Spatiotemporally Resolved Proteomics and Biomaterial Customization via Image-guided Photochemistry
Abstract
Biology is four-dimensionally complex, varying both in 3D space and over time. 4D-regulated processes underscoring the central dogma of molecular biology drive this complexity. While emerging tools now exist to identify and quantify DNA/RNA in 4D, methods to do so with proteins have not been reported. Moreover, RNA abundance often does not correlate with protein expression, nor do transcription-based approaches allow for quantification of post-translational modifications. Though mass spectrometry-based proteomic analysis represents a powerful tool to investigate proteins within complex samples, such methods have been limited to probing proteins within specific 3D regions or in time – but not both. As such, the goal of my thesis is to develop a set of chemical, lithographic, and computational tools to enable spatiotemporally resolved proteomics, and then to demonstrate their applicability in vitro, ex vivo, and in vivo. In doing so, proteins are metabolically tagged with non-canonical amino acids over a user-defined time window, inserting a click reaction handle on newly synthesized proteins in a residue-specific manner. Then, directed photochemistry is used to append affinity tags onto these newly synthesized proteins, but only in regions exposed to light. To accomplish 3D spatial labeling in a user-defined manner, I helped to establish automated image-guided patterning workflows utilizing two-photon lithography. After spatial labeling, 4D-tagged proteins are enriched through affinity purification and subjected to shotgun proteomics via liquid chromatography with tandem mass spectrometry (LC/MS-MS). These methods were successfully applied in mammalian cell culture, rodent brain tissue slice models, and in vivo rat studies. When combined with new highly sensitive two-photon chemistries, these methodologies exhibit tremendous utility towards spatiotemporal proteomics, as well as in advanced biomaterial fabrication.
Description
Thesis (Ph.D.)--University of Washington, 2023
