Yadalam Pradeep Kumar, Natarajan Prabhu Manickam, Ardila Carlos M
Department of Periodontics, Saveetha Institute of Medical and Technical Sciences, Saveetha Dental College and Hospital, Saveetha University, Chennai, 600077, Tamil Nadu, India.
Department of Clinical Sciences, Center of Medical and Bio-allied Health Sciences and Research, College of Dentistry, Ajman University, Ajman, 0971, United Arab Emirates.
Sci Rep. 2025 Jul 1;15(1):22339. doi: 10.1038/s41598-025-08027-w.
Periodontitis, a chronic inflammatory condition of the periodontium, is associated with over 60 systemic diseases. Despite advancements, precision medicine approaches have had limited success, emphasizing the need for deeper insights into cellular subpopulations and structural immunity, particularly gingival keratinocytes. This study employs autoencoder models and data augmentation techniques to explore the transcriptomic diversity of gingival keratinocytes at the single-cell level. Single-cell RNA sequencing data from GSE266897 were processed using the Scanpy library, with quality control implemented to filter cells based on predefined metrics. Clustering was performed using principal component analysis (PCA) and k-nearest neighbor (KNN) algorithms. Marker gene identification and differential expression analysis were used to characterize cell clusters. Visualization techniques, including UMAP, heatmaps, dot plots, and violin plots, provided insights into gene expression patterns. The autoencoder architecture featured an encoder reducing input size to 256 units with ReLU activation, a bottleneck layer, and a decoder restoring data dimensions. The basic Autoencoder (AE) demonstrated superior performance, achieving the lowest loss (0.725), the highest accuracy (0.695), and minimal false positives. The Test-Time Augmentation AE also performed robustly, achieving an F1 score of 0.642 and an AUC-ROC of 0.800. The Basic AE effectively modeled RNA-seq data complexity compared to Variational and Denoising Autoencoders. This study highlights advanced computational techniques to investigate gingival keratinocytes' transcriptomic diversity, revealing distinct subpopulations and differential gene expression profiles. These findings underscore the active role of keratinocytes in periodontal health and inflammatory responses, contributing to precision medicine approaches in periodontology.
牙周炎是一种牙周组织的慢性炎症性疾病，与60多种全身性疾病相关。尽管取得了进展，但精准医学方法的成效有限，这凸显了深入了解细胞亚群和结构免疫（尤其是牙龈角质形成细胞）的必要性。本研究采用自动编码器模型和数据增强技术，在单细胞水平上探索牙龈角质形成细胞的转录组多样性。使用Scanpy库处理来自GSE266897的单细胞RNA测序数据，并根据预定义指标实施质量控制以过滤细胞。使用主成分分析（PCA）和k近邻（KNN）算法进行聚类。通过标记基因鉴定和差异表达分析来表征细胞簇。包括UMAP、热图、点图和小提琴图在内的可视化技术提供了对基因表达模式的见解。自动编码器架构的编码器通过ReLU激活将输入大小减少到256个单元，有一个瓶颈层，以及一个恢复数据维度的解码器。基本自动编码器（AE）表现出卓越性能，实现了最低损失（0.725）、最高准确率（0.695）和最少误报。测试时增强自动编码器也表现稳健，F1分数为0.642，AUC-ROC为0.800。与变分自动编码器和去噪自动编码器相比，基本自动编码器有效地模拟了RNA测序数据的复杂性。本研究突出了先进的计算技术，用于研究牙龈角质形成细胞的转录组多样性，揭示了不同的亚群和差异基因表达谱。这些发现强调了角质形成细胞在牙周健康和炎症反应中的积极作用，为牙周病学中的精准医学方法做出了贡献。
