A MALDI-MS methodology for studying metabolic heterogeneity of single cells in a population

Jasmin Krismer, Jens Sobek, Robert F. Steinhoff, Rolf Brönnimann, Martin Pabst, Renato Zenobi*

*Corresponding author for this work

    Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review

    11 Citations (Scopus)

    Abstract

    Mass spectrometry based metabolomics is the highly multiplexed, label-free analysis of small molecules such as metabolites or lipids in biological systems, and thus one of the most direct ways to characterize phenotypes. However, the phenotyping of populations with single-cell resolution is a great challenge due to the small number of molecules contained in an individual cell. Here we describe a microarray-based sample preparation workflow for MALDI mass spectrometry that has single-cell sensitivity and allows high-throughput analysis of lipids and pigments in single algae cells. The microarray targets receive individual cells in 1430 separate spots that allow the cells to be lysed individually without cross-contamination. Using positive ion mode and 2,5-dihydroxybenzoic acid as the MALDI matrix, the mass spectra unveil information about the relative composition of more than 20 different lipids/pigments in each individual cell within the population. Thus, the method allows the analysis of cellular phenotypes in a population on a completely new level.

    Original languageEnglish
    Title of host publicationSingle Cell Metabolism
    PublisherSpringer
    Pages113-124
    Number of pages12
    ISBN (Print)978-1-4939-9829-6
    DOIs
    Publication statusPublished - 2020

    Publication series

    NameMethods in Molecular Biology
    Volume2064
    ISSN (Print)1064-3745
    ISSN (Electronic)1940-6029

    Keywords

    • Chlamydomonas reinhardtii
    • High-throughput analysis
    • Lipid profiling
    • MALDI-mass spectrometry
    • Single-cell analysis

    Fingerprint

    Dive into the research topics of 'A MALDI-MS methodology for studying metabolic heterogeneity of single cells in a population'. Together they form a unique fingerprint.

    Cite this