The adult human breast is comprised of an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue1,2,3. Although most previous studies have focused on the breast epithelial system4,5,6, many of the non-epithelial cell types remain understudied. Here we constructed the comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution. Our single-cell transcriptomics study profiled 714,331 cells from 126 women, and 117,346 nuclei from 20 women, identifying 12 major cell types and 58 biological cell states. These data reveal abundant perivascular, endothelial and immune cell populations, and highly diverse luminal epithelial cell states. Spatial mapping using four different technologies revealed an unexpectedly rich ecosystem of tissue-resident immune cells, as well as distinct molecular differences between ductal and lobular regions. Collectively, these data provide a reference of the adult normal breast tissue for studying mammary biology and diseases such as breast cancer.
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The HBCA website can be viewed at http://www.breastatlas.org. The data are available at the Gene Expression Omnibus (GSE195665). The data are also available from CZI in the CELLxGENE database (https://cellxgene.cziscience.com/collections/4195ab4c-20bd-4cd3-8b3d-65601277e731).
The scripts associated with the analysis are available at GitHub (https://github.com/navinlabcode/HumanBreastCellAtlas).
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This work was supported by major funding from the Chan-Zuckerberg Initiative (CZI) SEED Network Grant (CZF2019-002432); and grants to N. Navin from the NIH National Cancer Institute (RO1CA240526, RO1CA236864, 1R01CA234496, F30CA243419), the CPRIT Single Cell Genomics Center (RP180684), the American Cancer Society (132551-RSG-18-194-01-DDC). N. Navin is an AAAS Fellow, AAAS Wachtel Scholar, Damon-Runyon Rachleff Innovator, Andrew Sabin Fellow and Jack & Beverly Randall Innovator. T.K. is funded by the NCI T32 Translational Genomics Fellowship and Rosalie B. Hite fellowship. This study was supported by the MD Anderson Sequencing Core Facility Grant (CA016672). M.P. is supported by a fellowship from the CIRM Training Grant (EDUC4-12822). The work was also supported by a Damon-Runyon Quantitative Biology Postdoctoral Fellow to R.W. We thank B. Marshall, N. Tavares and J. Cool for their guidance and support; J. Waters, S. Stingley and L. Vann from for their support on this project; J. Wiley, A. Wood, A. Alexander and A. Contreras for clinical support; A. Longworth, S. Mallya and M. Curran for their advice; Y. Lin and R. Ye at MD Anderson for help with experiments; J. Zamanian and J. Yu-Sheng Chien for assistance with data depositing; and all of the women who participated in the HBCA and donated their breast tissue to this project. We thank Enable Medicine for their help with CODEX data generation. This publication is part of the HCA (www.humancellatlas.org).
N. Navin previously served on the scientific advisory board for Resolve Biosciences (2020–2022) but did not receive any compensation. O.B., B.B.C. and N. Nikulina are employees at Akoya Biosciences. B.N. and N.K. are employees of Resolve Biosciences. The other authors declare no competing interests.
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Extended data figures and tables
a, Experimental workflow for breast tissue processing for scRNA-seq, showing different conditions used for digestion times and trypsin treatments. b, Pie chart showing ethnic backgrounds of women who provided tissue samples for the breast atlas. c, Cell type frequencies for different tissue sources (reduction mammoplasties - RM, prophylactic mastectomies - PM and contralateral mastectomies - CM), cells vs nuclei single cell RNA-seq protocols, and experimental dissociation protocol. d, Major cell type frequencies of matched left and right breasts from 22 women (left) and averages across all left and all right breast tissues (right). e, Stacked barplot showing the variation of cell type frequencies across the 126 women in scRNA-seq data. The top annotation bar shows the experimental workflow used (short/medium/long). f, Top regulons identified with SCENIC for each cell type cluster from the snRNA-seq data. g, Top regulons identified with SCENIC for each cell type cluster from the scRNA-seq data. h, Multi-dimensional scaling and Procrustes analysis to determine the concordance of left and right breast cell type frequencies and Pearson correlations for 22 women with matched breast tissue samples. P-value was calculated based on a two-sided test.
Ligand-receptor interaction plots predicted from scRNA-seq data using CellPhoneDB between the major breast cell types. a, Interaction plot between the epithelial (Basal, LumHR and LumSec) cell types. b, Interaction plot between the epithelial and immune (B-cells, T-cells, Myeloid cells) cell types. c, Interaction plot between the epithelial and stromal (Fibroblasts, Perivascular and Vascular endothelial cells) cell types. d, Interaction plot within the stromal (Fibroblasts, Myeloid, Lymphatic, Vascular and Perivascular cells) cell types. e, Interaction plot between adipocytes and stromal cell types.
a, Integrated UMAP and unbiased clustering of ST data from 10 breast samples, showing 9 ST clusters. b, Histopathological images, and spatial distribution of ST clusters in the ST data from the breast tissues. c, Concordance of ST clusters and the scRNA-seq clusters of the major cell types using Fisher’s exact test. d, Pearson correlation analysis of marker gene expression levels between the ST clusters and the scRNA-seq data for different cell types. All p-values were calculated based on two-sided tests.
a, Cell segmentation results of smFISH (Resolve) data across 12 tissue samples profiled from 5 different women. Cells were annotated based on combinations of markers for each cell type as described in Supplementary Table 6. b, Densities of cell types across three topographic areas using 12 tissues profiled by smFISH (Resolve). c, Heatmap of the top 5 targeted maker genes for each cell type in the smFISH (Resolve) data from 12 combined tissue samples. d, Cell segmentation results of CODEX data from 8 different women. Cells were annotated based on combinations or single protein markers to identify different cell types. e, Densities of cell types across three topographic areas from 8 different women by CODEX. f, Heatmap showing protein levels for markers that were used to identify different cell types in the CODEX data. (D: ducts, L: lobules and C: connective regions).
a, UMAPs of snRNA-seq data showing the expression of hormone receptor genes. b, Epithelial cell state frequencies across the 126 women in scRNA-seq data, where the top annotation bar represents the dissociation protocol. c, UMAP feature plots showing the expression of previously reported stem cell marker genes in the scRNA-seq epithelial dataset. d, Ligand-receptor interactions within the epithelial cell states predicted with CellPhoneDB. e, Cell cycle scoring of S-phase for different epithelial cell states detected in the scRNA-seq data. f, Cell cycle scoring for S-phase in the epithelial cell type clusters detected in the snRNA-seq data. g, smFISH (Resolve) data showing the expression of the MKI67 proliferation marker in the epithelial cells of the ducts and lobules from 4 different breast tissues. h, UMAP of different LumSec cell states and ELF5, LTF signature scores, respectively. i, Histopathological image of adjacent H&E section showing the anatomic annotation of ducts and lobules (left) and smFISH MERFISH (right panel) from P101 showing the spatial distribution of different LumSec cell states across different regions. j, Stacked barplot showing the distribution of different LumSec cell proportions in ducts and lobules across 3 MERFISH samples. k, Histopathological image (left panel) and smFISH MERFISH (right panel) from P101 showing the spatial distribution of the LumHR-SCGB population in a specific region of epithelium.
a, Spatial transcriptomic analysis showing clusters labelled as duct or lobule/TDLU from 3 breast tissues (P10, P35 and P47). b, smFISH (Resolve) data (P46-S1 and P46-S4) showing a subset of Keratin markers (left) and hormone receptor genes (right) and their localization to different breast tissue regions annotated as either duct or lobule/TDLU. c, CODEX data from P131 showing KRT5 in ducts and KRT19 in lobules/TDLU regions, with enlarged panels of the right. d, CODEX analysis from P130 of ductal and lobular/TDLU regions, showing differences for KRT14 levels in ducts and lobules. e, CODEX data from P131 showing protein levels of KRT8 and progesterone receptor (PR) in epithelial cells in the ducts and lobular/TDLU regions.
a, H&E staining of plasma B-cells, T-cells, mast cell and macrophages (arrows) in human breast tissues. b, Stacked barplot showing the cell type frequencies of T, B and myeloid cells across 126 women in scRNA-seq data. Top annotation bar represents different tissue dissociation protocols that were utilized. c-e, Stacked barplots showing the cell state frequencies of T, B and myeloid cells across 126 women in scRNA-seq data respectively. f, Dot plot showing expression of checkpoint/exhaustion markers in NK and T cell states from the scRNA-seq data of 126 women. g, Ligand-receptor interaction analysis predicted with CellPhoneDB between the fibroblasts cell states and macrophage cell states.
a, CODEX data from patient P130 and P131 showing localization of different immune cells with epithelial marker KRT19 and vascular marker CD31. Yellow and white arrows indicate CD4 Tregs and DCs, respectively. b, Frequency of T cells with the RUNX3 tissue residency marker in CODEX data. c, CODEX data (P130) showing immune cells in ductal, lobular and connective regions. d, Stacked barplots of CODEX data showing the density of immune cell types in each spatial region in 8 women. e, smFISH (Resolve) data (P46-S1) showing RNA localization of T, B and myeloid cells. f, Segmented smFISH (Resolve) data (P46-S1) showing cell localization of T, B and myeloid cells. g, smFISH (Resolve) data (P46-S1 and P47-S1) showing immune cell localization of B, T and myeloid cells across ducts, lobules and connective regions. h, Stacked barplots of smFISH (Resolve) data showing the density and proportion of immune cell types in different spatial regions. i, Adjacent histopathological tissue section (left) and segmented smFISH MERFISH data (right) from patient P91 showing the spatial distribution of m1, m2 macrophages and cDC2 populations in different regions of human breast tissue. j, Stacked barplot showing the density of m1, m2 macrophages and cDC2 populations in different regions across 3 smFISH MERFISH samples. k, Adjacent histopathological tissue section (left) and segmented smFISH MERFISH data (right) from patient P96 showing the spatial distribution of different B-cell states in different regions of human breast tissue. l, Stacked barplot showing the density of different B-cell states in different regions across three smFISH MERFISH samples.
a, Stacked barplot showing the fibroblasts cell state frequencies across 126 women in scRNA-seq data with top annotation bar representing the tissue dissociation protocol. b, Gene ontology enrichment analysis showing top enriched biological process gene sets associated with each cell state (Pos: positive; Neg: negative; Reg: regulation; RSTK: receptor protein serine/threonine kinase; TGF: transforming growth factor; IGF: insulin-like growth factor). c, CODEX data from P132 showing fibroblasts marked by VIM in the connective tissue (I) and interlobular (II) regions. d, smFISH (Resolve) data showing fibroblast markers in areas of connective tissue regions (I) and epithelial regions (II) from two women (P47-S1 and P69-S3). e, smFISH (Resolve) data (P35-S1) indicating spatial proximity regions with epithelial-proximal (Epi-prox), epithelial-middle (Epi-mid) and epithelial-distant (Epi-Dist) regions for 4 marker genes. f, Percentages of 4 markers that are proximal, middle or distant to the epithelial cells, quantified from the smFISH (Resolve) data. g, RNAscope in situ hybridization of breast tissues using an MMP3 probe in combination with anti-Vimentin and anti-PanCK immunofluorescent staining, with enlarged panel (right). h, Ligand-receptor interactions between fibroblasts, adipocytes and myeloid cell states predicted using CellPhoneDB.
a, Stacked barplot showing the endothelial cell state frequencies across the 126 women in scRNA-seq data, with top annotation bar showing the tissue dissociation protocol. b, Dot plot of gene ontology enrichment results for 4 lymphatic cell states. c, Heatmap showing top gene expression for vascular and lymphatic endothelial clusters detected in the ST data. d, smFISH (Resolve) data showing veins (ACKR1) and capillaries (RBP7), as well as a canonical vascular marker (VWF) in two different HBCA samples (P46-S3 and P69-S3). e, Adjacent H&E tissue section with pathological annotations (left panel) and segmented smFISH MERFISH data (right panel) from P101 showing the spatial distribution of vascular endothelial cell states in different regions of human breast tissue. f, Stacked barplot showing the density of vascular endothelial states in different regions across 3 smFISH MERFISH samples.
a, Stacked barplot showing the perivascular cell state frequencies across the 126 women in scRNA-seq data with top annotation bars indicating the tissue dissociation protocol. b, UMAPs of pericytes and vascular smooth muscle cells (VSMCs) and feature plots of the VSMCs marker genes (SYNM and ACTG2). c-e, smFISH (Resolve) data showing expression of pericyte marker RGS5, together with vascular marker VWF and fibroblast marker COL1A1 in lobular and ductal regions from 2 different breast tissue samples (P47-S1 and P46-S3). f, CODEX results from P131 showing vascular cells (anti-CD31) and pericytes (anti-LIF) in a TDLU region. g, smFISH MERFISH from P96 showing the spatial distribution of vascular endothelial cell states (left panel) and perivascular cell states (right panel) in different regions of human breast tissue. h, smFISH (MERFISH) data showing arteries (SOX17) and VSMCs (ATCG2 and SYNM) in breast tissue.
a, Boxplots showing the major cell type frequencies across ethnicity status in the n = 69 women using Wilcoxon rank sum test (top). Significant associations of cell states with ethnicity status using Fisher’s exact test (bottom). b, Boxplots showing the major cell type frequencies across pre- and post-menopause status in the n = 71 women using Wilcoxon rank sum test (top). Significant associations of cell states with menopause status using Fisher’s exact test (bottom). c, Boxplots showing the major cell type frequencies across different age groups using Wilcoxon rank sum test, young (<50 years) and old (>50 years) for n = 76 women (top). Significant associations of cell states with age groups using Fisher’s exact test (bottom). d, Boxplots showing the major cell type frequencies across different breast density (high, low) groups in the n = 16 women using Wilcoxon rank sum test (top). Significant associations of cell states with breast density using Fisher’s exact test (bottom). e, Boxplots showing the major cell type frequencies across different BMI status in 73 women using Wilcoxon rank sum test, overweight (BMI >= 25 and < 30) and obese (BMI >= 30). f, Boxplots showing the major cell type frequencies across different parity status (nulliparous, parous) status in the n=64 women using Wilcoxon rank sum test. All p-values were calculated based on two-sided tests. Boxplots show the median with interquartile ranges (25–75%), while whiskers extend to 1.5× the interquartile range from the box.
This illustration summarizes all of the breast cell types and cell states that were identified in the HBCA study. a, Summary of cell lineages from cell types to cell states. b, Mapping of cell types and cell states to the four major spatial regions (Adipose, Connective, Ductal, Lobular) that were supported by the spatial technologies. Not all cell states were assigned to specific spatial regions, in cases where the data did not support their assignment. Individual figures were created with BioRender.com.
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Kumar, T., Nee, K., Wei, R. et al. A spatially resolved single-cell genomic atlas of the adult human breast. Nature 620, 181–191 (2023). https://doi.org/10.1038/s41586-023-06252-9
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