Tag Archives: mCherry

Design guidlines for tandem fluorescent timers

Almost 4 years ago, I wrote a post on tandem fluorescent timers (tFTs). The idea is to have two different fluorescent proteins fused together to the protein of interest. In the paper from 4 years ago, it was superfolder GFP (sfGFP) and mCherry. sfGFP matures very fast (within minutes) and mCherry  matures more slowly (t1/2 ~40min). The ratio beween green to red fluorescent signal indicates the percentage of new vs old proteins, thus acts as a “timer”.  This latests paper on tFTs from the same group of Michael Knop’s lab, found that analyzing tFTs might be more complicated due to some possible problems of this system.

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This month’s Nature methods (part 1): Spinach, blue transcription & photoacoustic imaging

This month’s Nature Methods issue has several interesting imaging items & articles, including two super-resolution reviews, two optogenetics articles, and more.

This post will be dedicated to three items in the “tools in brief” section.

Blue transcription

Optogenetics usually refers to control of ion flux via light sensitive channels. However, there are other light-responsive molecules. The item titles “Optimized optogenetic gene expression” describe a work from Kevin Gardner’s lab. They fused the transcription activating domain of the protein VP16 to the protein EL222. EL222 is a light-oxygen-voltage protein from the bacterium Erythrobacter litoralis. This protein binds to DNA when illuminated by blue light, and detaches from the DNA when the light is removed. Using this system, they could induce and repress transcription of a specific gene of interest (harboring a specific promoter recognized by EL222) in mammalian cells in tissue culture and in zebra-fish embryo. This can be a great tool.

Zebra fish egg and embryo harboring  an mCherry gene under the control of the VP-EL222 (or not) under dark or blue-light conditions. Source: Motta-Mena LB et al. (2014) Nat Chem. Biol. 10:196.

Zebra fish egg and embryo harboring an mCherry gene under the control of the VP-EL222 (or not) under dark or blue-light conditions. Source: Motta-Mena LB et al. (2014) Nat Chem. Biol. 10:196.

Photoacoustic imaging

Fluorescent molecules absorb light, and then emit light at a different wavelength. Photoacoustic molecules absorb light and emit sound waves. This is called the photoacoustic effect. This effect can be utilized to image inside whole animals, and the hope of the field is to get deep tissue penetration and a high resolution. The item titled “Activatable photoacoustic probes” presents a paper by the J. Roa’s lab at Stanford university. They developed a new polymer which absorbs at near-infrared (thus allowing good tissue penetration) and these produce a higher signal than commonly used materials for such imaging. They were also able for the first time to create a photoacoustic sensor of reactive oxygen species. This new field is very interesting and very exciting.

Spinach2

Spinach may deserve its own post, but briefly, Spinach and Spinach2 are RNA aptamers that can be used for the genetic encoding of fluorescent RNA. This aptamers form a unique structure which binds a specific molecule which then fluoresce. However, the optical properties of this dye were not suitable for common microscope filters. So now the group that developed Spinach developed several new dyes to enhance the fluorescent range of Spinach2.

The main problem I have with Spinach is that most of their work is based on an artificial RNA composed of 60 repeats of CGG trinucleotide and the ribosomal 5S rRNA. I haven’t followed the literature of Spinach much, but haven’t seen any single molecule imaging using Spinach. but, I guess I owe Spinach a post of its own.

ResearchBlogging.orgTools in brief (2014). Chemical biology: Optimized optogenetic gene expression Nature Methods, 11 (3), 230-230 DOI: 10.1038/nmeth.2867
Tools in brief (2014). Sensors and probes: Expanding Spinach2’s spectral properties Nature Methods, 11 (3), 230-230 DOI: 10.1038/nmeth.2865
Tools in brief (2014). Imaging: Activatable photoacoustic probes Nature Methods, 11 (3), 230-230 DOI: 10.1038/nmeth.2868
Pu K, Shuhendler AJ, Jokerst JV, Mei J, Gambhir SS, Bao Z, & Rao J (2014). Semiconducting polymer nanoparticles as photoacoustic molecular imaging probes in living mice. Nature nanotechnology PMID: 24463363
Motta-Mena LB, Reade A, Mallory MJ, Glantz S, Weiner OD, Lynch KW, & Gardner KH (2014). An optogenetic gene expression system with rapid activation and deactivation kinetics. Nature chemical biology, 10 (3), 196-202 PMID: 24413462
Song W, Strack RL, Svensen N, & Jaffrey SR (2014). Plug-and-Play Fluorophores Extend the Spectral Properties of Spinach. Journal of the American Chemical Society, 136 (4), 1198-201 PMID: 24393009
Strack RL, Disney MD, & Jaffrey SR (2013). A superfolding Spinach2 reveals the dynamic nature of trinucleotide repeat-containing RNA. Nature methods, 10 (12), 1219-24 PMID: 24162923

High res imaging of DNA double strand breaks: Clearing the nucleus or marking the spot

DNA can be damaged in many ways. Consequently, there are numerous mechanisms to repair it. It is a fascinating field full of innovative concepts (“DNA repair” was my favorite course during my undergrad studies).  Double strand breaks (DSBs) are considered the most genotoxic, which is why many DNA damaging drugs and treatments intended to treat cancer are intended to create DSBs. On the other hand, DSBs can lead to chromosome translocations which can promote cancer, and can actually be viewed as a hallmark of cancer cells. DSBs also occur naturally during recombination events at Meiosis, and are important intermediates in immune system development.

DSBs are recognized by repair complexes that act to mark and repair the damage. Though the sequence of event has been studied biochemically, knowledge on the in vivo temporal and spatial arrangement has been limited due to lack of good high-res methods to visualize DSBs and repair proteins at DBS sites.

Two papers recently published take two different approaches to get  high-resolution images of events at DSBs.

The first paper, from Stephen Jackson’s lab, implemented a simple method to reduce the background fluorescence in the nucleus, thus increasing the signal/noise ratio. They study the non-homologous end joining (NHEJ) process which initiates with the Ku complex recognizing the DSB, followed by recruitment of the DNA dependent protein kinase (DNA-PK) and ending with ligation by XRCC4. DNA-PK phosphorylates the histone H2AX near DSB (gamma-H2AX). This is a known marker for DSB.

They used laser illumination to create a streak of DSB, and immunofluorescence (IF) against gamma-H2AX and Ku. They used a buffer called CSK that is known to release soluble proteins, thus removing background. Though the gamma-H2AX appears as a clear streak, Ku is all over the nucleus. Ku is also an RNA binding protein. So they added an RNase to the CSK wash. Miraculously, only DNA-bound Ku was now visible.

Look at the image – it looks amazing!

Hi-res image of Ku at DSB

Addition of RNase to the CSK buffer during IF allows for high-resolution imaging of DNA repair proteins. Source: Britton et al (2013) JCB vol 202(3):579-595.

Using this simple upgrade to the protocol, which removed most of the background, and combined with 3D structured illumination (3D-SIM) super resolution microscopy*, they were able to study co-localization (here’s a video), measure distances and study composition of different components of the repair complex. They also used Ku foci count to asses the effect of different drugs on DSBs repair efficiency.

I found this paper important mostly because  this simple improvement in their protocol yielded a vast improvement in imaging. This technique could be useful for studying many other nuclear processes which involve proteins that associate with both DNA and RNA (e.g. transcription complexes).

The second paper, from Tom Misteli’s lab, took a very different approach. They wanted to see sites of chromosomal translocations  following DSBs. First, instead of getting random, or multiple DSBs using drugs, radiation or laser (as in Jackson’s paper), they introduced restriction sites for the rare-cutting IsceI restriction enzyme. One site, on chromosome 7, was surrounded by multiple repeats (256) of the LacO sequence. Other sites, on chromosomes 1 & 10, were surrounded by 96 TetO repeats. The LacO and TetO arrays can be viewed by fusion of GFP to the Lac repressor (which binds LacO) and mCherry to the Tet repressor which binds TetO. Thus, each potential DSB site is marked by a fluorescent array in distinct colors. Expression of ISceI enzyme from a plasmid induces DSB. They then followed the temporal position of the green and red spots, until they get co-localization of the colors – meaning chromosomal translocation has occurred.

Time-Lapse microscopy of cells after transfection with ISceI enzyme, showing the translocation of two distinct sites (marked by GFP and mCherry) into a co-localized spot. Source: Roukus et al. (2013) Science vol 341:660-664.

Time-Lapse microscopy of cells after transfection with ISceI enzyme, showing the translocation of two distinct sites (marked by GFP and mCherry) into a co-localized spot. Source: Roukus et al. (2013) Science vol 341:660-664.

Like in Jackson’s paper, they used this system to look at temporal and spatial events, including recruitment of proteins (tagged with a third color: BFP), and to study the effect of different mechanisms that perturb the DSB repair machinery.

In conclusion: both approaches yielded beautiful and informative images about DSBs behavior and repair. Jackson’s method has a more global and immediate application, since it can be used to study other nuclear processes. Misteli’s system requires a lot of “genetic engineering” but can provide a more precise temporal resolution (since it enables live-cell imaging, unlike Jackson’s modified IF which only uses fixed cells). Misteli’s method can also provide insight into specificity (studying DSBs at specific genomic locations). These arrays can also be used to study chromosome dynamics (i.e. movements within the nucleus, during cell division, or transport of specific genomic regions during transcription induction e.g. to the nuclear pore complex). I am actually using such an array myself, to mark the location of my gene of interest.

* 3D-SIM uses laser light that passes through an optical grating. A series of images created by these striped patterns, generated at high spatial frequency, can then be processed by a computer algorithm into a high resolution imaging. See ref. below for more details.

ResearchBlogging.orgBritton S, Coates J, & Jackson SP (2013). A new method for high-resolution imaging of Ku foci to decipher mechanisms of DNA double-strand break repair. The Journal of cell biology, 202 (3), 579-95 PMID: 23897892
Roukos V, Voss TC, Schmidt CK, Lee S, Wangsa D, & Misteli T (2013). Spatial dynamics of chromosome translocations in living cells. Science (New York, N.Y.), 341 (6146), 660-4 PMID: 23929981
Schermelleh L, Heintzmann R, & Leonhardt H (2010). A guide to super-resolution fluorescence microscopy. The Journal of cell biology, 190 (2), 165-75 PMID: 20643879

Folding and maturation (part 3) – fluorescent timers

In the previous two parts (1)(2), I described the directed evolution of fast folding fluorescent proteins. But why is it important? Why do we need fast folding GFP? Why do we need to know the maturation time?

For most applications, it usually doesn’t matter. If we express these proteins constitutively, then we should already have enough fluorescent protein in the cells when we get to the experiment. Even in induced systems, we rarely take into account the maturation time of the protein. We follow fluorescent as it appears; usually without taking into account that the immature protein may have been present in the cells minutes or hours before.

However, when studying cellular dynamics, timing is important. The spatiotemporal dynamics of a protein is not an easy task to determine. Fluorescent timers (FT) are fluorescent proteins which change color with time due to a chemical conversion of the chromophore. Early FTs were first developed on the basis of mCherry. These FTs change color from blue to red at rates ranging from 10 min (fast-FT) to 28hr (slow FT). How are FT used? Basically, the ratio between the two colors should indicate the passage of time. For instance, a ration of red/blue=0 will indicate that the FT is a nascent protein that has not converted yet. A ratio of red/blue=1 indicates that there are no new proteins – all have converted. The ratio between 0 and 1 will tell us how much new protein still exists at the specific cellular location.

However, because of their tendency to oligomerize, or because of low brightness, these FTs were not widely used. Another recently developed monomeric green to orange FT is called Kusabira green orange (mK-GO). However, the 10h color transition is not suitable to measure fast dynamics.

A new paper sent to me by one of the blog readers (thanks Andrius) has taken a unique approach to develop FTs which they call tandem FT (tFT).

FT – regular fluorescent timer. tFT – tandem FT. m1, m2 – maturation rates. If m1<<m2 than option 2 is negligable compared to option 1.

Instead of using directed evolution to develop a novel FT, they took two known fluorescent proteins which have significantly different maturation times (sfGFP and mCherry) and fused them together in tandem. The logic of this system is quite beautiful: the sfGFP will mature fast, and the green signal will remain consistent. The mCherry will mature slower (half-time of 40min). Thus, they get a two color timer. If the signal is only green, it means that the protein was recently synthesized. A larger ratio of red/green will indicate passage of time based on mCherry maturation rate. A ratio of 1 will indicate that no new protein is present at that particular location.

They then started playing with in in different scenarios in yeast. First, they followed the formation of the spindle pole body (SPB) (the yeast equivalent to centrosomes). It is already known that in yeast, the SPB duplicates during metaphase and the “old” SPB travels to the bud tip during anaphase. They tagged an SPB protein with their tFT. Indeed, they see that the ratio of red/green in the bud is greater than in the mother cell. A mutation known to affect this segregation similarly reduces the ratio in the bud compared to the mother. In contrast, proteins that are known to be retained at the mother, whereas newly synthesized a transported to the bud show the exact opposite – i.e. red/green signal higher in the mother vs. bud cell.

A very neat experiment used a bud-scar protein tagged with tFT which shows several bud scars forming on the same cell over time, with different color blends (the old scar is red, newer in “yellow” and newest in green. This presents a nice “clock” of for the relative age of each scar. It’s too bad that the authors didn’t add the real time line (in minutes) in their image and tried to correlate the actual time with the tFT “time”.

A protein localized to bud scars is fused to tFT. the older the scar, the more red it is. Source: Khmelinski et al. (2012) Nat Biotech 30:708.

They then started to explore other stuff. First, they looked at the segregation of nuclear pores during mitosis. Nuclear pores (NPC) are complex structures that allow transfer of proteins and RNA in and out of the nucleus in a regulated manner. The question they asked is whether “old” NPCs remain in the mother cell and the bud get newly synthesized proteins, or vice versa (or equal distribution, i.e. non discriminated transfer). They individually tagged each of the NPC proteins (plus some control) – a total of 36 proteins- with their tFT and measured whether we see older proteins at the mother or the bud. Surprisingly, the bud gets the older proteins. Their analysis suggests that there is active transport of the “old” NPCs into the bud. How this is achieved and why the bud should receive old and possibly damaged NPCs are good questions for further research.

NPC proteins tagged with tFT show preferential localization of “older” proteins at the bud compared to mother cell. source: Khmelinski (2012), Nat Biotech. 30:708.

Despite of their interesting results, the authors didn’t stop there and when to test another application. They show that a single readout of the red/green ratio can indicate the stability of the protein. Stabilization/de-stabilization by mutations or different inherent stability can easily be distinguished based on the red/green ratio. They further utilize this approach to screen for protein stability regulators, which was quite successful as they identified most known factors in the specific pathway they studied, as well as identified new factors in this pathway.

In conclusion – this paper shows how one can utilize “ordinary” fluorescent proteins as fluorescent timers. Moreover, this method is much easier and much more flexible than trying to evolve FTs by random or directed mutagenesis. In fact, using different pairs with different maturation times would easily enable us to create FTs for any time intervals we wish and in any color we wish.

They show here how to utilize this system to follow cell cycle events. Obviously, this system can be used to study other scheduled, localized events in the cell.

A straightforward application is to follow protein stability. However, this system can also be used to study mRNA stability, by combining these tFTs with the MS2 system.

I’m sure other applications will be developed in the future.
All in all, a good paper with a great idea!

ResearchBlogging.org Khmelinskii A, Keller PJ, Bartosik A, Meurer M, Barry JD, Mardin BR, Kaufmann A, Trautmann S, Wachsmuth M, Pereira G, Huber W, Schiebel E, & Knop M (2012). Tandem fluorescent protein timers for in vivo analysis of protein dynamics. Nature biotechnology, 30 (7), 708-14 PMID: 22729030

Red alert: the three color dilemma continues

So, I sat with the biophysicist who built the microscope that I intend to use. We discussed my proposed experiment and the different capabilities of that particular microscope.

Immediately we discovered that I will not be able to use LSSmKat1. If you recall, the emission peak of LSSmKate1 is at 624nm. However, the dichromatic mirrors set in that microscope for the red channels are from ~500 to 620 and from 650 to 750. So the 624 falls just in the waveband that I cannot detect.

So I remain with LSSmKate2, with em. Peak at 605.

Ok, now, the available lasers in this microscope are 407, 436, 488, 561 and 640nm.

GFP is excited at 488. That the peak and that’s good. We will use the 525/30 filter (i.e. from 510 to 540).

mCherry is excited at 587, so using the 561 laser we can get a fair signal (~60%). We will use the 593/40 filter (i.e. from 573 to 613).

This means that in both cases I collect only part of the emitted photons (the ones within the filter range, see fig 1).

spectra of GFP & mCherry, with filters. (image: BD spectra viewer)

LSSmKate2 can be excited with the 436 or 488 laser.  The 488 will give a ~8% signal for mCherry.  But the 436 will not. Although LSSmKate2 is 3-fold less bright than mCherry, and is less photostable, to object using this marker is only for time zero, when the GFP and mCherry signal should be low. However, this needs to be experimentally tested. It could be that the mCherry signal could be mathematically subtracted later.

The problem is that I would not be able to excite simultaneously at 488 and 561 to get simultaneous data of GFP and mCherry. This is because I would also have LSSmKate2 excited. However, for my purposes, I do not have to take the images simultaneously. The microscope can take images with the different lasers at 50-100ms time intervals. Since I expect to detect changes in the seconds to minutes range, a 0.1sec difference between the green and red channel is acceptable.

There is another option and that is to use a far-red protein, such as TagRFP657.

FP Max ex. Max em. QY EC(M-1 cm-1) Brightness Relative brightness Photostability
TagRFP657 611 657 0.10 34,000 3400 0.1 55 sec
LSSmOrange 437 572 0.45 52,000 21400 0.82 10 sec

The advantage is that we can use the 650-750 dichromatic, and use excitation at 640, which still gives a fair excitation, and it will not excite mCherry.  The down side is that the 561 laser will excite TagRFP657 too. However, the TagRFP657 emission at the 593/40 filter is fairly low and with a less than maximum excitation to begin with, and a lower brightness and photostability (compared to mCherry), this should not be a big problem (see figure 2) . But again – should be tested experimentally.

figure 2: TagRFP657 spectra (From: Morozova et al. Biophys J. 2010 99(2):L13-5.)

So here is another lesson: when designing a multi-color experiment, you need to take into account both the FP properties, and the capabilities of your microscope (lasers, filters, dichromatics, speed etc…).

Update 22.6.12:

After further discussion, another LSS FP was suggested, LSSmOrange. I added its characteristics to the table above.  It is brighter than TagRFP657, and the Stokes shift of LSSmOrange is better than LSSmKate2 in relation to mCherry (bluer excitation, which means NO excitation of mCherry.) So I will test all three, and hopefully have an answer which is better within 2-3 weeks.

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.

Basics in Confocal microscopy and image analysis

Nowadays, confocal microscopy is possibly the most widely used optical method in biological research. This methods creates better (and prettier) images than widefield microscopy (whether transmitted light or epifluorescence). The main advantages of confocal vs. widefield microscopy is the elimination of out-of-focus glare (thus increasing resolution and increasing signal-to-noise ratio) and the ability to collect serial optical sections of the specimen (z-sections).

The basic configuration of the optics is similar to that of the epifluorescnece microscope. The addition that created the confocal microscope, invented by Marvin Minsky in 1955, was to add two pinholes. The light produced by lamp (or laser) passes through the first pinhole on the way to the specimen. The light that is reflected (bright light) or emitted (fluorescent light) from the specimen passes through a second pinhole on the way to the detector (eyepiece, camera or any recording device). The two pinholes have the same focus – thus they are confocal. The light from other focal planes cannot go through the second pinhole, and this reduces the background “glare” of out-of-focus fluorescence seen in epifluorescence widefield microscopes.

Since biological samples usually have thickness of a few microns at least, one can get an image of a thin slice of the sample (e.g. 0.1 µm) without physically slicing the sample (optical section). We can then move the focus along the Z axis to get clear images of up or down sections. Thus, for a cell 3µm thick, we can have 30 hi-resolution images 0.1 µm thick from bottom to top (z-sections). These images can then be stacked one on top of the other (z-stacking) to create a single 2D image or to reconstruct a 3D image of the sample.

Here’s an example from an experiment I did last week (note that this is a widefield, not confocal microscope):

Above is a composite image of 31 Z sections of U2OS cells, create by the ImageJ program. The”pseudo-blue” represents the blue fluorescnce of a dye called DAPI (4′,6-diamidino-2-phenylindole)  which intercalates into DNA, and is therefore a popular nuclear dye. When bound to DNA, it is excited by UV light (peak at 358nm) and emits blue/cyan light (peak at 461nm). The “pseudo-red” color represents the fluorescence of mCherry-ZBP1 fusion protein. mCherry is an RFP.

You can see in the image that the first and last few images in the series are out of focus. You can therefore choose the best or sharpest Z-section according to your needs.

However, most people do not show a single Z section since then we miss a lot of information that is found in other sections. The available programs today allow “stacking” the section to create a projection of all the sections into a single image.

Here is the maximum projection of the Z sections shown above:

Maximum projection means that the algorithm chooses, for each pixel, the highest value found in any of the 31 Z sections. However, since we chose all 31 sections, we can still see a “glare” or halo. This is a result of the “halos” from the out of focus sections.

I therefore choose only a few sections to create the next image:

This image is now sharper and better looking.

The program allows you other options besides maximu projection: you can choose minimum, average, median, and even standrad deviation, seen in the next image (DAPI channel only):

It looks very cool. I stacked the entire 31 sections (of a differnt field), so you can see the halo from the out-of-focus sections sorounding the “black” rim of the nucleus (black since it has the minimal standrd deviation value for all the images). The blue zones, with high SD, suggest a larger differnce in fluorescence between the differnt sections.

Above, I mentions that the blue and red are pseudo colors. What actually happened was that the images I took with the microscope at each channel (range of wavelengths) is actually maintained as a greyscale image.

Using the image analysis program you can then merge the images of the differnt channels (up to 4 in ImageJ) to create a color image. When creating the merged image, you determine what color to assign to each channel. Here is the same image, but with the colors reversed:

You should take that into account when you see pretty pictures in sceintific journals.

The program also allows to creade 3D representations of your Z stack. but I haven’t learned how to do that.

There are many other tools that one can use with the image analysis program besides creating the image. One important feture is the ability to measure the intensity of the fluorescent signal (actually, the pixels) in certain areas within the cell. You can measure distances and angles between objects and probably many moer that I still have to learn.