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Detection of Copy Number Alterations Using Single Cell Sequencing
Published on: February 17, 2017
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Mapping recurrent mosaic copy number variation in human neurons.
Chen Sun1, Kunal Kathuria2, Sarah B Emery3
1Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, 100 Washtenaw Avenue, Ann Arbor, MI, 48109, USA.
Nature Communications
|May 17, 2024
Summary
Somatic copy number variants (CNVs) occur in human neurons during development. A new method, SCOVAL, identified 226 CNV neurons, revealing nonrandom genomic rearrangement patterns.
Area of Science:
- Neuroscience
- Genetics
- Genomics
Background:
- Somatic cells with complex karyotypes are usually eliminated by the immune system.
- While cancer typically doesn't originate in neurons, somatic mutations in neurons are linked to neurodevelopmental, neuropsychiatric, and neurodegenerative diseases.
- Previous studies on copy number variant (CNV) neurons were limited by small sample sizes.
Purpose of the Study:
- To develop and validate an allele-based approach (SCOVAL) for identifying copy number variants (CNVs) in single human neurons.
- To analyze the prevalence and characteristics of CNV neurons in the human brain.
Main Methods:
- Developed SCOVAL, an allele-based validation method for copy number variant (CNV) detection in single neurons.
- Applied SCOVAL to 2,125 frontal cortical neurons from a neurotypical human brain.
Main Results:
- Identified 226 neurons with copy number variants (CNVs), including 65 with highly aberrant karyotypes involving multiple chromosome losses.
- Found that the locations of CNVs in neuronal genomes are nonrandom.
- Observed that recurrent regions of neuronal genome rearrangement are characterized by fewer, but longer, genes.
Conclusions:
- The SCOVAL method provides a robust approach for validating CNV calls in single human neurons.
- The findings reveal a significant population of neurons with CNVs during neurotypical brain development.
- The nonrandom distribution of CNVs suggests specific genomic vulnerabilities or selection mechanisms in neurons.
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