Molecular phenotypic profiling of a <i>Saccharomyces cerevisiae</i> strain at the single-cell level

Schmidt, Anna Mareike; Fagerer, Stephan R.; Jefimovs, Konstantins; Buettner, Florian; Marro, Christian; Siringil, Erdem; Boehlen, Karl L.; Pabst, Martin; Ibáñez, Alfredo J.

doi:10.1039/c4an01119h

Cited by 19 publications

(28 citation statements)

References 27 publications

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“…As also cells originating from the same colony monitored under identical conditions (same device, same day, and same culture medium) differed in growth rates, a significant influence of the microfluidic device itself can be neglected. Thus, our findings confirm the heterogeneity in the growth behavior of individual S. cerevisiae cells, which has been reported previously .…”

Section: Resultssupporting

confidence: 92%

Cultivation and quantitative single‐cell analysis of Saccharomyces cerevisiae on a multifunctional microfluidic device

et al. 2017

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Here, we present a multifunctional microfluidic device whose integrative design enables to combine cell culture studies and quantitative single cell biomolecule analysis. The platform consists of 32 analysis units providing two key features; first, a micrometer-sized trap for hydrodynamic capture of a single Saccharomyces cerevisiae (S. cerevisiae) yeast cell; second, a convenient double-valve configuration surrounding the trap. Actuating of the outer valve with integrated opening results in a partial isolation in a volume of 11.8 nL, i.e. the cell surrounding fluid can be exchanged diffusion-based without causing shear stress or cell loss. Actuation of the inner ring-shaped valve isolates the trapped cell completely in a small analysis volume of 230 pL. The device was used to determine the growth rate of yeast cells (S. cerevisiae) under under optimum and oxidative stress conditions. In addition, we successfully quantified the cofactor beta-nicotinamide adenine dinucleotide phosphate (NAD(P)H) in single and few cells exposed to the different microenvironments. In conclusion, the microdevice enables to analyze the influence of an external stress factor on the cellular fitness in a fast and more comprehensive way as cell growth and intracellular biomolecule levels can be investigated.

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Section: Resultssupporting

confidence: 92%

Cultivation and quantitative single‐cell analysis of Saccharomyces cerevisiae on a multifunctional microfluidic device

et al. 2017

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“…While highly sensitive and quantitative, fluorescence‐based measurements require analyte pre‐selection prior to analysis , which means that the researcher can target only a limited range of biomolecules. In contrast, mass spectrometry (a label‐free method) provides high sensitivity, while at the same time being able to monitor simultaneously multiple biomolecules . In recent years, mass spectrometry platforms have been interfaced with micro dissection tools, microfluidic platforms, microarrays, microstructures, etc.…”

Section: Available Technologies For Single‐cell Analysismentioning

confidence: 99%

“…For example, Nemes and co‐workers used a microfluidic platform (suitable for cell immobilization, and cultivation) and coupled it to a mass spectrometry to monitor secreted compounds . With respect to arrays, ionization of compounds by laser ablation and desorption directly from the predefined areas improves the overall sensitivity of the mass spectrometry, hence enhancing it in terms of throughput and data analysis .…”

Section: Available Technologies For Single‐cell Analysismentioning

confidence: 99%

“…In this regard, matrix‐assisted laser/desorption ionization mass spectrometry (MALDI‐MS) has been successfully used to study the natural occurring cell‐to‐cell heterogeneity in isogenic populations . Urban et al developed a microarray platform called “Microarrays for Mass Spectrometry” (MAMS), which allows for the unsupervised aliquoting of cell suspensions .…”

Section: Available Technologies For Single‐cell Analysismentioning

confidence: 99%

“…Additionally, in the case of comparative studies, it is important to efficiently and reliably remove the extracellular metabolites without compromising the integrity or modifying the metabolism of the cell . Only if a good protocol for sample handling is employed, then we can clearly assign the source of the cell‐to‐cell heterogeneity without analytical artifacts even at the single‐cell level . Furthermore, in case we aim for the detection of a specific, but possibly low abundant metabolite(s), we have to consider that with the exception of DNA and RNA, all other biomolecules in the cell lack a direct amplification protocol to increase the analyte number available to us.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of metabolites in single cells—what is the best micro‐platform?

Dittrich

Ibáñez

2015

Electrophoresis

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This review covers new innovations and developments in the field of single-cell level analysis of metabolites, involving the role of microfluidic and microarray platforms to manipulate and handle the cells prior their detection. Microfluidic and microarray platforms have shown great promise. The latest developments demonstrate their potential to identify a particular cell or even an ensemble of cells (sharing a common property or phenotype) that co-exist in a much larger cell population. The reason for this is the capability of these platforms to perform several complex analytical processes, such as: cleanup, sorting, derivatization, separation, and detection, with great robustness, speed, and reduced sample/reagent consumption. Here, we present several examples that illustrate the rapid strides that have been made for the routine analysis of metabolites by coupling different microfluidics and microarrays devices to a wide range of analytical detectors (e.g. fluorescent microscopy, electrochemical, and mass spectrometry). Herein, we also present selected examples detailing the use of microfluidics and microarrays in the visualization of the natural occurring cell-to-cell heterogeneity in isogenic populations, in particular during the response to external cues. The possibility to accurate monitor the cell-to-cell heterogeneity based on different levels of key metabolites is of clinical relevance, since cell-to-cell heterogeneity can influence, for example, the outcome of a drug treatment.

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Cell‐to‐cell heterogeneity emerges as consequence of metabolic cooperation in a synthetic yeast community

Campbell

Vowinckel

Ralser

2016

Biotechnology Journal

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Cells that grow together respond heterogeneously to stress even when they are genetically similar. Metabolism, a key determinant of cellular stress tolerance, may be one source of this phenotypic heterogeneity, however, this relationship is largely unclear. We used self‐establishing metabolically cooperating (SeMeCo) yeast communities, in which metabolic cooperation can be followed on the basis of genotype, as a model to dissect the role of metabolic cooperation in single‐cell heterogeneity. Cells within SeMeCo communities showed to be highly heterogeneous in their stress tolerance, while the survival of each cell under heat or oxidative stress, was strongly determined by its metabolic specialization. This heterogeneity emerged for all metabolite exchange interactions studied (histidine, leucine, uracil, and methionine) as well as oxidant (H2O2, diamide) and heat stress treatments. In contrast, the SeMeCo community collectively showed to be similarly tolerant to stress as wild‐type populations. Moreover, stress heterogeneity did not establish as sole consequence of metabolic genotype (auxotrophic background) of the single cell, but was observed only for cells that cooperated according to their metabolic capacity. We therefore conclude that phenotypic heterogeneity and cell to cell differences in stress tolerance are emergent properties when cells cooperate in metabolism.

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Molecular phenotypic profiling of a Saccharomyces cerevisiae strain at the single-cell level

Cited by 19 publications

References 27 publications

Cultivation and quantitative single‐cell analysis of Saccharomyces cerevisiae on a multifunctional microfluidic device

Cultivation and quantitative single‐cell analysis of Saccharomyces cerevisiae on a multifunctional microfluidic device

Analysis of metabolites in single cells—what is the best micro‐platform?

Cell‐to‐cell heterogeneity emerges as consequence of metabolic cooperation in a synthetic yeast community

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