EWCE

EWCE identifies cell types enriched for expression in gene lists by using single-cell transcriptome data to reveal cellular origins and pathologies in brain disorders.

Key Features:

• Single-Cell Transcriptome Utilization: Uses single-cell RNA sequencing/transcriptome data to compute probability distributions indicating the likelihood that a gene list is associated with average expression in specific cell types.
• Application to Polygenic Disorders: Detects enrichment of susceptibility genes expressed within particular cell populations in complex polygenic conditions.
• Versatility Across Diseases and Tissues: Has been applied to human genetic data from epilepsy, schizophrenia, autism, intellectual disability, Alzheimer's disease, multiple sclerosis, and anxiety disorders.
• Insight into Cellular Pathology: Identifies primary cell type pathologies and hypothesizes secondary changes by applying the method to transcriptome data from diseased tissue.
• Validation and Application: Has revealed cell-type associations such as microglia enrichment in Alzheimer's disease and multiple sclerosis, interneuron and pyramidal neuron enrichment in autism and schizophrenia, and broad enrichment (notably interneurons) in intellectual disability and epilepsy.
• Model Validation: Can be applied to validate mouse models to assess translational relevance to human conditions.

Scientific Applications:

• Cellular mechanism discovery: Pinpoints cell types most affected by genetic susceptibility to elucidate disease etiology and progression.
• Secondary pathology detection: Identifies transcriptional changes in additional cell types within diseased tissue to infer downstream cellular effects.
• Therapeutic and diagnostic informatics: Provides cell-type–level insights that can inform development of targeted therapies and improve diagnostic interpretation.
• Model evaluation: Assesses concordance between human disease-associated cell-type enrichment and mouse model transcriptional profiles.

Methodology:

Computes expression-weighted enrichment by comparing gene lists to single-cell RNA-seq/transcriptome profiles to generate probability distributions of average expression per cell type and applies the same analysis to diseased tissue transcriptomes (and to mouse model data where available).