||4UH3CA255132-03 Interpret this number
||Novel Platform for Quantitative Subcellular Resolution Imaging of Human Tissues Using Mass Spectrometry
Novel Platform for Quantitative Subcellular Resolution Imaging of Human Tissues Using
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
Spatial Segmentation of Mass Spectrometry Imaging Data by Combining Multivariate Clustering and Univariate Thresholding.
, Yin R.
, Brown H.M.
, Laskin J.
Analytical chemistry, 2021-02-23; 93(7), p. 3477-3485.