Exploring Brain Somatic Mosaicism Through Robust Copy Number Variation Detection in Single Cell Whole Genome Sequencing Data

The presence of intra-individual genomic diversity in neurons and other brain cells, known as brain somatic mosaicism, has gone largely unnoticed and unappreciated historically. But growing evidence accumulated in recent years shows that neurons are highly genomically heterogeneous as a consequence of mutations ranging in scale from single nucleotide variants to whole chromosome copy number variations (CNVs). Mosaicism can disrupt particular cell signaling pathways to cause disease but is also common in healthy individuals, wherein its role is currently unknown. Given the long lifespan of neurons and their critical role in neural circuits and networks, it is probable that mosaic mutations affect neurodevelopment and brain function in ways that are not yet understood. Instrumental in identifying these genomic variations have been single cell technology and next generation sequencing, which allow for high resolution, high throughput measurement of individual genomes.

To quantify the extent of mosaicism in healthy brains and explore its biological implications, we developed a robust analysis pipeline for CNV detection and analyzed single cell whole genome sequencing (scWGS) data from over 1200 human brain cells from 15 individuals. Notably, our results showed a significant anticorrelation between the percent of an individual’s neurons containing CNVs and age. We also sequenced over 800 mouse neurons from 10 mice to examine mosaic CNVs in relationship to aging and perturbations of various genes of interest. In brief, our work demonstrates the value of large datasets in constructing a rigorous CNV detection pipeline, documents the prevalence and characteristics of mosaic CNVs in the brain, and contributes insights to our understanding of the causes and consequences of somatic mosaicism in the brain.