Yang Changbo, Liu Yujie, Wang Xiaohua, Jia Qing, Fan Yuqi, Lu Zhenglin, Shi Jingyi, Liu Zhaoxin, Chen Gengdong, Li Jianing, Lu Weijian, Zhou Weiwei, Lv Dezhong, Zou Haozhe, Xu Juan, Li Yongsheng, Jiang Qinghua, Wang Tao, Shao Tingting
College of Bioinformatics Science and Technology, Harbin Medical University, No.157 Baojian Road, Harbin, Heilongjiang 150081, China.
School of Interdisciplinary Medicine and Engineering, Harbin Medical University, No.157 Baojian Road, Harbin, Heilongjiang 150081, China.
Nucleic Acids Res. 2025 Jan 6;53(D1):D1224-D1234. doi: 10.1093/nar/gkae945.
Single nucleotide variants (SNVs), as important components of genetic variation, affect gene expression, function and phenotype. Mining and summarizing the spatial distribution of SNVs in diseased and normal tissues for a better understanding of their characteristics and potential roles in cell-lineage determination, aging, or disease occurrence is significant. Herein, we have developed a comprehensive spatial mutation resource stSNV (http://bio-bigdata.hrbmu.edu.cn/stSNV/index.jsp), which provides an atlas of spatial SNVs in major diseased and normal tissues of human and mouse. stSNV documents 42 202 spatial mutated genes involving 898 908 SNVs called from 730 067 spots within 450 slices from 19 diseased and 28 normal tissues. Importantly, potential characteristics of SNVs are explored and provided by analyzing the perturbation of the SNVs to gene expression, spatial communication, biological function, region-specific mutated genes, spatial mutant signatures, SNV-cell co-localization and mutation core region. All these spatial mutation data and in-depth analyses have been integrated into a user-friendly interface, visualized through intuitive tables and various image formats. Flexible tools are developed to explore co-localization among clusters, genes, cell types and SNVs in the same slice. In summary, stSNV as a valuable resource helps to dissect intra-tissue genetic heterogeneity and lays the groundwork for understanding the SNVs' biological regulatory mechanisms.
单核苷酸变异(SNV)作为遗传变异的重要组成部分,会影响基因表达、功能和表型。挖掘并总结患病组织和正常组织中SNV的空间分布,以更好地了解它们在细胞谱系确定、衰老或疾病发生中的特征和潜在作用,具有重要意义。在此,我们开发了一个全面的空间突变资源库stSNV(http://bio-bigdata.hrbmu.edu.cn/stSNV/index.jsp),它提供了人类和小鼠主要患病组织和正常组织中空间SNV的图谱。stSNV记录了42202个空间突变基因,涉及从19种患病组织和28种正常组织的450个切片中的730067个位点中检测到的898908个SNV。重要的是,通过分析SNV对基因表达、空间通讯、生物学功能、区域特异性突变基因、空间突变特征、SNV-细胞共定位和突变核心区域的扰动,探索并提供了SNV的潜在特征。所有这些空间突变数据和深入分析都已整合到一个用户友好的界面中,通过直观的表格和各种图像格式进行可视化。还开发了灵活的工具来探索同一切片中簇、基因、细胞类型和SNV之间的共定位。总之,stSNV作为一种有价值的资源,有助于剖析组织内的遗传异质性,并为理解SNV的生物调控机制奠定基础。