Tag Archives: STORM

Poking holes into membranes to label proteins for live imaging

There are two major way to label inner proteins, structures or organnelles for live cell imaging. The most common method is fusing the studied protein to a fluorescent protein. A second approach is the addition of labeling agents from outside the cells. However, many labels cannot penetrate through the cell membrane. This is true to some, but not all dyes, but more importantly, to larger agents, such as antibodies or DNA/RNA oligos. To allow these agents to enter cells, researchers can use microinjection, electroporation, bead-loading, or transfection (e.g. of short oligos).

In a paper just published in eLife, a new technique is described to form temporary holed in the cell membrane. These holes allow delivery of any labeling agent into cells. Continue reading

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Nobel prize in chemistry 2014

The Nobel prize in chemistry 2014 was awarded to Eric Betzig, Stefan W. Hell & William E. Moerner for the development of Super resolution fluorescent microscopy!

See here for details on the history of this discovery.

As readers of this blog know, Super-resolution microscopy has made a revolution in the field of fluorescent microscopy in a very short time.

This is a justified award.

Nuclear pore complex at super resolution

It is very difficult to decipher the spatial arrangement of proteins in a given complex. The most common methods are X-ray crystallography (which is very difficult to achieve for single proteins, let alone complexes), cryo-electron microscopy  and electron tomography (ditto) or  biochemical approaches (e.g. which protein domains interact with each other).  A paper published in today’s Science, shows a different approach, using super-resolution microscopy.

The Nuclear Pore Complex (NPC) is a large complex that enables movement of large particles in and out of the nucleus. Though electron tomography previously showed the general structure of NPC, the spatial order of individual proteins was unclear. Here, the researchers used stochastic optical reconstruction microscopy (STORM), a super resolution microscopy method, of either antibody or nanobody labeled samples. The idea of STORM is to get to the point where fluorophores begin to photobleach, and then take a thousands of images very fast (here they used 100 frames/second). the idea is that only a few fluorophores will lit up in each image, thus reducing background.

NPC viewed by regular confocal microscopy (A) or STORM (B). Scale bars indicate 3 μm and 300 nm, respectively. (C)- images after quality control. (D) - average "ring". (E) Normalized mean radius. (F) Precision of determining the radial position was estimated by cross-validation, performed by averaging 17 sets of 500 pores each (red line marks the median). The standard deviation of the distribution is 0.1 nm. The whiskers on the box plot encompass 99.3% of the distribution.  Source: Szymborska et al. (2013) Science: Vol. 341  no. 6146  pp. 655-658

NPCs viewed by regular confocal microscopy (A) or STORM (B). Scale bars indicate 3 μm and 300 nm, respectively. (C)- images after quality control. (D) – average “ring”. (E) Normalized mean radius. (F) Precision of determining the radial position was estimated by cross-validation, performed by averaging 17 sets of 500 pores each (red line marks the median). The standard deviation of the distribution is 0.1 nm. The whiskers on the box plot encompass 99.3% of the distribution. Source: Szymborska et al. (2013) Science: Vol. 341 no. 6146 pp. 655-658

Furthermore, for each protein, they were able to average thousands of NPCs from the same cell, thus improving statistical analysis compared to cryo-EM (which usually compares only a few good images). They claim to had a standard deviation of ~0.1nm(!).

Since the NPC is ring shaped, they calculated the radius of the ring for each protein they tested. Thus they were able to spatially align the proteins based on their distance from the center of the ring. systematic labeling of different components allowed them to create a model of the NPC scaffold structure.

The potential for their method is big, particularly for structures that are abundant in cells (ribosomes, transcription pre-initiation complex, gated channels, photosynthetic centers – choose your favorite complex) to allow for good statistical analysis.

Though in this paper they used fixed cells, it would be most interesting to implement similar methods to live cells, and view structural changes in real time.

ResearchBlogging.orgSzymborska A, de Marco A, Daigle N, Cordes VC, Briggs JA, & Ellenberg J (2013). Nuclear Pore Scaffold Structure Analyzed by Super-Resolution Microscopy and Particle Averaging. Science (New York, N.Y.), 341 (6146), 655-658 PMID: 23845946

Ries J, Kaplan C, Platonova E, Eghlidi H, & Ewers H (2012). A simple, versatile method for GFP-based super-resolution microscopy via nanobodies. Nature methods, 9 (6), 582-4 PMID: 22543348

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

Nature methods special issue (Part IV): systems biology in single cells

Systems biology is a field that aims to understand the behavior and interactions between multiple biological components, in a quantitative manner. Thus, systems biology routinely uses “-omics” analyses, on whole cell populations, yielding an average behavior of heterogenic populations.  Little is known about the interaction of multiple components in a single cell.

Fluorescent microscopy is a great tool to study events in whole populations at the single cell level: i.e. determine the exact behavior of 100 or 1000 single cells in the population and extrapolate from that on the heterogeneity of the population, and which different parameters may affect one cell and not another in the same population (which could be isogenic or hetrogenic). However, fluorescent microscopy is limited by the number of spectrally distinct fluorophores. Therefore, most experiments make use of 1-3 colors to study 1-3 different components (proteins or RNA).  Thus, systems biology on a single cell level is a challenge not easily met. Here, Eric Lubeck and Long Cai describe a novel method that allows visualizing multiple RNA species in single cells.

Before I go on to their paper, I want to briefly discuss mRNA detection by optical means. There are two major methods to optically detect mRNAs in cells. The first method, suitable for live-cell imaging, is by tagging an RNA binding protein with a fluorescent protein. Thus, by following the protein we are following the RNA it binds to. However, RNA binding proteins typically associate with multiple mRNA species in the cells. Therefore, an artificial system was developed (commonly called the MS2 system). In this method, a specific sequence, MS2 binding sequence (MBS) is genetically inserted into the RNA we wish to study. This sequence is recognized specifically by the bacteriophage MS2 coat protein (MCP). Thus, MCP, fused to a fluorescent protein, specifically recognizes only the one MBS-tagged RNA. Similar systems, based on the PP7 coat protein and the U1A protein were developed, allowing visualization of two-three mRNAs in the same cell.

The second method, termed Fluorescent in situ hybridization (FISH), uses the inherent specificity of nucleic acid binding – complementing single-strand sequences can hybridize to form a double stranded (ds)DNA, dsRNA of DNA:RNA hybrid. Thus, with FISH, we use short, fluorescently labels, DNA oligonucleotides (typically 20-50 bases long) that are complimentary to the RNA species we wish to study. The disadvantage of this method is that you must fix the cells (thus, it is not used for live imaging).

Figure depicting the basic principles of the MS2 and FISH methods. On the left: diagram fo the MS2 principle and image of yeast cells expressing Rpb7-mCherry (nuclear marker) and MFA2-MS2-GFP. blue arrows indicate ingle mRNAs. Right: diagram of FISH method. Below: image of FISH for TEF4 mRNA in yeast cells. Blue area is nucleus (DAPI). Source: MS2 – myself. FISH – Xavier Darzacq (my collaborator).

Up until now, FISH probes for a single mRNA were labeled with the same color (in some cases, people use two colors to distinguish the 5’ and 3’ of an mRNA). Lubeck & Cai took the FISH method a huge leap forward, devising a nanoscale colored “barcode” system which allowed them to visualize 32 different mRNA species simultaneously in the same yeast cell.

Their initial strategy was to spatially order probes in different colors along the same mRNA. The length of each probe was 20 bases, which is equal to about 7nm. Super resolution microscopy can get to 1-20nm resolution, so theoretically, if different mRNAs have probes in different orders along its length (see figure), the order of the probes on each mRNA can be resolved, thus identifying that mRNA.  For the first attempt, they hybridized 12 probes, four of each color, in a spatially order way (four green, four red, four blue or four red, four green four blue) to the PUN1 or GFP mRNAs. They calculated that four probes for each color increase the probability of at least one probe hybridizing to 99%, and probes from all three colors to 96%, which is what they detected (i.e. 96% of the PUN1 mRNAs showed discrete spots that showed co-localization in all three colors. Using Gaussian fitting, they were able to correctly determine the barcode of 74% of the mRNAs. That is pretty good!

Spatial ordering of fluorophores on mRNAs can be resolved by Gaussian centroid localization. Source: Lubeck & Cui Nature Methods 9,743–748(2012)

This method is good enough if you have a few labeled mRNAs in the cytoplasm. However, when the transcript density increases spots may overlap thus making it impossible to read the barcode. The authors then used a different strategy to label and visualize the mRNAs. This time they used photoswitchable dye pairs. What it means is that you have probed that are labeled with activator dyes and probes that are labeled with emitter dyes. Only when an activator dye is close to the emitter dye you will get fluorescence of the emitter. This method reduces background, since you must have both probes hybridizing one next to the other. They then turned to an imaging method called stochastic optical reconstruction microscopy (STORM) which is used for super-resolution microscopy. Again, the dye pairs were spatially distributed along the mRNA so that the spatial order will create a barcode.  This method also allowed for proper barcode identification of ~72% of the mRNAs.

A major problem with both methods is that to get better barcode identification, the RNAs need to be linearized. They do that by compressing the cells between the coverglass. However, they also destroy the spatial organization of the cell. Another drawback is the obvious need for high-resolution microscopy that also requires high photon emission by the fluorophores.

So they turned to a third strategy: spectral barcoding. In spectral barcoding, the order in which the fluorophores are distributed along the mRNA molecule is not important, only the presence of the correct colors is relevant. This reduces the need for super-resolution (since 100nm is sufficient for single mRNAs), no need for linearization of the mRNA, and less photons are required. With this technique, they got 100% positive identifications (compared to 72-74% with the spatial barcode).

As proof of principle, they profiled the transcripts from 32 stress-responsive genes in single yeast cells, in response to calcium stress: 5 general stress response genes, 14 regulated by Crz1 transcription factor and 13 which are related to ageing and other stress markers. All in all they use barcodes of 3 colors combinations out of 7 possible colors (which gives a total of 35 possibilities).

First, they validated their results by comparing their spectral barcoding to single mRNA FISH, to qPCR and by comparing to different sets of barcodes.  Their results seem pretty convincing. The main concern was “cross-talk” among the barcodes due to background fluorescence, blinking of non-specifically bound probes, cross-talk among fluoreophores and computational errors.

To overcome these concerns, they only accepted emission values 3 standard deviations above background “cross-talk”; in their imaging scheme the inserted frames with no activation of the activators (to reduce auto fluorescence); and they intentionally left 3 barcodes unassigned so they could measure the false-positive rate. This appeared to be 0.67±0.84 copies per cell. twelve genes were within that range of  ~1 copy per cell, so they left them out of future calculations.

They then compared the entire expression profiles of the 32 genes in ~60 cells. From their results it is evident that there is a great deal of heterogeneity. Actually, it looks like each cell responded differently to the same stress!

Single-cell expression profiles of 32 mRNAs using spectral barcode FISH. Each column corresponds to the expression profile of a single yeast cell. Source: Lubeck & Cui Nature Methods 9,743–748(2012)

They then go on to try and find correlations between genes that are differentially regulated, transcription bursts in wild type & mutant strains and so on.

So, here they have proof of principle of “only” 32 genes (a great achievement on it own). In their discussion, they mention that the total area covered by the mRNAs that they imaged was only 2% of the cell area. They claim that addition of another color to the barcode would enable to profile 792 different genes. Scaling up will require 3D super-resolution microscopy, an additional color (which gives 18,532 possibilities) but also more sophisticated algorithms to identify single mRNAs. If true ( and I believe we can get there within a few years) that would be amazing, and will open an entire new area of research for single-cell systems biology.

We could then see how single cancer cells react to a drug, and maybe determine why the cells who didn’t die survived, and find a compatible treatment.

We could determine how single neurons in a neural circuit in the brain differ from one another, and maybe get more clues as to how memories are formed.

ResearchBlogging.org
Lubeck E, & Cai L (2012). Single-cell systems biology by super-resolution imaging and combinatorial labeling. Nature methods, 9 (7), 743-748 PMID: 22660740

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

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

In the second part of this series, I review four “Brief communicaions” papers.

Unsupervised modeling of cell morphology dynamics for time-lapse microscopy

This work, from the lab of Daniel Gerlich, provides a tool for automatic prediction and annotation of cell morphologies from time-lapse microscopy experiments.

Their initial task was to annotate the six different cell-cycle mitotic phases of human HeLa cells which express histone 2B tagged with mCherry. Tagging H2B enabled us to visualize the chromosomes. Following time-lapse microscopy, they let three biologists to annotate the images – determine the stage of the cell in each image. They found mild inconsistencies in the annotations of the same person on different days, and an extensive variation between the three biologists.

This is not surprising. Although scientists are supposed to be as objective and impartial as can be, sometimes different people notice different things in the same image.

Therefore, the authors thought it is a good idea to develop a program for objective data annotation in an automated fashion. I am not qualified to judge the quality of their work. I could not even follow some of the terminology. However, the authors claim that their program can objectively annotate cell morphologies in a time lapse experiment (i.e. changes in the same cell over time) quite successfully.

The problem with this program, if I understand correctly (I’m not sure I am), is that you need to give the program some standard base-line annotation. In this paper, they used a “majority vote” for each stage to achieve a sort of consensus base-line for the program. But once you get that baseline, the program does better than individual researchers. In their assays, it could successfully detect deviations from the time cell-cycle when they used RNAi to affect cell-cycle factors. That’s very nice.

An image analysis toolbox for high-throughput C. elegans assays

C. elegans is a useful organism to study many biological processes. There are many genetic and other tools that are used in worm research, particularly in large-scale screens. One of the first phenotypes that researchers look at is worm morphology.  However, such morphological distinctions are still done manually, in most cases. The lab of Anne Carpenter developed an open-source toolbox to identify different morphologies, both from bright-field images and fluorescent images. Worm scientists will love it!

Fluorescently tagged worms. (a) worms in their natural form. (b) All the worms in (a) are juxtaposed after automated detection and digital straightening. (c) A low-resolution worm atlas enables feature measurements in relation to the worm’s anatomy. Source: Wahlby et al. Nature Methods 9,714–716 (2012)

Elastic volume reconstruction from series of ultra-thin microscopy sections

The lab of Pavel Tomancak developed a program that creates 3D reconstructions from electron microscopy serial images. I did not understand the math and programming terminology, but their videos are cool.

Faster STORM using compressed sensing

STORM, STochastic Optical Reconstruction Microscopy, aka Photoactivated localization microscopy (PALM), is a super resolution microscopy method which is based on single molecule stochastic switching. What does it mean? Most fluorophores “active” all the time, meaning that they emit light in response to the excitation light. However, several photoactivatable fluorophores have been developed. This means that the molecules are dark even when shined upon by the excitation light. Only after activating the molecules by shining a certain wavelength (usually UV light, but not exclusively), the molecules are emit light. This is a huge advantage in fluorescent microscopy, since it reduces the background level; it allows activation of the fluorophore only at a certain region of interest; it allows very accurate time-resolution experiments and is very usefull for super-resolution experiments. This paper, from the lab of Bo Huang, deals with a technical problem in super-resolution microcopy which uses STORM. Apparently, to achieve super-resolution, one needs to take thousands of frames or acquire for tens of seconds. Thus, the temporal resolution is pretty low. The temporal resolution can be improved by increasing the excitation intensity (but risking photodamage to the camera and/or fluorophores) or increasing the density of the fluorophore (thus risking them to overlap, invalidating to common single-molecule algorithms). There are other algorithms, but if I understand their claims, these algorithms require an estimation of the number of molecules in the image. Here’ they describe  novel approach, which they claim can get them to a three second time resolution.

To be continued….

ResearchBlogging.orgZhong Q, Busetto AG, Fededa JP, Buhmann JM, & Gerlich DW (2012). Unsupervised modeling of cell morphology dynamics for time-lapse microscopy. Nature methods PMID: 22635062
Wählby C, Kamentsky L, Liu ZH, Riklin-Raviv T, Conery AL, O’Rourke EJ, Sokolnicki KL, Visvikis O, Ljosa V, Irazoqui JE, Golland P, Ruvkun G, Ausubel FM, & Carpenter AE (2012). An image analysis toolbox for high-throughput C. elegans assays. Nature methods, 9 (7), 714-716 PMID: 22522656
Saalfeld S, Fetter R, Cardona A, & Tomancak P (2012). Elastic volume reconstruction from series of ultra-thin microscopy sections. Nature methods, 9 (7), 717-720 PMID: 22688414
Zhu L, Zhang W, Elnatan D, & Huang B (2012). Faster STORM using compressed sensing. Nature methods, 9 (7), 721-723 PMID: 22522657