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Boehmler, D. J.

Publications and source records attributed to Boehmler, D. J..

3 recordsLinked to original sources

Enabling subcellular DESI-MSI for broad adoption: Acquisition, analysis, and application

Subcellular mass spectrometry imaging (MSI) has required specialized instrumentation and approaches. Additionally, data analysis has required expensive proprietary software, or code-based open-source tools. These barriers render spatial metabolomics less accessible than peer spatial omics techniques (transcriptomics, proteomics). Here we demonstrate subcellular MSI on standard commercial hardware, analyzed using code-free, open-source software. An inexpensive, fully reversible modification of a stock DESI source takes the platform past its typical 5-10 m pixels to routine 2x2 m pixels, and 1x1 m in proof-of-principle experiments. At a tissue-glass boundary imaged at 2x2 m, tissue-specific ions showed no measurable spillover onto the adjacent glass within the {+/-}4.4 m uncertainty of the edge position. To make these data interpretable, we extend MSI.EAGLE, an open-source, vendor-agnostic application with two new methods: i) Spatial UMAP preserves tissue architecture alongside subcellular detail, where conventional clustering breaks apart at high resolution; ii) Histology Fit performs landmark-free co-registration, and recovers synthetic misalignments of up to 8 m to within one pixel of a reference registration. Together, these enable previously high-burden spatial metabolomics experiments. In mouse brain tissue, we demonstrate genuine subcellular signal by resolving mitochondrial cardiolipin into perinuclear puncta, quantifying cytoplasmic enrichment across 6,073 cells. Cell identities are assigned to MSI pixels using histology-predicted transcriptomics, linking cell provenance to metabolism in 2D space directly. Finally, we recover cell-type-specific metabolomes consistent with the transcriptomic identities. This workflow allows cellular and subcellular spatial metabolomics linked to cell provenance, with only stock MSI hardware, open-source GUI-based software, and common histology techniques, putting it within reach of many researchers and laboratories.

molecular biology↗

MSI.EAGLE: An Open-Source GUI for Streamlined Mass Spectrometry Imaging Analysis

Mass Spectrometry Imaging (MSI) is an emergent tool for analyzing spatial molecular distributions, yet data complexity often hinders effective analysis. MSI.EAGLE, an open-source R-Shiny application, makes analysis accessible for non-specialists by integrating advanced tools in a user-friendly interface. The workflow leverages tools from the Cardinal MSI package, with enhanced phenotyping, segmentation, statistical analysis and visualization. The application addresses a gap in spatial biology research by empowering a broader scientific community.

pathology↗

Differential mosquito attraction to humans is associated with skin-derived carboxylic acid levels

Female Aedes aegypti mosquitoes feed on human blood, which they use to develop their eggs. It has been widely noted that some people are more attractive to mosquitoes than others, but the mechanistic basis of this phenomenon is poorly understood. Here we tested mosquito attraction to skin odor collected from human subjects and identified people who are exceptionally attractive or unattractive to mosquitoes. Notably, these preferences were stable over several years, indicating consistent longitudinal differences in skin odor between subjects. We carried out gas chromatography/quadrupole time of flight-mass spectrometry to analyze the chemical composition of human skin odor in these subjects and discovered that highly attractive people produce significantly increased levels of carboxylic acids. Mosquitoes could reliably distinguish a highly attractive human from their weakly attractive counterparts unless we substantially diluted the odor of the "mosquito magnet." This is consistent with the hypothesis that odor concentration is a major driver of differential attraction, rather than the less-favored skin odor blend containing repellent odors, although these are not mutually- exclusive. Mosquitoes detect carboxylic acids with a large family of odor-gated ion channels encoded by the Ionotropic Receptor gene superfamily. Mutant mosquitoes lacking any of the Ionotropic Receptor (IR) co-receptors Ir8a, Ir25a, and Ir76b, were severely impaired in attraction to human scent but retained the ability to differentiate highly and weakly attractive people. The link between elevated carboxylic acids in "mosquito-magnet" human skin odor and phenotypes of genetic mutations in carboxylic acid receptors suggests that such compounds contribute to differential mosquito attraction. Understanding why some humans are more attractive than others provides insights into what skin odorants are most important to the mosquito and could inform the development of more effective repellents.

neuroscience↗