Data!

I have data! Finally! Right now the question I’ve answered is pretty preliminary: where is my protein located in normal cells? And here’s a pretty picture (unusable for data analysis or publication unfortunately due to some missing information, but pretty nonetheless):

gc1

This is a frog growth cone (the growth cone is the part of the developing neuron at the end of the growing axon). The green is my protein. The purple is actin, which is a component of the cytoskeleton (the cytoskeleton is basically the structural scaffold of the cell, giving it shape and the rigidity necessary for directed movement).

And the answer to where my protein is located is that it’s not exactly where we expected it to be (though it’s not super weird either). Basically it seems to be concentrated in the axon (see the bright green line in the top left? That’s an axon), which is where the major protein highway of the neuron is located: microtubules. Neurons have a cell body (where the DNA is located and where most proteins are made) and a very long (relative to their size) axon that extends out. To send proteins down to the end of the axon, the proteins are attached to motor proteins with little feet that basically walk them down the microtubule highway (microtubules are basically what they sound like: small tubes). So seeing a ton of our protein in the axon suggests that it’s probably being moved via this highway system. Which is cool! Figuring out how a cell decides how much of a protein it needs where (and how the cell actually accomplishes it) can be just as interesting as figuring out what the protein actually does once it’s there.

And this evening I’ll finally (hopefully) get data with my morpholino experiments. Those will answer the question of “what happens when neurons don’t have much of this protein?” Asking this type of question and taking this approach is called a loss of function experiment. And there are basically only 3 types of experiments:

1) Observation: What happens under normal conditions? Where is the thing located? When is it there? What is it doing? That last one may or may not be answerable with observation alone, especially not in molecular biology. The experiment I got data on was observational; I just looked at where my protein was under normal conditions.
2) Loss of function: What happens when this thing is gone or broken? This includes things like gene knockouts, morpholinos, and drug inhibitors. They are how you can determine necessity: is this thing necessary for a given process or outcome to occur?
3) Gain of function: What happens when you have extra of this thing, or have this thing where/when it isn’t normally there? This is how you determine sufficiency: is this thing sufficient to cause a given process or outcome? This is often accomplished by inducing overexpression of a protein (if you inject in extra DNA or RNA, the protein will make extra) or by breaking something that prevents the protein from working (removing the inhibitor should increase the activity).

So I have an observational experiment done, and I’m about to get data on a loss of function experiment. I actually don’t have a specific gain of function experiment planned right now (though I know of at least one thing that’s inhibiting my protein, so I could remove that). Actually my next steps are probably going to be observational: what is my protein interacting with? There are a few ways I can do that. One simple method is by staining and seeing what’s in the same place as my protein. Another approach is looking for functional association: if I break two things at the same time and they cause a change that is less than the sum of breaking each of them individually, it suggests that they may be interacting or part of the same pathway. To explain that another way:

If A activates B in order to cause C, breaking either A or B would cause C to not happen. And breaking both A or B would also cause C to not happen. However if A and B caused C independently from one another, breaking either of them would cause a decrease in C but would not get rid of C entirely. However breaking both A and B would still cause C to stop completely. And you can use that logic to construct experiments to determine if A and B are in the same or separate pathways.

So that’s what I’ve been up to! It’s definitely exciting finally having data to analyze. I get to make pretty graphs! And I actually really like data crunching days; it can be really relaxing to not worry about timers going off or making sure I regulate my caffeine intake enough to avoid shaky hands during dissections. Instead I can just hang out at the computer, drink coffee, and listen to music and I measure and play around with images and graphs. And then seeing my data graphed and being able to visualize my results is the most exciting part! And that gives me a jumping off point to hypothesize about what might be causing the results and what might actually be happening, which is easily one of my favorite parts of science. Yay for data and data interpretation!

data-star-trek-smile

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