Skip to main content

Advertisement

SpringerLink
Log in

Metabolomic Applications in Stem Cell Research: a Review

  • Published:
Stem Cell Reviews and Reports Aims and scope Submit manuscript

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 function with potential translation to in vivo clinical practice.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

adapted from Servier Medical Art (https://smart.servier.com/) licensed under a Creative Commons Attribution 3.0 Unported (CC BY 3.0) license

Fig. 2
Fig. 3

Adapted from reference [17], licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) license

Fig. 4

source of the dataset variance. Score plot (PC1 and PC2; n = 11, R2 = 0.962, and Q2 = 0.843 on 5 PC). (B and C) Orthogonal partial least squares discriminant analysis (OPLS-DA, supervised multivariate analysis) shows a strong discrimination between subcutaneous mAMSCs (S-ASC) and visceral mAMSCs (V-ASC), characterized by high values of goodness-of-fit model parameters (R2X = 0.796, R2Y = 0.991, and Q2 = 0.969). The discrimination robustness was validated by resampling 1000 times the model under the null hypothesis (data not shown), and the analysis of variance (CV-ANOVA) of the model led to a p-value of 1.20 × 10−4. (B) Score plot discriminating S-ASC (in green) and V-ASC (in blue). (C) Loading plot complemented by color-coded correlation indicating statistically significant signals. (1) leucine, (2) valine, (3) lactate, (4) alanine, (5) acetate, (6) glutamine, (7) citrate, (8) glucose, (9) tyrosine, (10) phenylalanine. (D) Simplified non-quantitative representation of the main metabolic pathways (represented in italics) in actively dividing mAMSCs. Colors identify the metabolic pathways analysed in this study. EAA, essential amino acids; mAMSCs, mouse adipose-derived mesenchymal stem cells; TCA, tricarboxylic acid cycle. Adapted from reference [17], licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) license

Fig. 5

Adapted from reference [32]

Fig. 6

Adapted from reference [45], licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) license

Fig. 7

Adapted from reference [56], licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) license

Similar content being viewed by others

Abbreviations

AA :

Arachidonic acid

ADA :

Alginate di-aldehyde

ALA :

α-linolenic acid

AST :

Astaxanthin

BMP-2 :

Bone morphogenetic protein 2

CE :

Capillary electrophoresis

CNS :

Central nervous system

CoA :

Coenzyme A

DAG :

Diacylglycerols

Dex :

Dexamethasone

DFO :

Desferrioxamine

DHA :

Docosahexaenoic acid

DI :

Direct infusion

DPSCs :

Dental pulp stem cells

EBs :

Embryoid bodies

ECM :

Extracellular matrix

EFs :

Embryonic fibroblasts

EpSCs :

Epidermal Stem cells

ESCs :

Embryonic stem cells

FAs :

Fatty acids

FLS :

Fibroblast-like synoviocyte

GC :

Gas chromatography

GdnF :

Glial cell line-derived neurotrophic factor

GHK :

Glycine-histidine-lysine

GPC :

Glycerophosphocholine

GPMVs :

Of giant plasma membrane vesicles

GWAS :

Genome-Wide-Association-Studies

hAMSCs :

Human adipose-derived mesenchymal stem cells

hBMMSCs :

Human bone marrow mesenchymal stem cells

hBTSCs :

Human biliary tree stem/progenitor cells

hHBs :

Human hepatoblasts

hHpSCs :

Human hepatic stem cells

hPDLSCs :

Human periodontal ligaments stem cells

hPMSCs :

Human placenta-derived mesenchymal stem cells

hPSC-CMs :

Cardiomyocytes derived from human pluripotent stem cell

HRMAS :

High resolution magic angle spinning

HSCs :

Haematopoietic stem cells

hSGSCs :

Human salivary gland stem cells

htt :

Huntington locus

IB :

Inclusion bodies

IFN-\(\gamma\) :

Interferon gamma

ILK :

Integrin linked kinase

iMSCs :

Mesenchymal stem cells derived from iPSCs

iPSCs :

Induced pluripotent stem cells

LA :

Linoleic acid

LC :

Liquid chromatography

LIPUS :

Low-intensity pulsed ultrasound

mESCs :

Murine Embryonic stem cells

MRI :

Magnetic resonance imaging

MS :

Mass Spectrometry

MSC :

Mesenchymal stem cell

MVA :

Multivariate analysis

M w :

Molecular weight

NAM :

Nicotinamide

NMR :

Nuclear Magnetic Resonance

nMSCs :

Native mesenchymal stem cells

NSCs :

Neural stem cells

OECs :

Olfactory Ensheathing Cells

P13K :

Phosphoinositide 3-kinase

PC :

Phospchocholine

PCA :

Principal component analysis

PD :

Pompe disease

PDH :

Pyruvate dehydrogenase

PKB :

Protein kinase B

PLC :

Poly(DL-lactide-ε-caprolactone)

PLs :

Phospholipids

PLS-DA :

Partial least-squares discriminant analysis

PPP :

Pentose phosphate pathway

PSCs :

Pluripotent stem cells

PTCs :

Phosphatidylcholines

PTEs :

Phosphatidylethanolamines

PTGs :

Phosphatidylglycerols

PUFA :

Polyunsaturated fatty acid

ROCK :

Rho kinase inhibitor

SC :

Stem cell

SCRM :

Single-cell Raman microspectroscopy

SMs :

Sphingomyelins

SR-FTIR :

Synchrotron radiation-based Fourier transform infrared

SSCs :

Spermatogonial stem cells

SVZ :

Subventricular zone

TAG :

Triacylglycerols

TCA :

Tricarboxylic acid

TRPV :

Transient receptor potential vallanoid sub-family 1

UCMSC :

Umbilical Cord-Derived Mesenchymal Stem Cell

VOCs :

Volatile organic compounds

YAP :

Yes-associated protein

References

  1. Martano, G., Borroni, E. M., Lopci, E., Cattaneo, M. G., Mattioli, M., Bachi, A., Decimo, I., & Bifari, F. (2019). Metabolism of stem and progenitor cells: Proper methods to answer specific questions. Frontiers in Molecular Neuroscience, 12, 151 (17 pages). https://doi.org/10.3389/fnmol.2019.00151

  2. Palacios-Ferrer, J. L., García-Ortega, M. B., Gallardo-Gómez, M., García, M. Á., Díaz, C., Boulaiz, H., Valdivia, J., Jurado, J. M., Almazan-Fernandez, F. M., Arias-Santiago, S., Amezcua, V., Peinado, H., Vicente, F., Pérez del Palacio, J., & Marchal, J. A. (2021). Metabolomic profile of cancer stem cell-derived exosomes from patients with malignant melanoma. Molecular Oncology, 15(2), 407–428. https://doi.org/10.1002/1878-0261.12823

    Article  CAS  PubMed  Google Scholar 

  3. Segers, K., Declerck, S., Mangelings, D., Heyden, Y. V., & Eeckhaut, A. V. (2019). Analytical techniques for metabolomic studies: A review. Bioanalysis, 11(24), 2297–2318. https://doi.org/10.4155/bio-2019-0014

    Article  CAS  PubMed  Google Scholar 

  4. Srivastava, A., Evans, K. J., Sexton, A. E., Schofield, L., & Creek, D. J. (2017). Metabolomics-based elucidation of active metabolic pathways in erythrocytes and HSC-derived reticulocytes. Journal of Proteome Research, 16(4), 1492–1505. https://doi.org/10.1021/acs.jproteome.6b00902

    Article  CAS  PubMed  Google Scholar 

  5. Zhang, H., Badur, M. G., Divakaruni, A. S., Parker, S. J., Jäger, C., Hiller, K., et al. (2016). Distinct metabolic states can support self-renewal and lipogenesis in human pluripotent stem cells under different culture conditions. Cell Reports, 16(6), 1536–1547. https://doi.org/10.1016/j.celrep.2016.06.102

    Article  CAS  PubMed  Google Scholar 

  6. Del Favero, G., Bonifacio, A., Rowland, T. J., Gao, S., Song, K., Sergo, V., Adler, E. D., Mestroni, L., Sbaizero, O., & Taylor, M. R. G. (2020). Danon Disease-Associated LAMP-2 Deficiency Drives Metabolic Signature Indicative of Mitochondrial Aging and Fibrosis in Cardiac Tissue and hiPSC-Derived Cardiomyocytes. Journal of Clinical Medicine, 9(8), 2457 (20 pages). https://doi.org/10.3390/jcm9082457

  7. Kostidis, S., Addie, R. D., Morreau, H., Mayboroda, O. A., & Giera, M. (2017). Quantitative NMR analysis of intra- and extracellular metabolism of mammalian cells: A tutorial. Analytica Chimica Acta, 980, 1–24. https://doi.org/10.1016/j.aca.2017.05.011

    Article  CAS  PubMed  Google Scholar 

  8. Duarte, I., Lamego, I., Marques, J., Marques, M., Blaise, B., & Gil, A. M. (2010). A Nuclear Magnetic Resonance (NMR) study of the effect of Cisplatin on the metabolic profile of MG-63 osteosarcoma cells. Journal of Proteome Research, 9(11), 5877–5886. https://doi.org/10.1021/pr100635n

    Article  CAS  PubMed  Google Scholar 

  9. De Luca, M., Aiuti, A., Cossu, G., Parmar, M., Pellegrini, G., & Robey, P. G. (2019). Advances in stem cell research and therapeutic development. Nature Cell Biology, 21(7), 801–811. https://doi.org/10.1038/s41556-019-0344-z

    Article  CAS  PubMed  Google Scholar 

  10. Andrzejewska, A., Lukomska, B., & Janowski, M. (2019). Concise review: mesenchymal stem cells: from roots to boost. Stem Cells, 37(7), 855–864. https://doi.org/10.1002/stem.3016

    Article  PubMed  PubMed Central  Google Scholar 

  11. Goodarzi, P., Alavi-Moghadam, S., Payab, M., Larijani, B., Rahim, F., Gilany, K., Bana, N., Tayanloo-Beik, A., Heravani, N. F., Hadavandkhani, M., & Arjmand, B. (2019). Metabolomics analysis of mesenchymal stem cells. International Journal of Molecular and Cellular Medicine, 8(1), 30–40. https://doi.org/10.22088/IIJMCM.BUMS.8.2.30

  12. Lapid, K., & Graff, J. M. (2017). Form(ul)ation of adipocytes by lipids. Adipocyte, 6(3), 176–186. https://doi.org/10.1080/21623945.2017.1299298

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Clémot, M., Sênos Demarco, R., & Jones, D. L. (2020). Lipid mediated regulation of adult stem cell behavior. Frontiers in Cell and Developmental Biology, 8, 115 (17 pages). https://doi.org/10.3389/fcell.2020.00115

  14. Liaw, L., Prudovsky, I., Koza, R. A., Anunciado-Koza, R. V., Siviski, M. E., Lindner, V., et al. (2016). Lipid profiling of in vitro cell models of adipogenic differentiation: relationships with mouse adipose tissues. Journal of Cellular Biochemistry, 117, 2182–2193. https://doi.org/10.1002/jcb.25522

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lee, S. J., Yi, T. G., Ahn, S. H., Lim, D. K., Kim, S., & na, Lee, H. J., Cho, Y. K., Lim, J. Y., Sung, J. H., Yun, J. H., Lim, J., Song, S. U., & Kwon, S. W. . (2018). Comparative study on metabolite level in tissue-specific human mesenchymal stem cells by an ultra-performance liquid chromatography quadrupole time of flight mass spectrometry. Analytica Chimica Acta, 1024, 112–122. https://doi.org/10.1016/j.aca.2018.04.018

    Article  CAS  PubMed  Google Scholar 

  16. Li, J. Z., Qu, H., Wu, J., Zhang, F., Jia, Z. B., Sun, J. Y., Lv, B., Kang, Y., Jiang, S. L., & Kang, K. (2018). Metabolic profiles of adipose-derived and bone marrow-derived stromal cells from elderly coronary heart disease patients by capillary liquid chromatography quadrupole time-of-flight mass spectrometry. International Journal of Molecular Medicine, 41(1), 184–194. https://doi.org/10.3892/ijmm.2017.3198

    Article  CAS  PubMed  Google Scholar 

  17. Lefevre, C., Panthu, B., Naville, D., Guibert, S., Pinteur, C., Elena-Herrmann, B., Vidal, H., Rautureau, G. J. P., & Mey, A. (2019). Metabolic phenotyping of adipose-derived stem cells reveals a unique signature and intrinsic differences between fat pads. Stem Cells International, 2019, Article ID 9323864 (16 pages). https://doi.org/10.1155/2019/9323864

  18. Caseiro, A. R., Pedrosa, S. S., Ivanova, G., Branquinho, M. V., Almeida, A., Faria, F., Amorim, I., Pereira, T., & Maurício, A. C. (2019). Mesenchymal Stem/Stromal Cells metabolomic and bioactive factors profiles: A comparative analysis on the umbilical cord and dental pulp derived Stem/Stromal Cells secretome. PLos One, 14(11), e0221378 (33 pages). https://doi.org/10.1371/journal.pone.0221378

  19. Mastrangelo, A., Panadero, M. I., Perez, L. M., Galvez, B. G., Garcia, A., Barbas, C., & Ruperez, F. J. (2016). New insight on obesity and adipose-derived stem cells using comprehensive metabolomics. Biochemical Journal, 473(14), 2187–2203. https://doi.org/10.1042/BCJ20160241

    Article  CAS  Google Scholar 

  20. Devito, L., Klontzas, M. E., Cvoro, A., Galleu, A., Simon, M., Hobbs, C., Dazzi, F., Mantalaris, A., Khalaf, Y., & Ilic, D. (2019). Comparison of human isogeneic Wharton’s jelly MSCs and iPSC-derived MSCs reveals differentiation-dependent metabolic responses to IFNG stimulation. Cell Death and Disease, 10(4), 277 (13 pages). https://doi.org/10.1038/s41419-019-1498-0

  21. Shi, C., Wang, X., Wu, S., Zhu, Y., Chung, L. W. K., & Mao, H. (2008). HRMAS 1 H-NMR measured changes of the metabolite profile as mesenchymal stem cells differentiate to targeted fat cells in vitro : implications for non-invasive monitoring of stem cell differentiation in vivo. Journal of Tissue Engineering and Regenerative Medicine, 2(8), 482–490. https://doi.org/10.1002/term.120

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Xu, Z.-F. (2012). Human umbilical mesenchymal stem cell and its adipogenic differentiation: Profiling by nuclear magnetic resonance spectroscopy. World Journal of Stem Cells, 4(4), 21–27. https://doi.org/10.4252/wjsc.v4.i4.21

    Article  PubMed  PubMed Central  Google Scholar 

  23. Bojin, F. M., Gruia, A. T., Cristea, M. I., Ordodi, V. L., Paunescu, V., & Mic, F. A. (2012). Adipocytes differentiated in vitro from rat mesenchymal stem cells lack essential free fatty acids compared to adult adipocytes. Stem Cells and Development, 21(4), 507–512. https://doi.org/10.1089/scd.2011.0491

    Article  CAS  PubMed  Google Scholar 

  24. Gruia, A. T., Suciu, M., Barbu-Tudoran, L., Azghadi, S. M. R., Cristea, M. I., Nica, D. V., et al. (2016). Mesenchymal stromal cells differentiating to adipocytes accumulate autophagic vesicles instead of functional lipid droplets. Journal of Cellular Physiology, 231(4), 863–875. https://doi.org/10.1002/jcp.25177

    Article  CAS  PubMed  Google Scholar 

  25. Klemenz, Meyer, Ekat, Bartels, Traxler, Schubert, Kamp, Miekisch, & Peters. (2019). Differences in the emission of volatile organic compounds (VOCs) between non-differentiating and adipogenically differentiating mesenchymal stromal/stem cells from human adipose tissue. Cells, 8(7), 697 (14 pages). https://doi.org/10.3390/cells8070697

  26. Ouellette, M. È., Bérubé, J. C., Bourget, J. M., Vallée, M., Bossé, Y., & Fradette, J. (2019). Linoleic acid supplementation of cell culture media influences the phospholipid and lipid profiles of human reconstructed adipose tissue. PLos One, 14(10), e0224228 (22 pages). https://doi.org/10.1371/journal.pone.0224228

  27. Rampler, E., Egger, D., Schoeny, H., Rusz, M., Pacheco, M. P., Marino, G., Kasper, C., Naegele, T., & Koellensperger, G. (2019). The power of LC-MS based multiomics: exploring adipogenic differentiation of human mesenchymal stem/stromal cells. Molecules, 24(19), 3615 (19 pages). https://doi.org/10.3390/molecules24193615

  28. Silva, C. G. da, Barretto, L. S. de S., Lo Turco, E. G., Santos, A. de L., Lessio, C., Martins Júnior, H. A., & Almeida, F. G. de. (2020). Lipidomics of mesenchymal stem cell differentiation. Chemistry and Physics of Lipids, 232, 104964 (9 pages). https://doi.org/10.1016/j.chemphyslip.2020.104964

  29. Mitchell, A., Ashton, L., Yang, X. B., Goodacre, R., Smith, A., & Kirkham, J. (2015). Detection of early stage changes associated with adipogenesis using Raman spectroscopy under aseptic conditions. Cytometry Part A, 87(11), 1012–1019. https://doi.org/10.1002/cyto.a.22777

    Article  CAS  Google Scholar 

  30. Liu, Z., Tang, Y., Chen, F., Liu, X., Liu, Z., Zhong, J., Hu, J., & Lü, J. (2016). Synchrotron FTIR microspectroscopy reveals early adipogenic differentiation of human mesenchymal stem cells at single-cell level. Biochemical and Biophysical Research Communications, 478(3), 1286–1291. https://doi.org/10.1016/j.bbrc.2016.08.112

    Article  CAS  PubMed  Google Scholar 

  31. Lorthongpanich, C., Thumanu, K., Tangkiettrakul, K., Jiamvoraphong, N., Laowtammathron, C., Damkham, N., U-Pratya, Y., & Issaragrisil, S. (2019). YAP as a key regulator of adipo-osteogenic differentiation in human MSCs. Stem Cell Research and Therapy, 10(1), 402 (12 pages). https://doi.org/10.1186/s13287-019-1494-4

  32. Surrati, A., Linforth, R., Fisk, I. D., Sottile, V., & Kim, D. H. (2016). Non-destructive characterisation of mesenchymal stem cell differentiation using LC-MS-based metabolite footprinting. The Analyst, 141(12), 3776–3787. https://doi.org/10.1039/c6an00170j

    Article  CAS  PubMed  Google Scholar 

  33. Klontzas, M. E., Vernardis, S. I., Heliotis, M., Tsiridis, E., & Mantalaris, A. (2017). Metabolomics analysis of the osteogenic differentiation of umbilical cord blood mesenchymal stem cells reveals differential sensitivity to osteogenic agents. Stem Cells and Development, 26(10), 723–733. https://doi.org/10.1089/scd.2016.0315

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Levental, K. R., Surma, M. A., Skinkle, A. D., Lorent, J. H., Zhou, Y., Klose, C., Chang, J. T., Hancock, J. F., & Levental, I. (2017). ω-3 polyunsaturated fatty acids direct differentiation of the membrane phenotype in mesenchymal stem cells to potentiate osteogenesis. Science Advances, 3(11), eaao1193 (15 pages). https://doi.org/10.1126/sciadv.aao1193

  35. Gaur, D., Yogalakshmi, Y., Kulanthaivel, S., Agarwal, T., Mukherjee, D., Prince, A., Tiwari, A., Maiti, T. K., Pal, K., Giri, S., Saleem, M., & Banerjee, I. (2018). Osteoblast‐Derived Giant Plasma Membrane Vesicles Induce Osteogenic Differentiation of Human Mesenchymal Stem Cells. Advanced Biosystems, 2(9), 1800093 (12 pages). https://doi.org/10.1002/adbi.201800093

  36. Zhao, G., Zhong, H., Rao, T., & Pan, Z. (2020). Metabolomic analysis reveals that the mechanism of Astaxanthin improves the Osteogenic differentiation potential in bone marrow mesenchymal stem cells. Oxidative Medicine and Cellular Longevity, 2020, Article ID 3427430 (11 pages). https://doi.org/10.1155/2020/3427430

  37. Tsimbouri, P. M., McMurray, R. J., Burgess, K. V., Alakpa, E. V., Reynolds, P. M., Murawski, K., Kingham, E., Oreffo, R. O. C., Gadegaard, N., & Dalby, M. J. (2012). Using nanotopography and metabolomics to identify biochemical effectors of multipotency. ACS Nano, 6(11), 10239–10249. https://doi.org/10.1021/nn304046m

    Article  CAS  PubMed  Google Scholar 

  38. McNamara, L. E., Sjöström, T., Burgess, K. E. V., Kim, J. J. W., Liu, E., Gordonov, S., Moghe, P. V., Meek, R. M. D., Oreffo, R. O. C., Su, B., & Dalby, M. J. (2011). Skeletal stem cell physiology on functionally distinct titania nanotopographies. Biomaterials, 32(30), 7403–7410. https://doi.org/10.1016/j.biomaterials.2011.06.063

    Article  CAS  PubMed  Google Scholar 

  39. Seras-Franzoso, J., Tsimbouri, P. M., Burgess, K. V., Unzueta, U., Garcia-Fruitos, E., Vazquez, E., Villaverde, A., & Dalby, M. J. (2014). Topographically targeted osteogenesis of mesenchymal stem cells stimulated by inclusion bodies attached to polycaprolactone surfaces. Nanomedicine, 9(2), 207–220. https://doi.org/10.2217/nnm.13.43

    Article  CAS  PubMed  Google Scholar 

  40. Roberts, J. N., Sahoo, J. K., McNamara, L. E., Burgess, K. V., Yang, J., Alakpa, E. V., et al. (2016). Dynamic surfaces for the study of mesenchymal stem cell growth through adhesion regulation. ACS nano, 10(7), 6667–6679. https://doi.org/10.1021/acsnano.6b01765

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Amer, M. H., Alvarez-Paino, M., McLaren, J., Pappalardo, F., Trujillo, S., Wong, J. Q., Shrestha, S., Abdelrazig, S., Stevens, L. A., Lee, J. B., Kim, D. H., González-García, C., Needham, D., Salmerón-Sánchez, M., Shakesheff, K. M., Alexander, M. R., Alexander, C., & Rose, F. R. (2021). Designing topographically textured microparticles for induction and modulation of osteogenesis in mesenchymal stem cell engineering. Biomaterials, 266 (June 2020), 120450 (17 pages). https://doi.org/10.1016/j.biomaterials.2020.120450

  42. Klontzas, M. E., Reakasame, S., Silva, R., Morais, J. C. F., Vernardis, S., MacFarlane, R. J., Heliotis, M., Tsiridis, E., Panoskaltsis, N., Boccaccini, A. R., & Mantalaris, A. (2019). Oxidized alginate hydrogels with the GHK peptide enhance cord blood mesenchymal stem cell osteogenesis: A paradigm for metabolomics-based evaluation of biomaterial design. Acta Biomaterialia, 88, 224–240. https://doi.org/10.1016/j.actbio.2019.02.017

    Article  CAS  PubMed  Google Scholar 

  43. Bow, A., Jackson, B., Griffin, C., Howard, S., Castro, H., Campagna, S., et al. (2020). Multiomics Evaluation of Human Fat-Derived Mesenchymal Stem Cells on an Osteobiologic Nanocomposite. BioResearch Open Access, 9(1), 37–50. https://doi.org/10.1089/biores.2020.0005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Tsimbouri, P. M., Childs, P. G., Pemberton, G. D., Yang, J., Jayawarna, V., Orapiriyakul, W., Burgess, K., González-García, C., Blackburn, G., Thomas, D., Vallejo-Giraldo, C., Biggs, M. J. P., Curtis, A. S. G., Salmerón-Sánchez, M., Reid, S., & Dalby, M. J. (2017). Stimulation of 3D osteogenesis by mesenchymal stem cells using a nanovibrational bioreactor. Nature Biomedical Engineering, 1(9), 758–770. https://doi.org/10.1038/s41551-017-0127-4

    Article  CAS  PubMed  Google Scholar 

  45. Orapiriyakul, W., Tsimbouri, M. P., Childs, P., Campsie, P., Wells, J., Fernandez-Yague, M. A., Burgess, K., Tanner, K. E., Tassieri, M., Meek, D., Vassalli, M., Biggs, M. J. P., Salmeron-Sanchez, M., Oreffo, R. O. C., Reid, S., & Dalby, M. J. (2020). Nanovibrational Stimulation of Mesenchymal Stem Cells Induces Therapeutic Reactive Oxygen Species and Inflammation for Three-Dimensional Bone Tissue Engineering. ACS Nano, 14(8), 10027–10044. https://doi.org/10.1021/acsnano.0c03130

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Gärtner, A., Pereira, T., Armada-da-Silva, P. A. S., Amorim, I., Gomes, R., Ribeiro, J., França, M. L., Lopes, C., Porto, B., Sousa, R., Bombaci, A., Ronchi, G., Fregnan, F., Varejão, A. S. P., Luís, A. L., Geuna, S., & Maurício, A. C. (2012). Use of poly(DL-lactide-ε-caprolactone) membranes and mesenchymal stem cells from the Wharton’s jelly of the umbilical cord for promoting nerve regeneration in axonotmesis: In vitro and in vivo analysis. Differentiation, 84(5), 355–365. https://doi.org/10.1016/j.diff.2012.10.001

    Article  CAS  PubMed  Google Scholar 

  47. Jang, M. Y., Chun, S. I., Mun, C. W., Hong, K. S., & Shin, J. W. (2013). Evaluation of Metabolomic Changes as a Biomarker of Chondrogenic Differentiation in 3D-cultured Human Mesenchymal Stem Cells Using Proton (1H) Nuclear Magnetic Resonance Spectroscopy. PLos One, 8(10), e78325 (12 pages). https://doi.org/10.1371/journal.pone.0078325

  48. Rocha, B., Cillero-Pastor, B., Eijkel, G., Bruinen, A. L., Ruiz-Romero, C., Heeren, R. M. A., & Blanco, F. J. (2015). Characterization of lipidic markers of chondrogenic differentiation using mass spectrometry imaging. Proteomics, 15(4), 702–713. https://doi.org/10.1002/pmic.201400260

    Article  CAS  PubMed  Google Scholar 

  49. Kwon, S. Y., Chun, S. Y., Ha, Y. S., Kim, D. H., Kim, J., Song, P. H., Kim, H. T., Yoo, E. S., Kim, B. S., & Kwon, T. G. (2017). Hypoxia Enhances Cell Properties of Human Mesenchymal Stem Cells. Tissue Engineering and Regenerative Medicine, 14(5), 595–604. https://doi.org/10.1007/s13770-017-0068-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Muñoz, N., Kim, J., Liu, Y., Logan, T. M., & Ma, T. (2014). Gas chromatography-mass spectrometry analysis of human mesenchymal stem cell metabolism during proliferation and osteogenic differentiation under different oxygen tensions. Journal of Biotechnology, 169(1), 95–102. https://doi.org/10.1016/j.jbiotec.2013.11.010

    Article  CAS  PubMed  Google Scholar 

  51. Georgi, N., Cillero-Pastor, B., Eijkel, G. B., Periyasamy, P. C., Kiss, A., Van Blitterswijk, C., et al. (2015). Differentiation of mesenchymal stem cells under hypoxia and normoxia: lipid profiles revealed by time-of-flight secondary ion mass spectrometry and multivariate analysis. Analytical Chemistry, 87(7), 3981–3988. https://doi.org/10.1021/acs.analchem.5b00114

    Article  CAS  PubMed  Google Scholar 

  52. Lakatos, K., Kalomoiris, S., Merkely, B., Nolta, J. A., & Fierro, F. A. (2016). Mesenchymal Stem Cells Respond to Hypoxia by Increasing Diacylglycerols. Journal of Cellular Biochemistry, 117(2), 300–307. https://doi.org/10.1002/jcb.25292

    Article  CAS  PubMed  Google Scholar 

  53. Wang, D., Chen, D., Yu, J., Liu, J., Shi, X., Sun, Y., Pan, Q., Luo, X., Yang, J., Li, Y., Cao, H., Li, L., & Li, L. (2018). Impact of Oxygen Concentration on Metabolic Profile of Human Placenta-Derived Mesenchymal Stem Cells As Determined by Chemical Isotope Labeling LC-MS. Journal of Proteome Research, 17(5), 1866–1878. https://doi.org/10.1021/acs.jproteome.7b00887

    Article  CAS  PubMed  Google Scholar 

  54. Showalter, M. R., Wancewicz, B., Fiehn, O., Archard, J. A., Clayton, S., Wagner, J., Deng, P., Halmai, J., Fink, K. D., Bauer, G., Fury, B., Perotti, N. H., Apperson, M., Butters, J., Belafsky, P., Farwell, G., Kuhn, M., Nolta, J. A., & Anderson, J. D. (2019). Primed mesenchymal stem cells package exosomes with metabolites associated with immunomodulation. Biochemical and Biophysical Research Communications, 512(4), 729–735. https://doi.org/10.1016/j.bbrc.2019.03.119

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Fujisawa, K., Takami, T., Okada, S., Hara, K., Matsumoto, T., Yamamoto, N., et al. (2018). Analysis of metabolomic changes in mesenchymal stem cells on treatment with desferrioxamine as a hypoxia mimetic compared with hypoxic conditions. Stem Cells, 36(8), 1226–1236. https://doi.org/10.1002/stem.2826

    Article  CAS  PubMed  Google Scholar 

  56. Pereira, T., Ivanova, G., Caseiro, A. R., Barbosa, P., Bártolo, P. J., Santos, J. D., Luís, A. L., & Maurício, A. C. (2014). MSCs conditioned media and umbilical cord blood plasma metabolomics and composition. PLos One, 9(11), e113769. https://doi.org/10.1371/journal.pone.0113769

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Liu, Y., Muñoz, N., Bunnell, B. A., Logan, T. M., & Ma, T. (2015). Density-Dependent Metabolic Heterogeneity in Human Mesenchymal Stem Cells. Stem Cells, 33(11), 3368–3381. https://doi.org/10.1002/stem.2097

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Huang, D., Gao, Y., Wang, S., Zhang, W., Cao, H., Zheng, L., Chen, Y., Zhang, S., & Chen, J. (2020). Impact of low-intensity pulsed ultrasound on transcription and metabolite compositions in proliferation and functionalization of human adipose-derived mesenchymal stromal cells. Scientific Reports, 10(1), 13690 (19 pages). https://doi.org/10.1038/s41598-020-69430-z

  59. Kim, J. S., Kim, E. J., Kim, H. J., Yang, J. Y., Hwang, G. S., & Kim, C. W. (2011). Proteomic and metabolomic analysis of H2O2-induced premature senescent human mesenchymal stem cells. Experimental Gerontology, 46(6), 500–510. https://doi.org/10.1016/j.exger.2011.02.012

    Article  CAS  PubMed  Google Scholar 

  60. Lu, X., Chen, Y., Wang, H., Bai, Y., Zhao, J., Zhang, X., et al. (2019). Integrated lipidomics and transcriptomics characterization upon aging-related changes of lipid species and pathways in human bone marrow mesenchymal stem cells. Journal of Proteome Research, 18(5), 2065–2077. https://doi.org/10.1021/acs.jproteome.8b00936

    Article  CAS  PubMed  Google Scholar 

  61. Fernandez-Rebollo, E., Franzen, J., Goetzke, R., Hollmann, J., Ostrowska, A., Oliverio, M., et al. (2020). Senescence-associated metabolomic phenotype in primary and iPSC-derived mesenchymal stromal cells. Stem Cell Reports, 14(2), 201–209. https://doi.org/10.1016/j.stemcr.2019.12.012

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Sun, Y., Chen, D., Liu, J., Xu, Y., Shi, X., Luo, X., Pan, Q., Yu, J., Yang, J., Cao, H., Li, L., & Li, L. (2018). Metabolic profiling associated with autophagy of human placenta-derived mesenchymal stem cells by chemical isotope labeling LC−MS. Experimental Cell Research, 372(1), 52–60. https://doi.org/10.1016/j.yexcr.2018.09.009

    Article  CAS  PubMed  Google Scholar 

  63. Folmes, C. D. L., Nelson, T. J., Martinez-Fernandez, A., Arrell, D. K., Lindor, J. Z., Dzeja, P. P., Ikeda, Y., Perez-Terzic, C., & Terzic, A. (2011). Somatic oxidative bioenergetics transitions into pluripotency-dependent glycolysis to facilitate nuclear reprogramming. Cell Metabolism, 14(2), 264–271. https://doi.org/10.1016/j.cmet.2011.06.011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Sherstyuk, V. V., Yanshole, L. V., Zelentsova, E. A., Melnikov, A. D., Medvedev, S. P., Tsentalovich, Y. P., & Zakian, S. M. (2020). Comparative Metabolomic Profiling of Rat Embryonic and Induced Pluripotent Stem Cells. Stem Cell Reviews and Reports, 16(6), 1256–1265. https://doi.org/10.1007/s12015-020-10052-3

    Article  CAS  PubMed  Google Scholar 

  65. Park, H., Haynes, C. A., Nairn, A. V., Kulik, M., Dalton, S., Moremen, K., & Merrill, A. H. (2010). Transcript profiling and lipidomic analysis of ceramide subspecies in mouse embryonic stem cells and embryoid bodies. Journal of Lipid Research, 51(3), 480–489. https://doi.org/10.1194/jlr.M000984

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Panopoulos, A. D., Yanes, O., Ruiz, S., Kida, Y. S., Diep, D., Tautenhahn, R., Herrerías, A., Batchelder, E. M., Plongthongkum, N., Lutz, M., Berggren, W. T., Zhang, K., Evans, R. M., Siuzdak, G., & Belmonte, J. C. I. (2012). The metabolome of induced pluripotent stem cells reveals metabolic changes occurring in somatic cell reprogramming. Cell Research, 22(1), 168–177. https://doi.org/10.1038/cr.2011.177

    Article  CAS  PubMed  Google Scholar 

  67. Wu, Y., Chen, K., Xing, G., Li, L., Ma, B., Hu, Z., Duan, L., & Liu, X. (2019). Phospholipid remodeling is critical for stem cell pluripotency by facilitating mesenchymal-to-epithelial transition. Science Advances, 5(11), eaax7525 (11 pages). https://doi.org/10.1126/sciadv.aax7525

  68. Meissen, J. K., Yuen, B. T. K., Kind, T., Riggs, J. W., Barupal, D. K., Knoepfler, P. S., & Fiehn, O. (2012). Induced pluripotent stem cells show metabolomic differences to embryonic stem cells in polyunsaturated phosphatidylcholines and primary metabolism. PLos One, 7(10), e46770 (9 pages). https://doi.org/10.1371/journal.pone.0046770

  69. Park, S. J., Lee, S. A., Prasain, N., Bae, D., Kang, H., Ha, T., et al. (2017). Metabolome profiling of partial and fully reprogrammed induced pluripotent stem cells. Stem Cells and Development, 26(10), 734–742. https://doi.org/10.1089/scd.2016.0320

    Article  CAS  PubMed  Google Scholar 

  70. Nagasaka, R., Gotou, Y., Yoshida, K., Kanie, K., Shimizu, K., Honda, H., & Kato, R. (2017). Image-based cell quality evaluation to detect irregularities under same culture process of human induced pluripotent stem cells. Journal of Bioscience and Bioengineering, 123(5), 642–650. https://doi.org/10.1016/j.jbiosc.2016.12.015

    Article  CAS  PubMed  Google Scholar 

  71. Vernardis, S. I., Terzoudis, K., Panoskaltsis, N., & Mantalaris, A. (2017). Human embryonic and induced pluripotent stem cells maintain phenotype but alter their metabolism after exposure to ROCK inhibitor. Scientific Reports, 7(October 2016), 42138 (11 pages). https://doi.org/10.1038/srep42138

  72. Tanosaki, S., Tohyama, S., Fujita, J., Someya, S., Hishiki, T., Matsuura, T., Nakanishi, H., Ohto-Nakanishi, T., Akiyama, T., Morita, Y., Kishino, Y., Okada, M., Tani, H., Soma, Y., Nakajima, K., Kanazawa, H., Sugimoto, M., Ko, M. S. H., Suematsu, M., Fukuda, K. (2020). Fatty Acid Synthesis Is Indispensable for Survival of Human Pluripotent Stem Cells. iScience, 23(9), 101535 (13 pages). https://doi.org/10.1016/j.isci.2020.101535

  73. Yanes, O., Clark, J., Wong, D. M., Patti, G. J., Sánchez-Ruiz, A., Benton, H. P., Trauger, S. A., Desponts, C., Ding, S., & Siuzdak, G. (2010). Metabolic oxidation regulates embryonic stem cell differentiation. Nature Chemical Biology, 6(6), 411–417. https://doi.org/10.1038/nchembio.364

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Patsch, C., Challet-Meylan, L., Thoma, E. C., Urich, E., Heckel, T., O’Sullivan, J. F., Grainger, S. J., Kapp, F. G., Sun, L., Christensen, K., Xia, Y., Florido, M. H. C., He, W., Pan, W., Prummer, M., Warren, C. R., Jakob-Roetne, R., Certa, U., Jagasia, R., Freskgard, P., Adatto, I., Kling, D., Huang, P., Zon, L. I., Chaikof, E. L., Gerszten, R. E., Graf, M., Iacone, R., Cowan, C. A. (2015). Generation of vascular endothelial and smooth muscle cells from human pluripotent stem cells. Nature Cell Biology, 17(8), eaax7525 (6 pages). https://doi.org/10.1038/ncb3205

  75. Danoy, M., Poulain, S., Jelalli, R., Gilard, F., Kato, S., Plessy, C., Kido, T., Miyajima, A., Sakai, Y., & Leclerc, E. (2020). Integration of metabolomic and transcriptomic profiles of hiPSCs-derived hepatocytes in a microfluidic environment. Biochemical Engineering Journal, 155(January), 107490. https://doi.org/10.1016/j.bej.2020.107490

    Article  CAS  Google Scholar 

  76. Pöhö, P., Lipponen, K., Bespalov, M. M., Sikanen, T., Kotiaho, T., & Kostiainen, R. (2019). Comparison of liquid chromatography-mass spectrometry and direct infusion microchip electrospray ionization mass spectrometry in global metabolomics of cell samples. European Journal of Pharmaceutical Sciences, 138, 104991 (7 pages).https://doi.org/10.1016/j.ejps.2019.104991

  77. Elena-Herrmann, B., Montellier, E., Fages, A., Bruck-Haimson, R., & Moussaieff, A. (2020). Multi-platform NMR Study of Pluripotent Stem Cells Unveils Complementary Metabolic Signatures towards Differentiation. Scientific Reports, 10(1), 1622 (11 pages). https://doi.org/10.1038/s41598-020-58377-w

  78. Hsu, C. C., Xu, J., Brinkhof, B., Wang, H., Cui, Z., Huang, W. E., & Ye, H. (2020). A single-cell Raman-based platform to identify developmental stages of human pluripotent stem cell-derived neurons. Proceedings of the National Academy of Sciences of the United States of America, 117(31), 18412–18423. https://doi.org/10.1073/pnas.2001906117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Nakamura, Y., Shimizu, Y., Horibata, Y., Tei, R., Koike, R., Masawa, M., Watanabe, T., Shiobara, T., Arai, R., Chibana, K., Takemasa, A., Sugimoto, H., & Ishii, Y. (2017). Changes of plasmalogen phospholipid levels during differentiation of induced pluripotent stem cells 409B2 to endothelial phenotype cells. Scientific Reports, 7(1), 9377 (9 pages). https://doi.org/10.1038/s41598-017-09980-x

  80. Tohyama, S., Fujita, J., Hishiki, T., Matsuura, T., Hattori, F., Ohno, R., Kanazawa, H., Seki, T., Nakajima, K., Kishino, Y., Okada, M., Hirano, A., Kuroda, T., Yasuda, S., Sato, Y., Yuasa, S., Sano, M., Suematsu, M., & Fukuda, K. (2016). Glutamine Oxidation Is Indispensable for Survival of Human Pluripotent Stem Cells. Cell Metabolism, 23(4), 663–674. https://doi.org/10.1016/j.cmet.2016.03.001

    Article  CAS  PubMed  Google Scholar 

  81. D’Aniello, C., Habibi, E., Cermola, F., Paris, D., Russo, F., Fiorenzano, A., Di Napoli, G., Melck, D. J., Cobellis, G., Angelini, C., Fico, A., Blelloch, R., Motta, A., Stunnenberg, H. G., De Cesare, D., Patriarca, E. J., & Minchiotti, G. (2017). Vitamin C and L-Proline Antagonistic Effects Capture Alternative States in the Pluripotency Continuum. Stem Cell Reports, 8(1), 1–10. https://doi.org/10.1016/j.stemcr.2016.11.011

    Article  CAS  PubMed  Google Scholar 

  82. Zhao, X., Chen, H., Xiao, D., Yang, H., Itzhaki, I., Qin, X., Chour, T., Aguirre, A., Lehmann, K., Kim, Y., Shukla, P., Holmström, A., Zhang, J. Z., Zhuge, Y., Ndoye, B. C., Zhao, M., Neofytou, E., Zimmermann, W. H., Jain, M., & Wu, J. C. (2018). Comparison of Non-human Primate versus Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes for Treatment of Myocardial Infarction. Stem Cell Reports, 10(2), 422–435. https://doi.org/10.1016/j.stemcr.2018.01.002

    Article  PubMed  PubMed Central  Google Scholar 

  83. Rohani, L., Borys, B. S., Razian, G., Naghsh, P., Liu, S., Johnson, A. A., Machiraju, P., Holland, H., Lewis, I. A., Groves, R. A., Toms, D., Gordon, P. M. K., Li, J. W., So, T., Dang, T., Kallos, M. S., & Rancourt, D. E. (2020). Stirred suspension bioreactors maintain naïve pluripotency of human pluripotent stem cells. Communications Biology, 3(1), 492 (22 pages). https://doi.org/10.1038/s42003-020-01218-3

  84. Ismailoglu, I., Chen, Q., Popowski, M., Yang, L., Gross, S. S., & Brivanlou, A. H. (2014). Huntingtin protein is essential for mitochondrial metabolism, bioenergetics and structure in murine embryonic stem cells. Developmental Biology, 391(2), 230–240. https://doi.org/10.1016/j.ydbio.2014.04.005

    Article  CAS  PubMed  Google Scholar 

  85. Sato, Y., Kobayashi, H., Higuchi, T., Shimada, Y., Ida, H., & Ohashi, T. (2017). Metabolomic Profiling of Pompe Disease-Induced Pluripotent Stem Cell-Derived Cardiomyocytes Reveals That Oxidative Stress Is Associated with Cardiac and Skeletal Muscle Pathology. Stem Cells Translational Medicine, 6(1), 31–39. https://doi.org/10.5966/sctm.2015-0409

    Article  CAS  PubMed  Google Scholar 

  86. Yoshida, T., Awaya, T., Jonouchi, T., Kimura, R., Kimura, S., Era, T., Heike, T., & Sakurai, H. (2017). A skeletal muscle model of infantile-onset pompe disease with patient-specific iPS cells. Scientific Reports, 7(1), 13473 (13 pages). https://doi.org/10.1038/s41598-017-14063-y

  87. Warren, C. R., O’Sullivan, J. F., Friesen, M., Becker, C. E., Zhang, X., Liu, P., Wakabayashi, Y., Morningstar, J. E., Shi, X., Choi, J., Xia, F., Peters, D. T., Florido, M. H. C., Tsankov, A. M., Duberow, E., Comisar, L., Shay, J., Jiang, X., Meissner, A., … Cowan, C. A. (2017). Induced Pluripotent Stem Cell Differentiation Enables Functional Validation of GWAS Variants in Metabolic Disease. Cell Stem Cell, 20(4), 547–557. https://doi.org/10.1016/j.stem.2017.01.010

    Article  CAS  PubMed  Google Scholar 

  88. Kim, J., Kang, S. C., Yoon, N. E., Kim, Y., Choi, J., Park, N., Jung, H., Jung, B. H., & Ju, J. H. (2019). Metabolomic profiles of induced pluripotent stem cells derived from patients with rheumatoid arthritis and osteoarthritis. Stem Cell Research and Therapy, 10(1), 319 (13 pages). https://doi.org/10.1186/s13287-019-1408-5

  89. Castiglione, F., Ferro, M., Mavroudakis, E., Pellitteri, R., Bossolasco, P., Zaccheo, D., Morbidelli, M., Silani, V., Mele, A., Moscatelli, D., & Cova, L. (2017). NMR Metabolomics for Stem Cell type discrimination. Scientific Reports, 7(1), 15808 (12 pages). https://doi.org/10.1038/s41598-017-16043-8

  90. Lee, H., Lee, H. R., Kim, H. Y., Lee, H., Kim, H. J., & Choi, H. K. (2019). Characterization and classification of rat neural stem cells and differentiated cells by comparative metabolic and lipidomic profiling. Analytical and Bioanalytical Chemistry, 411(21), 5423–5436. https://doi.org/10.1007/s00216-019-01922-y

    Article  CAS  PubMed  Google Scholar 

  91. Drago, D., Basso, V., Gaude, E., Volpe, G., Peruzzotti-Jametti, L., Bachi, A., Musco, G., Andolfo, A., Frezza, C., Mondino, A., & Pluchino, S. (2016). Metabolic determinants of the immune modulatory function of neural stem cells. Journal of Neuroinflammation, 13(1), 232 (18 pages). https://doi.org/10.1186/s12974-016-0667-7

  92. Baumann, H. J., Betonio, P., Abeywickrama, C. S., Shriver, L. P., & Leipzig, N. D. (2020). Metabolomic and Signaling Programs Induced by Immobilized versus Soluble IFN γin Neural Stem Cells. Bioconjugate Chemistry, 31(9), 2125–2135. https://doi.org/10.1021/acs.bioconjchem.0c00338

    Article  CAS  PubMed  Google Scholar 

  93. Fang, Q., Zhang, Y., Chen, X., Li, H., Cheng, L., Zhu, W., Zhang, Z., Tang, M., Liu, W., Wang, H., Wang, T., Shen, T., & Chai, R. (2020). Three-Dimensional Graphene Enhances Neural Stem Cell Proliferation Through Metabolic Regulation. Frontiers in Bioengineering and Biotechnology, 7, 436 (14 pages). https://doi.org/10.3389/fbioe.2019.00436

  94. Alakpa, E. V., Jayawarna, V., Lampel, A., Burgess, K. V., West, C. C., Bakker, S. C. J., Roy, S., Javid, N., Fleming, S., Lamprou, D. A., Yang, J., Miller, A., Urquhart, A. J., Frederix, P. W. J. M., Hunt, N. T., Péault, B., Ulijn, R. V., & Dalby, M. J. (2016). Tunable Supramolecular Hydrogels for Selection of Lineage-Guiding Metabolites in Stem Cell Cultures. Chem, 1(2), 298–319. https://doi.org/10.1016/j.chempr.2016.07.001

    Article  CAS  Google Scholar 

  95. Alakpa, E. V., Jayawarna, V., Burgess, K. E. V., West, C. C., Péault, B., Ulijn, R. V., & Dalby, M. J. (2017). Improving cartilage phenotype from differentiated pericytes in tunable peptide hydrogels. Scientific Reports, 7(1), 6895 (11 pages). https://doi.org/10.1038/s41598-017-07255-z

  96. Agathocleous, M., Meacham, C. E., Burgess, R. J., Piskounova, E., Zhao, Z., Crane, G. M., Cowin, B. L., Bruner, E., Murphy, M. M., Chen, W., Spangrude, G. J., Hu, Z., DeBerardinis, R. J., & Morrison, S. J. (2017). Ascorbate regulates haematopoietic stem cell function and leukaemogenesis. Nature, 549(7673), 476–481. https://doi.org/10.1038/nature23876

    Article  PubMed  PubMed Central  Google Scholar 

  97. Kumar, A., Kumar, Y., Sevak, J. K., Kumar, S., Kumar, N., & Gopinath, S. D. (2020). Metabolomic analysis of primary human skeletal muscle cells during myogenic progression. Scientific Reports, 10(1), 11824 (14 pages). https://doi.org/10.1038/s41598-020-68796-4

  98. Rudan, M. V., Mishra, A., Klose, C., Eggert, U. S., & Watt, F. M. (2020). Human epidermal stem cell differentiation is modulated by specific lipid subspecies. Proceedings of the National Academy of Sciences of the United States of America, 117(36), 22173–22182. https://doi.org/10.1073/pnas.2011310117

    Article  CAS  Google Scholar 

  99. Turner, W. S., Seagle, C., Galanko, J. A., Favorov, O., Prestwich, G. D., Macdonald, J. M., & Reid, L. M. (2008). Nuclear Magnetic Resonance Metabolomic Footprinting of Human Hepatic Stem Cells and Hepatoblasts Cultured in Hyaluronan-Matrix Hydrogels. Stem Cells, 26(6), 1547–1555. https://doi.org/10.1634/stemcells.2007-0863

    Article  CAS  PubMed  Google Scholar 

  100. Costantini, D., Overi, D., Casadei, L., Cardinale, V., Nevi, L., Carpino, G., Di Matteo, S., Safarikia, S., Valerio, M., Melandro, F., Bizzarri, M., Manetti, C., Berloco, P. B., Gaudio, E., & Alvaro, D. (2019). Simulated microgravity promotes the formation of tridimensional cultures and stimulates pluripotency and a glycolytic metabolism in human hepatic and biliary tree stem/progenitor cells. Scientific Reports, 9(1), 5559 (10 pages). https://doi.org/10.1038/s41598-019-41908-5

  101. Li, B.-B., Chen, Z. Y., Jiang, N., Guo, S., Yang, J. Q., Chai, S. B., Yan, H. F., Sun, P. M., Hu, G., Zhang, T., Xu, B. X., Sun, H. W., Zhou, J. L., Yang, H. M., & Cui, Y. (2020). Simulated microgravity significantly altered metabolism in epidermal stem cells. Vitro Cellular and Developmental Biology - Animal, 56(3), 200–212. https://doi.org/10.1007/s11626-020-00435-8

    Article  CAS  Google Scholar 

  102. Takubo, K., Nagamatsu, G., Kobayashi, C. I., Nakamura-Ishizu, A., Kobayashi, H., Ikeda, E., Goda, N., Rahimi, Y., Johnson, R. S., Soga, T., Hirao, A., Suematsu, M., & Suda, T. (2013). Regulation of glycolysis by Pdk functions as a metabolic checkpoint for cell cycle quiescence in hematopoietic stem cells. Cell Stem Cell, 12(1), 49–61. https://doi.org/10.1016/j.stem.2012.10.011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Xu, B., Wei, X., Chen, M., Xie, K., Zhang, Y., Huang, Z., Dong, T., Hu, W., Zhou, K., Han, X., Wu, X., & Xia, Y. (2019). Glycylglycine plays critical roles in the proliferation of spermatogonial stem cells. Molecular Medicine Reports, 20(4), 3802–3810. https://doi.org/10.3892/mmr.2019.10609

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors are grateful to Dr. Iola F. Duarte for help in organizing some of the relevant literature.

Funding

The authors acknowledge the Portuguese Foundation for Science and Technology (FCT) for co-funding the BIOIMPLANT project (PTDC/BTM-ORG/28835/2017) through the COMPETE2020 program and European Union fund FEDER (POCI-01–0145-FEDER-028835). CSHJ and KR are grateful to the same project for funding their contracts with the University of Aveiro. DSB acknowledges the Sociedade Portuguesa de Química and FCT for her PhD grant SFRH/BD/150655/2020. AMG acknowledges the CICECO-Aveiro Institute of Materials project, with references UIDB/50011/2020 & UIDP/50011/2020, financed by national funds through the FCT/MEC and when appropriate co-financed by FEDER under the PT2020 Partnership Agreement. The NMR spectrometer used in this work is part of the National NMR Network (PTNMR) and, partially supported by Infrastructure Project Nº 022161 (co-financed by FEDER through COMPETE 2020, POCI and PORL and FCT through PIDDAC).

Author information

Authors and Affiliations

  1. Department of Chemistry, CICECO - Aveiro Institute of Materials (CICECO/UA), University of Aveiro, Campus Universitario de Santiago, 3810-193, Aveiro, Portugal

    Daniela S. C. Bispo, Catarina S. H. Jesus, Inês M. C. Marques, Katarzyna M. Romek, Mariana B. Oliveira, João F. Mano & Ana M. Gil

Authors
  1. Daniela S. C. Bispo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Catarina S. H. Jesus
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Inês M. C. Marques
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Katarzyna M. Romek
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mariana B. Oliveira
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. João F. Mano
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ana M. Gil
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AMG had the idea for this review article; DSB, CSHJ, IMCM, and KMR performed the literature search and data analysis; AMG and DSB drafted the manuscript; JFM and MBO critically revised the  manuscript; all authors read, revised, and approved the final version of this work.

Corresponding author

Correspondence to Ana M. Gil.

Ethics declarations

Conflicts of Interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bispo, D.S.C., Jesus, C.S.H., Marques, I.M.C. et al. Metabolomic Applications in Stem Cell Research: a Review. Stem Cell Rev and Rep 17, 2003–2024 (2021). https://doi.org/10.1007/s12015-021-10193-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12015-021-10193-z

Keywords

Search

Navigation