Exploring chemical communication and chemical heterogeneity in biological systems with mass spectrometry imaging
Chemical heterogeneity is present in all biological systems—whether caused by lipid regulation and expression between individual mammalian single cells or by dynamic chemical communication in bacterial communities. Mass spectrometry imaging (MSI) is an untargeted and unlabeled analytical tool that enables us to look at molecular constituents in two or three dimensions. Exploring these additional spatial dimensions of heterogeneity is essential to understanding of specific molecular pathways or metabolic regulation at the single cell level. Here, we use a combination of secondary ion mass spectrometry (SIMS) imaging and matrix-assisted laser desorption/ionization (MALDI) MSI to detect, visualize, and piece together important chemical variations in biological systems. This thesis covers two main biological systems: a) bacterial communities and b) single mammalian cells with three main aims: 1) developing instrumentation and methodologies for visualizing chemical heterogeneity, 2) improving computational handling, visualization, and analysis of multidimensional mass spectrometry data, and 3) exploring these approaches to answer complex questions regarding chemical signaling and regulation and discovery of significant heterochemical variations between single cells. Development includes quantitative imaging techniques for SIMS imaging and an analytical workflow for characterization of microbially-influenced corrosion during co-culture interactions with correlated SIMS and MALDI MSI. Additionally, we have explored microbial communication during interactions with various antibiotics to map quorum sensing (QS) pathways in Pseudomonas aeruginosa and to observe how the initial arrangement and polymer surface interactions of P. aeruginosa biofilms alter their quinolone profile. Finally, we utilize microMS, an optically-guided mass spectrometry single cell profiling software, to obtain thousands of individual chemical profiles of single mammalian cells. We then developed laboratory-built bioinformatics tools to perform multivariate and clustering analyses and to uncover at least three cell types in rodent DRGs by their lipid, peptide, and protein content. This method was expanded upon and using metabolic information with high mass accuracy and high mass resolution obtained from tens-of-thousands of rat cerebellum cells to discover 101 unique clusters along with rare lipids present in only a small fraction of cells.
secondary ion mass spectrometry, SIMS, matrix-assisted laser desorption/ionization, MALDI, mass spectrometry imaging, MSI, chemical signaling, chemical heterogeneity, quorum sensing, Pseudomonas aeruginosa, biofilm, swarming, single cell mass spectrometry
University of Illinois - Urbana-Champaign
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University of Illinois at Urbana-Champaign