only observes a femtoliter volume in which the concentration of the two proteins is in the nanomolar range. Proteins diffusing in and out of the focal volume are detected by detecting the emitted photons. Since big molecules diffuse slower than small ones, two different “fluorescence vs. time” curves result. If both proteins interact, one might observe a third curve which indicates even slower diffusion. However, in practice it is not so trivial to interpret the FCCS results. This is the approach of a master student to make some sense out of the matter and therefore not all translations from the mathematical formulas into English might be 100% correct. Comments on mistakes or misunderstandings are greatly appreciated!

Principle of a confocal microscope with a cross-correlation setup

In order to study protein-protein interactions a dual-color setup (Fig. 1) is the method of choice, since differences in diffusion time for two molecules are hard to measure accurately if the molecular masses do not differ by factor four at least. To be able to excite the two different fluorophores two different excitation wavelengths are required. Two lasers fulfill this task. For a dual-color setup an additional normal mirror and an additional dicroic mirror are necessary. A dicroic mirror reflects light of one wavelength spectrum, but allows passage of another (Fig. 1). Via such a mirror therefore both laser beams can be focused on the sample volume (Fig. 2). After excitation the fluorophores emit photons of a higher wavelength due to photochemical energy losses. A second dicroic mirror allows only the passage of the desired emitted wavelength spectra. Since this spectrum contains the emission wavelengths of both fluorophores they need to be split by applying a third dicroic mirror which reflects the orange light in this examples and allows passage of the green light. Two emission filters that are located in the now separated light beams further “purify” the incoming light. Lenses now focus the light on a photoreceptor with one-photon sensitivity.

Fig. 1: Functional parts of a confocal microscope suitable for FCCS while making use of a dual-color setup (1).

Molecules now pass through the focal volume (Fig. 2). This leads to random photon noise which is depicted in Fig. 3. Sometimes signals arise that have a higher intensity (photons/time) than the noise which is always present. The strength of the statistical method behind FCCS is to extract meaning out of these fluctuations. By correlating the intensity fluctuations to the noise information about the molecule concentration (number of molecules) and molecular size (diffusion time) can be obtained. A brief overview about how this is done can be found below.

Fig. 2: Molecules of different sizes pass through the focal volume (with circle) of the microscope and emit photons. The longer the diffusion takes, the more photons are emitted and detected (2).

What is measured and what it means

When molecules move through the focal volume they emit photons that are detected by for example a photomultiplier device (see above). The measured amount of photons is plotted against the time which yields an intensity curve that can be seen in the top left corner of Fig. 3. Since there are always photons detected that do not originate from the sample molecule, but are for example reflected there is a constant mean intensity measured, which is called noise. However, the signal also consists out of a fluctuating contribution which is significantly higher than the average signal. These fluctuations are the photons that are emitted by the protein linked fluorophores and therefore quite some informational value is linked to them. The two curves in the top portion of Fig. 2 essentially depict the same (photon amount per time), however the curves differ because different molecules are responsible for them. The left curve shows a small molecule which diffuses fast, the right curve a bigger and therefore slower molecule.  When now the signal at one point in time (a fluctuation) is correlated to the average intensity over an increasing time period, a so-called auto-correlation curve results (Fig. 3, bottom curve). With the average measuring time being the same as the amount of time at which the fluctuating signal was measured the correlation is of course 1 (100%). However, when average intervals are increased the results resemble more and more the background noise and the measured fluctuation intensity at one point does not correlate with the average anymore or at least not perfectly (smaller than 1, bigger than 0). Plotting this number on a time log scale results in the autocorrelation curve shown below, which also depicts the decreasing correlation value between the measured signals as blue are between the curves.
In order to detect nice fluctuations in the first place the concentration of the to-be measured molecule in the focal volume needs to be in the nanomolar range. Otherwise the fluctuations combine and become background noise themselves. Just on a higher level.
The measured data, of course, in most cases does not perfectly overlay the theoretically determined curve which originates from the autocorrelation function. In order to achieve mathematical fitting is required. For FCCS a so-called nonlinear least square algorithm can be used which will not be explained here.

Behind the scenes of cross-correlation

The value that can be extracted from such a fitted curve is, that curves, when compared to each other, are differing. This is the result of different molecular properties like the concentration, the size (diffusion time) and triplet state. These features allow the characterization of the molecule.

Fig. 3: Schematic overview of the origin of the actual autocorrelation curve from detected photon intensities plotted against time and correlated with average values of different time spans. For details see the text (modified overview with graphical elements from (1) and (3)).

As already mentioned above, different molecular properties lead to (slightly) different curves. If, however the diffusion coefficients (size) of two molecules are very similar, also the resulting cross-correlation curves will be hardly distinguishable. Still, it is possible two examine the potential interaction of two molecules at the same time if both molecules are labeled with fluorophores that emit photons of different wavelengths. Fig. 1 shows how these photons with different wavelengths are detected individually. Mathematically these slightly different curves (they, however, DO DIFFER in wavelength origin) can now be correlated with each other (cross-correlated) which yields information about potential dimer formation if the “new” combined curve cannot be correlated significantly to one of the previous ones.

Taken together FCCS is a relatively easy method to handle and delivers fast clues about molecular interaction on a single-molecule level. A prerequisite is, however, that the previous cloning and biochemical steps worked and that the proteins are pure, nicely labeled, functional and non-agregated. Once this has been achieved great discoveries are possible 😉

(1)    Schwille & Haustein, Fluorescence Correlation Spectroscopy – An introduction to its concepts and applications,
(2)     Jay Unruh, Fluorescence Correlation Spectroscopy, http://research.stowers
(3)    Invitrogen, Fluorescence Correlation Spectroscopy (FCS) – Note 1.3,

Master’s Project

December 25, 2011

In the lab of Prof. A.J.M. Driessen (Molecular Microbiology) I am currently working on the first of my two master’s projects. This project has an approximate duration of six months and is centered on some molecular aspects of bacterial protein secretion across the cytoplasmic membrane. In order to give you the chance of learning something about what I do,  I summarized my work-plan and some of the theory behind it.  If you have questions, do not hesitate to contact me!


Cells are no closed and just internally highly complex entities that are sealed from the outside world. In fact in one way or the other every cell directly or indirectly communicates with its environment through processes which are mediated by channels or receptors. For multicellular eukaryotic organisms it is evident and well-known that cells secrete substances which serve as substrates for a wide array of physiological functions. These substances are ranging from simple ions for neuronal signaling purposes or pH homeostasis to more complex peptide hormones and other proteins. A general issue which was overlooked by science for decades is the fact that secretion and communication also appears in and among prokaryotic cells. Since these organisms most of the time consist of single cells, systemically relevant excretion of compounds which are synthesized within the cell is occurring in a different manner and on a different scale. Bacteria, representing one of the two prokaryotic domains, indeed possess highly complex systems which enable interaction with the environment in the form of other cells or an adherence substrate.

A key role for a bacterial cells ability to survive and to secrete molecules into the environment plays the bacterial secretory (Sec) system. Genetic, biochemical, biophysical, and structural approaches during the last two decades have shaped a detailed understanding of the functional elements and dynamics of this system. The so-called translocase system encompasses an array of proteins which are functionally centered around the translocon channel which mediates the export of proteins across the bacterial cytoplasmic membrane and the insertion of membrane proteins into it. The focus of this work lies on the study the SecA motor proteins that occur in bacteria that next to the general Sec system also possess a so-called accessory Sec system which is responsible for the export of a subset of proteins. A significant number of pathogenic bacteria make use of this subsystem to export protein virulence factors. SecA is a motor protein and ATPase which is thought to be mainly responsible for the movement of proteins across the membrane via the SecYEG translocon channel. How the two different SecA motor proteins that occur in the general and accessory Sec systems interact and what their exact functional relationship is, is currently not understood. By making use of genetic, biochemical, and fluorescence microscopic techniques the focus of the present study is to elucidate a possible interaction between Staphylococcus aureus’ general Sec systems SecA1 and its accessory Sec systems counterpart SecA2. Parts of the bacterial Sec system are conserved in all three kingdoms of life and among others also function in the eukaryotic endoplasmic reticulum in order to secrete proteins into the cytoplasm. Embedding the performed research into its context is essential and therefore brief introductions to bacterial cell structure and the two Sec systems follow with a focus on SecA structure and function.

It does, however, not lie within the scope of this work to formulate a wholesome review on the translocase system. A number of reviews have already been published on this topic, which offer an in-depth overview and cover this subject more explicitly (1, 2, 3).

The Sec translocase system

Since the SecA proteins of S. aureus, which stand central in my research project, are part of the functional complex Sec translocase system, here a brief overview is given about this system. Fig. 1 summarizes the most important aspects of this kind of bacterial protein translocation across the cytoplasmic membrane.

Fig. 1: Schematic overview of the bacterial protein translocation system termed Sec translocase. Proteins which are synthesized within the ribosome are exported from the cytoplasm over the cytoplasmic membrane into the periplasm by the proton motive force (PMF) and the ATPase SecA via the heterotrimeric membrane channel SecYEC. Two major ways of protein translocation exist: posttranslational secretion and cotranslational insertion into the cytoplasmic membrane. Secretory proteins are either directly targeted to SecA by means of their N-terminal signal sequence or are bound by the chaperone SecBfirst and translocate later via SecA and SecYEG. Membrane proteins are translocated cotranslational. Their C-terminal signal sequence is bound by the signal recognition particle (SRP) and targeted to the SRP receptor (FtsY). Consequently SecAtranslocates these proteins via a lateral gate in the SecYEG channel into the membrane. SecDF(yajC) is an accessory factor which seems to improve preprotein translocation. YidC associates with the translocon during protein insertion into the membrane (figure derived from (1)).

Some bacteria posses two different versions of SecA

In bacteria SecA universally functions as the ATPase which delivers the chemical energy to physically translocate proteins across the cytoplasmic membrane in a post-translational manner. However, some bacteria posses two homologues of this protein termed SecA1 and SecA2. SecA1 functions as the essential housekeeping protein for translocation, while SecA2 seems to be especially important for a subset of proteins. Studies on bacteria which are expressing  SecA2 have shown, that this subset of proteins often includes virulence factors, as for example adhesion molecules (4). In addition to SecA1/A2 some of these bacteria also contain a homologue monomer of the heterotrimeric translocon channel SecYEG (Fig. 1) termed SecY2.  Summarizing, bacteria containing either only SecA1/A2 or SecA1/A2 + SecY2 are said to express an accessory Sec system next to the general Sec system which is depicted in Fig. 1.

The question

The intriguing research questions of my project therefore is: Do SecA1 and SecA2 dimerize with each other or which other combinations are required to be functional? This is an important question, since SecA1/A2 might function as a link between the general and accessory Sec systems. As the accessory Sec system is important for virulence in some bacteria it is essential to learn more about its protein molecular basis. Fig. 2 summarizes the hypothetical dimers which SecA1/A2 might form in bacteria with or without an additional SecY2 homologue. Currently I am investigating the SecA proteins of S. aureus which is not expressing an additional SecY2. Some of the combinations depicted below (Fig. 2, (D) through (F)) therefore are not predicted to apply to the current state of my project.

Fig. 2: Schematic overview of the hypothetical combinations of two SecA motor proteins in S. aureus with the two SecYEG homologues when assuming a functional SecAdimer. Dimers (A) through (C) might function with the canonical SecY1EG translocation channel, while dimers (D) through (F) might also interact with the accessory SecY2EG translocation channel which is present in S. aureus. In addition it is also theoretically possible that dimer (A) binds to SecY2EG and dimer (D) to SecY1EG (created by myself, based on earlier work of Irfan Prabudiansyah).

How to get there

Here I will present a very brief work-plan, just to give you an idea about which techniques and experimental approaches my project entails in order to investigate the properties of SecA1/A2 and their functional relation to each other in S. aureus. The aim is to use molecular genetics to overexpress both proteins, which is followed by using knowledge of biochemistry to purify and fluorescently label the proteins. The final step is to use confocal microscopy (cross-correlation) and FRET microscopy to examine the potential interaction between both proteins.


The molecular cloning of of both secA1 and secA2 gene sequences from S. aureus into plasmid vectors involves some bioinformatics work and the designing of the needed primers. In addition DNA sequencing is performed to confirm correct sequence insertion. The Isopropyl β-D-1-thiogalactopyranoside (IPTG) inducible plasmids are isolated and heat-shock transformed into E. coli cells. These cells containing either secA1 or secA2 are consequently grown in large amounts at 25°C to 30°C to reduce inclusion body formation after overexpression induction by the addition of IPTG into the growth medium. After cell harvesting and cell membrane disruption by french pressing or sonication, the overexpressed protein-containing cytoplasmic fraction is isolated by ultracentrifugation. Results are confirmed by SDS-PAGE and Western blots.


Overexpressed SecA1/A2 is purified by HPLC mediated cation exchange and size-exclusion chromatography. After desalting on a size-exclusion column both proteins are labeled with two fluorophores (Cy5 & fluoresceine) via a maleimide group to their external cysteines (50% of purified SecA1 is labeled with first fluorophore, other half with second, same procedure for SecA2). Labeling efficiency is checked by photometry at different wavelengths specific to excitations spectra of fluorophores. Removal of excess fluorohore molecules is essential for avoiding too strong background noise during microscope steps. This is achieved by HPLC cation exchange at protein specific salt concentration. Results are checked on SDS-PAGE under fluorescence detection conditions.


Analysis of dimer formation is first achieved by an array of different concentrations of SecA1 and SecA2 combined to each other. Secondly, the potential dimers are analyzed with the help of a confocal microscope observing a volume of approximately 90 femtoliters. Fluorescence cross-correlation spectroscopy is applied to this volume of the solution in order to determine the diffusion coefficient of the fluorescently labeled particles (5). Potential dimer formation can then be described by altered dual color absorption/emission spectra of the proteins (FRET), as well as their unchanged or slower diffusion time through the observed volume in the case of dimer formation.

Final remarks

If you also consider the approach of my work as interesting as I do, you can always contact me back via mail. In the future I would like to continue my work and extend my knowledge into the direction of biophysics. Describing the properties of proteins by interdisciplinary means has definitively caught my eye.

(1) Du Plessis, Nouwen, Driessen, The Sec translocase, Biochimica et BiophysicaActa (2010).

(2) Driessen & Nouwen, Protein Translocation Accross the Bacterial Cytoplsmic Membrane. Annual Reviews of Biochemistry (2008).

(3) Papanikou, Karamanou, Economou, Bacterial protein secretion through the translocase nanomachine, Nature Reviews Microbiology (2007).

(4) Rigel & Braunstein, A new twist on an old pathway – accessory Sec systems, Molecular Microbiology (2008).

(5) Schwille, Mayer-Almes, Rigler, Dual-color fluorescence cross-correlation spectroscopy for multicomponent diffusional analysis in solution, Biophysical Journal (1997)

The biology of light

December 16, 2011

If you want to know something about the connection of biomolecules and light, then this might be of interest to you. I also thought it is pretty interesting, so I wrote this piece on proteins, linker-molecules, emission, and fluorophores. First of all because I wanted to understand the principles of fluorescence better since it is important for my project (FRET and confocal microscopy), but secondly also because I like the interplay between biology and physics. For my work on the hypothetical interaction of Staphylococcus areus SecA1 and SecA2 proteins I use two molecules that have fluorophore properties. Fig.1 depicts these molecules called fluorescein-5-maleimide (A) and Cy5-maleimide (B). Obviously both names are not the IUPAC nomenclature names for chemical compounds, however in everyday lab-life, these two names seemed more practical.


Fig. 1: Chemical structure of two fluorophores that can be used to label proteins via sulfur containing thiol groups on external cysteines. Left fluorescein-malamide is depicted and on the right side Cyanine (Cy5) maleimide. The maleimide group is the lowest identical portion of both molecules.  It functions as a linker between the thiol group and the actual fluorophores.

To be fluorescent actually describes a property that some molecules have. This includes the absorption of light at a specific wavelength (relatively high energy) and the subsequent emission of light with a longer wavelength and lower energy per photon. The relation between the energy of a photon and its wavelength is important when talking about fluorophores (Fig. 2). The frequency of a photon (≈ internal energy) is inversely proportional to its wavelength, thus photons with a short wavelength contain more energy than photons with longer wavelength.

 Fig. 2: The relation of a photon energy with its wavelength. Long wavelength means lower energy, because the frequency of the photon decreases (not shown in this formula) (1).

If one concentrates on the chemical structures depicted in Fig. 1 it becomes apparent that they both contain a large number of double bond containing cyclic carbon rings. And this is also the case for most fluorophores in nature. These structures are one secret of fluorescent properties.

Light low wavelength transfers energy on electrons in double bond which becomes excited and “jumps up” a band, thereby stabilizing the energy which has just been absorbed. This excited state of the electron does not last forever and it will fall back to its original and more stable state sooner or later. However, thermodynamics tells us that energy does not just disappear so the energy which is lost (because the electron has fallen back) must be somewhere. Indeed the energy is somewhere. The fluorescence effect that we see is the electromagnetic radiation in the form of photons that is emitted from the falling electron. Important to notice is, however, that not all energy is emitted as visible electromagnetic radiation. The physical turnover processes also create other emissions that cannot simply be seen by eye and in addition energy is “lost” through collisions of our fluorophores with other molecules. Since this is the case, the emitted energy is somewhat lower than the absorbed energy and therefore also the wavelength of the emitted photons becomes longer, as Fig. 2 tells us. As light color depends on wavelength, the emitted color that can be observed is different from the previously absorbed one (Fig. 3).

 Fig. 3: Emission spectra of green fluorescent protein (GFP). The wavelength of the emitted light is longer than the wavelength of the absorbed light. Therefore also the color of the light appears different (2).

Fluorescent properties of molecules enable biologists to mark proteins or other biomolecules in order to make them easily detectable by eye or with the help of photomultipliers. Two or more fluorescent markers can even be combined and might transfer energy to each other when their carrier molecules make contact. Changes emission spectra can consequently be observed. This is the basis of FRET microscopy and allows me to study the potential dimerization of proteins that are thought to function in the same protein translocation pathway.