Into the cold!

I am now in New England. I am visiting Ollie to write a grant and work on results. It snowed on the first day here. And now the clouds have cleared away, and it became colder. Today its about -10C and tomorrow it is supposed to get even colder.

Regulation of Blood Cell Differentiation

Another cause for celebration. A project that was active for several years has made it to publication. Today a paper with Noa as a first author appeared in Cell. This project was mostly done when Noa was at Aviv Regev's lab at the Broad Institute. She analyzed the expression profiles of genes through the differentiation of hematopoitic cell lineages --- how a bone marrow stem cell develops in to different types of blood cells.

                                                                          (Source: Cell)

The interesting part is that this analysis uncovered multiple "modules" --- groups of genes --- that are reused in different lineages. This shows that differentiation is much more complex than previously assumed about this system.

Congratulations to Noa!

NanoString Tryout

You might remember that previously I reported on our efforts on RNA extraction from yeast cells. Since then Assaf and Ayelet quantified the amounts and quality of RNA we get by doing RNA purification from the cell supernatant in Assaf's assay. They showed that the amount of RNA we get matches what literature reporters (about 1.2 pico-gram/cell), and that the RNA does not show any degradation.

Last week Assaf performed a time course experiment where he grew yeast, and then subjected them to salt stress, and collected cell at different time points following the stimulation. He froze samples of cells by droping them into very cold (< -40C) methanol, and then extracted supernatant using the technique I discussed in the previous entry. 

He packed the supernatant in a box with dry ice and we sent it to Ollie's lab at U. Mass. Medical School by carrier service. We were not sure if the samples will get there in good shape, and it was great to hear that they did, still frozen. Hsiuyi from Ollie's lab run the samples on the Nanostring nCounter machine they have there and sent us back the results.

Few words about the NanoString machine (you can read a nice overview on their website). The basic idea is that we order a set of probes that match genes we are interested in, and then using these probes can count individual molecules in a small volume sample. By counting how many times we see each type of probes we get a very accurate measure of the number of RNA molecules in the initial sample. The cool thing about this method is that it does not use any enzymatic reaction and thus does not have biases due to PCR preferences and such. Moreover, it is very simple to run and does not require RNA purification, labeling and such.

We were very happy that the results we got from Hsiuyi showed high degree of reproducability. Two repeats of the experiments looked almost identical (if they would have been more similar I would start being suspicious).

In this figure each dot correspond to a gene, and the x/y axis are the log (base 2) of the number of counts we had for that gene in the first or second replicate expriment. We can see that the dynamic range here is between 2 to 16, so we have 2^14 (= 4096) fold difference between the lowest probe and the highest one.

This success shows that Assaf's setup is ready for the big experiment of measuring many different mutants. Hopefully we will have this done in the next few weeks.

Visitors

This week the winter decided to give us a sneak visit. It has been a mostly sunny winter so far, but for a few days we had actual winter (rain and wind).

In the midst of this (mild) winter weather Tommy, an ex-student of mine who is now a postdoc at the Eisen lab at UC Berkeley, showed up for an interview. It was fun to spend time with Tommy again. He was involved with the early steps of thinking about the grant, and so it was nice to show off how much things have progressed.

We enjoyed a lull in the rain to sit outside among the fallen leafs and enjoy a simple lunch.

As part of his visit Tommy had to renew his visa, and seems that courtesy of the US embassy, we will have him visiting for a bit :-(

Here is a picture of wintery sunrise:

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!