Development of spatial and single-cell omics methods for biochemical profiling of the brain

Asadian, Marisa

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https://hdl.handle.net/2142/130150 Copy

Description

Title

Development of spatial and single-cell omics methods for biochemical profiling of the brain

Author(s)

Asadian, Marisa

Issue Date

2025-07-14

Director of Research (if dissertation) or Advisor (if thesis)

Sweedler, Jonathan V

Doctoral Committee Chair(s)

Sweedler, Jonathan V

Committee Member(s)

• Bhargava, Rohit
• Lam, Fan
• Chan, Jefferson

Department of Study

Chemistry

Discipline

Chemistry

Degree Granting Institution

University of Illinois Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Spatial omics
• Single-cell mass spectrometry

Abstract

Understanding the molecular basis of brain function and dysfunction requires analytical tools with both spatial precision and cell-type specificity, along with the ability to profile the diverse chemical composition underlying cellular heterogeneity. The brain is composed of diverse cell types, with molecular identities shaped by spatial context, dynamic metabolic states, and complex intercellular signaling. To dissect these layers of complexity, this dissertation presents the development and application of spatial and single-cell omics approaches for multiscale biochemical profiling of the brain across species and disease. In the first study (Chapter 2), expansion microscopy was integrated with multiplexed error-robust fluorescence in situ hybridization (MERFISH) to characterize the spatial transcriptome of the honeybee (Apis mellifera) brain. This approach enabled the mapping of aggression-associated genes linked to social behavior in this eusocial insect. The combined technique also demonstrated the feasibility of detecting and decoding mRNA fluorescence spots in the densely packed honeybee brain, which contains approximately one million neurons in a 1 mm³ volume, highlighting its ability to map molecular activity in small, compact brains. Importantly, this study establishes a framework for investigating the spatial molecular basis of social behavior using the honeybee as a model organism. The second study focuses on lipidomics, a key omics field given that lipids account for over 50% of the brain’s dry weight and play vital roles in maintaining homeostasis and cellular signaling. Presented in Chapter 3, a high-throughput sequential single-cell workflow was developed by leveraging the untargeted capability of matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) to obtain lipid signatures across thousands of cells. This was combined with multiplex antibody labeling using photocleavable mass-tags for cell classification. Applied to isolated hippocampal cells from the rodent brain, this approach demonstrated the feasibility of performing cell-type- and cell-state-specific lipidomic analyses in this functionally complex brain region, revealing lipid enrichments specific to both cell type and neuronal cell state. Finally, Chapter 4 presents a multimodal spatial lipidomic workflow that combines salt doping with MALDI-2 post-ionization for enhanced detection of neutral lipid remodeling associated with Alzheimer’s disease (AD) in postmortem human brain tissue. This method, combined with post–MS imaging immunohistochemistry (MSI-IHC) of amyloid-beta plaques and phosphorylated tau in neurofibrillary tangles, enabled the detection of lipids colocalized with AD pathology. Additionally, a correlation analysis of lipid changes in human AD tissue compared with the 5xFAD mouse model is presented, supporting the translational relevance of the mouse model for future AD studies. Together, this dissertation presents omics approaches that advance our understanding of spatial molecular activity in the brain, as well as subcellular biochemical diversity that contributes to cell heterogeneity, brain function, and disease processes.

Graduation Semester

2025-08

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/130150

Copyright and License Information

Copyright 2025 Marisa Asadian

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