Metabolomic Applications in Stem Cell Research: a Review

Abstract

This review describes the use of metabolomics to study stem cell (SC) characteristics and function (excluding SCs in cancer research, suited to a fully dedicated text). The interest in employing metabolomics in SC research has consistently grown and emphasis is, here, given to developments reported in the past five years. This text informs on the existing methodologies and their complementarity regarding the information provided, comprising untargeted/targeted approaches, which couple mass spectrometry or nuclear magnetic resonance spectroscopy with multivariate analysis (and, in some cases, pathway analysis and integration with other omics), and more specific analytical approaches, namely isotope tracing to highlight particular metabolic pathways, or in tandem microscopic strategies to pinpoint characteristics within a single cell. The bulk of this review covers the existing applications in various aspects of mesenchymal SC behavior, followed by pluripotent and neural SCs, with a few reports addressing other SC types. Some of the central ideas investigated comprise the metabolic/biological impacts of different tissue/donor sources and differentiation conditions, including the importance of considering 3D culture environments, mechanical cues and/or media enrichment to guide differentiation into specific lineages. Metabolomic analysis has considered cell endometabolomes and exometabolomes (fingerprinting and footprinting, respectively), having measured both lipid species and polar metabolites involved in a variety of metabolic pathways. This review clearly demonstrates the current enticing promise of metabolomics in significantly contributing towards a deeper knowledge on SC behavior, and the discovery of new biomarkers of SC behavior and function with potential translation to in vivo clinical practice.

Human metabolite detection by surface-enhanced Raman spectroscopy

“…The core set of metabolites includes amino acids, glucose, lactic acid, lactate dehydrogenase (LDH), pyruvate, adenosine triphosphate (ATP), reactive oxygen species (ROS), lipids, and nucleotides. They act as alternative fuels, signaling metabolites, stimulators, and inhibitors of enzymes during cell lifetime [ [9] , [10] , [11] , [12] , [13] ].

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NMR Metabolomics Assessment of Osteogenic Differentiation of Adipose-Tissue-Derived Mesenchymal Stem Cells

“…In a typical metabolomics strategy, analytical data obtained by nuclear magnetic resonance (NMR) spectroscopy or mass spectrometry (MS) for complex mixtures ( e.g. , biofluids, tissues and cells) are handled and interpreted with the aid of multivariate statistical analysis (MVA). , Because local metabolic changes are believed to be critical for tissue regeneration, metabolomics of MSCs (through cell extracts or fingerprinting and culture media or footprinting) has already provided valuable information on metabolic adaptations associated with differentiation into bone, adipose tissue, or cartilage cells . Indeed, in recent years, several metabolomic studies have been carried out mostly through MS-based approaches and typically using bone marrow MSCs (BMMSCs). − These reports involve the promotion of osteogenic differentiation, either by media supplementation or specific physical properties (biomaterial nanotopography , or mechanical stimuli , ), the associated metabolic adaptations having been studied both in traditional in vitro conditions , and within specific biomaterials, such as nanostructured surfaces ,,− or scaffolds. ,, The results suggest that regardless of the osteoinductive method or culture conditions, osteogenic differentiation seems to be consistently associated with a generalized metabolic upregulation, often shown by the initial accumulation of amino acids, carbohydrates, nucleotides, or lipids, among other compounds. ,− Some reports suggest a subsequent metabolic reversal toward the end of the process, with differentiated cells acquiring a metabolic profile resembling that of primary osteoblasts. , In addition, MSC differentiation seems to lead to unique lineage-specific lipidic profiles, with osteoblastic membrane phenotypes containing longer and more polyunsaturated fatty acids (PUFAs, such as docosahexaenoic acid (DHA)) compared with undifferentiated cells .…”

An Intracellular Metabolic Signature as a Potential Donor-Independent Marker of the Osteogenic Differentiation of Adipose Tissue Mesenchymal Stem Cells

“…To the best of our knowledge, only a few metabolomic studies of MSCs heterogeneity have been carried out [ 23 , 27 , 28 , 29 , 30 ], employing both targeted and untargeted approaches and mostly using mass spectrometry (MS)-based methods. Metabolomics may be carried out using either MS or nuclear magnetic resonance (NMR) spectroscopy, techniques which provide complementary information [ 31 ] and which have proved valuable in determining the detailed metabolic adaptations during cell differentiation [ 32 , 33 ], for instance, to find metabolites with inductive roles in differentiation [ 34 , 35 , 36 , 37 ] or aid in the comparison between different MSC donors [ 23 , 28 , 29 , 30 ]. For example, an MS-based untargeted study unveiled that human adipose tissue MSCs (hAMSCs) from obese individuals secreted higher amounts of metabolites associated with glycolysis, tricarboxylic acid cycle (TCA) cycle, pentose phosphate pathway (PPP) and polyol pathway while showing decreased proliferation, migration and differentiation abilities [ 23 ].…”

“…Inter-donor variability may also be evident in the metabolism of proliferating MSCs (used as controls) and understanding such dependence could help in the definition of specific metabolic markers of differentiation. In the context of osteogenesis, a limited number of metabolomic reports have analysed the stepwise time course of MSCs differentiation, as reviewed recently [ 33 ], only a few having considered undifferentiated cells cultured over time as control samples, to the best of our knowledge [ 35 , 48 , 49 ]. For example, mouse BMMSCs exometabolome changes measured by liquid chromatography (LC)-MS enabled the identification of metabolic biomarkers either (i) specific to control conditions or (ii) common to control and osteogenic conditions (e.g., increases in deoxyuridine and orotidine, possibly linked to cell proliferation) or (iii) specific of osteogenic conditions (e.g., increases in citrate, succinate and glycerol) [ 48 ].…”