Multiscale biochemical mapping of the brain through data-driven and machine learning enabled- mass spectrometry
Xie, Yuxuan
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Permalink
https://hdl.handle.net/2142/121309
Description
Title
Multiscale biochemical mapping of the brain through data-driven and machine learning enabled- mass spectrometry
Author(s)
Xie, Yuxuan
Issue Date
2023-06-28
Director of Research (if dissertation) or Advisor (if thesis)
Sweedler, Jonathan V.
Doctoral Committee Chair(s)
Sweedler, Jonathan V.
Committee Member(s)
Lam, Fan
Bhargava, Rohit
Kraft, Mary L.
Department of Study
Bioengineering
Discipline
Bioengineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Mass Spectrometry, Machine Learning
Abstract
For brain research, there is an ever-growing demand for new generations of analytical methods that can allow researchers to see beyond the morphology of neural tissues, but to understand the mechanisms of various brain functions and diseases. Mass spectrometry (MS) based measurements both provide unmatched chemical specificity and resolution. Laser-based MS have allowed spatial-omics profiling of tissues through imaging, improved mass resolution for outstanding detection specificity, and achieved attomole detection limits for small volume samples at the single-cell resolution. However, there exist several major technological challenges for comprehensive mapping of the brain biochemistry using MS: extremely low throughput for high- resolution mass spectrometry, lack of quantitative framework to extract biological knowledge, and no current methods to integrate multiscale and multimodal data sets. Here, we present a framework based on computational MS strategies to elucidate multiscale biochemistry of brain at both tissue and single-cell level. We first enabled high-throughput MS imaging via model-based approach to computationally reconstruct high-mass-resolution MS data, significantly accelerating data acquisition and improving signal-to-noise ratio by 10-fold. We further devised a data analysis pipeline based on machine learning to extract biologically relevant information from data collected on individual cells and organelles. Through the pipeline, we were able to classify and interpret the heterogeneity and variability present within and across different brain cell types or organelle types, as well as cells from different brain anatomical regions. Using the computational framework, we performed simultaneous three-dimensional brain-wide and single-cell biochemical mapping containing millions of pixels and large single-cell populations across rat brains. Via multimodal registration and data integration, we created 3D molecular distribution with rich chemical details, and identified cell-specific lipids depending on both cell types and anatomical origins of the cells.
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