In recent years, mass spectrometry-based imaging techniques have improved at unprecedented speeds, particularly in spatial resolution, and matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) experiments can now routinely image molecular profiles of single cells in an untargeted fashion. With the introduction of MALDI-immunohistochemistry (IHC), multiplexed visualization of targeted proteins in their native tissue location has become accessible and joins the suite of multimodal imaging techniques that help unravel molecular complexities. However, MALDI-IHC has not been validated for use with cell cultures at single-cell level. Here, we introduce a workflow for combining MALDI-MSI and MALDI-IHC on single, isolated cells. Patient-derived cells from glioblastoma tumor samples were imaged, first with high-resolution MSI to obtain a lipid profile, followed by MALDI-IHC highlighting cell-specific protein markers. The multimodal imaging revealed cell type specific lipid profiles when comparing glioblastoma cells and neuronal cells. Furthermore, the initial MSI measurement and its sample preparation showed no significant differences in the subsequent MALDI-IHC ion intensities. Finally, an automated recognition model was created based on the MALDI-MSI data and was able to accurately classify cells into their respective cell type in agreement with the MALDI-IHC markers, with triglycerides, phosphatidylcholines, and sphingomyelins being the most important classifiers. These results show how MALDI-IHC can provide additional valuable molecular information on single-cell measurements, even after an initial MSI measurement without reduced efficacy. Investigation of heterogeneous single-cell samples has the potential of giving a unique insight into the dynamics of how cell-to-cell interaction drives intratumor heterogeneity, thus highlighting the perspective of this work.

We introduce a novel approach for comprehensive molecular profiling in biological samples. Our single-section methodology combines quantitative mass spectrometry imaging (Q-MSI) and a single step extraction protocol enabling lipidomic and proteomic liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis on the same tissue area. The integration of spatially correlated lipidomic and proteomic data on a single tissue section allows for a comprehensive interpretation of the molecular landscape. Comparing Q-MSI and Q-LC-MS/MS quantification results sheds new light on the effect of MSI and related sample preparation. Performing MSI before Q-LC-MS on the same tissue section led to fewer protein identifications and a lower correlation between lipid quantification results. Also, the critical role and influence of internal standards in Q-MSI for accurate quantification is highlighted. Testing various slide types and the evaluation of different workflows for single-section spatial multiomics analysis emphasized the need for critical evaluation of Q-MSI data. These findings highlight the necessity for robust quantification methods comparable to current gold-standard LC-MS/MS techniques. The spatial information from MSI allowed region-specific insights within heterogeneous tissues, as demonstrated for glioblastoma multiforme. Additionally, our workflow demonstrated the efficiency of a single step extraction for lipidomic and proteomic analyses on the same tissue area, enabling the examination of significantly altered proteins and lipids within distinct regions of a single section. The integration of these insights into a lipid-protein interaction network expands the biological information attainable from a tissue section, highlighting the potential of this comprehensive approach for advancing spatial multiomics research.

Cells often adopt different phenotypes, dictated by tissue-specific or local signals such as cell-cell and cell-matrix contacts or molecular micro-environment. This holds in extremis for macrophages with their high phenotypic plasticity. Their broad range of functions, some even opposing, reflects their heterogeneity, and a multitude of subsets has been described in different tissues and diseases. Such micro-environmental imprint cannot be adequately studied by single-cell applications as cells are detached from their context, while histology-based assessment lacks the phenotypic depth due to limitations in marker combination. Here, we present a novel, integrative approach in which 15-color multispectral imaging allows comprehensive cell classification based on multi-marker expression patterns, followed by downstream analysis pipelines to link their phenotypes to contextual, micro-environmental cues such as their cellular (“community”) and metabolic (“local lipidome”) niches in complex tissue. The power of this approach is illustrated for myeloid subsets and associated lipid signatures in murine atherosclerotic plaque.