Looking at the brain at molecular scale

A new technique called “magnified analysis of proteome” (MAP) developed at MIT allows researchers to peer at molecules within cells or take a wider view of the long-range connections between neurones. Credit: MIT

A 3-dimensional image taken via the CLARITY technique showing a 1 millimeter slice of mouse hippocampus. The different colors represent proteins stained with fluorescent antibodies. Excitatory neurons are labeled in green, Inhibitory neurons in red, and astrocytes in blue. Credit: Kwanghun Chung and Karl Deisseroth

At MIT researchers have discovered a new technique to look at the brain at a molecular scale, hence with a higher resolution than “just” looking at neurons. At the same time this technique can highlight the connections among neurons, thus providing at the same  time views at different scales.

The technique, called MAP – Magnified Analyses of Proteome (the ensemble of proteins manufactured by our cells based on our genes instructions), leverages on CLARITY, a technology that makes cell transparent and observable in 3D, still preserving their structure.

Basically the technique involves the use of antibodies (there are hundreds of thousands of them, each one attaching to a specific protein) to mark the presence of a specific protein with a resolution of 60 nm (that is about 600 atoms in a linear dimension, but 200 million in volume!) that is sufficiently small to pinpoint a protein.  With this technique researchers have been able to provide unprecedented detail in a relatively large area of brain tissue: 2mm.  It turns out that this approach can provide a better imaging result than the current approach based on electron microscopy.

Interestingly, this technique acts as an amplifier: the gel that is inserted in the tissue (acrylamide polymer) carrying the antibody markers expand the tissues without altering its structure by a factor of 5, thus increasing the visibility of the whole.

We are really making progress in our understanding of the brain at different levels and the hope is that we will start to make use of this understanding in the next decade, with far reaching implications, including a revolution in computer science and its applications.

Author - Roberto Saracco

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