ZEBRA

ZEBRA integrates single-cell and single-nucleus RNA sequencing data from human and murine brains to provide a harmonized molecular atlas for investigating cellular and molecular mechanisms of neurodegenerative diseases.

Key Features:

• Extensive Data Integration: Consolidates gene expression and metadata from 33 studies encompassing over 4.2 million cells sampled across 39 brain regions in humans and mice, including patients with Alzheimer's disease, Parkinson's disease, and Multiple sclerosis, alongside relevant mouse models.
• Normalization and Integration: Normalizes and integrates heterogeneous datasets using the scVI deep probabilistic auto-encoder model to enable cross-study and cross-region comparisons.
• Comprehensive Metadata Curation: Includes curated cell- and sample-level metadata to support cell-type annotation, disease-state labeling, and comparative analyses.
• Analysis Capabilities: Provides processed data and annotations to enable cell-type and disease-specific marker discovery, cell composition analysis, and gene-wise feature mappings.

Scientific Applications:

• Comparative Transcriptomics: Enables comparison of gene expression across cell types, disease states, brain regions, and species (human and mouse).
• Marker and Pathway Identification: Supports identification of cell-type and disease-specific markers and molecular pathways implicated in Alzheimer's disease, Parkinson's disease, Multiple sclerosis, and related mouse models.
• Cellular Composition Analysis: Facilitates analysis of cell composition changes and the roles of neural, glial, and immune cell subtypes in aging and neurodegeneration within the central nervous system.
• Cross-study Integration for Hypothesis Generation: Allows integrated cross-study comparisons to generate hypotheses about cellular mechanisms underlying neurodegenerative disorders.

Methodology:

Normalization and integration of heterogeneous single-cell and single-nucleus RNA-seq datasets using the scVI deep probabilistic auto-encoder model.