Abstract
Single-nucleus analysis allows robust cell-type classification and helps to establish relationships between chromatin accessibility and cell-type-specific gene expression. Here, using samples from 92 women of several genetic ancestries, we developed a comprehensive chromatin accessibility and gene expression atlas of the breast tissue. Integrated analysis revealed ten distinct cell types, including three major epithelial subtypes (luminal hormone sensing, luminal adaptive secretory precursor (LASP) and basal-myoepithelial), two endothelial and adipocyte subtypes, fibroblasts, T cells, and macrophages. In addition to the known cell identity genes FOXA1 (luminal hormone sensing), EHF and ELF5 (LASP), TP63 and KRT14 (basal-myoepithelial), epithelial subtypes displayed several uncharacterized markers and inferred gene regulatory networks. By integrating breast epithelial cell gene expression signatures with spatial transcriptomics, we identified gene expression and signaling differences between lobular and ductal epithelial cells and age-associated changes in signaling networks. LASP cells and fibroblasts showed genetic ancestry-dependent variability. An estrogen receptor-positive subpopulation of LASP cells with alveolar progenitor cell state was enriched in women of Indigenous American ancestry. Fibroblasts from breast tissues of women of African and European ancestry clustered differently, with accompanying gene expression differences. Collectively, these data provide a vital resource for further exploring genetic ancestry-dependent variability in healthy breast biology.
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Data availability
Most of the data are included in the paper. High-throughput data are available through the NCBI database with SuperSeries accession no. GSE244594. In addition, these data are publicly available through the CellXGene database of the Chan Zuckerberg Initiative. Source data are provided with this paper.
Code availability
No unique code was used in the study.
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Acknowledgements
We thank the countless number of women who donated normal breast tissues for research. We also thank the volunteers who facilitated this tissue collection. We offer special thanks to members of the KTB, including J. Henry, E. Nelson, M. Huynh, V. Rodriguez, A. Hughes, P. Rockey and J. Rose von Arx, as well as the Indiana University Simon Comprehensive Cancer Center (IUSCCC) tissue procurement facility for providing tissues and related data. We thank the flow cytometry core of IUSCCC for timely sorting of nuclei. We also thank D. Scoville of NanoString Technologies for processing the GeoMx data. H.N. acknowledges support for the research from the funders, the Catherine Peachey Fund and the Chan Zuckerberg Initiative Human Atlas Project. A.M.S. acknowledges funding from the Susan G. Komen Foundation to support the Susan G. Komen Tissue Bank at IUSCCC. The breast cancer research infrastructure at Indiana University School of Medicine is supported by the Vera Bradley Foundation for Breast Cancer Research.
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H.N. conceived and designed the study. P.B.-N., D.C., A.S.K., A.K.A., G.J., P.C.M., H.G., C.E., R.G., F.N., Y.L. and H.N. developed the methodology. P.B.-N., F.N., A.K.A., H.G., L.E. and G.S. acquired the data. P.B.-N., A.S.K., A.K.A., C.E., G.J., F.N., H.G., Y.L., G.S., A.M.S. and H.N. analyzed and interpreted the data. P.B.-N., D.C., R.G., H.G., F.N., A.K.A., A.M.S. and H.N. wrote, reviewed or revised the paper. A.M.S., Y.L. and H.N. provided administrative, technical or material support. H.N. and Y.L. supervised the study.
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Nature Medicine thanks Andrey Krokhotin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Sonia Muliyil, in collaboration with the Nature Medicine team.
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Extended data
Extended Data Fig. 1 Experimental workflow of single nucleus atlas generation.
Twelve major steps that were used in creation of single nucleus atlas of breast tissues are shown.
Extended Data Fig. 2 Expression pattern of epithelial subtypes identity gene.
Expression pattern of LHS, LASP and BM cell identity genes is shown. These genes have not been previously reported to be expressed in specific subtypes of breast epithelial cells.
Extended Data Fig. 3 DNA binding motif analyses using Signac.
a) DNA binding motifs differentially active in every cell type of the breasts are shown. b) Expression patterns of select transcription factors whose DNA binding motifs are enriched in epithelial subtypes. c) DNA binding motifs differentially active in epithelial cell types. d) Footprinting analyses show lack of Tn5 integration in regions that carry epithelial cell specific motifs. e) Representative immunohistochemistry images of breast tissues stained with antibodies against ERα (n=17), FOXA1 (n = 18) and GATA3 (n = 20). Nuclei in ducts and lobules analyzed has been marked.
Extended Data Fig. 4 Spatial transcriptomics to determine differences in gene expression between ductal and lobular breast epithelial cells.
a) UMAP showing differences in gene expression patterns between timepoint 1 and timepoint 2. b) Age and BMI of donors at two timepoints of tissues collected for spatial transcriptomics are also indicated. c) Staining pattern of breast tissues with antibodies against pan-keratin, FABP4 and smooth muscle actin. N = 10. d) Representative regions of interest related to ducts, lobules and adipocytes selected for RNA extraction and sequencing. N = 10. e) Deconvolution of spatial transcriptomics data show elevated Adi-2, macrophages and Endo-2 at timepoint 2 compared to timepoint 1 in most samples.
Extended Data Fig. 5 Gene expression and signaling differences between epithelial cells of ducts and lobules.
a) Expression pattern of 10 genes that showed differential expression in ductal epithelial cells compared to lobular epithelial cells assessed using multiome data. b) Differences in signaling pathways in ductal and lobular epithelial cells. Data from all samples were used to generate these networks. c) PTBP1 whose expression in normal breast epithelial cells was reduced in timepoint 2 compared to timepoint 1, is overexpressed in all breast cancer subtypes compared to normal breast. Statistical significance was derived using Unpaired t-test. Samples are biologically independent. (Normal: N = 114, low- 55.146, First quartile (Q1)-87.064, median- 109.154, Third quartile (Q3) - 123.208, high- 163.066; Luminal: N = 566, low- 85.382, q1- 138.168, median- 159.404, q3- 180.133, high- 242.444; HER2 positive: N = 37, low- 105.775, q1- 122.596, median- 132.549, q3- 148.043, high- 188.8; TNBC Basal-like 1: N = 13, low- 152.31, q1- 166.83, median- 182.37, q3- 206.14, high- 220.45; TNBC Basal-like 2: N = 11, low- 119.54, q1- 161.645, median- 179.97, q3- 210.075, high- 217.12; TNBC Immunomodulatory: N = 20, low- 123.85, q1- 139.06, median- 155.46, q3- 179.92, high- 242.18; TNBC luminal androgen receptor: N = 8, low- 123.99, q1- 129.368, median- 136.68, q3- 142.92, high- 153.48; TNBC mesenchymal stem-like: N = 8, low- 96.39, q1- 129.857, median- 154.925, q3- 177.99, high- 203.06; TNBC Mesenchymal: N = 29, low- 75.03, q1- 140.838, median- 165.18, q3- 200.85, high- 260.73; TNBC unspecified: N = 27, low- 100.17, q1- 144.165, median- 167.22, q3- 193.375, high- 264.97).
Extended Data Fig. 6 Age-dependent signaling pathway alterations in ductal and lobular epithelial cells of the breast.
Genes differentially expressed in ductal and lobular epithelial cells at timepoint 2 compared to timepoint 1 from sample #3 were subjected to Ingenuity Pathway Analysis. a) EIF2 signaling pathway enrichment with age. b) Oxidative phosphorylation pathway enrichment with age.
Extended Data Fig. 7 Chromatin accessibility and expression patterns of BM cell-enriched markers.
a) Expression and chromatin accessibility pattern of KRT14 and TP63 in various genetic ancestry and BRCA1/2 mutation carriers. b) Signaling pathways uniquely active in alveolar progenitor cells enriched in Indigenous Americans. Legend within the figure provides details of relationship between molecules of the signaling network.
Extended Data Fig. 8 Genetic ancestry dependent variability in expression of fibroblast-enriched genes.
a) Differences in expression of fibroblast-enriched genes in breast tissue fibroblasts of African ancestry compared to European ancestry. Fourteen clusters (0-13) are shown in Fig. 5g of the main text. b) Expression levels of genes that classify fibroblasts into four subtypes are also shown.
Extended Data Fig. 9 Relationship between breast epithelial gene signatures derived from this study with gene signatures derived from single cell analysis of breast tumors.
a) Gene signature of LHS cells overlap with gene expression modules of LumA, LumB and HER2+ breast cancers, whereas gene signatures of LASP and BM cells overlap with gene expression of modules of cancer cycling and cancer basal, respectively. b) Expression patterns of genes that identify myCAFs, iCAFs, dPVLs and iPVLs among fibroblast subclusters.
Supplementary information
Supplementary Tables 1–9
Supplementary files containing Excel spreadsheets with data on genes differentially expressed in several cell types of breast lobular and ductal epithelial cells and details of breast tissue donors.
Source data
Source Data Fig. 1
Flow cytometry gating strategy for the isolation of nuclei from the breast tissues of different genetic ancestry groups.
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Bhat-Nakshatri, P., Gao, H., Khatpe, A.S. et al. Single-nucleus chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry. Nat Med (2024). https://doi.org/10.1038/s41591-024-03011-9
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DOI: https://doi.org/10.1038/s41591-024-03011-9
