Dataset Information

Single-cell and bulk level metabolomic study of the glycolysis inhibition by 2-deoxy-D-glucose in Saccharomyces cerevisiae

ABSTRACT: Single-cell level measurements are necessary to characterize the intrinsic biological variability in a population of cells. In a study of the metabolic response of a clonal Saccharomyces cerevisiae population challenged by the glycolytic inhibitor 2-deoxy-D-glucose (2-DG), this variability is shown here, by comparing measurements at the population and the single-cell level. A novel microarray platform and MALDI mass spectrometric read-out allow us to follow 24 parameters simultaneously, in up to 100 single-cell measurements. Correlation plots for metabolites from the glycolytic pathway, the pentose phosphate pathway, and the observed ATP/ADP ratio as a measure of cellular energy charge give new insight into biochemical and metabolic pathways, which would not be accessible from population-level metabolomics data. 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 (MAMS) platform we are able to observe this variability. We monitor environmentally-(2-deoxy-D-glucose) and genetically-(∆PFK2) perturbed Saccharomyces cerevisiae cells both at the population and near single-cell 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 new biological insight that is not accessible from population-level metabolomic data.

INSTRUMENT(S): ABI 4800 Plus MALDI-TOF/TOF MS/MS, ABI 4800 Plus MALDI-TOF/TOF

SUBMITTER: Alfredo Ibanez

PROVIDER: MTBLS29 | MetaboLights | 2013-05-22

REPOSITORIES: MetaboLights

ACCESS DATA

Dataset's files

Source:

Items per page:

1 - 5 of 9

Publications

Mass spectrometry-based metabolomics of single yeast cells.

Ibáñez Alfredo J AJ Fagerer Stephan R SR Schmidt Anna Mareike AM Urban Pawel L PL Jefimovs Konstantins K Geiger Philipp P Dechant Reinhard R Heinemann Matthias M Zenobi Renato R

Proceedings of the National Academy of Sciences of the United States of America 20130513 22

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 wit ...[more]

PMID: 23671112

Similar Datasets

Project description:Yao2016_Calcium_Signaling This model is described in the article: Distinct cellular states determine calcium signaling response Jason Yao, Anna Pilko, Roy Wollman Molecular Systems Biology Abstract: The heterogeneity in mammalian cells signaling response is largely a result of preexisting cell to cell variability. It is unknown whether cell to cell variability rises from biochemical stochastic fluctuations or distinct cellular states. Here, we utilize calcium response to adenosine trisphosphate as a model for investigating the structure of heterogeneity within a population of cells and analyze whether distinct cellular response states coexist. We use a functional definition of cellular state that is based on a mechanistic dynamical systems model of calcium signaling. Using Bayesian parameter inference, we obtain high confidence parameter value distributions for several hundred cells, each fitted individually. Clustering the inferred parameter distributions revealed three major distinct cellular states within the population. The existence of distinct cellular states raises the possibility that the observed variability in response is a result of structured heterogeneity between cells. The inferred parameter distribution predicts, and experiments confirm that variability in IP3R response explains the majority of calcium heterogeneity. Our work shows how mechanistic models and single cell parameter fitting can uncover hidden population structure and demonstrate the need for parameter inference at the single cell level. This model is hosted on BioModels Database and identified by: MODEL1611150001. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

			Action	DRS
	MTBLS29	Other
	FILES	Other
	a_MTBLS29_MSMS.txt	Txt
	a_MTBLS29_single-cell.txt	Txt
	i_Investigation.txt	Txt