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Cairns, J. L.

Publications and source records attributed to Cairns, J. L..

3 recordsLinked to original sources

Fast Single-Cell MALDI Imaging of Low-Mass Metabolites Reveals Cellular Activation Markers

Single-cell MALDI mass spectrometry imaging (MSI) of lipids and metabolites >200 Da has recently come to the forefront of biomedical research and chemical biology, but fast metabolome-preserving methods without paraformaldehyde fixation for analysis of low mass, hydrophilic metabolites (<200 Da) in large cell populations are lacking. Introducing giant unilamellar vesicles (GUVs) as MSI ground truth for cell-sized objects and Monte Carlo reference-based consensus clustering for data-dependent identification of cell subpopulations. The PRISM-MS (PRescan Imaging for Small Molecule - Mass Spectrometry) dual-scan MSI workflow is presented, enabling space-efficient and therefore faster lipid analysis in single GUVs and cells. Beyond lipids, PRISM-MS enables MSI and on-cell MS2-based identification of low-mass metabolites like amino acids or Krebs cycle intermediates involved in stimulus-dependent cell activation. The utility of PRISM-MS is demonstrated through the characterization of complex metabolome changes in lipopolysaccharide (LPS)-stimulated microglial cells and human-induced pluripotent stem cell-derived microglia. Translation of single cell results to endogenous microglia in organotypic hippocampal slice cultures indicates that LPS-activation involves changes of the itaconate-to-taurine ratio and alterations in neuron-to-glia glutamine-glutamate shuttling. The data suggests that PRISM-MS could serve as a standard method in single cell metabolomics, given its capability to characterize larger cell populations and low-mass metabolites.

bioinformatics↗

Deep MALDI-MS Spatial Omics guided by Quantum Cascade Laser Mid-infrared Imaging Microscopy

In spatial omics, highly confident molecular identifications are indispensable for the investigation of complex biology and for spatial biomarker discovery. However, current mass spectrometry (MS)-based spatial omics must compromise between data acquisition speed and biochemical profiling depth, thus often leading to only "putative" molecular identifications. Here, we introduce fast quantum cascade laser mid-infrared imaging microscopy to guide MS imaging to confined tissue areas of high interest, e.g., multicellular spheroid cores or kidney glomeruli, for spatial lipidomics profiling at maximized analytical depth utilizing magnetic resonance-MS imaging at >106 resolution or prm-PASEF-MS2 fragmentation imaging. Instigating selective sulfatide accumulation in arylsulfatase A-deficient mice as ground truth concept, we demonstrate that deep QCL-infrared-guided on-tissue spatial omics unequivocally identifies 120 sulfatides. This approach enables identifications of odd-chain sulfatides and studies of structure-ion mobility-relationships that provide chemical rationales for improvements to current ion mobility prediction algorithms. Workflows and data processing tools are provided as community resources.

biochemistry↗

Spatial Probabilistic Mapping of Metabolite Ensembles in Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) vows to enable simultaneous spatially-resolved investigation of hundreds of metabolites in tissue sections, but it still relies on poorly defined ion images for data interpretation. Here, we outline moleculaR, a computational framework (https://github.com/CeMOS-Mannheim/moleculaR) that introduces probabilistic mapping and point-for-point statistical testing of metabolites in tissue. It enables collective molecular projections and consequently spatially-resolved investigation of ion milieus, lipid pathways or user-defined biomolecular ensembles within the same image.

bioinformatics↗