Determination of yeast glycogen content by individual cell spectroscopy using image analysis

Cahill, Gearoid; Walsh, Padraig K.; Donnelly, Dan

doi:10.1002/1097-0290(20000805)69:3<312::aid-bit9>3.0.co;2-y

Cited by 15 publications

(14 citation statements)

References 33 publications

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“…For instance, glycogen metabolism was studied in yeast cells by single-cell spectroscopy in conjunction with image analysis [86]. In other work, Shinohara and Wang [87] studied dopamine release from a mammalian nerve cell, after incubation with a drug, by using an enzyme-catalyzed luminescence method.…”

Section: Other Techniquesmentioning

confidence: 99%

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

2010

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Single-cell metabolomics is an emerging field that addresses fundamental biological questions and allows one to observe metabolic phenomena in heterogeneous populations of single cells. In this review, we assess the suitability of different detection techniques and present considerations on sample preparation for single-cell metabolomics. Although targeted analysis of single cells can readily be conducted using fluorescent probes and optical instruments (microscopes, fluorescence detectors), a comprehensive metabolomic approach requires a powerful label-free method, such as mass spectrometry (MS). Mass-spectrometric techniques applied to study small molecules in single cells include electrospray MS, matrix-assisted laser desorption/ionization MS, and secondary ion MS. Sample preparation is an important aspect to be taken into account during further development of methods for single-cell metabolomics.

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Section: Other Techniquesmentioning

confidence: 99%

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

2010

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“…Other methods used in single cells including single-cell spectroscopy in conjunction with image analysis for glycogen metabolism in yeast cells (Cahill et al, 2000), enzyme-catalyzed luminescence method for dopamine release from a mammalian nerve cell (Shinohara & Wang, 2007), synchrotron Fourier transform infrared spectromicroscopy for ethanol formation in single living cells of unicellular algae (Goff et al, 2009), raman spectroscopy (Schuster et al, 2000;Buckmaster et al, 2009;Hermelink et al, 2009) for detecting nucleic bases and amino acids in single cells, and nuclear magnetic resonance (NMR) for structural characterization of organic compounds, including metabolites (Beckonert et al, 2007;Motta et al, 2010). Brief summary and comparison for various metabolomics techniques discussed above are listed in Table 1.…”

Section: Single-cell Metabolomicsmentioning

confidence: 99%

Computational Methods to Interpret and Integrate Metabolomic Data

Li¹,

Wang²,

Zhang³

2012

Metabolomics

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“…Preferential retention of oxidatively damaged proteins within the mother cell has recently been described for Saccharomyces cerevisiae, suggesting a mechanism for enhancing the fitness of newborn cells (1). Quantities of certain macromolecular components such as carotenoids (10), intracellular carbohydrate, or lipid storage polymers may also vary among individual cells, contributing to their biochemical heterogeneity (44,184,204).…”

Section: Biochemical or Metabolic Heterogeneitymentioning

confidence: 99%

“…This provides a means of cell identification or characterization and generation of highcontrast images suitable for further processing (44,54,61,198,202). Alternatively, intrinsic changes in the light-scattering characteristics of a microorganism as it undergoes certain physiological processes may be sufficient to allow analysis.…”

Section: Cytometrymentioning

confidence: 99%

“…Advantages of such thorough image processing may include the ability to differentiate target cells from background material, particularly in "difficult" sample matrices such as soil (202). Through the use of special algorithms, images may be automatically processed on the basis of user-defined criteria or artificial neural networks may be trained for the automatic classification of objects (34,44). Such automation can greatly aid image processing, especially where manual data extraction would be impossible, tedious, or error prone (61).…”

Section: Cytometrymentioning

confidence: 99%

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Single-Cell Microbiology: Tools, Technologies, and Applications

Brehm-Stecher

Johnson

2004

Microbiol Mol Biol Rev

447

316

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The field of microbiology has traditionally been concerned with and focused on studies at the population level. Information on how cells respond to their environment, interact with each other, or undergo complex processes such as cellular differentiation or gene expression has been obtained mostly by inference from population-level data. Individual microorganisms, even those in supposedly “clonal” populations, may differ widely from each other in terms of their genetic composition, physiology, biochemistry, or behavior. This genetic and phenotypic heterogeneity has important practical consequences for a number of human interests, including antibiotic or biocide resistance, the productivity and stability of industrial fermentations, the efficacy of food preservatives, and the potential of pathogens to cause disease. New appreciation of the importance of cellular heterogeneity, coupled with recent advances in technology, has driven the development of new tools and techniques for the study of individual microbial cells. Because observations made at the single-cell level are not subject to the “averaging” effects characteristic of bulk-phase, population-level methods, they offer the unique capacity to observe discrete microbiological phenomena unavailable using traditional approaches. As a result, scientists have been able to characterize microorganisms, their activities, and their interactions at unprecedented levels of detail

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Determination of yeast glycogen content by individual cell spectroscopy using image analysis

Cited by 15 publications

References 33 publications

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

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

Computational Methods to Interpret and Integrate Metabolomic Data

Single-Cell Microbiology: Tools, Technologies, and Applications

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