Novel Platform for Quantitative Subcellular Resolution Imaging of Human Tissues Using Mass Spectrometry

人体组织定量亚细胞分辨率成像的新平台

Novel Platform for Quantitative Subcellular Resolution Imaging of Human Tissues Using Mass Spectrometry Mass spectrometry imaging (MSI) is a powerful technique that enables label-free spatial mapping of different classes of biomolecules in biological systems. Because it does not require any special sample pretreatment, ambient MSI is particularly attractive for high throughput automated imaging applications. The throughput of ambient MSI experiments is typically limited by the inherently slow microprobe-type sampling from surfaces, which is a characteristic shortcoming of many chemical imaging modalities. This project will combine several highly innovative approaches to address challenges associated with the high-throughput high- resolution ambient MSI of lipids and metabolites using nanospray desorption electrospray ionization (nano-DESI). Nano-DESI is an ambient ionization technique, which relies on gentle localized liquid extraction of molecules from tissue sections into a flowing solvent confined between two glass capillaries. The extracted molecules are efficiently delivered to a mass spectrometer inlet and ionized by soft electrospray ionization. Nano-DESI MSI enables detection of hundreds of metabolites, lipids, and peptides in tissue sections with high sensitivity, high spatial resolution, and without special sample pretreatment. Furthermore, on-the-fly quantification of lipids and metabolites in tissue sections during nano-DESI imaging experiments is achieved by doping the working solvent with appropriate standards of known concentration. This project will extend these powerful capabilities of nano-DESI MSI to enable high-throughput imaging of large tissue sections of interest to the HubMAP Consortium. This will be achieved using a combination of a conceptually different nano-DESI probe design optimized for robustness, ease of fabrication, and spatial resolution and a suite of advanced machine learning and compressed sensing computational approaches. These developments will be applicable to different types of human tissues and will transform quantitative molecular imaging of multiple classes of biomolecules in tissue sections. Although the capabilities of the new imaging platform will be demonstrated using non-diseased tissue, these developments will be broadly applicable to scientific problems associated with understanding health and disease