中国神经再生研究(英文版) ›› 2026, Vol. 21 ›› Issue (2): 677-678.doi: 10.4103/NRR.NRR-D-24-01201

• 观点:退行性病与再生 • 上一篇    下一篇

利用阿尔茨海默病单细胞和空间RNA测序数据库(ssREAD)进行假设驱动查询

  

  • 出版日期:2026-02-15 发布日期:2025-05-23

Utilizing Single-cell and Spatial RNAseq databasE for Alzheimer’s Disease (ssREAD) in hypothesis-driven queries

Diana Acosta, Cankun Wang, Qin Ma* , Hongjun Fu*   

  1. University, Columbus, OH, USA (Acosta D, Fu H) Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA (Wang C, Ma Q) Chronic Brain Injury Program, The Ohio State University, Columbus, OH, USA (Fu H)
  • Online:2026-02-15 Published:2025-05-23
  • Contact: Qin Ma, PhD, qin.ma@osumc.edu; Hongjun Fu, PhD, hongjun.fu@osumc.edu.

摘要: https://orcid.org/0000-0002-3264-8392 (Qin Ma) https://orcid.org/0000-0001-5346-7075 (Hongjun Fu)

Abstract: Alzheimer’s disease (AD) is the most common form of dementia. In addition to the lack of effective treatments, there are limitations in diagnostic capabilities. The complexity of AD itself, together with a variety of other diseases often observed in a patient’s history in addition to their AD diagnosis, make deciphering the molecular mechanisms that underlie AD, even more important. Large datasets of single-cell RNA sequencing, singlenucleus RNA-sequencing (snRNA-seq), and spatial transcriptomics (ST) have become essential in guiding and supporting new investigations into the cellular and regional susceptibility of AD. However, with unique technology, software, and larger databases emerging; a lack of integration of these data can contribute to ineffective use of valuable knowledge. Importantly, there was no specialized database that concentrates on ST in AD that offers comprehensive differential analyses under various conditions, such as sex-specific, region-specific, and comparisons between AD and control groups until the new Single-cell and Spatial RNA-seq databasE for Alzheimer’s Disease (ssREAD) database (Wang et al., 2024) was introduced to meet the scientific community’s growing demand for comprehensive, integrated, and accessible data analysis.