High-density micro-arrays for mass spectrometry

Urban, Pawel L.; Jefimovs, Konstantins; Amantonico, Andrea; Fagerer, Stephan R.; Schmid, Thomas; Mädler, Stefanie; Puigmartí‐Luis, Josep; Goedecke, Nils; Zenobi, Renato

doi:10.1039/c0lc00211a

Cited by 106 publications

(128 citation statements)

References 24 publications

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“…MAMS represent a type of substrate for matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS) recently introduced by our group (14,15). MAMS uses a combination of hydrophilic reservoirs surrounded by an omniphobic surface (SI Text) for massively parallel, automated sample spotting.…”

Section: Resultsmentioning

confidence: 99%

“…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.…”

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confidence: 99%

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Mass spectrometry-based metabolomics of single yeast cells

Ibáñez¹,

Fagerer²,

Schmidt³

et al. 2013

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

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216

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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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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Mass spectrometry-based metabolomics of single yeast cells

Ibáñez¹,

Fagerer²,

Schmidt³

et al. 2013

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

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216

123

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“…This approach provides true high-throughput operation: the sample deposition is an automated, parallel process, and the readout of the spectra is fast, limited only by the speed of the MS instrument ( 2 spots per second, that is analysis of 1000s of cells/hour) [39 ]. (d) Imaging mass spectrometry.…”

Section: Ms-based Approaches For Single Cell Metabolomicsmentioning

confidence: 99%

Single cell metabolomics

Heinemann

Zenobi

2011

Current Opinion in Biotechnology

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117

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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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“…High-throughput analyses of single cells deposited from suspension and picoliter-volume aliquots have been demonstrated using high-density microarrays for MS. This approach can anchor samples on 100 mm spots, which is equal to 250 sample recipients per cm 2 , or 250-fold higher sample density than what is currently available from commercial sample plates for MALDI MS (Urban et al, 2010). Applications of MALDI MS to small-volume analyses of signaling molecules are discussed in the next section.…”

Section: Detection Platforms Compatible With Microanalysismentioning

confidence: 99%

Small-Volume Analysis of Cell–Cell Signaling Molecules in the Brain

Romanova

Aerts

Croushore

et al. 2013

Neuropsychopharmacol

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Modern science is characterized by integration and synergy between research fields. Accordingly, as technological advances allow new and more ambitious quests in scientific inquiry, numerous analytical and engineering techniques have become useful tools in biological research. The focus of this review is on cutting edge technologies that aid direct measurement of bioactive compounds in the nervous system to facilitate fundamental research, diagnostics, and drug discovery. We discuss challenges associated with measurement of cell-to-cell signaling molecules in the nervous system, and advocate for a decrease of sample volumes to the nanoliter volume regimen for improved analysis outcomes. We highlight effective approaches for the collection, separation, and detection of such small-volume samples, present strategies for targeted and discovery-oriented research, and describe the required technology advances that will empower future translational science.

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High-density micro-arrays for mass spectrometry

Abstract: Functional high-density micro-arrays for mass spectrometry enable rapid picolitre-volume aliquoting and ultrasensitive analysis of microscale samples, for example, single cells.

Cited by 106 publications

References 24 publications

Mass spectrometry-based metabolomics of single yeast cells

Mass spectrometry-based metabolomics of single yeast cells

Single cell metabolomics

Small-Volume Analysis of Cell–Cell Signaling Molecules in the Brain

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