Spatial single-cell profiling of intracellular metabolomesin situ

Abstract

The recently unveiled extent of cellular heterogeneity demands for single-cell investigations of 15 intracellular metabolomes to reveal their roles in intracellular processes, molecular 16 microenvironment and cell-cell interactions. To address this, we developed SpaceM, a method 17 for in situ spatial single-cell metabolomics of cell monolayers which detects >100 metabolites in 18 >10000 individual cells together with fluorescence and morpho-spatial cellular features. We 19 discovered that the intracellular metabolomes of co-cultured human HeLa cells and mouse 20 NIH3T3 fibroblasts predict the cell type with 90.4% accuracy and revealed a short-distance 21 metabolic intermixing between HeLa and NIH3T3. We characterized lipid classes composing 22 lipid droplets in steatotic differentiated human hepatocytes, and discovered a preferential 23 accumulation of long-chain phospholipids, a co-regulation of oleic and linoleic acids, and an 24 association of phosphatidylinositol monophosphate with high cell-cell contact. SpaceM provides 25 single-cell metabolic, phenotypic, and spatial information and enables spatio-molecular 26 investigations of intracellular metabolomes in a variety of cellular models. 27 28 Keywords 29 Spatial single-cell metabolomics, imaging mass spectrometry, microscopy, heterogeneity, 30 metabolic intermixing, macrovesicular steatosis, lipid droplets, cell-cell contact, SpaceM 31 32 77 6 interrogate changes of single-cell metabolomes upon perturbations, and discover spatio-78 molecular associations. 79 80 Results 81The SpaceM method 82 SpaceM relies on using Matrix Assisted Laser Desorption Ionization (MALDI)-imaging mass 83 spectrometry, a spatially-resolved mass spectrometry technology for detection of a wide range of 84 molecules (Baker et al., 2017). MALDI-imaging is increasingly used for spatial metabolomics 85 (Palmer et al., 2016) and was demonstrated to achieve the femtomolar-levels sensitivity 86 (Soltwisch et al., 2015). This, together with soft ionisation preventing excessive in-source 87 molecular fragmentation makes it a perfect choice for single-cell metabolomics as demonstrated 88 by others (Do et al., 2017; Ibáñez et al., 2013). The experimental part of SpaceM combines 89 MALDI-imaging with microscopy as well as with collecting supporting information to integrate 90 these two sources of data ( Figure 1; for a detailed workflow see Figure S1). The cells for 91 SpaceM are cultured on a labtek chamber glass slide in a monolayer, with the cell confluence 92 sufficient to allow cells to interact between each other but at the same time preventing the growth 93 of cells on top of each other. After washing, cells are fixed to halt enzymatic activity, stained 94 with a fluorescent dye with the staining protocol compatible with metabolomics, and dried in a 95 desiccator following regular cell preparation protocols. SpaceM requires the Hoechst (or any 96 similar) staining for nuclei detection. For investigation of the steatotic hepatocytes, we also used 97 the lipophilic LD540 staining to d...

Cell‐intrinsic metabolic regulation of mononuclear phagocyte activation: Findings from the tip of the iceberg

“…65 Increased emphasis on studying immune cell metabolism in vivo, or in more sophisticated culture systems such as those provided by organoids, 107,108 is therefore warranted. In this general context, the application of advances in single-cell metabolomics 109 and spatial metabolomics [110][111][112][113] has the potential to greatly improve the resolution of future studies.…”

Combining Machine Learning and Metabolomics to Identify Weight Gain Biomarkers

“…Machine learning has been intensively used in MS imaging (Hanselmann et al, 2009;Rappez et al, 2019), and is becoming a key methodology in untargeted metabolomics (Li et al, 2019). The strategy applied in this article is an extension of the MLbased platform used by our group for screening ZIKV molecules in blood serum (Melo et al, 2018).…”

Emerging Roles of Single-Cell Multi-Omics in Studying Developmental Temporal Patterning

“…Metabolite structures are highly heterogeneous, have a fast-turnover and are currently largely uncharacterized, making their analysis and sample preparation particularly arduous. Therefore, several methods have been recently developed to generate single-cell high-throughput and spatial metabolomics datasets [ 113 , 114 , 115 ]. In the not-so-distant future, technological advances could extend our ability to describe the heterogeneity of cell types beyond genes and proteins, allowing researchers to tailor their analysis for the modality according to their questions and needs.…”