D. SARKAR, A. T. WASSIE, A. PAYNE, K. D. PIATKEVICH, D. ORAN, J.-B. CHANG, E. S. BOYDEN. Society for Neuroscience, 2016.

Abstract

Understanding in 3-D how molecules are configured throughout neurons, and how neurons are configured in circuits, may not only enable the discovery of new targets and technologies for treating neural diseases, but could help reveal fundamental principles of neural computation. Since biomolecules are nanoscale, however, and configured with nanoscale precision, this has remained difficult to study. For example, with electron microscopy, fantastic spatial resolution is possible, but it is difficult to identify the biomolecules in a protein complex.

Ideally, one would be able to achieve electron microscopy resolution, but with the additional capability of single-molecule biomolecular identification.

While conventional microscopy techniques involve magnification of images of samples, recently, we discovered that it was possible to physically magnify the biological specimen itself (Science 347(6221):543-548). This is done by embedding the specimens in dense swellable polymers, associating key biomolecules or labels with the polymer, mechanically disrupting the sample, and adding water to swell the biomolecule-polymer composite. This process, which we call expansion microscopy (ExM), was originally shown by us to enable ~60 nm resolution, achieved through ~4.5x linear expansion (i.e., a 300 nm diffraction limited lens would now have a resolution of 300 / 4.5 ~ 60 nm), and is in increasingly widespread use because it enables nanoscale imaging of a wide variety of preserved specimens on conventional, diffraction-limited, hardware.

Here we report a new polymeric chemistry that enables 100x linear expansion, which would ideally give an effective resolution of 300 / 100 ~ 3 nm, comparable to that of electron microscopy. This method does not require any hardware except that found in conventional biology laboratories, and is compatible with existing dyes, fluorophores, and antibodies. Our approach may be useful for analyzing protein complexes and cellular architectures with high precision, thus helping link nanoscale biological mechanisms with large-scale circuit architectures in the brain.