Chemical and biological single cell analysis

Schmid, Andreas; Kortmann, Hendrik; Dittrich, Petra S.; Blank, Lars M.

doi:10.1016/j.copbio.2010.01.007

Cited by 171 publications

(139 citation statements)

References 47 publications

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Section: Challenges For Single Cell Metabolomicsmentioning

confidence: 99%

“…In the budding yeast, with a volume of about 65 fL [29], amounts in the two-to three-digit attomole range must be detected. In mammalian cells, with a 500 fL volume [29], metabolites would be expected in the two-digit femtomole range (assuming similar metabolite concentration levels as in microbes). Compared to the metabolite amounts that are typically used for classical populationlevel metabolomics (nanomoles), single cell metabolomics for E. coli has to handle amounts that are approximately 10 9 times lower.…”

Section: Challenges For Single Cell Metabolomicsmentioning

confidence: 99%

“…In E. coli, a bacterium with about 1 fL of volume [29], even highly abundant glycolytic intermediates, which are present in the low mM concentration range, require detection sensitivity in the low attomole range. In the budding yeast, with a volume of about 65 fL [29], amounts in the two-to three-digit attomole range must be detected. In mammalian cells, with a 500 fL volume [29], metabolites would be expected in the two-digit femtomole range (assuming similar metabolite concentration levels as in microbes).…”

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confidence: 99%

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Single cell metabolomics

Heinemann

Zenobi

2011

Current Opinion in Biotechnology

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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Section: Challenges For Single Cell Metabolomicsmentioning

confidence: 99%

Section: Challenges For Single Cell Metabolomicsmentioning

confidence: 99%

mentioning

confidence: 99%

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Single cell metabolomics

Heinemann

Zenobi

2011

Current Opinion in Biotechnology

117

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“…Technologies for high-throughput, quantifiable single cell analysis is highly demanding in modern biological research [15,118]. The capability of single cell analysis would allow for (1) spotting stochastic events among a cell population, (2) tracing spatiotemporal changes on cells to elucidate mechanistically cellular functions, and (3) developing high throughput technology for identifying and purifying rare cells from a cell population.…”

Section: Single Cell Analysismentioning

confidence: 99%

Basic Technologies for Droplet Microfluidics

Zeng

Liu

Xie

et al. 2011

Topics in Current Chemistry

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In recent years droplet microfluidics has become a quickly evolving research field. The availability of a wide range of technologies for droplet generation and manipulation has enabled the applications of droplet microfluidics in a wide variety of fields, from single cell analysis to material synthesis. In this review we summarize the main technologies for droplet microfluidics and discuss the recent advances in technologies that enable droplets as microreactors with complete functions. Applications of microdroplets in chemical reactions, particle synthesis, and single cell analysis are also briefly reviewed.

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“…At this point, we want to emphasize the many comprehensive review articles that discuss the challenges and current approaches of single cell analysis (e.g., Zenobi [5] Fritzsch et al [6], Schmid et al [7], Borland et al [8]). Furthermore, in a recent issue of ABC (406/14) entitled "Multiplex platforms in diagnostics and bioanalytics," the state of the art of those methods is discussed, which are already fit for purpose of measuring at cellular levels.…”

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confidence: 99%