Multi-Omics Insights Into Androgenetic Alopecia

Data from the 2025 White Paper on China's Scalp Health Industry reveals that the hair-loss population in China has exceeded 340 million. Notably, the 20-to-35 age group accounts for nearly 70% of this demographic (a 23% increase compared to 2020). The post-90s and post-00s generations have become the primary consumers of anti-hair loss products, highlighting an increasingly prominent trend of hair loss occurring at a younger age and correlating with specific occupational profiles. Further demographic analysis indicates that finance and internet professionals enduring chronic high stress and late nights, postpartum women experiencing hormonal fluctuations, dieters with nutritional deficiencies, and individuals who frequently dye or perm their hair collectively constitute a massive and diverse market for anti-hair loss solutions. However, among the various types of hair loss, androgenetic alopecia (AGA), primarily driven by genetic factors, accounts for 80% of young outpatient cases. Epidemiological surveys indicate that the prevalence of AGA in China has reached 21.3% in males and 6% in females. The pathogenesis of AGA is highly complex, involving the interplay of multiple factors such as genetic susceptibility, abnormal androgen metabolism, cellular senescence, neuroendocrine dysregulation, and immune microenvironment imbalances; its precise molecular regulatory networks have yet to be fully elucidated.

In recent years, the rapid advancement of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics has made them cutting-edge tools for characterizing the hair follicle microenvironment and cellular heterogeneity. In the field of hair aging (graying), research has predominantly focused on the single-cell level. For instance, the first human gray hair single-cell atlas constructed by Wu et al. revealed the synergistic exhaustion mechanism of melanocyte stem cells (McSCs) and matrix hair progenitors. Furthermore, the release of noradrenaline by sympathetic nerves under acute stress, or DNA damage-induced McSC "seno-differentiation" and autoimmune responses, have all been proven to be key drivers leading to the depletion of the stem cell pool.

In exploring the mechanisms of hair loss diseases, the research horizon has further expanded from single-cell analysis to "spatial multi-omics." Spatial omics has demonstrated significant advantages, particularly for hair loss types accompanied by immune infiltration. For example, in scarring alopecia (lichen planopilaris, LPP), the combined application of scRNA-seq and spatial transcriptomics precisely mapped the abnormal accumulation of CD8+ T cells and macrophages in the hair follicle bulge region, elucidating the spatial mechanisms by which IFN-γ and oncostatin M (OSM) drive follicular fibrosis. In alopecia areata (AA), spatial omics revealed that the loss of regulatory T cells around hair follicles is the core cause of immune privilege collapse. Regarding AGA, the primary focus of this study, previous researchers have utilized spatial technologies to preliminarily characterize the fibrotic microenvironment in balding areas and, combined with mouse models, explored the molecular mechanisms by which exosomes promote hair follicle regeneration.

In summary, integrating single-cell resolution with spatial positional information to deeply investigate hair loss mechanisms has become a mainstream trend in this field. Building upon this, the present study proposes a combined application of scRNA-seq and spatial transcriptomics to systematically compare the differences in cellular heterogeneity and spatial architecture of the hair follicle microenvironment between healthy individuals and AGA patients. This study aims to pinpoint the key cell subpopulations and their spatial interaction networks driving hair follicle stem cell (HFSC) exhaustion, comprehensively elucidate the core molecular mechanisms leading to HFSC functional decline, and identify potential therapeutic targets. Ultimately, this will provide a solid theoretical foundation and robust data support for the precision diagnosis and targeted intervention of AGA.