Dataset Information

Aging-Associated Alterations in Mammary Epithelia and Stroma Revealed by Single-Cell RNA Sequencing.

ABSTRACT: Aging is closely associated with increased susceptibility to breast cancer, yet there have been limited systematic studies of aging-induced alterations in the mammary gland. Here, we leverage high-throughput single-cell RNA sequencing to generate a detailed transcriptomic atlas of young and aged murine mammary tissues. By analyzing epithelial, stromal, and immune cells, we identify age-dependent alterations in cell proportions and gene expression, providing evidence that suggests alveolar maturation and physiological decline. The analysis also uncovers potential pro-tumorigenic mechanisms coupled to the age-associated loss of tumor suppressor function and change in microenvironment. In addition, we identify a rare, age-dependent luminal population co-expressing hormone-sensing and secretory-alveolar lineage markers, as well as two macrophage populations expressing distinct gene signatures, underscoring the complex heterogeneity of the mammary epithelia and stroma. Collectively, this rich single-cell atlas reveals the effects of aging on mammary physiology and can serve as a useful resource for understanding aging-associated cancer risk.

SUBMITTER: Li CM

PROVIDER: S-EPMC7898263 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Aging-Associated Alterations in Mammary Epithelia and Stroma Revealed by Single-Cell RNA Sequencing.

Li Carman Man-Chung CM Shapiro Hana H Tsiobikas Christina C Selfors Laura M LM Chen Huidong H Rosenbluth Jennifer J Moore Kaitlin K Gupta Kushali P KP Gray G Kenneth GK Oren Yaara Y Steinbaugh Michael J MJ Guerriero Jennifer L JL Pinello Luca L Regev Aviv A Brugge Joan S JS

Cell reports 20201201 13

Aging is closely associated with increased susceptibility to breast cancer, yet there have been limited systematic studies of aging-induced alterations in the mammary gland. Here, we leverage high-throughput single-cell RNA sequencing to generate a detailed transcriptomic atlas of young and aged murine mammary tissues. By analyzing epithelial, stromal, and immune cells, we identify age-dependent alterations in cell proportions and gene expression, providing evidence that suggests alveolar matura ...[more]

PMID: 33378681

Similar Datasets

Project description:BackgroundType 2 Diabetes Mellitus (T2DM) represents a major global health challenge, marked by chronic hyperglycemia, insulin resistance, and immune system dysfunction. Immune cells, including T cells and monocytes, play a pivotal role in driving systemic inflammation in T2DM; however, the underlying single-cell mechanisms remain inadequately defined.MethodsSingle-cell RNA sequencing of peripheral blood mononuclear cells (PBMCs) from 37 patients with T2DM and 11 healthy controls (HC) was conducted. Immune cell types were identified through clustering analysis, followed by differential expression and pathway analysis. Metabolic heterogeneity within T cell subpopulations was evaluated using Gene Set Variation Analysis (GSVA). Machine learning models were constructed to classify T2DM subtypes based on metabolic signatures, and T-cell-monocyte interactions were explored to assess immune crosstalk. Transcription factor (TF) activity was analyzed, and drug enrichment analysis was performed to identify potential therapeutic targets.ResultsIn patients with T2DM, a marked increase in monocytes and a decrease in CD4+ T cells were observed, indicating immune dysregulation. Significant metabolic diversity within T cell subpopulations led to the classification of patients with T2DM into three distinct subtypes (A-C), with HC grouped as D. Enhanced intercellular communication, particularly through the MHC-I pathway, was evident in T2DM subtypes. Machine learning models effectively classified T2DM subtypes based on metabolic signatures, achieving an AUC > 0.84. Analysis of TF activity identified pivotal regulators, including NF-kB, STAT3, and FOXO1, associated with immune and metabolic disturbances in T2DM. Drug enrichment analysis highlighted potential therapeutic agents targeting these TFs and related pathways, including Suloctidil, Chlorpropamide, and other compounds modulating inflammatory and metabolic pathways.ConclusionThis study underscores significant immunometabolic dysfunction in T2DM, characterized by alterations in immune cell composition, metabolic pathways, and intercellular communication. The identification of critical TFs and the development of drug enrichment profiles highlight the potential for personalized therapeutic strategies, emphasizing the need for integrated immunological and metabolic approaches in T2DM management.

Dataset Information

Aging-Associated Alterations in Mammary Epithelia and Stroma Revealed by Single-Cell RNA Sequencing.

Publications

Aging-Associated Alterations in Mammary Epithelia and Stroma Revealed by Single-Cell RNA Sequencing.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets