Hyperspectral Stochastic Optical Reconstruction Raman Microscopy for Label-Free Super-Resolution Imaging Using Surface Enhanced Raman Spectroscopy

Super-resolution imaging is an emerging field that has attracted attention in the recent years due to far the reaching impact in biology. All super-resolution techniques use fluorescent labels to image nanoscale biomolecular structures. In contrast, label-free nanoscopic imaging of the chemical environment of biological specimens would readily bridge the supramolecular and the cellular scales, if a chemical fingerprint technique such as Raman scattering can be coupled with super-resolution imaging, overcoming the diffraction limit. In order to achieve this goal, we propose to develop a super-resolved stochastic hyperspectral Raman microscopy technique for imaging of biological architectures. The surface enhanced Raman spectroscopy (SERS) signal contains information about the presence of various Raman bands, allowing for the discrimination of families of biomolecules such as lipids, proteins, DNA. The rich, fluctuating spectral information contained in the single molecule SERS signal possesses a great potential in label-free imaging, using stochastic optical reconstructionmicroscopy (STORM) methods. In a recently published work, we demonstrated 20 nm spatial resolution using the spectrally integrated Raman signal on highly uniform SERS substrates. A mature version of our method would require development of spectrally resolved nanoscale Raman imaging. Development of stochastic Raman imaging addresses the issue by design and construction of a Raman microscope with hyperspectral imaging capability that will allow imaging of different Raman bands of the SERS signal. Novel computational techniques must also be developed that will enable extraction of hyperspectral STORM images corresponding to different Raman bands, while simultaneously allowing conventional STORM data to be collected using the wellestablished labelling techniques. The resulting technique (Hyperspectral Raman STORM or HyperSTORRM) has the potential to complement the available labeled stochastic imaging methods and enable chemically resolved nanoscopy.