Busy Bee and Internship Reflections

I almost forgot to post this week! I’m finally making progress on my first project (the morpholino one) which means I have time to work on the second one (the optogenetics one)! While it’s technically possible to work on both simultaneously, they both require a lot of the same first steps (injecting embryos, dissecting spinal cords), and I don’t yet trust myself to have the mental dividers to not mix them up, haha.

All this progress means I have more to do, which means I’ve been in lab a lot more the past two weeks (and still smooshing in small dance trips on the weekends). This is probably true in other fields as well, but certainly in science, progress seems to be exponential. Because once something works, even just a little bit, there are 5 ways you could verify it and 10 follow-up questions and 1,000 variations on the original experiment, all of which would be valuable to some degree.

Oh, but I have one thing I meant to post about a couple weeks ago and can talk about now: summer internships.

I was recently asked how valuable I thought my summer internship was (I did the Summer Undergraduate Research Fellowship at Mayo Graduate School the summer before my senior year) in terms of preparing me for grad school. My immediate response was, “A million*.” Which is to say that internship 1) is the reason I realized I wanted to go to grad school, 2) showed me what actually doing biological science was like day-to-day, and 3) was what changed my focus from behavioral neuroscience to cell biology. Basically without this internship, I’m quite sure I wouldn’t be in a cell biology program. Not just because they wouldn’t have let me in (undergrads who want to go to grad school in the sciences: research experience is a prerequisite for grad school these days; but also know that being a lab technician for a few years totally counts, so you don’t necessarily need to get that experience during undergrad; it’s never too late to go to grad school and your current state will not lock you into any one thing!), but also because I just wouldn’t have realized that cell biology was what I was really interested in and so at best I would have ended up in a program I didn’t like as much.

I also think that my internship was extra valuable because my project really was my own. This is often not true of undergraduate research opportunities (some other people doing the same program basically just did what their grad student told them to do). I basically got lucky. I was working with a grad student, but my project wasn’t just supporting his. This meant that I was expected to plan experiments myself and do them all myself. Which threw me off the first week or two (I spent a bit waiting for someone to tell me what to do because that’s what my lab experience up to that point had been**). But once I realized it was up to me to read the literature and make decisions about dosages and experimental design, I got going and learned a ton. I also had some excellent advice from my PI that helped set the tone for the summer: ask a question during every talk you go to. Because that forces you to 1) pay attention so you can come up with a good question (harder than you might think at 4pm with the lights off) and 2) think about what information you’re missing. And it may be a question about their methods in order to personally verify that their conclusions make sense, or it may be a question about the next directions and the broader context of the work. And both of these are super important questions to ask about other people’s work because it helps your understanding and gives you practice so you can ask those questions well about your own work.

At the end of the summer, there was a poster session where all the undergrads presented their work. Most people had fancy looking posters that they had gotten printed up by printing services, and they had pretty pictures and fancy coloring schemes. I printed out powerpoint slides on normal paper (a suggestion from a grad student) because I was still collecting and analyzing data the day before the poster session, so I had to be able to add or take away information as it changed or got updated.

So I have these 8.5×11 sheets pinned up to the board, and while they looked nice and had a cohesive color scheme, they didn’t look nearly as fancy as anyone else’s. But one person gave me my favorite (admittedly passive aggressive and slightly backhanded) compliment, “It looks like you actually did the work for your poster!”

Moral of the story: research experience is absolutely essential for grad school, but I also highly recommend seeking out (or creating) opportunities where you are in charge of your project.

*I was looking for a way to say “a lot” but even more emphatically, and this was the phrase my brain chose.
**During my junior year, I did research in a psych lab with human participants. While it was a really valuable experience in terms of teaching me about hypotheses and proper experimental design, I was literally working off a script (to make sure the interaction with each participant is as similar as possible) and I jumped in partway through years worth of data collection, so I had no direct role in the actual planning of the experiment. And lab courses were always just “Do X, Y, and Z in this order.”

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