Technique named 'clarity' makes chunks of dead brain transparent, allowing fluorescent labeling

This technique takes a dead brain and permeates it with a transparent hydrogel matrix to keep proteins and nucleic acids in place. Then it removes the lipids. I guess the lipids are all that makes the brain opaque. At this point the brain is transparent but maintains its original structure so you can still label the proteins and nucleic acids.

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.)

PNAS roundup: Superresolution in 3D and fetal testosterone of traders

PNAS has some interesting articles that I came across today:

  1. 3D PALM (open access): Using 2-photon and photoactivatable proteins, the authors image beyond the usual sub-wavelength TIRF limits. They image over multiple microns with 50nm resolution.
  2. Neuroeconomics:  Low digit ratio (2d:4d) predicts financial success in traders. Okay, measure the length of your index and ring fingers. (Not sure if this analysis applies for the ladies; the authors only used men in the study.) Calculate the ratio (2d/4d); longer ring fingers signify greater fetal androgen exposure. The mean value is about 0.96. As the authors say,

    Digit ratios have been found to predict performance in competitive sports, such as soccer, rugby, basketball, and skiing, so 2D:4D may also predict the risk preferences and physical speed required for high-frequency trading.

    A strong correlation (r~0.5) was found between low digit ratios and profits in short-term trading. So, they take on more risk and make more money. What I want to know is how well the low 2d:4d ratio traders did over the last 6 months!

Social neuroscience fMRI: Specious correlations?

Nature is reporting on potential flaw in multiple imaging (fMRI) studies of social neuroscience. Ed Vul (a graduate student in my dept) and colleagues have a paper in press that says that many of the high correlations between brain regions and social behavior are implausible, given the inherent variability/noise in fMRI. Furthermore, based on a survey of methods from individual investigators, they created a list of papers that commit, in their view, a statistical mistake (non-independence). Naturally, the authors named in the paper aren’t happy and, according to the Nature article, several rebuttals are in the works. At the very least, to my non-expert eyes, this seems like an important discussion to have about data analysis and methodology.