Tag Archives: PALM

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.

Those repair crews work fast!

Super-resolution microscopy can potentially allow imaging of single protein molecules. A new paper now tracks single Pol and Lig proteins in E. coli, as they repair DNA damage.

The researchers replaced the endogenous proteins with proteins tagged with a photoactivatable mCherry (PAmCherry). PAmCherry is non-fluorescent, unless activated by UV light (in this case, a 405 nm laser). By using very short pulses, they activate on average less than one molecule per cell at a time. This method is called photoactivated localization microscopy (PALM).

Using this method, they were able to follow single molecules (see this movie), count the number of Pol & Lig proteins per cell, to measure their binding rates to DNA (at optimal and DNA damage states) and importantly, to measure the timing and rates of the different steps of the base excision repair process (BER).

Looking at single repair proteins by PALM. Source: Uphoff S et al. PNAS 2013;110:8063-8068

Looking at single repair proteins by PALM. Source: Uphoff S et al. PNAS 2013;110:8063-8068

Their findings show that Pol & Lig diffuse by Brownian motion near undamaged DNA, but immobilize next to damaged DNA.  Only a small fraction of the proteins (<5%) are involved in repair under normal conditions. But upon severe damage, the excess proteins come into play, to process >1000 DNA damage sites per minute!

The single proteins fix the damage at about 2 seconds per site. That’s pretty fast!

I think that as PALM and other super-resolution methods become more accessible and prevalent, we will be able to collect amazing data on the function of single proteins and other macro-molecules in vivo.

ResearchBlogging.orgUphoff S, Reyes-Lamothe R, Garza de Leon F, Sherratt DJ, & Kapanidis AN (2013). Single-molecule DNA repair in live bacteria. Proceedings of the National Academy of Sciences of the United States of America, 110 (20), 8063-8068 PMID: 23630273

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