2026-09-19T20:26:35Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1623002025-08-12T03:04:10Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Park, Peter J.
Zhao, Yifan
Harvard-MIT Program in Health Sciences and Technology
2025-08-11T14:16:53Z
2025-08-11T14:16:53Z
2025-05
2025-06-05T14:32:26.938Z
https://hdl.handle.net/1721.1/162300
https://orcid.org/0000-0003-4829-1428
Copy number variants (CNVs) represent a significant but understudied form of somatic variation in the human brain, with potential implications for neurodevelopment, aging and disease. While single-cell whole-genome sequencing (scWGS) enables genome-wide profiling at single-cell resolution, existing computational methods struggle to accurately detect non-clonal CNVs, limiting our understanding of genomic mosaicism in the brain. In this thesis, I present two novel and complementary computational approaches for high-resolution CNV analysis in single cells. The first, HiScanner, is a CNV detection method that integrates single-cell assay-specific characteristics and introduces innovations in bin size optimization, read depth normalization, and joint segmentation across cells. Through extensive benchmarking experiments, I demonstrate HiScanner’s superior performance compared to existing tools. The second is a validation method that leverages unique molecular patterns from tagmentation-based scWGS, representing the first tool that exploits fragment overlap patterns to corroborate CNV predictions. I then apply these tools to investigate CNVs in three biological contexts: tumor evolution in paired initial and recurrent meningiomas, age-related genomic changes in neurotypical human brains, and developmental patterns in fetal and postnatal brain tissues. By analyzing both scWGS and multimodal single-cell data (paired RNA-seq and ATAC-seq), I characterize cell-type-specific CNV patterns and their potential functional implications. This work establishes a robust framework for studying somatic CNVs at single-cell resolution and provides insights into genomic instability in brain development, aging, and disease.
Ph.D.
Massachusetts Institute of Technology
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https://rightsstatements.org/page/InC-EDU/1.0/
Decoding Brain Somatic Mosaicism with New Single-Cell Copy Number Analysis Methods
Thesis
application/pdf
Doctoral
Doctor of Philosophy
Publication
unknown
unknown
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<Abstract>Copy number variants (CNVs) represent a significant but understudied form of somatic variation in the human brain, with potential implications for neurodevelopment, aging and disease. While single-cell whole-genome sequencing (scWGS) enables genome-wide profiling at single-cell resolution, existing computational methods struggle to accurately detect non-clonal CNVs, limiting our understanding of genomic mosaicism in the brain. In this thesis, I present two novel and complementary computational approaches for high-resolution CNV analysis in single cells. The first, HiScanner, is a CNV detection method that integrates single-cell assay-specific characteristics and introduces innovations in bin size optimization, read depth normalization, and joint segmentation across cells. Through extensive benchmarking experiments, I demonstrate HiScanner’s superior performance compared to existing tools. The second is a validation method that leverages unique molecular patterns from tagmentation-based scWGS, representing the first tool that exploits fragment overlap patterns to corroborate CNV predictions. I then apply these tools to investigate CNVs in three biological contexts: tumor evolution in paired initial and recurrent meningiomas, age-related genomic changes in neurotypical human brains, and developmental patterns in fetal and postnatal brain tissues. By analyzing both scWGS and multimodal single-cell data (paired RNA-seq and ATAC-seq), I characterize cell-type-specific CNV patterns and their potential functional implications. This work establishes a robust framework for studying somatic CNVs at single-cell resolution and provides insights into genomic instability in brain development, aging, and disease.</Abstract>
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