Lecture

Mass-Guided Single-Cell and 3D MALDI Imaging Spatial Omics in Biological Systems

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  • ICM Saal 2
  • Type: Lecture

Lecture description

S.A. Iakab, J. L. Cairns, J. Cordes, J. Huber, T. Enzlein, S. Schmidt, C. Hopf

Spatial omics has revolutionized our understanding of biological heterogeneity, yet matrixassisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) faces persistent challenges in detecting low-mass metabolites (<500 DA)  at single-cell and volumetric resolution. Traditional MSI workflows suffer from matrix interference, ion suppression in complex environments, and limited throughput for large-scale 3D organoid analysis — critical barriers for translating spatial metabolomics into routine pharma screening and clinical diagnostics. Here, we present advanced PRISM-MS (Precision Raster Imaging with Single-cell Mass spectrometry) which enables space-efficient single cell lipid and metabolite analysis [1] and a 3D MALDI imaging platform for advanced 3D spatial omics analysis of organoids [2]. These integrate mass-guided targeted rastering over large cell populations, increased sensitivity for small analytes, and multidimensional imaging (x, y, z, m/z, intensity) to achieve cellular spatial resolution (<10 μm) in 2D tissues and full volumetric reconstructions in organoids. We identified stimulus‑dependent cellular activation states and metabolically defined cell subpopulations in large cell populations as well as demonstrated molecular profiling of patient-derived colorectal cancer organoids.

[1] J. L. Cairns, J. Huber, A. Lewen, J. Jung, S. J. Maurer, T. Bausbacher, S. Schmidt, P. A. Levkin, D. Sevin, K. Göpfrich, P. Koch, O. Kann, C. Hopf, Mass-Guided Single-Cell MALDI Imaging of Low-Mass Metabolites Reveals Cellular Activation Markers. Adv. Sci. 2025, 12, 2410506. https://doi.org/10.1002/advs.202410506
[2] S. A. Iakab, J. Cordes, T. Enzlein, et al. “ From Sample to Mixed Reality: A Translational 3D MALDI Imaging Platform for Advanced 3D Spatial Omics Analysis of 3D Cell Culture Disease Models.” Adv. Sci. (2025): e16098. https://doi.org/10.1002/advs.202516098

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