SEAM is a spatial single nuclear metabolomics method for dissecting tissue microenvironment

Abstract

Spatial metabolomics can reveal intercellular heterogeneity and tissue organization. To achieve highest spatial resolution, we reported a novel Spatial single nuclEar metAboloMics (SEAM) method, a scalable platform combining high resolution imaging mass spectrometry (IMS) and a series of computational algorithms, that can display multiscale/multicolor tissue tomography together with identification and clustering of single nuclei by their in situ metabolic fingerprints. We firstly applied SEAM to a range of wild type mouse tissues, then delineate a consistent pattern of metabolic zonation in mouse liver. We further studied spatial metabolome in human fibrotic liver. Intriguingly, we discovered novel subpopulations of hepatocytes with special metabolic features associated with their proximity to fibrotic niche, which was further validated by spatial transcriptomics with Geo-seq. These demonstrations highlight how SEAM may be used to explore the spatial metabolome and tissue anatomy at single cell level, hence leading to a deeper understanding of the tissue metabolic organization.

Principles of Spatial Transcriptomics Analysis: A Practical Walk-Through in Kidney Tissue

“…In this manuscript, we have reviewed the biological and computational basis of spatial transcriptomics analysis, with an example of cell type mapping and downstream applications in kidney tissue. Spatial transcriptomics technologies are evolving at a rapid pace and extending beyond transcriptomics into metabolomics and proteomics ( Lundberg and Borner, 2019 ; Ganesh et al, 2021 ; Yuan et al, 2021 ). The integration of unbiased spatial omics technologies will provide a powerful set of tools to characterize disease processes in intact tissue ( Dries et al, 2021a ).…”

SOTIP: a Unified Framework for Microenvironment Modelling with Spatial Omics Data

“…Based on SOTIP detected tumorimmune boundary, we also detected proteins with previously unreported spatial polarization. In the novel task of DME analysis, we firstly compared a fibrotic liver sample with healthy liver sample 7 , then identified MECNs specifically enriched in the fibrotic sample. The spatial localization and cellular composition of the detected MECN is consistent with the histology image.…”

“…For instance, in healthy vertebrate liver, the zonation patterns of hepatocytes perform mutual effect on the microenvironment within liver lobules 4 . While during liver fibrosis, there occurs metabolic alteration around the liver-fibrotic microenvironment 7 . In cancer, researchers also reported that different patterns of immune infiltration were associated with patient survival [11][12][13] .…”

“…This discovery showed high consistency across a 41human patient cohort 12 . Unlike most methods, SOTIP is highly scalable to a wide range of technologies, our experiments covered various types of spatial omics data [45][46][47][48] , including FISH-based 49 and sequencing-based 50 spatial transcriptomics, TOF mass spectrometry based spatial proteomics 12,51 , and secondary ion mass spectrometry (SIMS) based spatial metabolomics 7 .…”

X-dimensional Mass Spectrometry Imaging Discovers Spatially Resolved Metabolic Response

“…MALDI, SIMS, DESI, LA-ESI), MSI technique has been successfully applied to detect synthetic drugs, metabolites, lipids, peptides, proteins, metals, glycans, etc 7,8,9,10 . A multiplex molecular changes caused by disease progression or drug intervention can be visualized at the whole-body, organ, tissue, cell or even sub-cellular resolution 11,12,13,14 . This unique advantage in spatially resolved multiplex molecular detection makes MSI promising in drug research and development (R&D) 15,16 .…”