Transcriptional diversity and bioenergetic shift in human breast cancer metastasis revealed by single-cell RNA sequencing

Abstract

Although metastasis remains the cause of most cancer-related mortality, mechanisms governing seeding in distal tissues are poorly understood. Here, we establish a robust method for the identification of global transcriptomic changes in rare metastatic cells during seeding using single-cell RNA sequencing and patient-derived-xenograft models of breast cancer. We find that both primary tumours and micrometastases display transcriptional heterogeneity but micrometastases harbour a distinct transcriptome program conserved across patient-derived-xenograft models that is highly predictive of poor survival of patients. Pathway analysis revealed mitochondrial oxidative phosphorylation as the top pathway upregulated in micrometastases, in contrast to higher levels of glycolytic enzymes in primary tumour cells, which we corroborated by flow cytometric and metabolomic analyses. Pharmacological inhibition of oxidative phosphorylation dramatically attenuated metastatic seeding in the lungs, which demonstrates the functional importance of oxidative phosphorylation in metastasis and highlights its potential as a therapeutic target to prevent metastatic spread in patients with breast cancer.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Single-cell RNA sequencing of micrometastatic and primary tumour cells.
Fig. 2: Transcriptional diversity in micrometastatic and primary tumour cells.
Fig. 3: Micrometastatic cells display a distinct transcriptome program.
Fig. 4: Micrometastatic cells display increased mitochondrial OXPHOS.
Fig. 5: Metastatic cells display a distinct metabolic profile.
Fig. 6: Oxidative phosphorylation is critical for lung metastasis.

Data availability

The authors declare that all data supporting the findings of this study are available within the article and its supplementary information files or from the corresponding author on reasonable request. All RNA-seq data files along with their associated metadata have been deposited in the GEO database under the accession code GSE123837. Previously published microarray data that were reanalysed here with KM Plotter26,67 are available under the following accession codes: E-MTAB-365, E-TABM-43, GSE11121, GSE12093, GSE12276, GSE1456, GSE16391, GSE16446, GSE16716, GSE17705, GSE17907, GSE18728, GSE19615, GSE20194, GSE20271, GSE2034, GSE20685, GSE20711, GSE21653, GSE2603, GSE26971, GSE2990, GSE31448, GSE31519, GSE32646, GSE3494, GSE37946, GSE41998, GSE42568, GSE45255, GSE4611, GSE5327, GSE6532, GSE7390 and GSE9195.

Code availability

Custom scripts are available at https://github.com/lawsonlab/Single_Cell_Metastasis.

References

  1. 1.

    Bianchini, G., Balko, J. M., Mayer, I. A., Sanders, M. E. & Gianni, L. Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease. Nat. Rev. Clin. Oncol. 13, 674–690 (2016).

  2. 2.

    Weigelt, B., Peterse, J. L. & van’t Veer, L. J. Breast cancer metastasis: markers and models. Nat. Rev. Cancer 5, 591–602 (2005).

  3. 3.

    Oskarsson, T., Batlle, E. & Massagué, J. Metastatic stem cells: sources, niches, and vital pathways. Cell Stem Cell 14, 306–321 (2014).

  4. 4.

    Lawson, D. A., Kessenbrock, K., Davis, R. T., Pervolarakis, N. & Werb, Z. Tumour heterogeneity and metastasis at single-cell resolution. Nat. Cell Biol. 20, 1349–1360 (2018).

  5. 5.

    Hermann, P. C. et al. Distinct populations of cancer stem cells determine tumor growth and metastatic activity in human pancreatic cancer. Cell Stem Cell 1, 313–323 (2007).

  6. 6.

    Lodhia, K. A., Hadley, A. M., Haluska, P. & Scott, C. L. Prioritizing therapeutic targets using patient-derived xenograft models. Biochim. Biophys. Acta 1855, 223–234 (2015).

  7. 7.

    Hochhauser, D. & Caldas, C. Of mice and men: patient-derived xenografts in cancer medicine. Ann. Oncol. 28, 2330–2331 (2017).

  8. 8.

    Izumchenko, E. et al. Patient-derived xenografts effectively capture responses to oncology therapy in a heterogeneous cohort of patients with solid tumors. Ann. Oncol. 28, 2595–2605 (2017).

  9. 9.

    DeRose, Y. S. et al. Tumor grafts derived from women with breast cancer authentically reflect tumor pathology, growth, metastasis and disease outcomes. Nat. Med. 17, 1514–1520 (2011).

  10. 10.

    Lawson, D. A. et al. Single-cell analysis reveals a stem-cell program in human metastatic breast cancer cells. Nature 526, 131–135 (2015).

  11. 11.

    Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

  12. 12.

    Ziegenhain, C. et al. Comparative analysis of single-cell RNA sequencing methods. Mol. Cell 65, 631–643.e4 (2017).

  13. 13.

    Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

  14. 14.

    Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

  15. 15.

    Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).

  16. 16.

    Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

  17. 17.

    Puram, S. V. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611–1624 (2017).

  18. 18.

    Venteicher, A. S. et al. Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Science 355, eaai8478 (2017).

  19. 19.

    Bruna, A. et al. A biobank of breast cancer explants with preserved intra-tumor heterogeneity to screen anticancer compounds. Cell 167, 260–274 (2016).

  20. 20.

    Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

  21. 21.

    The Gene Ontology Consortium. Expansion of the Gene Ontology knowledgebase and resources. Nucleic Acids Res. 45, D331–D338 (2017).

  22. 22.

    Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).

  23. 23.

    Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

  24. 24.

    Ikwegbue, P. C., Masamba, P., Oyinloye, B. E. & Kappo, A. P. Roles of heat shock proteins in apoptosis, oxidative stress, human inflammatory diseases, and cancer. Pharmaceuticals 11, 1–18 (2018).

  25. 25.

    Creagh, E. M., Sheehan, D. & Cotter, T. G. Heat shock proteins-modulators of apoptosis in tumour cells. Leukemia 14, 1161–1173 (2000).

  26. 26.

    Györffy, B. et al. An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients. Breast Cancer Res. Treat. 123, 725–731 (2010).

  27. 27.

    Tunster, S. J., Creeth, H. D. J. & John, R. M. The imprinted Phlda2 gene modulates a major endocrine compartment of the placenta to regulate placental demands for maternal resources. Dev. Biol. 409, 251–260 (2016).

  28. 28.

    Moon, H.-G. et al. Prognostic and functional importance of the engraftment-associated genes in the patient-derived xenograft models of triple-negative breast cancers. Breast Cancer Res. Treat. 154, 13–22 (2015).

  29. 29.

    Scaduto, R. C. & Grotyohann, L. W. Measurement of mitochondrial membrane potential using fluorescent rhodamine derivatives. Biophys. J. 76, 469–477 (1999).

  30. 30.

    Liu, X. et al. High-resolution metabolomics with Acyl-CoA profiling reveals widespread remodeling in response to diet. Mol. Cell. Proteomics 14, 1489–1500 (2015).

  31. 31.

    Mayers, J. R. et al. Elevation of circulating branched-chain amino acids is an early event in human pancreatic adenocarcinoma development. Nat. Med. 20, 1193–1198 (2014).

  32. 32.

    Fernyhough, P. & McGavock, J. in Handbook of Clinical Neurology Vol. 126 (eds Zochodne, D.W. and Malik, R.A.) 353–377 (Elsevier, 2014).

  33. 33.

    Hao, W., Chang, C. P., Tsao, C. C. & Xu, J. Oligomycin-induced bioenergetic adaptation in cancer cells with heterogeneous bioenergetic organization. J. Biol. Chem. 285, 12647–12654 (2010).

  34. 34.

    Choi, S. W., Gerencser, A. A. & Nicholls, D. G. Bioenergetic analysis of isolated cerebrocortical nerve terminals on a microgram scale: spare respiratory capacity and stochastic mitochondrial failure. J. Neurochem. 109, 1179–1191 (2009).

  35. 35.

    Dayan, F. et al. Activation of HIF-1α in exponentially growing cells via hypoxic stimulation is independent of the Akt/mTOR pathway. J. Cell. Physiol. 218, 167–174 (2009).

  36. 36.

    Ward, M. W., Rego, A. C., Frenguelli, B. G. & Nicholls, D. G. Mitochondrial membrane potential and glutamate excitotoxicity in cultured cerebellar granule cells. J. Neurosci. 20, 7208–7219 (2000).

  37. 37.

    Digman, M. A., Caiolfa, V. R., Zamai, M. & Gratton, E. The phasor approach to fluorescence lifetime imaging analysis. Biophys. J. 94, L14–L16 (2008).

  38. 38.

    Sameni, S., Syed, A., Marsh, J. L. & Digman, M. A. The phasor-FLIM fingerprints reveal shifts from OXPHOS to enhanced glycolysis in Huntington Disease. Sci. Rep. 6, 34755 (2016).

  39. 39.

    Warburg, O. Origin of cancer cells. Oncol. 9, 75–83 (1956).

  40. 40.

    Koppenol, W. H., Bounds, P. L. & Dang, C. V. Otto Warburg’s contributions to current concepts of cancer metabolism. Nat. Rev. Cancer 11, 325–337 (2011).

  41. 41.

    Yang, L. et al. Metabolic shifts toward glutamine regulate tumor growth, invasion and bioenergetics in ovarian cancer. Mol. Syst. Biol. 10, 728 (2014).

  42. 42.

    Dornier, E. et al. Glutaminolysis drives membrane trafficking to promote invasiveness of breast cancer cells. Nat. Commun. 8, 2255 (2017).

  43. 43.

    Rodrigues, M. F. et al. Enhanced OXPHOS, glutaminolysis and β-oxidation constitute the metastatic phenotype of melanoma cells. Biochem. J. 473, 703–715 (2016).

  44. 44.

    Camarda, R. et al. Inhibition of fatty acid oxidation as a therapy for MYC-overexpressing triple-negative breast cancer. Nat. Med. 22, 427–432 (2016).

  45. 45.

    Antalis, C. J., Uchida, A., Buhman, K. K. & Siddiqui, R. A. Migration of MDA-MB-231 breast cancer cells depends on the availability of exogenous lipids and cholesterol esterification. Clin. Exp. Metastasis 28, 733–741 (2011).

  46. 46.

    Elia, I. et al. Proline metabolism supports metastasis formation and could be inhibited to selectively target metastasizing cancer cells. Nat. Commun. 8, 15267 (2017).

  47. 47.

    Christen, S. et al. Breast cancer-derived lung metastases show increased pyruvate carboxylase-dependent anaplerosis. Cell Rep. 17, 837–848 (2016).

  48. 48.

    Elia, I., Doglioni, G. & Fendt, S. M. Metabolic hallmarks of metastasis formation. Trends Cell Biol. 28, 673–684 (2018).

  49. 49.

    Teoh, S. T. & Lunt, S. Y. Metabolism in cancer metastasis: bioenergetics, biosynthesis, and beyond. Wiley Interdiscip. Rev. Syst. Biol. Med. 10, e1406 (2018).

  50. 50.

    LeBleu, V. S. et al. PGC-1α mediates mitochondrial biogenesis and oxidative phosphorylation in cancer cells to promote metastasis. Nat. Cell Biol. 16, 992–1003 (2014).

  51. 51.

    Basnet, H. et al. Flura-seq identifies organ-specific metabolic adaptations during early metastatic colonization. eLife 8, e43627 (2019).

  52. 52.

    Schafer, Z. T. et al. Antioxidant and oncogene rescue of metabolic defects caused by loss of matrix attachment. Nature 461, 109–113 (2009).

  53. 53.

    Porporato, P. E. et al. A mitochondrial switch promotes tumor metastasis. Cell Rep. 8, 754–766 (2014).

  54. 54.

    Ogura, M., Yamaki, J., Homma, M. K. & Homma, Y. Mitochondrial c-Src regulates cell survival through phosphorylation of respiratory chain components. Biochem. J. 447, 281–289 (2012).

  55. 55.

    Zielonka, J. & Kalyanaraman, B. ‘ROS-generating mitochondrial DNA mutations can regulate tumor cell metastasis’-a critical commentary. Free Radic. Biol. Med. 45, 1217–1219 (2008).

  56. 56.

    Dai, X. et al. Breast cancer intrinsic subtype classification, clinical use and future trends. Am. J. Cancer Res. 5, 2929–2943 (2015).

  57. 57.

    Giannoni, E. et al. Redox regulation of anoikis: reactive oxygen species as essential mediators of cell survival. Cell Death Differ. 15, 867–878 (2008).

  58. 58.

    Kumar, S. et al. Metformin intake is associated with better survival in ovarian cancer: a case-control study. Cancer 119, 555–562 (2013).

  59. 59.

    Bodmer, M., Becker, C., Meier, C., Jick, S. S. & Meier, C. R. Use of metformin and the risk of ovarian cancer: a case-control analysis. Gynecol. Oncol. 123, 200–204 (2011).

  60. 60.

    Romero, I. L. et al. Relationship of type II diabetes and metformin use to ovarian cancer progression, survival, and chemosensitivity. Obstet. Gynecol. 119, 61–67 (2012).

  61. 61.

    Col, N. F., Ochs, L., Springmann, V., Aragaki, A. K. & Chlebowski, R. T. Metformin and breast cancer risk: a meta-analysis and critical literature review. Breast Cancer Res. Treat. 135, 639–646 (2012).

  62. 62.

    Yap, T. A. et al. Phase I trial of IACS-010759 (IACS), a potent, selective inhibitor of complex I of the mitochondrial electron transport chain, in patients (pts) with advanced solid tumors. J. Clin. Oncol. 37, 3014–3014 (2019).

  63. 63.

    Hafeez, B. Bin et al. Plumbagin inhibits prostate cancer development in TRAMP mice via targeting PKCε, Stat3 and neuroendocrine markers. Carcinogenesis 33, 2586–2592 (2012).

  64. 64.

    Hafeez, B. Bin et al. Plumbagin inhibits prostate carcinogenesis in intact and castrated PTEN knockout mice via targeting PKCε, Stat3, and epithelial-to-mesenchymal transition markers. Cancer Prev. Res. 8, 375–386 (2015).

  65. 65.

    Fiorillo, M. et al. Repurposing atovaquone: targeting mitochondrial complex III and OXPHOS to eradicate cancer stem cells. Oncotarget 7, 34084–34099 (2016).

  66. 66.

    Lv, Z., Yan, X., Lu, L., Su, C. & He, Y. Atovaquone enhances doxorubicin’s efficacy via inhibiting mitochondrial respiration and STAT3 in aggressive thyroid cancer. J. Bioenerg. Biomembr. 50, 263–270 (2018).

  67. 67.

    Nagy, Á., Lánczky, A., Menyhárt, O. & Győrffy, B. Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets. Sci. Rep. 8, 9227 (2018).

  68. 68.

    Mah, E. J., Lefebvre, A. E. Y. T., McGahey, G. E., Yee, A. F. & Digman, M. A. Collagen density modulates triple-negative breast cancer cell metabolism through adhesion-mediated contractility. Sci. Rep. 8, 17094 (2018).

  69. 69.

    Rohart, F., Gautier, B., Singh, A. & Lê Cao, K. A. mixOmics: an R package for ‘omics feature selection and multiple data integration. PLoS Comput. Biol. 13, e1005752 (2017).

Download references

Acknowledgements

We thank G. Gutierrez and M. Masoud for technical assistance and animal handling. We thank N. Pervolarakis for helpful advice on data analysis. We thank K. Kessenbrock for thoughtful feedback on experimental design and careful review of the manuscript. We thank A. L. Welm for providing the PDX samples. Image acquisition was made possible in part through access to the Optical Biology Core Facility of the Developmental Biology Center, a shared resource supported by the Cancer Center Support Grant (grant no. CA-62203), with assistance from A. Syed and a Center for Complex Biological Systems Support Grant (grant no. GM-076516) at the University of California, Irvine. This study was supported by funds from the National Cancer Institute (grant nos R01 CA057621 and U01 CA199315 to Z.W., and grant no. K22 CA190511 to D.A.L.), National Institutes of Health (grant no. R01HD073179 to E.M., P41-GM103540 to M.A.D. and A.E.Y.T.L,, and T32CA009054 to M.B.G. and R.T.D., through matched university funds through matched university funds), National Science Foundation (grant no. 1847005 to M.A.D. and NSF GRFP DGE-1839285 to A.E.Y.T.L.), Team Michelle and Friends non-profit organization, Suzette Kirby Breast Cancer Research Fund, V Foundation (grant no. V2019-019) as well as an Opportunity Award funded by the UCI Center for Complex Biological Systems (CCBS; NIGMS, grant no. P50-GM076516 to R.T.D., K.B., D.Maurer, E.M. and D.A.L.). H.A. was supported by the University of Hail, Hail, Saudi Arabia for the PhD Fellowship. D.Ma was supported by a Canadian Institutes of Health Research Postdoctoral Fellowship.

Author information

D.A.L., E.M., M.K., M.A.D., J.W.L. and Z.W. designed and supervised the research. R.T.D., Y.Y., K.B., M.B.G., D.Ma, A.E.Y.T.L., A.T.P., H.A., G.A.H., J.L. and D.A.L. performed the research. R.T.D., K.B., Z.X. and D.Maurer performed the bioinformatic analyses. R.T.D. and D.A.L. wrote the manuscript, and all authors discussed the results and provided comments and feedback.

Correspondence to Devon A. Lawson.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Extended data

Extended Data Fig. 1 Quality control and exclusion criteria for single-cell RNA sequencing.

a, Identification and removal of poor-quality cell libraries. Plots show the number of genes detected in each cell from each PDX model. Cells (x axis) are ordered from fewest to most genes detected. Cells with fewer than 2500 genes detected (horizontal line) were excluded. b, Identification and removal of noisy/background genes. Plots show the distribution of genes detected per cell. Vertical line indicates that genes detected in fewer than 8 cells were excluded. c, Violin plots indicate the expression of mitochondrial genes as a percentage of total gene expression in each cell separated by HCI001 (n = 375 cells), HCI002 (n = 576 cells), and HCI010 (n = 756 cells). Cells were excluded if > 50% of genes detected were mitochondrial (horizontal line). Black line with dot indicates median. (d, e) Cells from HCI001 (d) and HCI010 (e) are plotted based on the relative expression of gene sets associated with G1/S (x-axis) and G2/M (y- axis) stages of the cell cycle. Left plots: Relative expression of the proliferation-associated gene MKI67 is shown in HCI001 and HCI010. Middle plots: Cell clustering before cell cycle regression. Cluster identities are shown in grey. Right plots: Cell clustering after cell cycle regression. Colours indicate new cluster identity of each cell and correspond with clusters shown in Fig. 2a. Bar graphs show quantification of cell clusters by cycling status before and after regression. Source data

Extended Data Fig. 2 Marker and GO term analysis of cell clusters from each PDX model.

a, tSNE plots display clustering of cells coloured by mouse of origin from PDX models HCI001 (n = 247 cells), HCI002 (n = 401 cells) and HCI010 (n = 471 cells). b, tSNE plots display clustering of cells coloured by tissue of origin from PDX models HCI001 (n = 247 cells), HCI002 (n = 401 cells) and HCI010 (n = 471 cells). c, Bar plots show selected top GO terms determined by the marker genes identified for each cell cluster. P values are determined by the Fisher exact test. Full marker gene lists utilized are listed in Supplementary Table 1. Specifically, for HCI001, n = 162 A1 genes, n = 107 A2 genes, and n = 199 A3 genes. For HCI002, n = 490 B1 genes, n = 173 B2 genes, n = 34 B3 genes, n = 181 B4 genes, and n = 194 B5 genes. For HCI010, n = 96 C1 genes, n = 247 C2 genes, n = 198 C3 genes, n = 357 C4 genes, n = 54 C5 genes, n = 110 C6 genes. d, Bar graphs show the log fold change (logFC) for selected genes from GO term pathways. Values indicate the logFC of the average gene expression for the indicated cell cluster relative to all other clusters within that PDX model. Source data

Extended Data Fig. 3 Prognostic value of micrometastasis- associated genes in basal-like breast cancer patients.

a, Kaplan-Meier curves show relapse free survival (RFS) in basal-like breast cancer patients from the KM plotter database (879 patients), based on their primary tumour expression of specified micrometastasis-associated genes. P values were determined via a log-rank test. b, Schematic for the construction of a stepwise logistic regression model to identify top biomarker candidates descriptive of primary tumour or micrometastatic cells. The data was subsampled to analyse equal numbers of micrometastatic and tumour cells from each mouse. The model was run on 10 subsamplings of the data, with the number of genes in each model determined by AIC. c, Plot demonstrating the AIC versus the number of genes included in the model. AIC is used to balance parameter additions (that is gene additions) with the descriptive power of a model. Data is presented as the 10% and 90% quantiles of the 10 data subsamplings. d, Bar plot showing the number of model appearances for each gene out of 10 data subsamplings.

Extended Data Fig. 4 Gene scoring identifies OXPHOS and glycolysis as top metabolic pathways differentially expressed between micrometastases and primary tumour cells.

Gene scores for each metabolic pathway in micrometastatic (red, n = 435 cells) or primary tumour cells (blue, n = 684 cells). Each cell in the dataset was scored by calculating the difference between the average gene expression for all the genes in each metabolic pathway versus the average gene expression of a randomly selected background gene set. Dotted line represents a zero score, which indicates the metabolic pathway is not differentially expressed relative to the background gene set. The boxed value (top right of each plot) indicates the percent of genes in the pathway that was detected in the dataset. The lower and upper hinges correspond to the first and third quartiles, and the midline represents the median. The upper and lower whiskers extend from the hinge up to 1.5 * IQR (inter-quartile range). Outlier points are indicated if they extend beyond this range.

Extended Data Fig. 5 Oligomycin treatment inhibits OXPHOS in MDA-MB-231 and 4T1-GFP cells.

a,b, Schematic of the experimental setup to determine the effects of oligomycin treatment on MDA-MB-231 (a) or 4T1-GFP (b) cells. Oligo. = oligomycin. (c-d) Bar graph (left) of the viability of MDA-MB-231 (c) or 4T1- GFP (d) cells 72-hours post-treatment determined via flow cytometry. Data is presented as the mean ± s.d. for n = 3 replicates. P values determined by unpaired, two- sided Student’s t-test. Brightfield images (right) of in vitro MDA-MB-231 (c) or 4T1-GFP (d) cells 72 hours after cessation of oligomycin treatment. Scale bar = 100 µm. e, Growth rate of MDA-MB-231 cells for the indicated time period after cessation of oligomycin treatment. Data is presented as mean ± s.d. of n = 3 replicates. P values determined by unpaired, two-sided Students t-test. f, Extracellular acidification rate (ECAR) of MDA-MB-231 treated with oligomycin compared to control cells. ECAR was measured at the conclusion of treatment with oligomycin as described in (a). Arrows indicate when drugs were added. O=oligomycin, F=FCCP, ROT/AA=Rotenone/ Antimycin A. Data is presented as mean ± s.d. of n = 4 replicates. g, Same as (e) for 4T1-GFP cells. h, Same as (f) for 4T1-GFP cells. Data is presented as mean ± s.d. of n = 3 replicates. P values determined by unpaired, two-sided Students t-test. (i, j) Additional FLIM images of the fluorescence lifetime of NADH in cultured MDA-MB-231 (i) or 4T1-GFP cells (j) as shown in Fig. 5e, f. Fields do not represent consecutive images of the same cell. O=oligomycin, C=control. Scale bar=10 µm. (k-l) Brightfield images of tumours from orthotopically injected control or oligomycin treated MDA-MB-231 (k) or 4T1-GFP (l) cells. Bar graphs indicate tumour weights (right). Data presented as mean ± s.d. of MDA-MB-231 (n = 6 oligomycin-treated, n = 6 control) and 4T1-GFP (n = 6 oligomycin-treated, n = 6 control) tumours. P-values were determined by unpaired, two-sided Student’s t-test. Scale bar = 0.5 cm. Source data

Extended Data Fig. 6 Model for metabolic shift associated with metastatic seeding in TNBC.

Metastatic cells in the lung and lymph nodes display increased OXPHOS, in contrast to primary tumour cells that express higher levels of genes associated with aerobic glycolysis. Pharmacological inhibition of OXPHOS with the complex V inhibitor oligomycin substantially attenuates lung metastasis in experimental models of TNBC, showing that OXPHOS is functionally important for metastasis.

Supplementary information

Reporting Summary

Supplementary Table 1

Marker genes characteristic of each cancer cell subpopulation (A1–A3; B1–B5; C1–C6) identified by clustering analysis using the Seurat pipeline. Values indicate the log[FC] of the average gene expression for the indicated cell cluster relative to all other clusters within that PDX model. Specifically, for HCI001, n = 87 A1 cells, n = 83 A2 cells and n = 77 A3 cells. For HCI002, n = 54 B1 cells, n = 102 B2 cells, n = 81 B3 cells, n = 76 B4 cells and n = 88 B5 cells. For HCI010, n = 102 C1 cells, n = 73 C2 cells, n = 64 C3 cells, n = 88 C4 cells, n = 84 C5 cells and n = 60 C6 cells. P values were determined with the bimod test in Seurat, which utilizes a likelihood-ratio test. Adjusted P values were determined using the default Bonferonni adjustment in the Seurat FindAllMarkers() function.

Supplementary Table 2

GO terms associated with each cancer cell subpopulation (A1–A3; B1–B5; C1–C6). Full marker gene lists utilized for GO analysis are listed in Supplementary Table 1. Specifically, for HCI001, n = 162 A1 genes, n = 107 A2 genes and n = 199 A3 genes. For HCI002, n = 490 B1 genes, n = 173 B2 genes, n = 34 B3 genes, n = 181 B4 genes and n = 194 B5 genes. For HCI010, n = 96 C1 genes, n = 247 C2 genes, n = 198 C3 genes, n = 357 C4 genes, n = 54 C5 genes and n = 110 C6 genes. For HCI001, n = 162 A1 genes, n = 107 A2 genes and n = 199 A3 genes. For HCI002, n = 490 B1 genes, n = 173 B2 genes, n = 34 B3 genes, n = 181 B4 genes and n = 194 B5 genes. For HCI010, n = 96 C1 genes, n = 247 C2 genes, n = 198 C3 genes, n = 357 C4 genes, n = 54 C5 genes and n = 110 C6 genes. P values were determined by the Fisher’s exact test in Enrichr. Adjusted P values were determined by Benjamini–Hochberg (BH) correction in Enrichr.

Supplementary Table 3

330 genes differentially expressed between micrometastases and primary tumour cells. Differential expression analysis of micrometastatic (n = 435) and primary tumour (n = 684) cells was performed using the tobit test in Seurat (additional details in Methods). P values were determined by the tobit test in Seurat, which utilizes a likelihood-ratio test. Adjusted P values were determined by the default Bonferonni adjustment in the Seurat FindAllMarkers() function.

Supplementary Table 4

GO terms for the 330 genes differentially expressed between micrometastases and primary tumour cells and conserved in all three PDX models. GO terms identified for primary tumour (n = 214 genes) and micrometastatic cells (n = 116 genes) based on the 330 micrometastasis gene signature. P values are determined by the Fisher’s exact test in Enrichr. Adjusted P values were determined by Benjamini–Hochberg (BH) correction in Enrichr.

Supplementary Table 5

Genes associated with the 37 metabolic pathways for gene scoring analysis.

Supplementary Table 6

Integrated peak intensities for metabolites detected in micrometastases and primary tumour cells by LC–HRMS.

Supplementary Table 7

Primer sequences used for quantitative real-time PCR.

Source data

Source Data Fig. 1

Statistical source data

Source Data Fig. 2

Statistical source data

Source Data Fig. 3

Statistical source data

Source Data Fig. 4

Statistical source data

Source Data Fig. 5

Statistical source data

Source Data Fig. 6

Statistical source data

Source Data Extended Data Fig. 1

Statistical source data

Source Data Extended Data Fig. 2

Statistical source data

Source Data Extended Data Fig. 5

Statistical source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Davis, R.T., Blake, K., Ma, D. et al. Transcriptional diversity and bioenergetic shift in human breast cancer metastasis revealed by single-cell RNA sequencing. Nat Cell Biol 22, 310–320 (2020). https://doi.org/10.1038/s41556-020-0477-0

Download citation