Tag Archives: Nat Methods

This month’s Nature methods (part 2): optogenetics

Optogenetic tools are light-sensitive genetically encoded proteins that, upon light activation, affect a molecular change in the cells. In the previous post I described an optogenetic system to induce transcription. However, the most common use is of channelrhodopsin (ChR) molecules, that alter ion homeostasis upon illumination, and when expressed in neurons, can affect neuronal activity and – in live animals – a change in behavior.

Though optogenetics were used extensively in neurons in culture or in mice, its use was limited in flies. The reason was that the most common ChRs are activated by blue light, which does not penetrate well into the brains of live flies. Therefore, theremogenetic control has been used. In thermogenetics, a thermosensitive channel is expressed in the neurons. However, theremogenetics is far less accurate than optogenetics in terms of temporal resolution and intensity. On top of that, one needs to consider the effect of changes in heat on behavior.

To overcome that, the group of David Anderson decided to use a newly developed red-shifter ChR, ReaChr.

Using ReaChr, but not other Chrs, the researchers were able to activate specific neurons and alter fly behavior. Here’s one of their videos. Using this tool, they learned about the sexual behavior of the male fly and came to the conclusion that there are two sets of neurons which can be separated, with social experience affecting only one set of neurons.

The second optogenetic paper in this issue is more technical. The group of Edward Boyden decided to screen for new candidate ChRs in over 100 species of alga. They sequenced transcriptomes of 127 alga species and isolated 61 candidate ChRs which they tested for light-induced currents using electrophysiology methods. They chose 20 candidates and tested them in a range of wavelength excitations looking at current maxima, kinetics and more parameters.

characteristics of selected channelrhodopsins from different alga species. A, B, C, D - currents amplitude at different excitation wavelengths in HEK293 (A-C) and neurons (D).  E - Chrimson is the most red-shifted ChR. F - off kinetics. G - on-kinetics. H - recovery kinetics.

characteristics of selected channelrhodopsins from different alga species. A, B, C, D – currents amplitude at different excitation wavelengths in HEK293 (A-C) and neurons (D). E – Chrimson is the most red-shifted ChR. F – off kinetics. G – on-kinetics. H – recovery kinetics. Source: Klapoetke et. al. (2014) Nat. Meth. 11:338-346.

Of these, they selected two unique ChR: Chronos (with very high activity after blue or green light excitation) and Chrimson (the most red-shifted ChR – 45nm more red-shifted than ReaChR). After further characterization they showed that Chrimson is a good optogenetic tool for live flies and showed that they can stimulate neurons in fly brains.

Last, they created a two-color system. One of the limitations of the current ChRs is that all of them can be stimulated to some extent by blue light. Although Chrimson can be activate by blue light, the kinetics of Chronos are 10 fold faster. They did a very detailed work in finding the best conditions for a two-color system, based on the excitation pulse and power as well as expression level of the different ChRs.

All in all, a very nice work.


ResearchBlogging.orgKlapoetke NC, Murata Y, Kim SS, Pulver SR, Birdsey-Benson A, Cho YK, Morimoto TK, Chuong AS, Carpenter EJ, Tian Z, Wang J, Xie Y, Yan Z, Zhang Y, Chow BY, Surek B, Melkonian M, Jayaraman V, Constantine-Paton M, Wong GK, & Boyden ES (2014). Independent optical excitation of distinct neural populations. Nature methods, 11 (3), 338-46 PMID: 24509633
Inagaki HK, Jung Y, Hoopfer ED, Wong AM, Mishra N, Lin JY, Tsien RY, & Anderson DJ (2014). Optogenetic control of Drosophila using a red-shifted channelrhodopsin reveals experience-dependent influences on courtship. Nature methods, 11 (3), 325-32 PMID: 24363022


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.


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

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

New and improved – the next generation of GFP?

A new and improved green fluorescent protein, named mNeonGreen, was developed.

It was engineered from a Yellow fluorescent protein (LanYFP) that was isolated from the cephalochordate Branchiostoma lanceolatum. Therefore, LanYFP is genetically unrelated to the commonly used Aequorea victoria GFP.

LanYFP has a high quantum yield (0.95) and extinction coefficient (~150,000 M−1 cm−1) – making it a very bright protein.  LanYFP is a tetramer – not useful for most applications.

Directed evolution to make it a monomer produced the new, monomeric protein, mNeonGreen. The ex/em of mNeonGreen is slightly shifted to the yellow compared to EGFP (509/516 compared to 488/509), making it a better choice to separate from CFP emission.

Though slightly less brighter than its parent protein LanYFP, mNeonGFP is 2-3 times brighter than EGFP and actually brighter than most green & yellow proteins.

Another great advantage of this new protein is that is it fast folding – the authors claim it is <10 min at 37C. This is fairly close to the superfolder GFP.

It is also very photostable (comparable to EGFP), performs well as a fusion construct at N & C termini many tested proteins, performs 4-times better that EGFP in stochastic single-molecule superresolution imaging and is a better FRET partner (both as acceptor and donor) than other proteins.

mNeonGreen fused to histone H2B shows the different stages of the chromosomes during cell division. Source: Shaner et al., (2013) Nature Methods 10:407-409.

mNeonGreen fused to histone H2B shows the different stages of the chromosomes during cell division. Source: Shaner et al., (2013) Nature Methods 10:407-409.

In short, this may very well be the “next generation” of fluorescent proteins. It has all the good qualities, and seems to have none of the bad ones. It performs better than most, if not all fluorescent proteins in every tested parameter.

Only its name is rather plain. I would call it something like wonderGFP or GreenLantern (hey, it even has the Lan from the animal they developed it from).

(Update: see here for details on how to get your hands on this protein).

ResearchBlogging.orgShaner NC, Lambert GG, Chammas A, Ni Y, Cranfill PJ, Baird MA, Sell BR, Allen JR, Day RN, Israelsson M, Davidson MW, & Wang J (2013). A bright monomeric green fluorescent protein derived from Branchiostoma lanceolatum. Nature methods, 10, 407-409 PMID: 23524392

FISH-quant, a new RNA-FISH analysis tool

RNA FISH is a powerfull tool to measure not only the amount of mature mRNAs in the cytoplasm (or other compartments) but also to asses the amount of nascent transcripts that are still at the transcription site.  These nascent transcripts are RNAs that are still in the elongation or termination step of transcription (i.e. have not moved out ot the transcription site, where the gene is located).

Typically, in order to estimate the the number of nascent transcripts, one needs to calculate the average fluorescent intensity of individual RNAs (that are located outside the transcription site) and then divide the intensity of the transcription site by that average.

For example, if we take the average intensity of a single mRNA as 1, and the intensity at the transcription site equals 5, it means that we have 5 RNAs at the transcription site.

However, the desnse co-localization of these nascent transcripts prevent accurate estimate of the number of transcripts. Furthermore, the simple comparison of intensity looses information on 3D, or even 2D arrangement of the transcripts at the transcription site, and may also pose a problem with assesing the quality of the FISH signal.

Now, the groups of Zimmer, Darzacq & Bertrand developed a new tool that allows much more accurate measurments of nascent mRNAs at transcription sites. Their program, FISH-quant (can be freely downloded here), utilizes two methods to measure the number of transcripts: 1. integrated intensity of the transcription site and 2. reconstruction of the transcription site signal by iterative superposition of weighted point-spread function of the RNA spots.

The results in their report at Nature methods show that the simple method used before underestimates the number of RNAs at the transcription site. Their program should facilitate better measurments of transcription sites or other FISH-signal aggregates. For example, they mention P-bodies & stress granules in their paper, but there are other RNA granules such as neuronal granuls and germ cell  granules.

Of course, this program should be similarly useful for live cell imaging of transcription sites or other RNA granuls.

I can think of other uses. For instance, the number of ribosomes on an mRNA (using super-resolution with fluorescently tagged ribosomes) or the nuber of RNAs in microvesicels and more.

I may even try it myself in a FISH experiment I’m doing now. If I use it, I’ll report on its ease of use.

ResearchBlogging.org Mueller, F., Senecal, A., Tantale, K., Marie-Nelly, H., Ly, N., Collin, O., Basyuk, E., Bertrand, E., Darzacq, X., & Zimmer, C. (2013). FISH-quant: automatic counting of transcripts in 3D FISH images Nature Methods, 10 (4), 277-278 DOI: 10.1038/nmeth.2406

Watching Neurons in action

Fluorescent sensors are important tools that can allow real-time, live, single molecule imaging of microscopic millisecond scale events. It is even better if these sensors are genetically encoded sensors (i.e. fluorescent proteins). We have already encountered the pH sensors pHluorin and pHTomato and the Ca2+ sensor GCaMP. There have been a few others, such as HyPer that detects H2O2 or ArcLight and ElectrikPk which are voltage sensors.

Now, the group of Loren Looger from HHMI Janelia  developed a sensor for a very important molecule: L-glutamate. Continue reading

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.

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