Spatially resolved 3D metabolomic profiling in tissues

• Authors: Shambavi Ganesh, Georgia Institute of Technology; Thomas Hu, Georgia Institute of Technology; Eric Woods, Georgia Institute of Technology; Mayar Allam, Georgia Institute of Technology; Shuangyi Cai, Georgia Institute of Technology; Walter Henderson, Georgia Institute of Technology; Ahmet F Coskun, Georgia Institute of Technology
• Journal Title: SCIENCE ADVANCES
• Volume: Volume 7, Number 5
• Publisher: AMER ASSOC ADVANCEMENT SCIENCE | 2021-01-01
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1126/sciadv.abd0957
• Subjects: Engineering, Biomedical; Computer Science
• Keywords: Science & Technology; Multidisciplinary Sciences; Science & Technology - Other Topics

Abstract

Spatially resolved RNA and protein molecular analyses have revealed unexpected heterogeneity of cells. Metabolic analysis of individual cells complements these single-cell studies. Here, we present a three-dimensional spatially resolved metabolomic profiling framework (3D-SMF) to map out the spatial organization of metabolic fragments and protein signatures in immune cells of human tonsils. In this method, 3D metabolic profiles were acquired by time-of-flight secondary ion mass spectrometry to profile up to 189 compounds. Ion beams were used to measure sub-5-nanometer layers of tissue across 150 sections of a tonsil. To incorporate cell specificity, tonsil tissues were labeled by an isotope-tagged antibody library. To explore relations of metabolic and cellular features, we carried out data reduction, 3D spatial correlations and classifications, unsupervised K-means clustering, and network analyses. Immune cells exhibited spatially distinct lipidomic fragment distributions in lymphatic tissue. The 3D-SMF pipeline affects studying the immune cells in health and disease.