Matrix‐free UV‐laser desorption/ionization (LDI) mass spectrometric imaging at the single‐cell level: distribution of secondary metabolites of <i>Arabidopsis thaliana</i> and <i>Hypericum</i> species

Hölscher, Dirk; Shroff, Rohit; Knop, Katrin; Gottschaldt, Michael; Crecelius, Anna C.; Schneider, Bernd; Heckel, David G.; Schubert, Ulrich S.; Svatoš, Aleš

doi:10.1111/j.1365-313x.2009.04012.x

Cited by 192 publications

(171 citation statements)

References 49 publications

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“…Interesting sample preparation protocols have been developed by the same group [43 ]. Another recent example is MS imaging of the distribution of secondary flavonoid metabolites such as kaempferol, quercetin, isorhamnetin, and their glycosides in individual plant tissue cells [44]. A spatial resolution of 10 mm was reached with state-of-the-art LDI instrumentation.…”

Section: Ms-based Approaches For Single Cell Metabolomicsmentioning

confidence: 99%

Single cell metabolomics

Heinemann

Zenobi

2011

Current Opinion in Biotechnology

116

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Recent discoveries suggest that cells of a clonal population often display multiple metabolic phenotypes at the same time. Motivated by the success of mass spectrometry (MS) in the investigation of population-level metabolomics, the analytical community has initiated efforts towards MS-based single cell metabolomics to investigate metabolic phenomena that are buried under the population average. Here, we review the current approaches and illustrate their advantages and disadvantages. Because of significant advances in the field, different technologies are now at the verge of generating data that are useful for exploring and investigating metabolic heterogeneity. IntroductionQuantitative metabolomics, the technology for large-scale quantification of intracellular metabolite concentrations, is a powerful tool in systems biology research that has recently led to a series of interesting findings (e.g. [1][2][3][4]). Because of the metabolome's chemical diversity, mass spectrometry (MS) is the analytical method of choice [5]. In addition to analytical challenges, quantitative metabolomics as required for addressing (systems) biology questions poses significant challenges in sample processing. One important challenge is the need to preserve the original metabolome during sample processing, which is often difficult because of the presence of enzymes in the sample and the fast metabolic turnover rates.For sensitivity reasons, current metabolomics methods require samples that contain a large number of cells. However, cell populations are not necessarily homogeneous. Besides genetic differences, several other sources for population heterogeneity exist, of which several are also known to cause metabolic differences. Today, methods capable of resolving differences in metabolite levels on the single cell level are provided, within limits, by molecular sensors such as FRET sensors [6,7] or aptamer-based technology [8 ,9]. Both types of molecular sensors, however, are difficult to develop, are limited to specific analytes, and quantitative analyses (e.g. in terms of mol/L) are hardly possible with them. Laserinduced fluorescence, as introduced by Dovichi for single cell proteomics, is limited to fluorescent compounds or labelled species [10,11]. In addition, all these existing methods share the limitation that they can never be extended to the ''-omics'' level, that is to measuring a large number of metabolites at the same time. They will thus not be applicable to discovery type research and research that requires a large number of metabolites to be measured in the same cell.Because of the success of mass spectrometry in population-level metabolome analyses, the analytical community has recently made great strides towards single cell level metabolite analyses (for a review, see [12]). So far, however, hardly any new biological insight has been generated from these endeavours. In this Current Opinion paper, we will thus not only review the current status of MS-based single cell metabolomics but also discuss which of the differ...

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Section: Ms-based Approaches For Single Cell Metabolomicsmentioning

confidence: 99%

Single cell metabolomics

Heinemann

Zenobi

2011

Current Opinion in Biotechnology

116

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“…A number of analytical approaches were developed with detection limits low enough for single-cell metabolite analyses [e.g., nanostructured surfaces (7,8), postionization techniques (9,10), modified laser optics (11), the use of microsampling tools (12,13), microarrays for MS measurements (14,15), etc.]. Until now, however, most MS studies targeting single-cell metabolite analysis have only shown the analytical capabilities, but have not demonstrated that true biological information can be retrieved from studying the metabolism of single cells.…”

mentioning

confidence: 99%

Mass spectrometry-based metabolomics of single yeast cells

Ibáñez¹,

Fagerer²,

Schmidt³

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

213

123

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Single-cell level measurements are necessary to characterize the intrinsic biological variability in a population of cells. In this study, we demonstrate that, with the microarrays for mass spectrometry platform, we are able to observe this variability. We monitor environmentally (2-deoxy-D-glucose) and genetically (ΔPFK2) perturbed Saccharomyces cerevisiae cells at the single-cell, few-cell, and population levels. Correlation plots between metabolites from the glycolytic pathway, as well as with the observed ATP/ADP ratio as a measure of cellular energy charge, give biological insight that is not accessible from population-level metabolomic data.single-cell measurements | MALDI mass spectrometry | baker's yeast E ven genetically identical cells present in the same microenvironment can express different phenotypes, for a number of reasons: cell-to-cell heterogeneity can stem from differences in the cell age and differences in the cell cycle stage, and stochastic effects together with feedback mechanisms can lead to distinctively different phenotypes, too (1-6). As population-level measurement techniques inherently average out such cell-to-cell differences, biochemical mechanisms underlying a studied system cannot be deduced from such measurements. Thus, to detect and exploit this heterogeneity, new analytical platforms with a sensitivity at the single-cell level and the ability to perform quantitative analyses must be developed and validated.Motivated by advances of mass spectrometry (MS) in metabolomics, the analytical chemistry community has stepped up its efforts toward realizing MS-based single-cell metabolomics (1, 2). A number of analytical approaches were developed with detection limits low enough for single-cell metabolite analyses [e.g., nanostructured surfaces (7, 8), postionization techniques (9, 10), modified laser optics (11), the use of microsampling tools (12, 13), microarrays for MS measurements (14, 15), etc.]. Until now, however, most MS studies targeting single-cell metabolite analysis have only shown the analytical capabilities, but have not demonstrated that true biological information can be retrieved from studying the metabolism of single cells.Here, using the unicellular eukaryotic model organism Saccharomyces cerevisiae, we present an analytical validation of a single-cell metabolite analysis using the microarrays for mass spectrometry (MAMS) platform. This validation concerns both the analytical methodology and the biological information, by monitoring expected cellular responses upon an environmental and a genetic perturbation. Furthermore, we present examples of biological insight that are only accessible with a platform such as MAMS. Specifically, we unravel metabolite-metabolite correlations, and visualize coexisting subpopulations in an isogenic cell culture. This technology can now be used to reveal metabolic differences in cells of isogenic cell populations, such as differences caused by cell cycles stages, cell ages, or stochastically induced phenotypic differences. Results and ...

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“…In parallel, experiments using HSI techniques for detection of fungal infections in fruits of citrus, leaves of sugar beet, wheat and maize have proven the applicability of the technique (Lorente et al, 2013, Mahlein et al, 2010Hillnhütter et al, 2011, Firrao et al, 2010Yao et al, 2010, Bauriegel and Herppich, 2014, Williams et al, 2012. Nowadays, HSI methodologies based on various indices have been mostly used for detection of photosynthetic pigments like chlorophylls, carotenoids as well as of the other major compounds in leaves and fruits, such as anthocyanins and flavonoids (Deepak et al, 2015;Matros and Mock, 2013, Hölscher et al, 2009, Zhao et al, 2005, Coops et al, 2003, Ferri et al, 2004.…”

Section: Hyperspectral Imaging and Its Utilization In Crop Phenotypinmentioning

confidence: 99%

Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance

Sytar

Brestič

Živčák

et al. 2017

Science of The Total Environment

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• Salinity represents an abiotic stress constraint affecting growth and productivity of plants • Better solutions is to improve the level of salt resistance using natural genetic variability within crop species • Phenomic methodology employing different non-invasive imaging systems for detecting quantitative and qualitative changes caused by salt stress at the whole plant and canopy level. Hyperspectral imaging techniques provide unique opportunities for fast and reliable evaluation of numerous characteristics associated both with various structural, biochemical and physiological traits • Salt-soil-plant interaction and sustainable coastal agriculture need powerful phenotyping tools Salinity represents an abiotic stress constraint affecting growth and productivity of plants in many regions of the world. One of the possible solutions is to improve the level of salt resistance using natural genetic variability within crop species. In the context of recent knowledge on salt stress effects and mechanisms of salt tolerance, this review present useful phenomic approach employing different non-invasive imaging systems for detection of quantitative and qualitative changes caused by salt stress at the plant and canopy level. The focus is put on hyperspectral imaging technique, which provides unique opportunities for fast and reliable estimate of numerous characteristics associated both with various structural, biochemical Science of the Total Environment 578 (2017) 90-99 ⁎ Corresponding authors at:

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Matrix‐free UV‐laser desorption/ionization (LDI) mass spectrometric imaging at the single‐cell level: distribution of secondary metabolites of Arabidopsis thaliana and Hypericum species

Cited by 192 publications

References 49 publications

Single cell metabolomics

Single cell metabolomics

Mass spectrometry-based metabolomics of single yeast cells

Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance

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