Date of Award

Fall 9-3-2024

Embargo Period

9-19-2029

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Cell and Molecular Pharmacology and Experimental Therapeutics

College

College of Graduate Studies

First Advisor

Anand Mehta

Second Advisor

Richard Drake

Third Advisor

Peggi Angel

Fourth Advisor

Shikhar Mehrotra

Fifth Advisor

Leonardo Ferreira

Abstract

Mass Spectrometry Imaging (MSI) is a powerful technique that enables the analysis of a wide variety of analytes from diverse samples such as tissue sections and spotted serum. This versatility fosters the development of innovative techniques, offering valuable tools for both research and clinical diagnostics. Our laboratory had initially developed a method for the MALDI-MSI glycan analysis of specific proteins following antibody capture. We hypothesized that we could also develop a MSI-compatible capture method for the affinity-based immobilization of cells, facilitating MALDI-MSI analysis. The initial version of this method successfully captured thousands of cells within a spotted antibody region via antibody-epitope interactions. This approach enabled rapid N-glycan imaging of the captured cells, with a particular emphasis on immune cells. Alternative methods for cellular N-glycan profiling are primarily catered towards homogenous samples, making the analysis of heterogenous samples such as PBMCs difficult for large scale and comprehensive N-glycan profiling. We successfully captured and profiled N-glycans from helper T cells, cytotoxic T cells and B cells from 30 cryopreserved PBMC samples, revealing a distinct N-glycan profile amongst the different cell types. MALDI-MSI is a soft ionization technique that preserves sample integrity during laser ablation. This allows for multiple rounds of imaging and analyte investigation to occur. We demonstrated that from captured cells, we could profile a range of molecules including lipids, N-glycans, glycogen, and tryptic peptides providing a comprehensive dataset from the same immobilized cells. We further hypothesized that our cell capture approach could be adapted for single-cell applications by reducing the capture spot size to enable the capture of individual cells. By integrating micro contact printing with polydimethylsiloxane (PDMS) stamps, we achieved capture spot diameters of 30-40 µm, suitable for single-cell capture. We developed an AI-based application, SoloCell, to facilitate the automated selection of captured single cells within the array. This method successfully captured thousands of single cells in a grid format, allowing for the rapid lipid and N-glycan profiling from the same cell at a rate of 6 cells per second. Overall, the workflow described in this thesis exhibits a sensitive, versatile and high throughput technique for bulk and single cell profiling.

Rights

Copyright is held by the author. All rights reserved.

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Available for download on Wednesday, September 19, 2029

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