Visualizing synaptic tagging and capture

A set of two articles recently came out in Science that directly visualize two different (and likely complementary) approaches to synapse specific delivery of gene products. Plasticity at specific synapses (input specificity — we’re restricting the discussion to the dendrites of the post-synaptic neuron) requires proteins (eg. new AMPA receptors) to get to those post-synaptic compartments and membranes. But the intructions for these new proteins are contained in the nucleus with the rest of the genome. Clearly, new proteins synthesized in the soma can’t just be sent everywhere, since only specific inputs (eg. particular dendritic spines) need these new proteins. How does this happen? Hence, the postulated synaptic tag.

Two approaches

Broadly, there are two approaches to synaptic tagging: 1) mRNA is distributed widely and translated locally at tagged locations; 2) protein products are distributed widely in the bodies of dendrites but only contact/off-load at tagged synaptic specializations. This News & Views gives a nice overview of the two papers, which find approach 1) in Aplysia cultures with sensorin mRNA and approach 2) in rat hippocampal neurons with Vesl-1S/Homer-1a protein. It amazes me that both were found pretty much simultaneously, but that might have more to do with the use of the photoconvertible Dendra2 protein than anything else.

With both approaches, we still don’t know why mRNA/protein is directed to a certain location. That is, we can visualize synaptic tagging but we don’t know what is the tag, its ontogeny, or the mechanism of tagging. But that might not be so important to understanding more about neural function. These new tools might allow us to image plasticity at many synapses at once, perhaps even in vivo. But before that, more work is needed… does the optical signal (from the Dendra fusion protein) correlate with degree of potentiation? Can we detect plasticity in the opposite direction, ie. synaptic depression, through another tag?  (As a sidenote to approach 1), the use of 5′ and 3′ UTRs as a sort of molecular zipcode is also intriguing.)

How to prepare for a PhD in neuroscience

UPDATED 7/26/2009: Click more (or scroll to the end of the post) to see what the student ended up choosing as a major.

Yesterday, I received this email from a freshman preparing for a future in neuroscience:

Dear Neville

My name is […]. I’m a freshman in Biomedical Engineering at the [university in] Mexico City.

After graduation, I am very interested in pursuing the Brain and Cognitive Sciences graduate program. Given your experience, I would like to ask you for some advice.

Over the last few months, I have been thinking about pursuing a major in Electrical Engineering instead. My goal would be to have more engineering tools for my further studies in Neuroscience. Based on your courses, which focus do you think would be more useful to have as an undergraduate? Are there any courses which you would recommend I take to build a stronger background?

I greatly appreciate any guidance you could provide.

Although there are other places to find advice on preparing for a PhD (for instance, economist N. Greg Mankiw has a few advice posts including this one on preparatory math classes), I figured that my take might be unique enough to share it with others. Neuroscience, like other fields, is becoming ever more interdisciplinary and being “a biologist that studies the brain” is just not enough anymore.

Here’s what I wrote back:

I think you’re on a good path for applying to BCS. I think it’s better to major in EE or BE rather than neuroscience or psychology to prepare for a BCS PhD. It’s easy to pick up the neuroscience in graduate school and harder to develop basic hard science and quantitative skills later on. Between BE and EE, I think you will have to decide. Either one should potentially give you a good background. Think about which one is more exciting to you and which program has better instructors.

Here are key quantitative areas I’d recommend:

  • Linear algebra,
  • probability theory/stats,
  • differential equations,
  • signal processing (Fourier transform and linear systems analysis)

A good command of these topics will serve you well in graduate school and far beyond. These topics are probably more closely aligned with an EE background, but, again, I think BE could be a great major if you make sure to add these kinds of rigorous engineering/applied math courses. Additional helpful quantitative topics would be electricity and magnetism and basic stat mech. (The nervous system is, in part, electrical and neuroscience makes extensive use of diffusion equations.)

More broadly, a neuroscientist is a type of biologist. With the age of genetics and genomics upon us, I think it is great to know some biochemistry, genetics, and organic chemistry (roughly in that order of importance). And, after all of that, if your schedule allows, take some courses that are specific to neuroscience, ethology, or cognitive science. I found it very beneficial to take a medical school neuroanatomy course before graduate school, which was really my only neuroscience course pre-MIT. A first course that emphasizes such raw memorization will get you up to speed with the field and its specialized lingo quite well.

And, since you speak Spanish, I recommend this wonderful book by the most pre-eminent neuroscientist (Santiago Ramon y Cajal)… it’s full of great advice about doing science well: Los tónicos de la voluntad (Reglas y consejos sobre investigación científica) (In English, there is a recent translation: Advice for a Young Investigator.)

Feel free to add your own sound advice in the comments below.
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Give it up for science

No, not that.

Neurodudes reader Deanna Saunders from the Sloman lab at Brown wants survey participants for a brief cognitive psychology survey. I took it and I must say it was kind of fun. She tells me that it’s about the effects of decision making on learning. (I’ve got to say I always appreciate the surveys that give you a little debriefing after the survey explaining some of the stimuli and the intended effects. Sadly, this survey doesn’t seem to have that.) Here it is for those of you with a few extra minutes for science: Click here for the survey. Better than the Colbert bump is the Neurodudes bump!

Longitudinal study on happiness and success

The Atlantic‘s Joshua Shenk has a fascinating story about a long-running study, started in the 1930s (!), that attempts to discern what makes people happy in life. The study has collected extensive data on subjects over a 70 year period. I couldn’t stop reading the article… what an amazing dataset. But, before I say more about that, here is Shenk’s synopsis of a single case file (ie. actual data) from the study:

Case No. 158

An attractive, amiable boy from a working-class background, you struck the study staff as happy, stable, and sociable. “My general impression is that this boy will be normal and well-adjusted—rather dynamic and positive,” the psychiatrist reported.

After college, you got an advanced degree and began to climb the rungs in your profession. You married a terrific girl, and you two played piano together for fun. You eventually had five kids. Asked about your work in education, you said, “What I am doing is not work; it is fun. I know what real work is like.” Asked at age 25 whether you had “any personal problems or emotional conflicts (including sexual),” you answered, “No … As Plato or some of your psychiatrists might say, I am at present just ‘riding the wave.’” You come across in your files as smart, sensible, and hard-working. “This man has always kept a pleasant face turned toward the world,” Dr. Heath noted after a visit from you in 1949. From your questionnaire that year, he got “a hint … that everything has not been satisfactory” at your job. But you had no complaints. After interviewing you at your 25th reunion, Dr. Vaillant described you as a “solid guy.”

Two years later, at 49, you were running a major institution. The strain showed immediately. Asked for a brief job description, you wrote: “RESPONSIBLE (BLAMED) FOR EVERYTHING.” You added, “No matter what I do … I am wrong … We are just ducks in a shooting gallery. Any duck will do.” On top of your job troubles, your mother had a stroke, and your wife developed cancer. Three years after you started the job, you resigned before you could be fired. You were 52, and you never worked again. (You kept afloat with income from stock in a company you’d done work for, and a pension.)

Seven years later, Dr. Vaillant spoke with you: “He continued to obsess … about his resignation,” he wrote. Four years later, you returned to the subject “in an obsessional way.” Four years later still: “It seemed as if all time had stopped” for you when you resigned. “At times I wondered if there was anybody home,” Dr. Vaillant wrote. Your first wife had died, and you treated your second wife “like a familiar old shoe,” he said.

But you called yourself happy. When you were 74, the questionnaire asked: “Have you ever felt so down in the dumps that nothing could cheer you up?” and gave the options “All of the time, some of the time, none of the time.” You circled “None of the time.” “Have you felt calm and peaceful?” You circled “All of the time.” Two years later, the study asked: “Many people hope to become wiser as they grow older. Would you give an example of a bit of wisdom you acquired and how you came by it?” You wrote that, after having polio and diphtheria in childhood, “I never gave up hope that I could compete again. Never expect you will fail. Don’t cry, if you do.”

What fascinates me is the absolute novelty of this kind of data. Normally, when someone relates their “life story,” we willingly participate in something of a shared lie. Both listener and story-teller know that the “life story” is being told in hindsight: Memory is not perfect and humans sometimes (often, perhaps) add meaning and create unifying themes in stories where they may be none. We emphasize the good parts and try to forget the not-so-good parts. In a sense, history recounted is never truly veridical but instead tainted with everything that happened after. Which is precisely why the availability of an objective history than spans an entire lifetime (or, as objective as possible) of both a qualitative (interview) and quantitative (medical) nature is so novel.

As you might expect, the data is confusing and hard conclusions are not easy to come by. There are however some tangible factors that seemed to correlate/predict success in life, which I’ve included after the jump. Continue reading