Wolf Julian, Boneva Stefaniya, Schlecht Anja, Lapp Thabo, Auw-Haedrich Claudia, Lagrèze Wolf, Agostini Hansjürgen, Reinhard Thomas, Schlunck Günther, Lange Clemens
Eye Center, Medical Center, Faculty of Medicine, University of Freiburg, Germany.
Eye Center, Medical Center, Faculty of Medicine, University of Freiburg, Germany.
Genomics. 2022 Mar;114(2):110286. doi: 10.1016/j.ygeno.2022.110286. Epub 2022 Feb 3.
The applications of deep sequencing technologies in life science research and clinical diagnostics have increased rapidly over the last decade. Although fast algorithms for data processing exist, intuitive, portable solutions for data analysis are still rare. For this purpose, we developed a web-based transcriptome database, which provides a platform-independent, intuitive solution to easily explore and compare ocular gene expression of 100 diseased and healthy human tissue samples from 15 different tissue types collected at the Eye Center of the University of Freiburg. To ensure comparability of expression between different tissues, reads were normalized across all 100 samples. Differentially expressed genes were calculated between each tissue type to determine tissue-specific genes. Unsupervised analysis of all 100 samples revealed an accurate clustering according to different tissue types and a high tissue specificity by analyzing known tissue-specific marker genes. Bioinformatic cell type deconvolution using xCell provided detailed insights into the cellular profiles of each tissue type. Several new tissue-specific marker genes were identified. These genes were involved in tissue- or disease-specific processes, such as myelination for the optic nerve, visual perception for retina, keratinocyte differentiation for conjunctival carcinoma, as well as endothelial cell migration for choroidal neovascularization membranes. The results are accessible at the Human Eye Transcriptome Atlas website at https://www.eye-transcriptome.com. In summary, this searchable transcriptome database enables easy exploration of ocular gene expression in healthy and diseased human ocular tissues without bioinformatics expertise. Thus, it provides rapid access to detailed insights into the molecular mechanisms of various ocular tissues and diseases, as well as the rapid retrieval of potential new diagnostic and therapeutic targets.
在过去十年中，深度测序技术在生命科学研究和临床诊断中的应用迅速增加。尽管存在用于数据处理的快速算法，但直观、便携的数据分析解决方案仍然很少。为此，我们开发了一个基于网络的转录组数据库，该数据库提供了一个独立于平台的直观解决方案，可轻松探索和比较来自弗莱堡大学眼科中心收集的15种不同组织类型的100个患病和健康人类组织样本的眼部基因表达。为确保不同组织之间表达的可比性，对所有100个样本的读数进行了标准化。计算每种组织类型之间差异表达的基因，以确定组织特异性基因。对所有100个样本进行无监督分析，通过分析已知的组织特异性标记基因，揭示了根据不同组织类型的准确聚类和高组织特异性。使用xCell进行的生物信息学细胞类型反卷积提供了对每种组织类型细胞图谱的详细见解。鉴定出了几个新的组织特异性标记基因。这些基因参与组织或疾病特异性过程，如视神经的髓鞘形成、视网膜的视觉感知、结膜癌的角质形成细胞分化以及脉络膜新生血管膜的内皮细胞迁移。结果可在人类眼转录组图谱网站https://www.eye-transcriptome.com上获取。总之，这个可搜索的转录组数据库无需生物信息学专业知识就能轻松探索健康和患病人类眼组织中的眼部基因表达。因此，它提供了快速获取对各种眼组织和疾病分子机制的详细见解的途径，以及快速检索潜在的新诊断和治疗靶点的方法。
