Sun George, Zhou Yi-Hui
Bioinformatics Research Center, North Carolina State University, 1 Lampe Drive, Raleigh, 27695, NC, USA.
Bioinformatics Research Center, North Carolina State University, 1 Lampe Drive, Raleigh, 27695, NC, USA; Departments of Biological Sciences and Statistics, North Carolina State University, 1 Lampe Drive, Raleigh, 27695, NC, USA.
Comput Biol Med. 2025 Mar;187:109748. doi: 10.1016/j.compbiomed.2025.109748. Epub 2025 Feb 8.
Lung disease remains a leading cause of morbidity and mortality in individuals with cystic fibrosis (CF). Despite significant advances, the complex molecular mechanisms underlying CF-related airway pathology are not fully understood. Building upon previous single-cell transcriptomics studies in CF patients and healthy controls, this study employs enhanced analytical methodologies to deepen our understanding of CF-associated gene expression.
We employed advanced single-cell transcriptomics techniques, integrating data from multiple sources and implementing rigorous normalization and mapping strategies using a comprehensive lung reference panel. These sophisticated methods were designed to enhance the accuracy and depth of our analysis, with a focus on elucidating differential gene expression and characterizing co-expression network dynamics associated with cystic fibrosis (CF).
Our analysis uncovered novel genes and regulatory networks that had not been previously associated with CF airway disease. These findings highlight new potential therapeutic targets that could be exploited to develop more effective interventions for managing CF-related lung conditions.
This study provides critical insights into the molecular landscape of CF airway disease, offering new avenues for targeted therapeutic strategies. By identifying key genes and networks involved in CF pathogenesis, our research contributes to the broader efforts to improve the prognosis and quality of life for patients with CF. These discoveries pave the way for future studies aimed at translating these findings into clinical practice.
肺部疾病仍然是囊性纤维化(CF)患者发病和死亡的主要原因。尽管取得了重大进展,但CF相关气道病理学背后的复杂分子机制尚未完全了解。基于先前对CF患者和健康对照的单细胞转录组学研究,本研究采用增强的分析方法来加深我们对CF相关基因表达的理解。
我们采用先进的单细胞转录组学技术,整合来自多个来源的数据,并使用全面的肺部参考面板实施严格的标准化和映射策略。这些复杂的方法旨在提高我们分析的准确性和深度,重点是阐明差异基因表达并表征与囊性纤维化(CF)相关的共表达网络动态。
我们的分析发现了以前与CF气道疾病无关的新基因和调控网络。这些发现突出了新的潜在治疗靶点,可用于开发更有效的干预措施来管理CF相关的肺部疾病。
本研究为CF气道疾病的分子格局提供了关键见解,为靶向治疗策略提供了新途径。通过识别参与CF发病机制的关键基因和网络,我们的研究有助于更广泛地努力改善CF患者的预后和生活质量。这些发现为未来旨在将这些发现转化为临床实践的研究铺平了道路。