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Mammary epithelial cells have lineage-rooted metabolic identities

Nature Metabolism volume 3, pages 665–681 (2021)Cite this article

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Abstract

Cancer metabolism adapts the metabolic network of its tissue of origin. However, breast cancer is not a disease of a single origin. Multiple epithelial populations serve as the culprit cell of origin for specific breast cancer subtypes, yet our knowledge of the metabolic network of normal mammary epithelial cells is limited. Using a multi-omic approach, here we identify the diverse metabolic programmes operating in normal mammary populations. The proteomes of basal, luminal progenitor and mature luminal cell populations revealed enrichment of glycolysis in basal cells and of oxidative phosphorylation in luminal progenitors. Single-cell transcriptomes corroborated lineage-specific metabolic identities and additional intra-lineage heterogeneity. Mitochondrial form and function differed across lineages, with clonogenicity correlating with mitochondrial activity. Targeting oxidative phosphorylation and glycolysis with inhibitors exposed lineage-rooted metabolic vulnerabilities of mammary progenitors. Bioinformatics indicated breast cancer subtypes retain metabolic features of their putative cell of origin. Thus, lineage-rooted metabolic identities of normal mammary cells may underlie breast cancer metabolic heterogeneity and targeting these vulnerabilities could advance breast cancer therapy.

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Fig. 1: Proteomics illustrates distinct metabolic networks of human MECs.
Fig. 2: Human breast single-cell transcriptomics reveals epithelial cell-state-specific metabolic networks.
Fig. 3: Mitochondrial structure and function vary with mammary lineage.
Fig. 4: Enhanced mitochondrial respiration is characteristic of MEC progenitors.
Fig. 5: Metabolic inhibitors expose lineage-restricted vulnerabilities of MECs.
Fig. 6: Breast cancer subtypes retain metabolic features of specific primary MECs.

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Data availability

MULTI-seq data is available from the NCBI Gene Expression Omnibus under accession number GSE168660. The human mammary proteome is available for download from ftp://massive.ucsd.edu/MSV000087042/ (MassiVE identifier: MSV000087042). The mouse mammary proteome data (Extended Data Fig. 4a,b) are published25 and can be downloaded from ftp://massive.ucsd.edu/MSV000079330/ (MassiVE identifier: MSV000079330). Source data are provided with this paper.

Code availability

All proteomic codes and MULTI-seq codes are available at https://github.com/mcclo/Mahendralingam-et-al.-Nat-Metab/.

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Acknowledgements

We are grateful to the Nanoscale Biomedical Imaging Facility at SickKids and the Princess Margaret and SickKids flow cytometry core facilities. We thank Z. Gartner for generously providing MULTI-seq reagents. This work is supported by funding from Canadian Institutes of Health Research (CIHR), Canadian Breast Cancer Foundation (CBCF) and Canadian Cancer Society Research Institute (CCSRI) grants to the laboratories of R.K. and T.K. M.A.P. is supported by Carlos III National Health Institute funded by FEDER funds—a way to build Europe (PI18/01029) and the Government of Catalonia (CERCA programme and 2017SGR449). M.J.M. received a CIHR Masters award, P.T. received a CBCF/CCS Doctoral Fellowship, and C.W.M. received a CIHR Banting Postdoctoral Fellowship.

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  1. These authors contributed equally: Mathepan Jeya Mahendralingam, Hyeyeon Kim, Curtis William McCloskey.

Authors and Affiliations

  1. Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada

    Mathepan Jeya Mahendralingam, Hyeyeon Kim, Curtis William McCloskey, Kazeera Aliar, Alison Elisabeth Casey, Pirashaanthy Tharmapalan, Vladimir Ignatchenko, Ankit Sinha, Jennifer Cruickshank, Ronak Shetty, Ravi N. Vellanki, Marianne Koritzinsky, Vid Stambolic, Mina Alam, Aaron David Schimmer, Hal Kenneth Berman, Thomas Kislinger & Rama Khokha

  2. Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada

    Mathepan Jeya Mahendralingam, Hyeyeon Kim, Curtis William McCloskey, Pirashaanthy Tharmapalan, Ankit Sinha, Marianne Koritzinsky, Aaron David Schimmer, Thomas Kislinger & Rama Khokha

  3. Terry Fox Laboratory, BC Cancer, Vancouver, British Columbia, Canada

    Davide Pellacani & Connie J. Eaves

  4. ProCURE, Catalan Institute of Oncology, Oncobell, Bellvitge Institute for Biomedical Research (IDIBELL), L’Hospitalet del Llobregat, Barcelona, Spain

    Mar Garcia-Valero, Luis Palomero & Miquel Angel Pujana

  5. Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada

    Marianne Koritzinsky

  6. Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada

    Marianne Koritzinsky

  7. Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada

    Hal Kenneth Berman & Rama Khokha

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  1. Mathepan Jeya Mahendralingam
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Contributions

M.J.M., A.E.C., H.K., C.W.M., T.K. and R.K. conceived the study. D.P., J.C., C.J.E. and H.K.B. provided participant samples, and A.D.S., R.N.V. and M.K. provided technical resources. M.J.M., H.K., C.W.M., A.E.C., A.S., V.S., P.T. and R.S. performed experiments, biological analyses and data interpretation. C.W.M. performed MULTI-seq analyses. A.E.C., A.S. and T.K. performed mass spectrometry. K.A., L.P., V.I., M.G.-V., C.W.M. and M.A.P. performed bioinformatics, and H.K. performed Amnis imaging flow cytometry. M.J.M., H.K., P.T., C.W.M., T.K. and R.K. wrote the paper. T.K. and R.K. supervised the study.

Corresponding authors

Correspondence to Thomas Kislinger or Rama Khokha.

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The authors declare no competing interests.

Additional information

Peer review information Primary Handling Editors: Elena Bellafante; George Caputa. Nature Metabolism thanks Ralph DeBerardinis, Fernando Martin Belmonte and Senthil Muthuswamy for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Characterization of proteomic datasets of primary FACS-purified human mammary epithelial cells.

a, Gating strategy for FACS-purifying human mammary epithelial cells (MECs). Total cells from dissected human breast tissue are gated to exclude debris. Doublet, dead cell and Lineage (Lin+) exclusion ensures sorting of single, live and non−immune cells. b, Venn diagram summarizing the distribution of the total 6040 detected proteins across human MEC proteomes. The numbers in brackets are the total number of proteins detected in that cell type. Uniquely detected genes are highlighted for each lineage. c, Heatmap shows unsupervised hierarchical clustering and z-scores for protein abundance of a set of known marker proteins well established for distinguishing mammary epithelial cell types. d, Distribution of protein abundance detected within each MEC lineage. Each point represents a protein and is colour-coded by the decile its median density is found. e, Pathway analysis using Enrichr was performed on each decile for each MEC type. The top 2 GO Biological Processes per decile are presented with associated adjusted p-values (Fisher’s Exact test) in brackets.

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Extended Data Fig. 2 Map of human mammary epithelial cell metabolism.

Metabolic map is adapted from a previously published template33. Proteins are color-coded to denote which mammary cell-type specific metabolic network exhibited their significantly increased abundance (Black = not significant or not detected; Light blue = luminal progenitors; Dark blue = mature luminal; Red = basal).

Extended Data Fig. 3 Age correlation with human proteome lineage signatures and expression of lineage-specific metabolic genes in scRNA-seq.

a, Linear regression of the mean metabolic protein abundance across age within each sample for the basal cell (BC), luminal progenitor (LP), and mature luminal (ML) metabolic signatures within their respective cell types. Linear equation (y), correlation coefficient (r2), and p-value are presented. Hormone status of each sample is represented by shapes indicated in legend. Error band represents 95% confidence interval and p-value was calculated by two-sided t-test. b, Violin plots show differentially expressed genes from Fig. 1f in basal, mature luminal, and luminal progenitor cells from our MULTI-seq dataset. Log10 expression values are presented with mean indicated by black lines. All groups are significantly different by one-way ANOVA followed by multiple pairwise comparisons, **p < 0.05, ***p < 0.01.

Extended Data Fig. 4 Characterization of human and mouse mammary mitochondria.

a, Heatmap showing unsupervised hierarchical clustering and z-scores of only metabolic proteins, determined by a curated list31, in our previously published mouse mammary proteomic dataset25. b, Heatmap showing unsupervised hierarchical clustering and z-scores of mitochondrial proteins present in the Mouse MitoCarta2.0 database in our previously published mouse mammary proteomic dataset25. c, Heatmap showing unsupervised hierarchical clustering and z-scores of only mitochondrial proteins present in the Human MitoCarta2.0 database in human mammary proteomes. d, Volcano plot highlighting the top 5 differentially expressed mitochondrial proteins in premenopausal compared to post-menopausal human samples within each of the basal (red), luminal progenitor (light blue), and mature luminal (blue) lineages. Significance is presented as log10(p-value) derived by two-sided t-test. e, Volcano plot highlighting the top 5 differentially expressed mitochondrial proteins in estrogen−treated compared to estrogen + progesterone-treated murine mammary proteomes within each of the basal (red), luminal progenitor (light blue), and mature luminal (blue) lineages. Significance is presented as log10(p-value) derived by two-sided t-test.

Extended Data Fig. 5 Gating strategies of conventional flow and imaging flow cytometry.

a, Conventional flow gating strategy for mouse mammary epithelial cells. Total cells from dissected mouse mammary gland are gated to exclude debris, doublets, dead cells and Lineage (Lin + ) to yield single, live, total mammary epithelial cells. b, General workflow of Amnis imaging flow cytometry. c, Gating strategy for image processing using the IDEAS™ software. Pre-defined features including Gradient RMS, Aspect Ratio, Area, and Raw Centroid X based on brightfield (CH01) images were used to gate focused, non−clipped, single cell images. Saturated mito-Denda2 events (based on CH02) were removed. d, The resulting live (Zombie UV–), Lin– (lineage (CD45 + CD31 + Ter119 + )-depleted) population was separated based on EpCAM (CH12), CD49f (CH06), CD49b (CH11), and Sca-1 (CH04) cell surface markers to acquire basal, luminal progenitor (LP), mature (ML), and stromal populations. Finally, distribution of mito-Dendra2 intensity from the 4 populations was plotted. e, Histogram illustrating proportional cell distribution across 4 foci bins for each population (basal: B; luminal progenitor: LP; mature luminal: ML). Each dot represents a biological replicate (n = 4 mice), and data are presented as mean ± SD.

Extended Data Fig. 6 CFC enumeration with metabolic inhibitors and exogenous metabolite rescue.

a, b, Quantification of absolute CFC counts at various concentrations of the specified a) OXPHOS inhibitor or b) glycolysis inhibitor Basal colonies are red and luminal colonies are blue. Each dot represents a mouse and number of biological replicates per drug is shown in brackets. P-values were calculated using two-way ANOVA and Sidak’s multiple comparisons test. Data are mean ± SEM. * P ≤ 0.05; ** P ≤ 0.01; *** P ≤ 0.001; ****P ≤ 0.0001. c, Boxplots of relative count of basal (red) and luminal (blue) colonies after addition of exogenous metabolites (FCCP, pyruvate, aspartate or α-ketobutyrate) following rotenone treatment (filled). Relative values were determined by normalization with matching Rotenone-negative, vehicle control basal or luminal colony (n = 4 mice). The centre line represents the median, box limits are the first and third quartiles, whiskers extend to 1.5×interquartile range and the points beyond the whiskers are outliers.

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Mahendralingam, M.J., Kim, H., McCloskey, C.W. et al. Mammary epithelial cells have lineage-rooted metabolic identities. Nat Metab 3, 665–681 (2021). https://doi.org/10.1038/s42255-021-00388-6

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