Tag Archives: oligomerization

FISEB 2014 meeting -day 1

FISEB meeting happens every three years, and it includes participants from 28 different experimental biology societies in Israel. It is the best meeting to learn about biological-medical research performed in Israel at all fields and doctrines.

4 days, 8-10 parallel sessions, hundreds of lectures, >1000 posters, >2200 participants.

The first day started by a plenary lecture by Aryeh Warshel, Nobel lauret. He is really far from my field, and his lecture was very much confusing to me. But he has nice cartoons 🙂 The bottom line – enzymes are able to catalyze reactions due to electrostatic connections that are maintained stable (unlike in water).

From the afternoon sessions, I chose “signaling pathways & networks”. Relevant to this blog:

Yoav Henis from Tel-Aviv Uni. talked about oligomerization of TGF-beta receptors. he used a method he calls “co-patching”, which is essentially IF with two different antibodies for two receptor subunits. homodimerization will yield single color “patch” whereas heterodimerization will yield an overlap of both colors (co-patch). He then looked at the % of co-patch with different receptor subunits with/without ligand, or with mutants.

Maya Schulinder from Weizmann Institute talked about the contacts between mitochondria and other organelles (ER, vacuole) in yeast. These contacts are important for lipid metabolism. She new about the mito-ER contact but found there must be a second contact (bypass mechanism). She used an interesting screen method to find the bypass mechanism to the mito-ER contact: she expressed one of the contact protein as a GFP fusion. She expected that if the bypass mechanism and the mito-ER contact “share the load” of lipid metabolism, then deletion of the bypass will increase the number of the mito-ER contacts to compensate. Using automation, she imaged 6200 deletion mutants (from the yeast deletion library) each expressing this GFP fusion. As expected, she found 4 candidates which turned out to be very interesting.

Roni Seger from Weizmann showed that targeting the nuclear localization signal of ERK can be a novel cure for certain pathologies, including certain types of cancer.

On the other hand, Maya Zigler from the Hebrew Uni. suggested another new idea to cure cancer – by inducing the surrounding immune cells to destroy the tumor.

Ido Amit from Weizmann as well told us that we may not really know all the different types of cells that exist. What most people do, particularly in immunology, is rely on one or two known “markers” and use FACS or other methods to sort the cells based on these markers. However, some of the markers overlap. and there may be cells for which we do not have any markers and they “disappear” in the crowd of unsorted cells. or, the could be further sub-types we do not know about. So he approached the problem in an unbiased way – he took all the cells in the spleen, and did single cell RNA seq to individual cells from the spleen. Thus, each cell type has several hundred/thousand “markers” based on gene expression profiles. Not only did this method agree with the common FACS sorting markers, but he identified several sub-types unknown before.  Expect his paper this month in Science. His paper just got published in Science.

Finally, Yaron Shav-tal from Bar-Ilan Uni. used the MS2 system to study how perturbing the signaling pathway of serum stimulation affects transcription of beta-actin gene. As per usual – very neat job and interesting results.

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Nature methods special issue: focus on bioimage informatics (Part III)

Continuing with the Brief communications section:
Rapid, accurate particle tracking by calculation of radial symmetry centers

Tracking single particles is a major challenge, since in many cases the particles are smaller than the pixel size. Several image analysis methods have been developed to analyze subpixel localization of particles. Here, Raghuveer Parthasarathy describes a new approach to calculate subpixel localization of particles, using radial symmetry analysis. Although the accuracy of his algorithm is similar to that of other algorithms (Gaussian fittings such as Non-Linear Least Square minimization – NLLS and Maximum likelihood estimations –MLE), his calculations are ~100 faster than other algorithms.

For those of you who do single particle analysis and super-resolution microscopy, this algorithm may be very helpful.

Rational design of true monomeric and bright photoactivatable fluorescent proteins

As already mentioned in the previous post, photoactivated localization microscopy (PALM) and Stochastic optical reconstruction microscopy (STORM) are two of the techniques used in super-resolution microscopy. In many cases PALM/STORM use photoactivatable fluorescent proteins (PA-FPs). The efficiency of super-resolution microscopy relies on the properties of these PA-FPs, such as brightness, photostability, pH stability, oligomeric state, maturation rate, photoswitching/activation yields etc…

EoSFP, which was cloned in 2004 from the scleractinian coral Lobophyllia hemprichii and furhter engineered, is a green-to-red photoswitchable protein with the highest photon output of all PA-FPs. Upon UV irradiation, it permanently switches its emission peak from 516 to 581 (excitation is at 505).  Monomeric form, mEoSFP and mEos2 were developed (mEoSFP is less used, since its chromofore does not maturate at 37°C, limiting the use to non-mammalian cells).

Here, the authors claim than mEoS2 forms oligomers at high concentrations, which may limit the use of this protein as a fusion partner to the studied protein, and can also skew super-resolution analysis that assumes only monomeric mEoS2 forms. Therefore, the authors solved the crystal structure of mEoS2. Based on the structure, they developed improved, true monomeric variants (mEoS3.1 & mEoS3.2), which are also brighter and mature faster.

Confocal images of HEK293 cells transiently transfected with plasmids encoding indicated fusions and imaged at the middle layer (top) or near the plasma membrane (bottom). See the differnces between mEoS2 and mEoS3! Source: Zhang et al. Nature Methods 9,727–729(2012)

In the supplementary data of this paper you will find a lot of data on the different mEoS variants (not only 2, 3.1 and 3.2 but others as well).

The lesson to be learned here – the properties of the fluorescent protein that you are using to tag your protein of interest may affect the properties of the studied fusion protein/organelle/cell and these factors should always be taken into consideration.

Tracking mitochondria dynamics in live HeLa cells. The large box: Mitochondria in HeLa cells tagged with mEoS3.2-mito prior to photoswitching. Rectangle – area of UV illumination to switch color. boxes on left: time-lapse of mEoS3.2-mito only in the activated region. Source: Zhang et al. Nature Methods 9,727–729(2012)

Multiview light-sheet microscope for rapid in toto imaging

Embryogenesis and morphogenesis are highly dynamic processes that are difficult to image since it involves multicellular samples in the millimeter range. In such cases, it is difficult to image subcellular processes on the one hand, and get a clear 3-D view of the entire sample (which need to be properly rotated). Some techniques that allow sample rotation exist, and an emerging method called selective plane illumination microscopy (SPIM), are helpful in following such processes on whole embryos. However, samples are required to rotate in several angles, often not keeping with the same axis, and the time resolution required for each rotation sometimes exceeds the biological dynamics. Here, research from the lab of Lars Hufnagel developed a new microscopy system, which they term MuVi-SPIM, consisting of four arms (with objectives) that can perform as illuminating or detecting objective. This allows rapid four 3D-view imaging of the sample. Very nifty!

3D image reconstruction of a Drosophila embryo expressing the membrane marker Gap43-mCherry in cycle 14. Alternating green and magenta colors correspond to the image contributions from the eight different views. Inset shows a close-up view of the image fusion on the boundary between two different views. Source: Krzic et al. Nature Methods 9,730–733(2012)

The next parts will review the four articles in this issue.

ResearchBlogging.orgParthasarathy R (2012). Rapid, accurate particle tracking by calculation of radial symmetry centers. Nature methods PMID: 22688415
Zhang M, Chang H, Zhang Y, Yu J, Wu L, Ji W, Chen J, Liu B, Lu J, Liu Y, Zhang J, Xu P, & Xu T (2012). Rational design of true monomeric and bright photoactivatable fluorescent proteins. Nature methods, 9 (7), 727-729 PMID: 22581370
Krzic U, Gunther S, Saunders TE, Streichan SJ, & Hufnagel L (2012). Multiview light-sheet microscope for rapid in toto imaging. Nature methods, 9 (7), 730-733 PMID: 22660739

The red color dilemma: how to choose the right fluorescent protein

This post continues the previous post.

I encountered a serious dilemma in choosing the right protein. This is also a great opportunity to learn about the many properties of fluorescent proteins.

Let us start with the obvious: excitation and emission maxima and spectra.

We all know that EGFP has an excitation maximum at 488nm. That is, EGFP protein that is excited with photons at 488nm will give its maximum emission intensity at its emission maximum, 509nm.

However, we must remember that this is the maximum emission. The emission spectra is much wider, and for GFP it goes from ~470nm up to ~630nm. Figure 1 shows the EGFP excitation (dashed line) and emission (full green) spectra.

Fig. 1: GFP specrum with 488 excitation

And now, what if we want to look simultaneously at two colors, EGFP and dTomato?

Figure 2 (upper panel) shows you that excitation at 488nm (the max ex. of EGFP) also excite dTomato, leading to almost 30% emission at its peak of 581nm. Therefore, if we would collect all the emitted photons, we would detect the combined excitation of EGFP and dTomato.

Fig. 2: GFP dTomato spectra with 488 excitation. Lower panel: with filters.

We therefore use filters with a narrow band of wavelength (fig. 2, bottom panel). Thus, if we use the 510/20 filter, we would detect only photons emitted at the band, in this case only from EGFP. If we use the 580/30 filter, we would detect photons coming from dTomato and EGFP.

Now what happens if we add the long-stokes shift protein LSSmKate1 to the system? Figure  3 shows LSSmKate1 is maximally excited at 463nm (red dashed), and has max. emission at 624nm (full red) [note- I drew the LSSmKate spectra, based on prior publications]. However, 460nm also excites EGFP and dTomato. The EGFP signal, at 624nm is negligible. However, dTomato gives emission at 8-9% of its maximal emission. Is this negligible? We will soon learn.

Fig. 3: GFP dTomato LSSmkate1 spetra with 460 excitation

Now let’s look at mCherry. With excitation laser at 460, we have virtually no excitation of mCherry. We would therefore prefer to use mCherry in our 3-color system, instead of dTomato.

Fig. 4: GFP mCherry LSSmkate1 spectra with 460 excitation

This table summarizes the excitation & emission maxima:

FP Max ex. Max em.
EGFP 488 509
dTomato 554 581
LSSmKate1 463 624
LSSmKate2 460 605
mCherry 587 610

This table shows the relative emission of the different FPs with different excitations (the LSS data is estimated based on publications, since the BD

spectrum viewer does not include LSSmKates in its database):

  % from max emission % from max emission

Em   (nm):

509 580 605 610 624 509 580 605 624
EGFP Ex.488 100% 8% <5% Ex.460 65% 5%
dTomato 27% 20% 19% 15% 15% 11% 9%
LSSmKate1 50%? 100
LSSmKate2 50%? 100
mCherry 8%

However, excitation & emission spectra are not the only considerations. There are other parameters to consider.

Brightness, QY and EC

Different fluorescent proteins have different brightness: some are very bright, some are dim. As a matter of history, we usually consider the relative brightness, compared to EGFP (which is set to 1.00). You can see in the table below the relative brightness of the proteins discussed above.

The brightness is determined by two parameters: quantum yield (QY) and Extinction coefficient (EC).

QY is simply the ration between the number of photons absorbed to the number of photons emitted. If QY=1, then for each absorbed photon you get one emitted photon.  However, we never get 100% efficiency. The protein with the highest QY that I encountered is called ZsGreen with QY=0.91.

EC (or ε) relates to the formula A = εcl in which A is the absorbance, l is the path length (in cm, usually) and c the concentration in Molar units. ε, in M-1cm-1 units, is a measurement of the capability of a certain fluorescent protein to absorb light at a certain wavelength.

The formula ε*QY gives a measure of the brightness, so for EGFP, the brightness is 55,000*0.60=33,000

For ZsGreen, the EC is only 43,000 so the relative brightness is only 1.18. This example shows that a high QY does not necessarily mean a very high brightness.

This table summarizes al the brightness data:

FP QY EC(M-1 cm-1) Brightness Relative brightness
EGFP 0.60 55,000 33,000 1.00
dTomato 0.69 69,000 47,610 1.44
LSSmKate1 0.08 26,000 2080 0.06
LSSmKate2 0.17 31,200 5304 0.16
mCherry 0.22 72,000 15,840 0.48

Let’s go back to our dilemma: dTomato or mCherry?

We now know that with a laser excitation of 460nm, we get 100% emission from LSSmKat1 at 624nm, and only 9% emission from dTomato at 624nm.

However, dTomato is 24-fold brighter than LSSmKate1.  If we look only on the QY data, at 8% emission, dTomato will produce ~6 photons for each 100 photons (100*0.69*9%), whereas LSSmKat1 will produce 8 photons (100*0.08*100%). In other words, although we get very low emission of dTomato, relative to its maximum, it is almost as high as the LSSmKate1 emission in that wavelength. Since eventually, we just “see” the photons with no knowledge of their source, the signal that we will get will be ~40% from dTomato. This will create a problem if we wish to detect changes in fluorescent intensity over time due to changes in protein localization (or other changes) because we will not be able to know if the change is due to dTomato or LssmKate1.

That is why mCherry is a better choice than dTomato for this experiment.

Photostability

Another parameter to take into consideration is the protein’s photostability.

Photostability is a measure (in seconds) of how long it takes for half of the number of proteins to bleach at the maximum excitation.  This matter is important for any experiment involving fluorescent proteins (or dyes) but is crucial when doing live imaging, particularly for time-lapse experiments.

LSSmKate1 is more stable than LSSmKate2 (see table below), and therefore, it might be a better choice than LSSmKate2 for my time-lapse experiment.

FP photostability
EGFP 174 sec
dTomato 98 sec
LSSmKate1 60 sec
LSSmKate2 44 sec
mCherry 96 sec

There are other parameters to take into account, some of them I discussed in earlier posts.

These include:

pH stability (and the effect of pH on the absorption and emission spectra).

Maturation rate – how long does it take your fluorescent protein to fold correctly and create the chromophore? If you are using your protein to measure expression rate, and the maturation time is longer than the time frame of your experiment, then you will not get the information you are looking for. Maturation rate  depends on protein characteristics, as well as oxygen levels and temperature. Several fast-folding FPs were developed in recent years.

Oligomerization state – many FPs are naturally monomeric. However some FPs particularly in the orange & red range) are dimeric or tetrameric.  Since in many cases the FP is genetically encoded as a fusion with a protein of interest, fusing a dimeric FP may cause your protein of interest to dimerize, through the FP, thus altering its biology. If your protein is naturally oligomeric, fusing it to a dimeric or tetrameric FP may create large protein aggregates. One solution for dimeric FPs is to create a tandem fusion proteins (i.e. two FPs are fused one to the other and to the your protein).

Generally, the letter m at the beginning of the FP name indicates that it is monomeric (e.g. mCherry, mKate), d indicates it is dimeric (dTomato) and td indicates tandem dimer (tdTomato). However, there are many FPs with no indication of their oligomeric state in their name (e.g. TurboRFP is dimeric). EGFP and its many derivatives are monomeric (except at high concentrations when they form weak dimers).

Phototoxicity  – although the FPs themselves do not emit harmful radiation, the excitation light can be harmful to the cell. For instance, UV light, that is used to excite BFPs, as well as some LSS FPs and photoactivatable FPs, can cause direct DNA damage, create reactive oxygen species that will cause an oxidative stress etc…  This is one reason why BFPs are the least popular FPs, particularly for live imaging.