Illuminating biology at the nanoscale with single-molecule and super-resolution imaging.

Speaker: Xiaowei Zhuang
Department: Chemistry Department of Harvard University
Subject: Microscopy imaging
Location: TU Delft

Date: 12 January 2017
Author: Gabriele Kockelkoren

At the Dies Natalis of the TU Delft, Xiaowei Zhuang is introduced to the public by Chirlmin Joo. Xiaowei Zhuang is principal investigator of a lab and professor at Harvard who contributed greatly to the design of the well-known techniques STORM and FRET. As a leading example of innovation and success in her field, she shared her valuable research.

The presentation starts with a short voyage back in history, leading to the first microscope ever created by Anthonie van Leeuwenhoek. This first ‘microscope’ consisted of a very tiny lens which allowed the visualization of bacteria and sperm cells. Since then, many new insights and techniques have arisen, which grant the possibility to ‘unravel’ even more details of the microscopic and nanoscopic world.

In order to image a complex system of proteins that give rise to cell life through collaboration, you require:  nanometer scale resolution, molecular specificity and dynamic imaging. The greatest problem is the diffraction limit for most conventional microscopes. In conventional fluorescence microscopy where all fluorophores in the sample are fluorescent, their diffraction limited images overlap, creating a smooth but blurred picture. This has been circumvented by the use of SIM and STORM. Xiaowei Zhuang developed the STORM technique.  STORM  is a type of super-resolution optical microscopy technique based on stochastic switching of single-molecule fluorescence signal. STORM/PALM utilizes fluorescent probes that can switch between fluorescent and dark states so that in every snapshot, only a small, optically resolvable fraction of the fluorophores is detected. This enables determining their positions with high precision from the centre positions of the fluorescent spots. With multiple snapshots of the sample, each capturing a random subset of the fluorophores, a final super-resolution image can be reconstructed from the accumulated positions.

 xiaowei-1

Figure 1: The principal of STORM. Stochastically fluorophores are excited. The overlap of all figures, results in a super-resolution image of the locations of the fluorophores. Source: http://huanglab.ucsf.edu/STORM.html

STORM has proven to be widely applicable, from single cells and sperm tail to neuron networks. During the talk Xiaowei Zhuang highlighted one application in her own lab. Shown in Figure 2 is actin in axons. The actin filaments are depicted as the blue stripes. In conventional microscopy techniques these filaments are not visible. The observation can be made that the rings of actin we see are extremely regularly spaced. This can also be seen in the periodic autocorrelation of peaks and defined Fourier peaks. This periodicity equals about 180nm-190nm.

fig2N copy

Figure 2: A. STORM image of actin rings on axon. B. Fourier transform to detect the most frequent periodicity. C. Histogram of measured spacing values (nm). Source Xu et al. Science 2013

This indicates the presence of a spacer that needs to be 180nm long and interacts with actin. This brought to spectrin tetramers. Regarding the functional role of the pattern, not much is known yet. It makes the axon very robust and still flexible. Furthermore, it is thought that the structure is important to maintain the integrity of the axon under stress.

The second part of Zhuang’s talk focussed on imaging of the transcriptome. The transcriptome is the total of all RNAs present in a cell. By understanding and analysing spatially-resolved single-cell transcriptomics, new insights can be gained in the subcellular organization of the transcriptome and of the spatial organization of transcriptome in tissue.  For transcriptome imaging, the lab started looking at FISH. Here the FISH probe is bound to the RNA. To distinguish thousands of RNA species only one type of RNAs are labelled in the first image and activated, then a second category is activated and so the cycles continues. Each RNA has a binary code to which it is connected. So in 16 rounds of imaging, you can distinguish 2^16=65535 RNAs. FISH is very accurate as it has a 5% error for 1 bit.

I have enjoyed this talk very much, as Xiaowei Zhuang shows innovation in all her projects. This innovation and out-of-box thinking, brought her to great success. She is an inspirational scientist for every Nanobiology student.

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