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Caitlin Cain
Assistant Professor of Chemistry
Education:
B.S. in Chemistry and Forensic Science, Virginia Commonwealth University, 2019
Ph.D. in Chemistry, University of Washington, 2024
NIH Postdoctoral Fellow, University of Michigan, 2024-2026
Biography:
Metabolomics provides a unique opportunity to uncover biochemical pathways connecting the genome, proteome, and organism function. Our lab aims to deepen understanding of the metabolome by developing high-resolution separations and novel computational algorithms. Specifically, we are focused on developing multidimensional gas (GC) and liquid chromatography (LC) methods to maximize metabolomic coverage and increase the number of compounds which can be reliably separated. However, coupling multidimensional separations with high-resolution mass spectrometry techniques generates large, complex datasets. Our lab applies chemometric and machine learning approaches to efficiently discover, identify, and quantify biomarkers.
By driving improvements in bioanalytical chemistry, our aim is to explore the brain metabolome and its connection to human health. The brain metabolome is a chemically diverse landscape, consisting of tens of thousands of small molecules that span several structural classes (e.g., amino acids, alcohols, nucleotides, carbohydrates, lipids). Metabolite concentrations can also span several orders of magnitude (pM to mM) and naturally fluctuate throughout the day, with changes also occurring in response to stimuli or disease. By uncovering neurotransmitters, neuromodulators, hormones, and other metabolites in the living brain, we can ultimately correlate their roles in biochemical pathways to broader brain function, behavior, and neurological disease.
Recent Publications:
Improving the Detection of Analyte Degeneracies in Untargeted Liquid Chromatography-Tandem Mass Spectrometry Data. Cain CN, Ma CH, Popov P, Kennedy RT. Anal Chem. 2025; 97(48): 26439-26448.
Sampling Probes Microfabricated from Parylene-C for In Vivo Neurochemical Monitoring. White T, Bain I, Cain CN, Popov P, Evans CR, Kennedy RT. ACS Meas Sci Au. 2025; 5(6):879-889.
Enhancing Gas Chromatography-Mass Spectrometry Resolution and Pure Analyte Discovery Using Intra-Chromatogram Elution Profile Matching. Cain CN and Synovec RE. Talanta. 2024; 278:126453.
Optimization of Parameters for ROI Data Compression for Nontargeted Analyses Using LC-HRMS. Schöneich S, Cain CN, Freye CE, Synovec RE. Anal Chem. 2023; 95(2): 1513-1521.
Enhancing Partial Least Squares Modeling of Comprehensive Two-Dimensional Gas Chromatography Time-of-Flight Mass Spectrometry Data by Tile-Based Variance Ranking. Cain CN, Ochoa GS, Synovec RE. J Chromatogr A. 2023; 1694: 463920.
Tile-Based Pairwise Analysis of GC × GC-TOFMS Data to Facilitate Analyte Discovery and Mass Spectrum Purification. Cain CN, Trinklein TJ, Ochoa GS, Synovec RE. Anal Chem. 2022; 94(14): 5658-5666.
Awards and Honors:
NIH Postdoctoral Individual National Research Service Award, 2024-2026
Rising Stars of Separation Science Award, 2024
NSF Graduate Research Fellowship, 2019-2024