About HSC Aging Atlas
A comprehensive platform for exploring the molecular landscape of hematopoietic stem cell aging
Hematopoietic stem cell (HSC) aging underlies age-related immune decline, anemia, and increased risk of hematologic malignancies, including clonal hematopoiesis and leukemia. Many available bulk RNA-sequencing (RNA-seq) studies have elucidated conserved transcriptional hallmarks, such as myeloid bias, inflammation dysregulation, and self-renewal reinforcement in aged HSCs across mouse and human models.
Here, we review key publicly available transcriptome and epigenome datasets from landmark studies, highlighting their contributions to defining molecular aging signatures. Despite these advances, fragmented data access, limited cross-species integration, and scarcity of multi-omics and single-cell contexts hinder progress.
We discuss strategies for dataset harmonization, incorporation of multi-omics (epigenomics, proteomics), and single-cell resolution to uncover heterogeneity and trajectories, as well as investigate the application of artificial intelligence (AI)/machine learning for predictive modeling and epigenome aging clocks.
Bridging insights from genetic mutant mouse models to emerging human bone marrow organoids offers translational potential for modeling HSC aging in vitro. We propose a centralized, interactive database as a community resource to integrate these layers, enabling meta-analyses, AI-driven discoveries, and accelerated therapeutic interventions for age-related hematopoietic disorders.
Paper Selection Criteria
Research papers are classified into three categories: Core papers represent landmark studies that have significantly advanced our understanding of HSC aging; Supplementary papers provide additional context and supporting evidence; Interventions papers focus on therapeutic approaches and experimental interventions.
Gene Signature Analysis
Core gene signatures are identified through rigorous meta-analysis across multiple studies. Each gene is assigned a consistency score representing the number of independent studies reporting dysregulation, and an average log2 fold change (log2FC) value indicating the magnitude and direction of expression change in aged versus young HSCs.
Platform Coverage
The database encompasses diverse experimental platforms including bulk RNA-seq, single-cell RNA-seq (10x and SMART-seq), ATAC-seq for chromatin accessibility, ChIP-seq for histone modifications, bisulfite sequencing and RRBS for DNA methylation, and microarray technologies.
Our AI agent leverages large language models to provide intelligent, context-aware responses to your questions about HSC aging research. The agent has access to the complete database of papers and gene signatures, enabling it to synthesize information across multiple studies and provide comprehensive answers.
Whether you're exploring specific genes, searching for papers on particular topics, or seeking to understand complex biological mechanisms, the AI agent can help guide your research journey.
Transcriptional Changes
- • Myeloid bias and lineage skewing
- • Inflammation and immune dysregulation
- • Self-renewal pathway alterations
Epigenetic Modifications
- • DNA methylation changes
- • Histone modification patterns
- • Chromatin accessibility shifts
Cellular Mechanisms
- • Mitochondrial dysfunction
- • DNA damage accumulation
- • Metabolic reprogramming
Therapeutic Interventions
- • Dietary restriction and fasting
- • NAD+ supplementation
- • Targeted molecular therapies
Scientific Goals
The HSC Aging Atlas aims to serve as a centralized community resource that accelerates discovery by providing unified access to curated data, enabling cross-study comparisons, facilitating meta-analyses, and supporting the development of predictive models for HSC aging and rejuvenation strategies.