{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/162300"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/162300","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Decoding Brain Somatic Mosaicism with New Single-Cell Copy Number Analysis Methods","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_html":"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_has_math":false,"creators":["Zhao, Yifan"],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Harvard-MIT Program in Health Sciences and Technology","school":null,"contributors":[],"advisors":["Park, Peter J."],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05","date_published":"2025-05","updated_at":"2026-07-22T22:21:09Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"rights_urls":["https://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/162300","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Park, Peter J."]},{"key":"dc:contributor.department","label":"Department","values":["Harvard-MIT Program in Health Sciences and Technology"]},{"key":"dc:creator","label":"Author","values":["Zhao, Yifan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-08-11T14:16:53Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-08-11T14:16:53Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-05"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctoral","Doctor of Philosophy"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/162300"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Decoding Brain Somatic Mosaicism with New Single-Cell Copy Number Analysis Methods"]}]}],"canonical_facts":{"dc:contributor.advisor":["Park, Peter J."],"dc:contributor.department":["Harvard-MIT Program in Health Sciences and Technology"],"dc:creator":["Zhao, Yifan"],"dc:date.accessioned":["2025-08-11T14:16:53Z"],"dc:date.available":["2025-08-11T14:16:53Z"],"dc:date.issued":["2025-05"],"dc:description.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. 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