Poster Presentation Multi-Omics Inaugural Conference 2022

Spatial lipidomics-guided proteomics using matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) and laser-capture microdissection (LCM) (#114)

Jacob JT Truong 1 2 3 4 , Paul PT Trim 3 4 , Sushma SR Rao 3 4 , Marten MS Snel 3 4 , Lisa LB Butler 1 2 3 4
  1. Freemasons Centre for Male Health and Well-being, Adelaide, SA, Australia
  2. SAiGENCI - South Australian immunoGENomics Cancer Institute, Adelaide, SA, Australia
  3. Proteomics, Metabolomics and MS Imaging Core Facilities, SAHMRI - South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
  4. The University of Adelaide, Adelaide, SA, Australia

Mass spectrometry (MS) is commonly used to identify and measure individual components of complex biological mixtures in the fields of metabolomics and proteomics. MS imaging (MSI) is an extension of this technology where thin tissue sections are sampled by a mass spectrometer at discrete x,y locations to generate spatially resolved data on the relative abundance and distribution of analytes. A current limitation of MSI technology is the difficulty in detecting different analyte types within the same sample. Sample preparation protocols for peptide imaging often involve the removal of lipids while lipid imaging does not require the enzymatic digestion typically used for peptide or glycan analysis. Furthermore, peptides, glycans, lipids and small metabolites all occupy different ranges on the m/z scale and therefore instrument method optimisation for one type will inevitably decrease the sensitivity to another. Here, we present a workflow for generating spatially resolved lipidomic data and subsequent lipid-image-guided proteomic data from the same tissue section using MALDI MSI followed by laser-capture microdissection (LCM) and nano liquid chromatography MS (nanoLC-MS). Prostate patient-derived explant tissues were mounted onto MSI and LCM compatible polyethylene naphthalate (PEN) slides, which were imaged using MALDI MSI. Unsupervised data mining on spatial lipidomic data identified regions of interest (ROIs), the co-ordinates of which were mapped and transferred seamlessly to an LCM system. Dissected tissues from ROIs were digested with trypsin and prepared for nanoLC-MS proteomic analysis. Encouragingly, the method presented could identify over 1000 proteins from dissected areas of less than 1 mm2. With this spatial multiomics workflow, a more comprehensive understanding of a tissue’s molecular composition can be achieved. Furthermore, generating spatially resolved lipidomic and proteomic data on the same tissue section can allow researchers to establish unambiguous links between changes in protein expression and emerging changes in lipid abundance and localisation.