Category Archives: Cell cycle

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! 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


Virtual cell biology

Ever wondered how all the processes inside a living cell work together as one system, in real time?  How they all collaborate with each other, affect each other?

Well, wonder no more.

Researchers from Stanford U. and the Craig Venter institute have created a computerized, simulated model of the entire life cycle of the bacterium Micoplasma genitalium (the bacteria with smallest genome [as far as we know]).

M.g. contains only 525 genes [but how many proto-genes?], and data on its genome, transcriptome, proteome and metabolome have been recently acquired. Karr et al. combined data from >900 publications to define >1900 parameters which were related to 28 modules of sub-cellular processes. Each module was first simulated according to the best fitted model (e.g. metabolism was modeled using flux-balance analysis, whereas RNA and protein degradation were modeled as Poisson processes). The difficult part, according to them, was to combine all 28 modules into one system.

They therefore assumed 1) that on short time scales (<1 sec) each module is independent of the others and 2) by running each module in 1 sec loops, they can use the variables created by all the modules from the previous loop to calculate the variables of the ongoing loop. Looping ends when the cell completes one division.

They ran the simulation 128 times, and then validated their model against databases which were not used to construct the model. They claim that the model predictions were significantly close to experimental data.

Now here comes the interesting part – their predictions. I won’t go over all of them – you can read that in the paper. But I do want to discuss a few unexpected predictions.

The metabolism regulates the cell-cycle

The cell-cycle in their model consists of three stages: initiation, replication and cytokinesis (cell division). While running the simulations, they noticed that there is a large variability between cells in the duration of the first and second stages (~65% & 38%), but very little variability in the duration of the last stage (~4%), or in the overall duration of the cell cycle (~9%).  This was strange. Why was there such a variability of the two stages, and how did this variability disappear when looking on the whole cell-cycle?

With respect to replication initiation, they found some correlation to the initial number of a protein called DnaA that must accumulates to a certain threshold at the replication start site in order to recruit DNA polymerase.  They also found that the stochastic nature of the transcription and translation modules creates variability in the number of DnaA monomers also during the simulation, not just when the simulation starts.

About the replication itself – since this is a deterministic process as they call it, they expected to see a straightforward correlation between replication and cell cycle progression. Instead, their model suggests a two-step replication process: the first step is fast. However, as soon as the availability of free deoxy-ribonucleotide-triphosphates (dNTPs) pool decreases beyond a certain threshold, replication slows to a rate equal to the rate of dNTPs production. Hence, the duration of the replication stage in individual cells is related to the free dNTP content at the start of replication than to the dNTP content at the start of the cell cycle. The availability of dNTPs imposes a sort of balance on the duration of the cell cycle – in cels with long initiation phase, the dNTP concentration will be high once replication begins, and replication duration will be short. But, if initiation starts early, then the pool of dNTP will be depleted quickly, thus slowing the replication phase. Yet, the total time of the cell cycle will not vary a great deal among individual cells.

Discovering new role and getting kinetic data for enzymatic activities

After reviewing the behavior of the wild-type cell, they went on to do simulations of single gene mutants for all 525 genes (a total of >3000 runs!).

For each gene they could determine if the cell remains viable. The simulation predictions were ~80% accurate compared to experimental data.

They then went on to experimentally test several mutants. An interesting case was the gene lpdA. The model found its deletion inviable, which was inconsistent with previous data. They therefore tested the viability of this mutant and found the strain to grow 40% slower than the wild type. They then reasoned that another protein must perform a similar task as LpdA. Indeed, they found that another gene, nox, showed similarity to lpdA in terms of sequence and function. Correcting the model with the added new task of Nox yielded a viable simulated cell.

In several other cases, there was a discrepancy between the growth rate of viable mutants in silico and in vivo.  These mutations were in gene coding certain enzymes. They then played with the kinetic parameters of the enzyme so that the outcome in silico, in terms of cell cycle duration, was similar to the outcome in vivo. Thus, they were able to calculate in silico the kinetic parameters of enzymes.

In each of these cases, the new catalytic values are consistent with experimental data.

To summarize:

Biologists are starting to realize more and more the importance of looking at the big picture, beyond their favorite protein or pathway. Systems biology is a field that is developing very fast.

Having a whole-cell in silico model is a dream of every systems biologist, indeed, of every biologist.

The fact that such a model is imperfect allows us to find discrepancies, and from them – new biological knowledge.

This model is a huge step forward in systems biology, and it is only the beginning.

Who knows, this may be the basis for designing new organisms in the future…

ResearchBlogging.orgJonathan R. Karr1, Jayodita C. Sanghvi, Derek N. Macklin, Miriam V. Gutschow, Jared M. Jacobs, Benjamin Bolival Jr., Nacyra Assad-Garcia, John I. Glass, & Markus W. Covert (2012). A Whole-Cell Computational Model Predicts Phenotype from Genotype Cell, 150 (2), 389-401 DOI: 10.1016/j.cell.2012.05.044

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