Spatially resolved mapping of cells associated with human complex traits.

Area of Science:

• Genomics
• Neuroscience
• Computational Biology

Background:

• Understanding the spatial organization of cells is vital for deciphering disease mechanisms.
• Integrating genomic data with spatial information can enhance our understanding of complex traits.

Purpose of the Study:

• To introduce genetically informed spatial mapping of cells for complex traits (gsMap), a novel computational method.
• To spatially map cells associated with human complex traits and diseases using integrated data.

Main Methods:

• gsMap integrates spatial transcriptomics data with genome-wide association study summary statistics.
• The method was benchmarked using simulations and validated on known trait-associated cells across 25 embryonic organs.
• Applied gsMap to brain spatial transcriptomics data to analyze cell distributions related to psychiatric disorders.

Main Results:

• gsMap successfully mapped cells to complex traits in a spatially resolved manner.
• Schizophrenia-associated glutamatergic neurons showed spatial resemblance to cognitive traits, distinct from depression-associated neurons.
• Schizophrenia-associated neurons were found near the dorsal hippocampus with specific gene expression patterns, while depression-associated neurons were near the medial prefrontal cortex with different gene signatures.

Conclusions:

• gsMap provides a powerful tool for spatially mapping trait-associated cells.
• This approach yields significant biological insights into the spatial distribution of relevant cells and their associated genes in complex diseases.
• The findings highlight distinct spatial cellular patterns for schizophrenia and depression in the brain.