Showing posts with label Assaf. Show all posts
Showing posts with label Assaf. Show all posts

Tuesday, July 31, 2012

Systematic Dissection of Roles for Chromatin Regulators in a Yeast Stress Response

The monster is out! 

A work that started more than 2 years ago, finally resulted in a publication. The paper came out in PLoS Biology. This work is tight collaboration between Ollie's lab and ours, with Assaf and Hsuiyi as first co-authors. Avital and Ayelet also played an important part and are coauthors on the paper.


This publication is also the first time we publish experimental work done in our own lab. We can say that we are officially an experimental lab! Figure 2 (below) is all about microscopy measurements that we collected on our system.


Thursday, June 7, 2012

IBS 2012

Today I was hosting the IBS 2012 meeting. Officially the "14th Israeli Bioinformatic Symposium" which is the annual meeting of the "Israeli Society for Bioinformatics and Computational Biology" of which I was the president until few hours ago.


The meeting brings together researchers in Bioinformatics, Computational Biology and Systems Biology from all the universities in Israel. Traditionally we have an invited speaker from abroad, and this year it was Manolis Kellis from MIT.

The last few weeks were hectic with setting up the website for registration and poster abstract submissions, selecting caterer, and such. Late last week we decided to order buses to transport people to/from the meeting from all the other major academic universities. This meant that we had to set up sign-up forms and make sure all the people made it to the bus. 

Yesterday, I was away, but my local help (Dikla and Cecile) made the preliminary arrangements. When I showed up at 7am, the outer area was already with poster stands. Since we had more than submitted posters, we needed quite a few stands.



Not long afterwards, the florist showed with a nice flower arrangement. I was working with the A/V people to set up the stage properly. The large Wise auditorium was ready for action.




Toward 8:30 we had our registration desk with name tags set up and waiting for people to arrive. We had large signs and posters up and ready. The first wave of attendees were on the bus from Rehovot. 




After welcome remarks by our Vice President, we had the pleasure of hearing Manolis. He did a great job of showing why the new flood of data can make a difference in personalized medicine and how this is going to come about.


Overall we had more than 350 registrations. Not everyone who registered showed up, but we also had quite a few walk-in. I think it is safe to say we had more than 300 attendees, maybe closer to 350. (We ordered food for 350 and it was all finished).



The closing ceremony involved the "Best Poster Awards" that were selected by a committee of faculty members (I was not involved). It was a nice surprise to find out that our very own Assaf was one of the poster winners.

By 5:30pm, the meeting was over. I helped wrap things up, and thenI headed to my office to find a water leak :-(

Monday, January 3, 2011

Growing yeasts (Robotically)

The first task we set to do with the robot is grow yeast cells for experiments. This sounds easy? No?

To understand the issue. Lets review the typical yeast life cycle. Suppose you pick a small number of yeasts from a colony or a saturated culture and put in a fresh "rich" media with glucose (sugar). Moreover, suppose that the media is kept in nice temperature (yeast like 30C) and shaken to make sure yeast cells and nutrients keep mixing.

Initially it will take the yeast cells time to realize that they are not in the nutrient-poor environment they were in. They will revitalize themselves and prepare to grow. This phase is called lag phase. During this phase the number of yeast cells will not change.

Once out of lag phase the yeast will start to grow on glucose. They will work hard to use this rich source of energy to grow as fast as possible. A typical yeast cell will divide every 90-120 min (depending on the exact conditions and temperature). This period is called doubling time as the number of yeast will double every fixed period. During this phase the yeast will experience exponential growth. For this reason this is known either as exponential phase or log phase (since it looks linear in logarithmic scale). 

After a while the yeast will start exhausting the sugar. If this was bacteria they will stop growing. But yeast has another trick up their sleeve. They switch from using sugar as fuel to using ethanol. During the fast growth phase the yeast use the glucose in a fast way by fermenting it to ethanol. This is what in human metabolism is known as anaerobic metabolism as it does not require oxygen. In nature this trick allows yeast to outgrow the competition (fast growth) and also kill it (by increasing ethanol concentration). Humans learned long time ago to use this property of yeasts to make alcoholic beverages. 

Returning to the yeast growth, the switch from glucose to ethanol is called the diauxic shift --- the yeast will go through it once it can no longer import glucose from the environment. Ethanol can be used for aerobic metabolism (or respiration), but requires more work to extract energy from it. As a result the yeast will grow slower. They still grow exponentially but the doubling time is much longer.  This phase is often referred to as saturated or early stationary phase although these description are inaccurate as the yeast still grows.

After a while (and this can take much longer), the ethanol reserves are consumed, and the yeast stops growing and enters in to stationary phase. The cells prepare for nutritional hardship and reduce their activity. 

When plot the number of yeasts in the tube during this phases we ideally see this type of curve:



For our experiment we want to take yeasts in the middle of the fast growing exponential phase. Moreover, to make sure that the yeast forgot its history, we want to make sure that there were several (>3) cell divisions since the lag phase. This means that we need to yeast to multiply itself by at least 8-fold from the initial amount.

Moreover, we want to make sure the yeast do not come close to diauxic shift, as this stage results in major changes in the yeast metabolism. This means avoiding over-crowded situation. Finally, we also want to ensure that we have sufficient number of cells to work with, so we do not want under-crowded cells either.

Sounds easy. If the lag phase is 60min and doubling time is say 90min, then we need 330min (5 1/2 hours) to grow the yeast. Calculate the desired amount at the end and seed the culture with 1/8th of that.

The problem is that we want to work with many strains of yeast. In fact, we want to grow 96 strains in one plate. Each strain has different lag time and doubling time. This means that while one strain has 90min doubling time, another might have 150min doubling time. This means that for the latter strain we need 450min to get 3 doubling (8-fold increase), but by then the fast strain has two more doublings and has grown by 32-fold from the original number of cells. Due to the exponential growth, small difference in growth rate can lead to dramatic differences in cell concentration.

So, how do we deal with the problem? Ideally, we can measure the relevant times for each strain and then plan the initial seeding to get things right. In fact, this is what we do, but using a robot.

During the last two weeks, Assaf and Avital developed a robotic protocol that grows the plate of yeast for 20 hours. Every half hour the robot took the plate out of the incubator, and put into a plate reader (spectrophotometer for plates) that measures the optical density (roughly equivalent to number of cells). After this incubation time most wells were past the diauxic shift. The program then used the plate to seed a new plate and again monitored growth for several hours. At this point Assaf and Avital's program computed what dillution it need to perform to each well to ensure that at the planned target time the cells will rich a desired density. The robot then applied a customized dilution step for each well.

At the end of this procedure we had a plate with 96 strains (with very different growth characteristics) all in roughly the same density. To our surprise/relief/joy the robot did all of this without a fault.

The end result can be seen like this. In this graph OD corresponds to yeast density, and each curve describe the density in one well on the plate. You can see the yeasts growing fast and then slowing down. You can also see the two dilution steps (the first dramatic one and then the "correction" step). Most importantly you can appreciate that in the end all the wells are fairly close to each other in density.



The nice growth can be more easily seen in a log-scale plot:



For some reason the empty wells (that do not grow :-) misbehave after the dilution step. Do not that before the final dilution there is a large variability in the density and that it mostly removed by the program.

And so now we can start doing experiment with tightly controlled yeast growth. Yey!


Wednesday, November 17, 2010

Blasting yeasts

In one of our projects we want to extract RNA from yeast for measurements using the nifty NanoString nCounter technology. One of the nice things about this assay is that it uses very small amounts of actual material to measure RNA quantities. Moreover, unlike other assays, we do not need to purify RNA before the assay, which saves a lot of headaches.

The challenge we posed to Assaf, who is about to do the experiment, is how to extract cellular material from a very small yeast sample. We actually need as few as 15,000 cells (just for reference, 1 microliter of happy growing yeast has about 1-2 x 10^7 cells). The problem is that yeast, like many other microorganisms, has a strong cell wall made from proteins. 

The classical way to break it is using mechanical action -- beating the yeast using small glass beads. This technique, however, does not scale down for small samples. And so, we decided to go with Plan B. Here we use an enzyme, Zymolase, that digests the cell wall. This creates spheroplasts -- cells without cell walls that are encapsulated by a thin membrane. We can then pop these open by diluting the sample with water with small amount of detergent.  This solution also includes protein denaturing agents that block any RNAses from breaking down the fragile RNA, and thus preserves our sample.

Assaf and Ayelet tried this technique, and it seems successful. The zymolase readily digested the cell wall and left with nice spheroplasts. Then when he dilluted these quickly disappeared. Assaf made a nice movie of this process under the microscope. What you see is a small drop with yeast spheroplasts, and midway through the movie this drop is dilluted.