Overview of HBCA

The goal of this project is to generate a comprehensive reference of cell types and cell states in the adult human breast tissues using single cell and spatial genomic methods. This is part of the greater Human Cell Atlas (HCA) effort and is generously funded by the Chan-Zuckerberg Initiative (CZI). This project has also established optimized tissue dissociation protocols and data analysis code that are freely available for download. In addition to our HBCA project, there are several other ongoing human breast atlas projects that focus on different areas.

Here, we profiled breast tissues from over 100 women with diverse biological states (parity, menopause, age, ethnicity), which has identified 12 major cell types and over 60 biological cell states. These cell types and cell states are spatially organized into four major areas of the breast: ductal, lobular, adipose and connective regions. Our analysis has revealed abundant pericyte, endothelial and immune cell populations in normal breast, as well as highly diverse luminal epithelial cell states. Our spatial mapping using multiple technologies revealed an unexpectedly rich ecosystem of tissue-resident immune cells in the ducts and lobules, as well as distinct epithelial cell state differences between ductal and lobular regions. Collectively, these data provide an unprecedented reference of adult normal breast tissue for studying mammary biology and disease states such as breast cancer.

UMAP Cells

All Cells

*note : these have been downsampled to 100k cells

UMAP projection of scRNA-seq data from 714,331 cells integrated across 167 tissues from 126 women, showing 10 clusters that correspond to the major cell types

Epithelial Cells

*note : these have been downsampled to 50K cells

UMAP of scRNA-seq data from 240,804 epithelial cells, showing three major epithelial types


UMAP of 102,228 basal epithelial cells


UMAP of 75,247 Luminal Hormone Responsive (LumHR) epithelial cells, showing 3 cell states


UMAP of 63,329 Luminal Secretory (LumSec) epithelial cells, showing 7 cell states

NK/T Cells

UMAP of 76,567 NK/T cells from scRNA-seq data showing 14 cell state

B Cells

UMAP of 12,510 B cells from scRNA-seq data showing 5 cell states

Myeloid Cells

UMAP of 30,789 myeloid cells from scRNA-seq data showing of 15 cell types and states


UMAP of 208,390 fibroblast cells, showing 4 cell states

Vascular Endothelial Cells

UMAP of 83,651 vascular endothelial cells, showing 3 major cell states

Lymphatic Endothelial Cells

UMAP of 8,982 lymphatic endothelial cells, showing 4 major cell states

Perivascular Cells

UMAP projection and clustering of 52,638 perivascular cells, showing 2 cell states

UMAP Nuclei


UMAP of snRNA-seq data from 120,024 nuclei from integrated across 20 tissues from 20 women, showing 11 cell type clusters

Epithelial Nuclei

UMAP of snRNA-seq data from 56,280 epithelial nuclei, showing 3 major epithelial types and 2 clusters of proliferating cells

Adipocyte Nuclei

UMAP of 4,917 adipocytes from snRNA- seq data

HBCA Team Members

Nicholas Navin

Lead PI

MD Anderson

Kai Kessenbrock


UC Irvine

Devon A. Lawson


UC Irvine

Bora Lim, MD


Baylor College of Medicine

Tapsi Kumar

Graduate Student

MD Anderson

Kevin Nee, PhD

MSTP Student

UC Irvine

Runmin Wei, PhD

Postdoctoral Associate

MD Anderson

Siyuan He

Graduate Student

MD Anderson

Quy H. Nguyen, PhD

Graduate Student

UC Irvine

Shanshan Bai

Research Laboratory Scientist

MD Anderson

Kerrigan Blake, PhD

Graduate Student

UC Irvine

Yanwen Gong, PhD

Graduate Student

UC Irvine

Maren Pein

Postdoctoral Scholar

UC Irvine

Anna Casasent

Computational Scientist

MD Anderson

Aatish Thennavan

Postdoctoral Associate

Ken Chen, PhD


UC Irvine

Sharmila Mallya

Research Associate/ manager

UC Irvine

Tatyana Lev

Graduate Student

UC Irvine

Other Helpful Links

*Acknowledgements : A big Shout Out to Ivan Yao-Yi Chang, Jenny Wu, and Jessica Gonzalez for their effort to make this website*