Stem cells can stay quiescent for a long period of time or proliferate and differentiate into multiple lineages. The activity of stage-specific metabolic programs allows stem cells to best adapt their functions in different microenvironments. Specific cellular phenotypes can be, therefore, defined by precise metabolic signatures. Notably, not only cellular metabolism describes a defined cellular phenotype, but experimental evidence now clearly indicate that also rewiring cells towards a particular cellular metabolism can drive their cellular phenotype and function accordingly. Cellular metabolism can be studied by both targeted and untargeted approaches. Targeted analyses focus on a subset of identified metabolites and on their metabolic fluxes. In addition, the overall assessment of the oxygen consumption rate (OCR) gives a measure of the overall cellular oxidative metabolism and mitochondrial function. Untargeted approach provides a large-scale identification and quantification of the whole metabolome with the aim to describe a metabolic fingerprinting. In this review article, we overview the methodologies currently available for the study of in vitro stem cell metabolism, including metabolic fluxes, fingerprint analyses, and single-cell metabolomics. Moreover, we summarize available approaches for the study of in vivo stem cell metabolism. For all of the described methods, we highlight their specificities and limitations. In addition, we discuss practical concerns about the most threatening steps, including metabolic quenching, sample preparation and extraction. A better knowledge of the precise metabolic signature defining specific cell population is instrumental to the design of novel therapeutic strategies able to drive undifferentiated stem cells towards a selective and valuable cellular phenotype.
Metabolism of Stem and Progenitor Cells: Proper Methods to Answer Specific Questions / G. Martano, E. Borroni, E. Lopci, M. Cattaneo, M. Mattioli, A. Bachi, I. Decimo, F. Bifari. - In: FRONTIERS IN MOLECULAR NEUROSCIENCE. - ISSN 1662-5099. - 12(2019 Jun 13). [10.3389/fnmol.2019.00151]
Metabolism of Stem and Progenitor Cells: Proper Methods to Answer Specific Questions
E. BorroniSecondo
;M. Cattaneo;F. Bifari
Ultimo
2019
Abstract
Stem cells can stay quiescent for a long period of time or proliferate and differentiate into multiple lineages. The activity of stage-specific metabolic programs allows stem cells to best adapt their functions in different microenvironments. Specific cellular phenotypes can be, therefore, defined by precise metabolic signatures. Notably, not only cellular metabolism describes a defined cellular phenotype, but experimental evidence now clearly indicate that also rewiring cells towards a particular cellular metabolism can drive their cellular phenotype and function accordingly. Cellular metabolism can be studied by both targeted and untargeted approaches. Targeted analyses focus on a subset of identified metabolites and on their metabolic fluxes. In addition, the overall assessment of the oxygen consumption rate (OCR) gives a measure of the overall cellular oxidative metabolism and mitochondrial function. Untargeted approach provides a large-scale identification and quantification of the whole metabolome with the aim to describe a metabolic fingerprinting. In this review article, we overview the methodologies currently available for the study of in vitro stem cell metabolism, including metabolic fluxes, fingerprint analyses, and single-cell metabolomics. Moreover, we summarize available approaches for the study of in vivo stem cell metabolism. For all of the described methods, we highlight their specificities and limitations. In addition, we discuss practical concerns about the most threatening steps, including metabolic quenching, sample preparation and extraction. A better knowledge of the precise metabolic signature defining specific cell population is instrumental to the design of novel therapeutic strategies able to drive undifferentiated stem cells towards a selective and valuable cellular phenotype.File | Dimensione | Formato | |
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