Analytical techniques for single-cell metabolomics: state of the art and trends

Amantonico, Andrea; Urban, Pawel L.; Zenobi, Renato

doi:10.1007/s00216-010-3850-1

Cited by 145 publications

(130 citation statements)

References 118 publications

(124 reference statements)

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“…[34]). Also, it has already been demonstrated for metabolites including UDP, ADP, GDP, UTP, ATP, GTP, acetyl-CoA, and butyryl/isobutyryl-CoA that the sensitivity for MALDI-based MS detection of metabolites will be sufficient to reach the single yeast cell level [30 ] and a suitable technology for sample deposition (''writing'' onto a MALDI plate) was also proposed [12,35,36] allowing for convenient off-line mass-spectrometric analysis. Coupling of the Figure 1 Schematics of the four MS-based approaches for single cell metabolomics.…”

Section: Ms-based Approaches For Single Cell Metabolomicsmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

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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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Section: Ms-based Approaches For Single Cell Metabolomicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Single cell metabolomics

Heinemann

Zenobi

2011

Current Opinion in Biotechnology

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117

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“…In ambient ionization MS, ions are formed outside the mass spectrometer without sample preparation or separation in unmodified samples [29]. Addressing the chemical composition of microscopic samples such as individual organisms or small cell populations is a bioanalytical challenge which is being overcome by diverse MS ionization strategies, such as nanoESI, MALDI, SIMS, laser ablation electrospray ionization, and DESI [24,30,31]. For example, MS analysis and identification of microorganisms, mostly performed by commercial platforms based on MALDI, is revolutionizing the field of microbiology [30].…”

Section: Introductionmentioning

confidence: 99%

Developmental phases of individual mouse preimplantation embryos characterized by lipid signatures using desorption electrospray ionization mass spectrometry

et al. 2012

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Knowledge of the lipids present in individual preimplantation embryos is of interest in fundamental studies of embryology, in attempts to understand cellular pluripotency and in optimization of in vitro culture conditions necessary for the application and development of biotechnologies such as in vitro fertilization and transgenesis. In this work, the profiles of fatty acids and phospholipids (PL) in individual mouse preimplantation embryos and oocytes were acquired using an analytical strategy based on desorption electrospray ionization mass spectrometry (DESI-MS). The methodology avoids sample preparation and provides information on the lipids present in these microscopic structures. Differences in the lipid profiles observed for unfertilized oocytes, two-and four-cell embryos, and blastocysts were characterized. For a representative set of embryos (N= 114) using multivariate analysis (specifically principal component analysis) unfertilized oocytes showed a narrower range of PL species than did blastocysts. Two-and four-cell embryos showed a wide range of PLs compared with unfertilized oocytes and high abundances of fatty acids, indicating pronounced synthetic activity. The data suggest that the lipid changes observed in mouse preimplantation development reflect acquisition of a degree of cellular membrane functional and structural specialization by the blastocyst stage. It is also noteworthy that embryos cultured in vitro from the two-cell through the blastocyst stage have a more homogeneous lipid profile as compared with their in vivo-derived counterparts, which is ascribed to the restricted diversity of nutrients present in synthetic culture media. The DESI-MS data are interpreted from lipid biochemistry and previous reports on gene expression of diverse lipids known to be vital to early embryonic development.

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“…Instrumentation and data processing for genomics and proteomics analyses of cells seem to be well-established. 1) Metabolomics based on so ionization mass spectrometry methods as the frontier of omics sciences, however, is emerging and it demands both methods and detection power.…”

Section: Introductionmentioning

confidence: 99%