Hematopoietic stem cell (HSC) aging underlies age-related immune decline, anemia and increased risk of hematologic malignancies, including clonal hematopoiesis and leukemia. Many available microarray and bulk RNA sequencing 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 HSC molecular aging signatures. We also summarize recent single-cell RNA sequencing, HSC aging intervention, and sex difference datasets. Despite these advances in technology and available sequencing datasets, 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 (transcriptome, epigenome, and proteome) and single-cell resolution to uncover heterogeneity and trajectories, as well as introduce the application of artificial intelligence and machine learning for predictive modeling, epigenome aging clocks, variant calling, clonal hematopoiesis detection, chromatin-based age prediction, and trajectory inference. 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 curated, centralized, interactive database as a community resource to integrate these layers, enabling meta-analyses, artificial intelligence-driven discoveries, and accelerated therapeutic interventions for age-related hematopoietic disorders.
