Microscopic visualization of sub-cellular structures and constituents plays a central role in cell biology. Synchrotron-based X-ray microscopy (XRM) provides a unique approach for direct imaging a whole cell with intrinsic nanoscale resolution. However, existing approaches to label biomolecules rely on the use of exogeneous tags that are multi-step and error-prone (e. g. antibody-based detection). Recently, Chunhai Fan from Shanghai Jiao Tong University, Ying Zhu, Jun Hu and Lihua Wang from the Shanghai Synchrotron Lightsource developed genetically-encoded tags for XRM imaging, which allows nanoscale localization of proteins in cells.
They repurposed peroxidases as genetically-encoded X-ray-sensitive tags for site-specific labeling of protein-of-interest in mammalian cells. They find that polymers that are in-situ catalytically formed by fusion-expressed peroxidases are visible under XRM (Fig. a). The major consequences of using this new tag can be categorized in three aspects: 1) The genetically encoded X-ray tags allow endogenous labeling of diverse molecules and subcellular structures for XRM imaging with an ultrahigh spatial resolution of ~30 nm (Fig. b). 2) The high photostability of X-ray tags enables long-term observation of intracellular and intercellular events. Especially, they visualize the changes of intercellular connections among tumor cells dependent on DNA methylation with XRM. 3) The high energy resolution of XRM provides a direct means to realize multi-colour imaging of cellular structures. This work enlightens the way to nanoscopic imaging for biological studies.
This work was supported by the 10ID-1 Soft X-ray Spectromicroscopy beamline of the Canadian Light Source (CLS), the 10A Soft X-ray Nanoscopy beamline of the Pohang Light Source (PLS) II and the BL08U1-A Soft X-ray Spectromicroscopy beamline of the Shanghai Synchrotron Radiation Facility (SSRF).
Genetically encoded X-ray cellular imaging for nanoscale protein
Huating Kong, Jichao Zhang, Jiang Li, Jian Wang, Hyun-Joon Shin,
Renzhong Tai, Qinglong Yan, Kai Xia, Jun Hu, Lihua Wang, Ying Zhu, Chunhai Fan