This study utilizes a multi-omics approach to systematically characterize the cellular heterogeneity and spatial architecture of the hair follicle microenvironment in patients with androgenetic alopecia (AGA). Our primary aim is to elucidate the key mechanisms driving hair follicle stem cell (HFSC) exhaustion and to identify potential therapeutic targets. Investigators will collect six groups of scalp tissue samples, which include healthy controls and AGA patients (stratified into younger and older cohorts). By integrating spatial transcriptomics, single - cell sequencing data, investigators will map aberrant cell subpopulations and their complex interaction networks. Furthermore, the identified core targets will be functionally validated using patient-derived organoids and animal models. Expected outcomes include the identification of 3-5 critical cell subpopulations and the discovery of 8-10 disease-associated targets. Additionally, investigators will establish an integrated clinical-omics-validation database, providing a robust theoretical foundation for the precision diagnosis and treatment of AGA.
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.
Study Type
OBSERVATIONAL
Enrollment
12
In the AGA group, scalp tissue samples were concurrently harvested from the affected balding area (vertex, Hamilton-Norwood III vertex) and an unaffected non-balding area (the central occipital region along the interauricular line). In healthy controls, scalp tissue was harvested solely from the occipital region.
Spatial Transcriptomics Sequencing Samples were embedded in OCT, cryosectioned (10 μm), fixed with methanol, and subjected to H\&E staining. After permeabilization (14 min) and RNA capture, libraries were constructed and sequenced on the Xenium platform (sequencing depth ≥ 5 million reads per sample). Single-Cell Sequencing Samples were minced and enzymatically digested using Collagenase IV, Dispase II, and DNAse I, followed by erythrocyte lysis, filtering, and resuspension. Single-cell suspensions were prepared with a cell viability ≥ 90% and a clump rate \< 5%. Sequencing was performed on the 10x Genomics platform (sequencing depth ≥ 10,000 reads per cell).
The First Affiliated Hospiatl of Henan Medical University
Xinxiang, Henan, China
RECRUITINGNumber of participants with type III vertex androgenetic aopecia (AGA) as assessed by the Hamilton-Norwood classification
Visually evaluated according to the Hamilton-Norwood classification, the presentation is consistent with Type III Vertex, manifesting as pronounced hair thinning or circumscribed alopecia at the vertex, alongside frontotemporal recession that does not surpass a standard Type III
Time frame: at enrollment
Cell viability percentage and RNA integrity number (RIN) / DV200 scores of scalp tissue samples
For single-cell RNA sequencing (scRNA-seq) sample quality control (QC), cell suspensions must demonstrate ≥80% viability, \<5% clumping, optimal concentrations of 700-1,200 cells/µL, cell sizes \<40 µm, and a debris-free background. For spatial transcriptomics QC, samples require an RNA Integrity Number (RIN) ≥7.0 for fresh frozen (FF) tissues or DV200 ≥50% for formalin-fixed paraffin-embedded (FFPE) tissues; furthermore, tissue sections (typically 10 µm for FF and 5 µm for FFPE) must be perfectly flat, free of folds, tears, or ice crystal artifacts, and fit strictly within the designated capture area of the chip.
Time frame: 30 days
Number of candidate biomarkers identified for the molecular diagnosis of hair loss
By integrating spatial transcriptomic and single-cell expression landscapes, investigators aim to construct a comprehensive 'clinical sample-omics data-target validation' relational database, thereby identifying 2-3 candidate biomarkers for the molecular diagnosis of hair loss
Time frame: About 150 days after all sample collected
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