Abstract
Metabolic characteristics of adult stem cells are distinct from their differentiated progeny, and cellular metabolism is emerging as a potential driver of cell fate conversions1,2,3,4. How these metabolic features are established remains unclear. Here we identified inherited metabolism imposed by functionally distinct mitochondrial age-classes as a fate determinant in asymmetric division of epithelial stem-like cells. While chronologically old mitochondria support oxidative respiration, the electron transport chain of new organelles is proteomically immature and they respire less. After cell division, selectively segregated mitochondrial age-classes elicit a metabolic bias in progeny cells, with oxidative energy metabolism promoting differentiation in cells that inherit old mitochondria. Cells that inherit newly synthesized mitochondria with low levels of Rieske iron–sulfur polypeptide 1 have a higher pentose phosphate pathway activity, which promotes de novo purine biosynthesis and redox balance, and is required to maintain stemness during early fate determination after division. Our results demonstrate that fate decisions are susceptible to intrinsic metabolic bias imposed by selectively inherited mitochondria.
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Data availability
The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD010667 (ref. 59). Proteomics data were searched against a human database (Swiss-Prot entries of the Uniprot KB database release 2016_01, 20198 entries) with a list of common contaminants appended (for details see the section ‘Proteomics analysis of old and new mitochondria’). The datasets generated and analysed in this study are included as Supplementary Information. Other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.
Code availability
To facilitate further analysis of the proteomics data, the code is available from the corresponding author in a ready to implement form per request.
References
Folmes, C. D. L., Dzeja, P. P., Nelson, T. J. & Terzic, A. Metabolic plasticity in stem cell homeostasis and differentiation. Cell Stem Cell 11, 596–606 (2012).
Vacanti, N. M. & Metallo, C. M. Exploring metabolic pathways that contribute to the stem cell phenotype. Biochim. Biophys. Acta 1830, 2361–2369 (2013).
Chandel, N. S., Jasper, H., Ho, T. T. & Passegué, E. Metabolic regulation of stem cell function in tissue homeostasis and organismal ageing. Nat. Cell Biol. 18, 823–832 (2016).
Shyh-Chang, N. & Ng, H. H. The metabolic programming of stem cells. Genes Dev. 31, 336–346 (2017).
Ron-Harel, N. et al. Mitochondrial biogenesis and proteome remodeling promote one-carbon metabolism for T cell activation. Cell Metab. 24, 104–117 (2016).
Tormos, K. V. et al. Mitochondrial complex III ROS regulate adipocyte differentiation. Cell Metab. 14, 537–544 (2011).
Jang, K.-J. et al. Mitochondrial function provides instructive signals for activation-induced B-cell fates. Nat. Commun. 6, 6750 (2015).
Yu, W.-M. et al. Metabolic regulation by the mitochondrial phosphatase PTPMT1 is required for hematopoietic stem cell differentiation. Cell Stem Cell 12, 62–74 (2013).
Knobloch, M. et al. A fatty acid oxidation-dependent metabolic shift regulates adult neural stem cell activity. Cell Rep. 20, 2144–2155 (2017).
de Almeida, M. J., Luchsinger, L. L., Corrigan, D. J., Williams, L. J. & Snoeck, H.-W. Dye-independent methods reveal elevated mitochondrial mass in hematopoietic stem cells. Cell Stem Cell 21, 725–729.e4 (2017).
Adams, W. C. et al. Anabolism-associated mitochondrial stasis driving lymphocyte differentiation over self-renewal. Cell Rep. 17, 3142–3152 (2016).
Ito, K. et al. A PML–PPAR-δ pathway for fatty acid oxidation regulates hematopoietic stem cell maintenance. Nat. Med. 18, 1350–1358 (2012).
Ryall, J. G. et al. The NAD+-dependent SIRT1 deacetylase translates a metabolic switch into regulatory epigenetics in skeletal muscle stem cells. Cell Stem Cell 16, 171–183 (2015).
Schell, J. C. et al. Control of intestinal stem cell function and proliferation by mitochondrial pyruvate metabolism. Nat. Cell Biol. 19, 1027–1036 (2017).
Ito, K. et al. Self-renewal of a purified Tie2+ hematopoietic stem cell population relies on mitochondrial clearance. Science 354, 1156–1160 (2016).
Ansó, E. et al. The mitochondrial respiratory chain is essential for haematopoietic stem cell function. Nat. Cell Biol. 19, 614–625 (2017).
Buck, M. D. D. D. et al. Mitochondrial dynamics controls T cell fate through metabolic programming. Cell 166, 63–76 (2016).
Khacho, M. et al. Mitochondrial dynamics impacts stem cell identity and fate decisions by regulating a nuclear transcriptional program. Cell Stem Cell 19, 232–247 (2015).
Chaffer, C. L. et al. Normal and neoplastic nonstem cells can spontaneously convert to a stem-like state. Proc. Natl Acad. Sci. USA 108, 7950–7955 (2011).
Raouf, A. et al. Transcriptome analysis of the normal human mammary cell commitment and differentiation process. Cell Stem Cell 3, 109–118 (2008).
Katajisto, P. et al. Asymmetric apportioning of aged mitochondria between daughter cells is required for stemness. Science 348, 340–343 (2015).
Keppler, A. et al. A general method for the covalent labeling of fusion proteins with small molecules in vivo. Nat. Biotechnol. 21, 86–89 (2003).
Pagliarini, D. J. et al. A mitochondrial protein compendium elucidates complex I disease biology. Cell 134, 112–123 (2008).
Calvo, S. E., Clauser, K. R. & Mootha, V. K. MitoCarta2.0: an updated inventory of mammalian mitochondrial proteins. Nucleic Acids Res. 44, D1251–D1257 (2016).
Hung, V. et al. Proteomic mapping of cytosol-facing outer mitochondrial and ER membranes in living human cells by proximity biotinylation. eLife 6, e24463 (2017).
Gottschling, D. E. & Nyström, T. The upsides and downsides of organelle interconnectivity. Cell 169, 24–34 (2017).
Murley, A. & Nunnari, J. The emerging network of mitochondria–organelle contacts. Mol. Cell 61, 648–653 (2016).
Guarani, V. et al. TIMMDC1/C3orf1 functions as a membrane-embedded mitochondrial complex I assembly factor through association with the MCIA complex. Mol. Cell. Biol. 34, 847–861 (2014).
Guerrero-Castillo, S. et al. The assembly pathway of mitochondrial respiratory chain complex I. Cell Metab. 25, 128–139 (2017).
Fernández-Vizarra, E. & Zeviani, M. Nuclear gene mutations as the cause of mitochondrial complex III deficiency. Front. Genet. 6, 134 (2015).
Diaz, F., Enríquez, J. A. & Moraes, C. T. Cells lacking rieske iron–sulfur protein have a reactive oxygen species-associated decrease in respiratory complexes I and IV. Mol. Cell. Biol. 32, 415–429 (2012).
Paradies, G., Paradies, V., De Benedictis, V., Ruggiero, F. M. & Petrosillo, G. Functional role of cardiolipin in mitochondrial bioenergetics. Biochim. Biophys. Acta 1837, 408–417 (2014).
Mejia, E. M. & Hatch, G. M. Mitochondrial phospholipids: role in mitochondrial function. J. Bioenerg. Biomembr. 48, 99–112 (2016).
Murphy, M. P. How mitochondria produce reactive oxygen species. Biochem. J 417, 1–13 (2009).
Patten, D. A. et al. OPA1-dependent cristae modulation is essential for cellular adaptation to metabolic demand. EMBO J. 33, 2676–2691 (2014).
Aguilaniu, H., Gustafsson, L., Rigoulet, M. & Nyström, T. Asymmetric inheritance of oxidatively damaged proteins during cytokinesis. Science 299, 1751–1753 (2003).
Shaw, F. L. et al. A detailed mammosphere assay protocol for the quantification of breast stem cell activity. J. Mammary Gland Biol. Neoplasia 17, 111–117 (2012).
Vannini, N. et al. The NAD-booster nicotinamide riboside potently stimulates hematopoiesis through increased mitochondrial clearance. Cell Stem Cell 24, 405–418.e7 (2019).
Sanderson, S. M. & Locasale, J. W. Revisiting the Warburg effect: some tumors hold their breath. Cell Metab. 28, 669–670 (2018).
Fendt, S. M. et al. Reductive glutamine metabolism is a function of the α-ketoglutarate to citrate ratio in cells. Nat. Commun. 4, 2236 (2013).
Faubert, B. et al. Lactate metabolism in human lung tumors. Cell 171, 358–371.e9 (2017).
Fan, J. et al. Quantitative flux analysis reveals folate-dependent NADPH production. Nature 510, 298–302 (2014).
Jalloh, I. et al. Glycolysis and the pentose phosphate pathway after human traumatic brain injury: microdialysis studies using 1,2-13C2 glucose. J. Cereb. Blood Flow Metab. 35, 111–120 (2015).
Miccheli, A. et al. Metabolic profiling by 13C-NMR spectroscopy: [1,2-13C2]glucose reveals a heterogeneous metabolism in human leukemia T cells. Biochimie 88, 437–448 (2006).
Ducker, G. S. et al. Reversal of cytosolic one-carbon flux compensates for loss of the mitochondrial folate pathway. Cell Metab. 23, 1140–1153 (2016).
Cox, J. et al. Andromeda: a peptide search engine integrated into the maxquant environment. J. Proteome Res. 10, 1794–1805 (2011).
Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).
Elias, J. E. & Gygi, S. P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 4, 207–214 (2007).
Gatto, L. & Lilley, K. S. MSnbase—an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation. Bioinformatics 28, 288–289 (2012).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
Strimmer, K. A unified approach to false discovery rate estimation. BMC Bioinformatics 9, 303 (2008).
Folch, J., Lees, M. & Sloane Stanley, G. A simple method for the isolation and purification of total lipides from animal tissues. J. Biol. Chem. 226, 497–509 (1957).
Tigistu-Sahle, F. et al. Metabolism and phospholipid assembly of polyunsaturated fatty acids in human bone marrow mesenchymal stromal cells. J. Lipid Res. 58, 92–110 (2017).
Haimi, P., Uphoff, A., Hermansson, M. & Somerharju, P. Software tools for analysis of mass spectrometric lipidome data. Anal. Chem. 78, 8324–8331 (2006).
Hernebring, M., Brolen, G., Aguilaniu, H., Semb, H. & Nystrom, T. Elimination of damaged proteins during differentiation of embryonic stem cells. Proc. Natl Acad. Sci. USA 103, 7700–7705 (2006).
Shaik, I. H. & Mehvar, R. Rapid determination of reduced and oxidized glutathione levels using a new thiol-masking reagent and the enzymatic recycling method: application to the rat liver and bile samples. Anal. Bioanal. Chem. 385, 105–113 (2006).
Millard, P., Letisse, F., Sokol, S. & Portais, J. C. IsoCor: correcting MS data in isotope labeling experiments. Bioinformatics 28, 1294–1296 (2012).
Partanen, J. I. et al. Tumor suppressor function of liver kinase B1 (Lkb1) is linked to regulation of epithelial integrity. Proc. Natl Acad. Sci. USA 109, E388–E397 (2012).
Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).
Acknowledgements
We thank J. Bärlund and M. Simula for technical assistance and all members of the Katajisto Laboratory for discussion and comments, Å.-L. Dackland at the FACS Facility at the Department of Laboratory Medicine at the Karolinska Institutet, J. Juutila at the Centre of Excellence in Stem Cell Metabolism Metabolomics Facility, University of Helsinki, and M. Lohela at the Biomedicum Imaging Unit at the University of Helsinki. Part of this study was performed at the Live Cell Imaging Facility, Karolinska Institutet, Sweden, and at the Light Microscopy Unit at the Institute of Biotechnology, University of Helsinki. A. Kamleh (Thermo Fisher Scientific), G. Mackay and K. Vousden (Beatson Institute) are acknowledged for their support on the metabolomics setup. This study was funded by grants from the European Research Council (ERC, 677809), the Academy of Finland (266869, 304591 and 312436), the Knut and Alice Wallenberg Foundation (KAW 2014.0207), the Swedish Research Council 2018-03078, Cancerfonden 190634, the Center for Innovative Medicine (CIMED), the Sigrid Juselius Foundation, the Cancer Society of Finland, the Doctoral Programme in Biomedicine at the University of Helsinki (J.D.), the Alfred Kordelin Foundation (J.D.) and the Finnish Cultural Foundation (J.D.).
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P.K., J.D., E.K., A.A. and J.I.E. conceived and designed the experiments. J.D., E.K., A.A., J.I.E., S.G., S.K., E.S.S., R.T.M., K.A., Y.Y. and H.B. performed the experiments. N.G. and A.O. performed the proteomics analysis. R.K. performed the lipidomics analysis. E.K., A.A. and A.I.N. performed the metabolomics data analysis. J.D., E.K. and P.K. wrote the manuscript with input from S.G., H.T., V.H., T.O. and all co-authors.
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Extended data
Extended Data Fig. 1 Age-specific labelling of mitochondria using Snap-tag for isolation and single-organelle FACS-sorting.
a, Related to Fig. 1a. Full cell, with the frame shown in Fig. 1a highlighted. Single confocal plane, scale bar, 1.5 μm. Representative of three experiments. b, Quality control for proteomics experiment in Fig. 1. Additional images (representative of 23) of isolated age-specifically labelled mitochondria with immunofluorescent staining for Snap-tag and staining control (representative of four images). The sample stained for Snap-tag shown here is the same as shown in Fig. 1a. Single confocal plane, scale bar, 2 μm. c, Control experiment to determine the gating strategy for enriching the sort sample for mitochondria, while excluding other organelles present in the mitochondrial fraction before FACS-sorting. Isolated mitochondria were labelled with MitoTracker Deep Red FM before analysis, or left unlabelled. To remove other organelles present in mitochondrial fractions after isolation, we excluded FSC-A high events (aggregates) as well as SSC-A low particles (other organelles with low internal complexity), and chose a SSC-A high population for further gating. Unlike SSC-A low events, SSC-A high events were nearly entirely positive for MitoTracker Deep Red FM, indicating a fraction highly enriched for mitochondria. d, FACS-sorting strategy for separating old and new mitochondria, the sample shown is the same as in Fig. 1a. Selection of SSC-A high events was used as a pre-gating strategy to enrich the parent population used for sorting for mitochondria, based on the control experiment shown in c. Population ‘Total mitochondria’ (corresponding to the population ‘SSC-hi’ in c) was used as parent population to gate for old and new mitochondria for single-organelle FACS-sorting.
Extended Data Fig. 2 Proteomic analysis of old and new mitochondria.
a–d, Analysis of enrichment in old and new mitochondrial samples of proteins localised to different subcellular compartments. All proteins that were detected shown in grey, proteins localised to indicated subcellular compartments are highlighted in black, and the number of proteins enriched in old and new mitochondrial samples (with log2 fold change < −0.5 or > 0.5) in each compartment is indicated. a, All proteins detected, and MitoCarta9,10 annotated proteins (317 of 1,001 proteins detected in total). b, Proteins localised to indicated subcellular compartments based on UniProt annotation (www.uniprot.org). c, Proteins implicated in mitochondria-ER contact sites due to their presence in both the outer mitochondrial membrane and the ER (OMMxERM11). d, Non-mitochondrial proteins (detected proteins not annotated in MitoCarta). e, Enrichment of proteins localised to submitochondrial compartments. Mitochondrial proteins9,10 are shown in grey, proteins localised to the indicated submitochondrial compartments (based on UniProt annotation) highlighted in black. f, Validation of enrichment of TIDC1 in old mitochondrial samples in comparison to OPA1 (similar levels in both age-classes). Samples in the immunoblot are from the same sort as the samples shown in the immunoblot in Fig. 1b. Representative of four experiments. g, Analysis of enrichment of complex I assembly factors14 in old and new mitochondria. The proteomics dataset shown in Fig. 1b and c was used for analysis shown here. Details of statistical analysis in Supplementary Data Table 1. See also Supplementary Data Tables 3–5. Numerical source data are available in Source Data Extended Data Table 2 and unprocessed blots are available in Source Data Extended Data Fig. 2.
Extended Data Fig. 3 Lipidomic analysis of old and new mitochondria.
a, Analysis of fatty acid composition of old and new mitochondria in comparison to whole cell extracts and cell culture medium. Amounts are expressed as fraction of total fatty acids detected. b, Relative enrichment of cardiolipin in old and new mitochondria in comparison to whole cell extract (Cell). Data shown as fraction of total phospholipids detected per sample (pooled analysis of four age-selective isolations). Cell and mitochondria samples are the same as used for the analysis shown in a. c, Phospholipid composition of whole cell extract, as well as old and new mitochondria. Shown here are additional phospholipid classes that were analysed in the same samples as in a and b. The analyses were performed using whole cell extract, as well as pooled lipid extracts from new and old mitochondria from four isolations. Additionally, fatty acid composition of MEGM cell culture medium was analysed. Amounts are expressed as fraction of total phospholipids detected. Numerical source data are available in Source Data Extended Data Table 3.
Extended Data Fig. 4 Analysis of properties of mitochondria segregated to Pop1 and Pop2 daughter cells in asymmetric divisions.
a, Gating strategy for flow cytometric analysis of membrane potential of isolated mitochondria using tetramethylrhodamine methyl ester (TMRM). Population ‘TMRM positive’ in sample ‘+ TMRM’ was used for analysis of membrane potential shown in Fig. 2a. For unlabelled control ‘- TMRM’, an aliquot of the same sample of isolated mitochondria was analysed without addition of TMRM. b, Mitochondrial DNA copy number expressed as mitochondrial DNA/nuclear DNA (mtDNA/nDNA) in Pop1 and Pop2 cells sorted by inheritance of old mitochondria. Data are mean ± s.d. of six biological replicates. c, Western blot showing expression of mitochondrial proteins in Pop1 and Pop2 cells from three biological replicates. PGC1α, HSP60 and GAPDH are from one blot and COXII and Vinculin form another. Details of statistical analysis in Supplementary Data Table 1. Numerical source data are available in Source Data Extended Data Table 4 and unprocessed blots are available in Source Data Extended Data Fig. 4.
Extended Data Fig. 5 Analysis of cell oxidative properties in relation to inheritance of old mitochondria.
a, Gating strategy for pre-gating before sorting Pop1 and Pop2 cells for Seahorse respirometry. The sample shown in Fig. 2d was gated to select for live cells. Population ‘FSC Singlets’ was then used for further gating based on inheritance of old mitochondria to sort Pop1 and Pop2 cells, as shown in Fig. 2d. b, Total cellular ROS levels in recently divided cells. Flow cytometric analysis of CellROX median fluorescence in Pop1 and Pop2 cells, fluorescence intensity normalised to the parent population, mean ± s.d. of five biological replicates. c, OxyBlot staining shows similar levels of protein carbonylation36,55 in P1 and P2 progeny cells. Representative images of recently divided cells with old mitochondrial label and immunofluorescent staining to detect protein carbonylation (OxyBlot). Representative images of staining controls (non-derivatised sample and secondary only control, derivatised sample incubated without primary antibody). Scale bar, 10 μm. Analysis of protein carbonylation in cell pairs for untreated cells and cells treated with 100 nM antimycin A (Anti A), and OxyBlot staining intensity (fraction per cell of total intensity for the corresponding division pair) for individual cells in relation to inheritance of old mitochondria (fraction of total per cell pair) for untreated cells (18 cell pairs for untreated cells, seven for cells treated with Anti A). Individual data points represent one progeny cell analysed as part of a cell pair as described above. Data are mean ± s.d. Details of statistical analysis in Supplementary Data Table 1. Numerical source data are available in Source Data Extended Data Table 5.
Extended Data Fig. 6 Effect of modulating respiration, redox state or glycolysis on stemness.
a, Basal respiration and spare respiratory capacity relative to mean of controls in same experiment in cells treated with UK-5099 alone or in combination with 2-Oxo for 26 h. Treatments were maintained during the assay. Data are mean ± s.d. from five experiments with individual repeats shown. b, Population frequencies, mean ± s.d., in cells treated with UK-5099 alone (ten sorts) or in combination with 2-Oxo (eight sorts), in comparison to DMSO (ten sorts). Sorting strategy for separating indicated cells. c, Cell population dynamics of UK-5099 ± 2-Oxo or DMSO (26 h) treated cells. No treatments present during the assay. Cell number was recorded in 24-hour intervals, and is expressed relative to the initial cell number at 0 h (nine samples per condition), mean ± s.d. UK-5099:10 µM, 2-Oxo 400 µM in a-c. d-h, Mammosphere formation of Pop1 and Pop2 cells and cell population dynamics of cells treated pharmacologically (T) or transfected according to the schematics. d, 100 μM NR for 5 h, mean ± s.d. of five (mammospheres, and NR cell population dynamics) or nine (DMSO cell population dynamics) biological replicates. e, 250 nM MitoTEMPO (MT) for 5 h. Data are mean ± s.d. of five (mammospheres) and nine (cell population dynamics) biological replicates. See scheamtics in d. f, siRNAs against glycolytic enzymes. Data are mean ± s.d. of six biological replicates, except for siGPI, five biological replicates for mammospheres and five (siFLUC, siGPI) or four (siGAPDH, siPFKL) repeats for cell population dynamics. g, 300 nM GAPDH inhibitor heptelidic acid (HepA) for 5 h, mean ± s.d. from five (mammospheres), three (HepA cell population dynamics) or nine (DMSO cell population dynamics) biological replicates. See scheamtics in d. DMSO controls for cell population dynamics are the same samples as shown in Extended Data Fig. 6d. h, Transfection with FLAG-HA-pcDNA3.1- (ctrl) or HA-HIF1alpha-pcDNA3 (Hif1α) in combination with pYFP_C1 as transfection control, mean ± s.d. of three (mammospheres) biological replicates or five (cell population dynamics) measurements. Details of statistical analysis in Supplementary Data Table 1. Numerical source data are available in Source Data Table 6.
Extended Data Fig. 7 Effect of selective inheritance of old mitochondria on the metabolism of daughter cells.
a, Related to Fig. 3a and d. LC–MS/MS analysis showing percent enrichment of representative metabolites tracing from a pulse of U-13C glucose given one or two hours before FACS-sorting of Pop1 (left) and Pop2 (right) cells. Data are mean ± s.d. of four biological replicates per time point. b, NAD+/NADH ratio (left) in cells treated with 10 µM UK-5099 for 26 hours compared to cells treated with DMSO. Data are mean ± s.d of six (UK-5099) or seven (DMSO) biological replicates. Lactate production (right) in cells treated with 10 µM UK-5099 (six biological replicates) relative to DMSO control (six biological replicates) for five hours. c, Related to Fig. 3a and d. LC–MS/MS analysis showing ratios of selected TCA cycle and glycolysis metabolites tracing from U-13C glucose. Data are mean ± s.d. of eight biological replicates. d, LC–MS/MS analysis showing (left) 6PG M+6 levels (peak area normalized to relative cell number) and ratios of Ru5P M+5 to 6PG M+6 (middle) and IMP M + 5 to Ru5P M+5 (right) in cells treated with a five-hour pulse of 50 or 200 µM 6-AN or DMSO and a two hour pulse of U-13C glucose according to the schematic. Data are from six (DMSO), five (50 µM 6-AN) and three (200 µM 6-AN) biological replicates. Empty dots denote metabolites undetected or below background level. Lines represent mean. Details of statistical analysis in Supplementary Data Table 1. Numerical source data are available in Source Data Extended Data Table 7.
Extended Data Fig. 8 Effect of PPP inhibition on metabolic state of HMECs.
a, LC–MS/MS showing 6PG peak area in HMECs treated with 10 or 200 µM 6-AN for 5 h. Data are mean ± s.d. of five samples. b, Cell population dynamics after 6-AN for 5 h in comparison to DMSO according to the schematic. Mean ± s.d. of nine repeats. Control samples are the same as in Extended Data Fig. 6e. c, Basal respiration (left), and spare respiratory capacity (middle) and basal Glycolysis rate (right) in cells following 6-AN for 5 h compared to DMSO controls from the same experiment. Mean ± s.d. of individual repeats from five (Mito stress test) and three (GlycoPER) experiments. Treatments were maintained during the assay. Control samples are partly the same as in Extended Data Fig. 6a for Mito stress test. d, Cell population dynamics (upper) after 2-DG or DMSO for 5 h. Mean ± s.d. of nine repeats. Control samples are the same as in Extended Data Figs. 6e and 8b. Population dynamics (middle) of cells maintained in 50 μM 2-DG (untreated: six repeats, 2-DG. 4 repeats). Cell morphology (lower) of cells supplemented with 1 mM 2-DG or untreated. CellIQ images of the same field of view with 24-hour intervals. Scale bar 100 µm e, Basal Glycolysis rate (left) after 26-h treatment with 50 μM 2-DG. Treatment was maintained during the assay. Mean ± s.d. of individual repeats in three experiments normalised to untreated controls for each experiment. Representative GlycoPER curves (right) of untreated (14 repeats) or cells treated for 26 h with 50 µM (eight repeats) or 1 mM 2-DG (six repeats). Treatments present during the assay. Mean ± s.d. f, Lactate production (left) in cells maintained in 50 µM (six biological replicates), or 1 mM (three biological replicates) 2-DG or untreated (six biological replicates). Two readings are missing due to technical measurement issues. Mean ± s.d. Effect of 50 μM 2-DG on NAD+/NADH ratio (right). Mean ± s.d. of seven biological replicates. Details of statistical analysis in Supplementary Data Table 1. Numerical source data are available in Source Data Extended Data Table 8.
Extended Data Fig. 9 Effect of manipulating PPP or the folate cycle on stemness.
a, Experimental strategy and representative FACS gating to separate progeny cells based on inheritance of old mitochondria. Mammosphere formation of cells treated with 2-DG or untreated for 5 h. Data are mean ± s.d. of four biological replicates. b, Representative FACS gating (left) for sorting Pop1 and Pop2 cells in untreated and 2-DG treated samples. Mammosphere formation and population frequencies (right) after treatment with 2-DG added 5 days before first addition of thymidine, and maintained until FACS. Mean ± s.d. of four biological replicates. c, Western blot of PPP enzymes in Pop1 and Pop2 cells. Quantification (Pop2/Pop1 normalized to β-actin) from eight repeats, except G6PDH: four repeats. Mean ± s.d. d, Mammosphere formation (left) and population dynamics (right) of cells transfected with pRK5 (ctrl) or G6PD/pRK5 (G6PD) and pYFP_C1 as transfection control for FACS gating. Data are mean ± s.d. of (mammospheres) three biological replicates and (population dynamics) three (G6PD) and five (control) replicates. Note, control samples are the same as in Extended Data Fig. 6h. e, Mammosphere formation (upper) and cell population dynamics (lower) after 10 nM methotrexate (Mtx) or DMSO for 5 h. Mean ± s.d. of five (mammospheres) biological replicates and nine (cell population dynamics) repeats. For cell population dynamics, DMSO samples are the same as in Extended Data Figs. 6e, 8b,d. f, Population dynamics in HMECs after 24-hour treatment with 6-AN or DMSO. Mean ± s.d. of nine samples. g, Mammosphere formation of HMECs following 24 h pretreatment with 6-AN or DMSO in 2D culture followed by low-adhesion mammosphere culture. Data are mean ± s.d. of five samples h, Cell viability (left) and mammosphere formation (right) of MMECs after 24 h pretreatment with 500 μM 6-AN or DMSO in 2D culture (see schematic). Mean ± s.d. of 2-3 frames analysed for 3 mice per group (cell viability) or seven mice each (mammospheres). Details of statistical analysis in Supplementary Data Table 1. Numerical source data are available in Source Data Extended Data Table 9 and unprocessed blots are available in Source Data Extended Data Figure 9.
Extended Data Fig. 10 Effect of partial RISP knockdown on metabolic state and asymmetric apportioning of mitochondria.
a, Cell population dynamics after transfection with the indicated siRNAs according to the schematic. Mean ± s.d. of five repeats. Note, the siFLUC control is the same as shown in Extended Data Fig. 6e. b, Related to Fig. 4e. LC–MS/MS analysis showing ratios of selected TCA cycle and glycolysis metabolites tracing from a two-hour pulse of U-13C glucose in cells transfected with RISP or control (siFLUC) siRNA. Mean ± s.d. of four biological replicates. c, Representative FACS-plots showing gating strategy for sorting Pop1 and Pop2 cells, and population frequencies in samples transfected with siRISP or siFLUC as control. Mean ± s.d. of eight biological replicates. d, Mammosphere forming capacity of Pop1 and Pop2 cells (left) treated with indicated siRNAs two days prior to FACS-sorting by inheritance of old mitochondria according to the schematic on top. Mean ± s.d. of eight biological replicates. Note, the control samples are the same as in Fig. 4f. Western blot (middle) showing TIDC1 expression in Pop1 and Pop2 cells treated with the indicated siRNAs two days prior to FACS-sorting by inheritance of old mitochondria. Note, the blot has been cut between the siFLUC and siTIDC1 samples and tubulin blot is the same as in Fig. 4c for siFLUC samples. Population dynamics (right) after transfection with the indicated siRNAs. Mean ± s.d. of five (siFLUC) and four (siTIDC1) repeats. Note, the siFLUC control is the same as shown in Extended Data Figs. 6e and 10a. Details of statistical analysis in Supplementary Data Table 1. Numerical source data are available in Source Data Extended Data Table 10 and unprocessed blots are available in Source Data Figure 10.
Supplementary information
Supplementary Tables
Supplementary Tables 1–5. Supplementary Table 1: Statistical analyses and sample sizes. Supplementary Table 2: Equipment and settings, fluorescence imaging. Supplementary Table 3: Proteomics analysis of old and new mitochondria—all hits. Supplementary Table 4: Peroxisomal proteins detected in old and new mitochondrial samples. Supplementary Table 5: ETC subunits detected in old and new mitochondrial samples.
Source data
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Statistical source data.
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Statistical source data.
Source Data Extended Data Fig. 7
Statistical source data.
Source Data Extended Data Fig. 8
Statistical source data.
Source Data Extended Data Fig. 9
Statistical source data.
Source Data Extended Data Fig. 9
Unprocessed western blots.
Source Data Extended Data Fig. 10
Statistical source data.
Source Data Extended Data Fig. 10
Unprocessed western blots.
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Döhla, J., Kuuluvainen, E., Gebert, N. et al. Metabolic determination of cell fate through selective inheritance of mitochondria. Nat Cell Biol 24, 148–154 (2022). https://doi.org/10.1038/s41556-021-00837-0
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DOI: https://doi.org/10.1038/s41556-021-00837-0
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