Spatial omics technologies are essential for characterizing molecular disruptions induced by pathological conditions or environmental stressors while preserving the native spatial context of biomolecules. Among these, mass spectrometry imaging (MSI) has become a central platform for spatial lipidomics. However, applying MSI to small and fragile organisms, such as zebrafish embryos (ZFEs), presents significant challenges. In this study, we introduce an integrative MSI-based workflow for spatial lipidomics, optimized for ZFEs but broadly adaptable to other delicate biological microsystems. The workflow integrates a refined sample preparation protocol that enabled high-quality consecutive tissue sections with a systematic comparison of ionization strategies, including matrix-assisted laser desorption/ionization (MALDI), surface-assisted laser desorption/ionization (SALDI), and advanced laser post-ionization (MALDI-2), achieving reproducible multimodal MSI analysis. Beyond comparative assessment, our results provide new insights into the ionization yields for each MSI platform. MALDI-2 stands out as a technique for spatial lipidomics, demonstrating superior ionization efficiency and enabling high-resolution imaging down to a pixel size of 5 μm. This pixel size enhancement improves visualization of anatomical structures and detailed lipid mapping, which are critical for understanding lipid biology in these small models. While MALDI proved highly effective for glycerophospholipids and sphingolipids, SALDI showed better performance for low-molecular-weight and neutral lipids. These results establish a conceptual model linking experimental conditions and analyte properties to optimize ionization across lipid classes, providing a robust framework for MSI-based spatial lipidomics in ZFEs. Therefore, this advancement contributes to the broader application of spatial omics using novel biological systems on biomedical, pharmacological and toxicological research.

The research leading to these results has received funding from the Spanish Ministry of Science and Innovation MCIN/AEI/10.13039/501100011033, PID2021-122929OB-C33 and CEX2018-000794-S. C·B has been supported by the European Union (MASS2 101067953 - HORIZON-MSCA-2021-PF-01). AMP also acknowledges a grant PRE2020-094656 funded by MCIN/AEI/10.13039/501100011033 by ESF Investing in your future. M.G.A acknowledges the grant RYC2022-036211-I funded by MICIU/AEI/10.13039/501100011033. The authors also acknowledge BioRender for providing the tools to create some of the illustrations included in this work.

Peer reviewed