ArticleNucleic acids research2024
ZEBRA: a hierarchically integrated gene expression atlas of the murine and human brain at single-cell resolution.
Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Review
- scAgeClock: a single-cell transcriptome-based human aging clock model using gated multi-head attention neural networks.npj aging · 2026Article
- MCPmed: a call for Model Context Protocol-enabled bioinformatics web services for LLM-driven discovery.Briefings in bioinformatics · 2026Article
- DicePlot: a package for high-dimensional categorical data visualization.Bioinformatics (Oxford, England) · 2026Article
- Angiopoietin signalling is a central axis of amyloid-driven vascular dysfunction in Alzheimer's disease.bioRxiv : the preprint server for biology · 2025Article
- A single-cell atlas to map sex-specific gene-expression changes in blood upon neurodegeneration.Nature communications · 2025Article
- Co-regulation of microglial subgroups in Alzheimer's amyloid pathology: Implications for diagnosis and drug development.PloS one · 2025Article
- The CEMIP Hyaluronidase is Elevated in Oligodendrocyte Progenitor Cells and Inhibits Oligodendrocyte Maturation.ASN neuro · 2025Article
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Authors and funding
7 authors.
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Abstract
The molecular causes and mechanisms of neurodegenerative diseases remain poorly understood. A growing number of single-cell studies have implicated various neural, glial, and immune cell subtypes to affect the mammalian central nervous system in many age-related disorders. Integrating this body of transcriptomic evidence into a comprehensive and reproducible framework poses several computational challenges. Here, we introduce ZEBRA, a large single-cell and single-nucleus RNA-seq database. ZEBRA integrates and normalizes gene expression and metadata from 33 studies, encompassing 4.2 million human and mouse brain cells sampled from 39 brain regions. It incorporates samples from patients with neurodegenerative diseases like Alzheimer's disease, Parkinson's disease, and Multiple sclerosis, as well as samples from relevant mouse models. We employed scVI, a deep probabilistic auto-encoder model, to integrate the samples and curated both cell and sample metadata for downstream analysis. ZEBRA allows for cell-type and disease-specific markers to be explored and compared between sample conditions and brain regions, a cell composition analysis, and gene-wise feature mappings. Our comprehensive molecular database facilitates the generation of data-driven hypotheses, enhancing our understanding of mammalian brain function during aging and disease. The data sets, along with an interactive database are freely available at https://www.ccb.uni-saarland.de/zebra.
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