{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/125548"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/125548","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Algorithms for single-cell phylogenetics to study tumor evolution and adaptive immune response","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","abstract_has_math":false,"creators":["Weber, Leah Leslie"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["El-Kebir, Mohammed","Warnow, Tandy","Milenkovic, Olgica","Navin, Nicholas"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-06-28","date_published":"2024-06-28","updated_at":"2026-07-22T22:25:02Z","subjects":["Tumor Evolution","Single-cell Sequencing","Phylogenetics","Somatic Hypermutation","Class Switch Recombination","Tumor Phylogeny Inference"],"languages":["en","eng"],"rights":["Copyright 2024 Leah Leslie Weber"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/125548","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["El-Kebir, Mohammed","Warnow, Tandy","Milenkovic, Olgica","Navin, Nicholas"]},{"key":"dc:creator","label":"Author","values":["Weber, Leah Leslie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-06-28","2024-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Tumor Evolution","Single-cell Sequencing","Phylogenetics","Somatic Hypermutation","Class Switch Recombination","Tumor Phylogeny Inference"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Leah Leslie Weber"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/125548"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","The student, Leah Weber, accepted the attached license on 2024-06-28 at 12:20.","The student, Leah Weber, submitted this Dissertation for approval on 2024-06-28 at 12:34.","This Dissertation was approved for publication on 2024-06-28 at 16:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20895 on 2025-02-04 at 21:03:59","Molecular phylogenetics, a field at the intersection of biology and computer science, utilizes molecular data such as DNA, RNA, and protein sequences to infer evolutionary relationships among various biological entities. This thesis focuses on advancing the study of microevolutionary processes in cellular populations by leveraging the capabilities of single-cell sequencing technologies. Microevolutionary processes are characterized by high mutation rates, rapid population dynamics, and strong selective pressures, distinguishing them from macroevolutionary processes that typically involve gradual changes over longer timescales. Two notable examples of cellular populations undergoing microevolutionary processes relevant to human health are (i) populations of tumor cells undergoing clonal evolution and (ii) B cells during the adaptive immune response. Single-cell sequencing technologies have transformed our ability to analyze these cellular populations at a granular level, enabling the sequencing of individual cells within a population. This high-resolution data provides a rich source of information for phylogenetic inference, allowing for the direct but noisy observation of genetic, epigenetic, proteomic, or transcriptomic profiles. This thesis develops novel algorithms that (i) account for the intricacies of the microevolutionary processes of interest and (ii) account for sequencing errors that may distort the signal from the noise. The algorithms proposed in this thesis are specifically designed to improve the accuracy of phylogenetic reconstruction in order to facilitate a deeper understanding of intra-tumor heterogeneity and B cell evolution. By integrating biological constraints with the intricacies of sequencing technologies, these algorithms aim to provide more accurate reconstructions of evolutionary histories, which are crucial for understanding cancer progression and immune response. One key contribution is the development of a method for optimizing single-cell DNA sequencing experimental design with the goal of accurate downstream phylogeny inference. This thesis presents a strategy to use a less expensive, prior bulk DNA sequencing sample to recommend the minimum number of single cells to sequence needed to resolve phylogenetic uncertainty. Another contribution is the proposal of two novel algorithms for identifying and correcting sequencing errors, such as doublets and allelic dropout, which are prevalent in single-cell DNA sequencing data. This thesis also proposes two new phylogenetic inference methods for reconstructing the evolution of a tumor from the latest generation of high-throughput but ultra-low coverage single-cell DNA sequencing technologies. The first algorithm scales well with the size of the input and requires minimal preprocessing steps but focuses solely on reconstructing the evolutionary history of point mutations. The second algorithm more directly utilizes the strengths of these technologies, i.e., the uniformity of sequencing coverage, to infer a comprehensive tumor phylogeny that captures both point mutations and large amplifications and deletions of genomic regions. Finally, this thesis proposes a new method of B cell phylogenetic inference for single-cell RNA sequencing data. Single-cell RNA sequencing enables the complete characterization of the microevolutionary processes of B cells during an adaptive immune response, yielding features for somatic hypermutation (SHM) and class switch recombination (CSR). Both of these microevolutionary processes play a critical role in yielding an effective short and long-term immune response. To date, existing phylogenetic approaches to reconstruct B cell evolution have primarily focused only on reconstructing the SHM process. Here, we develop an algorithm that optimally reconstructs the evolutionary history of B cell clonal lineages undergoing both SHM and CSR from single-cell RNA sequencing data. This proposed algorithm holds the potential to further advance our understanding of vaccine responses, disease progression, and the identification of therapeutic antibodies. In summary, this thesis advances the field of molecular phylogenetics by developing specialized algorithms for studying microevolutionary processes in cellular populations. By leveraging the capabilities of single-cell sequencing technologies, these algorithms provide a deeper understanding of cancer progression and immune responses, ultimately advancing our biological knowledge of these highly complex systems and facilitating the design of more effective cancer treatments and immunization strategies."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Algorithms for single-cell phylogenetics to study tumor evolution and adaptive immune response"]}]}],"canonical_facts":{"dc:contributor":["El-Kebir, Mohammed","Warnow, Tandy","Milenkovic, Olgica","Navin, Nicholas"],"dc:creator":["Weber, Leah Leslie"],"dc:date":["2024-06-28","2024-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","The student, Leah Weber, accepted the attached license on 2024-06-28 at 12:20.","The student, Leah Weber, submitted this Dissertation for approval on 2024-06-28 at 12:34.","This Dissertation was approved for publication on 2024-06-28 at 16:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20895 on 2025-02-04 at 21:03:59","Molecular phylogenetics, a field at the intersection of biology and computer science, utilizes molecular data such as DNA, RNA, and protein sequences to infer evolutionary relationships among various biological entities. This thesis focuses on advancing the study of microevolutionary processes in cellular populations by leveraging the capabilities of single-cell sequencing technologies. Microevolutionary processes are characterized by high mutation rates, rapid population dynamics, and strong selective pressures, distinguishing them from macroevolutionary processes that typically involve gradual changes over longer timescales. Two notable examples of cellular populations undergoing microevolutionary processes relevant to human health are (i) populations of tumor cells undergoing clonal evolution and (ii) B cells during the adaptive immune response. Single-cell sequencing technologies have transformed our ability to analyze these cellular populations at a granular level, enabling the sequencing of individual cells within a population. This high-resolution data provides a rich source of information for phylogenetic inference, allowing for the direct but noisy observation of genetic, epigenetic, proteomic, or transcriptomic profiles. This thesis develops novel algorithms that (i) account for the intricacies of the microevolutionary processes of interest and (ii) account for sequencing errors that may distort the signal from the noise. The algorithms proposed in this thesis are specifically designed to improve the accuracy of phylogenetic reconstruction in order to facilitate a deeper understanding of intra-tumor heterogeneity and B cell evolution. By integrating biological constraints with the intricacies of sequencing technologies, these algorithms aim to provide more accurate reconstructions of evolutionary histories, which are crucial for understanding cancer progression and immune response. One key contribution is the development of a method for optimizing single-cell DNA sequencing experimental design with the goal of accurate downstream phylogeny inference. This thesis presents a strategy to use a less expensive, prior bulk DNA sequencing sample to recommend the minimum number of single cells to sequence needed to resolve phylogenetic uncertainty. Another contribution is the proposal of two novel algorithms for identifying and correcting sequencing errors, such as doublets and allelic dropout, which are prevalent in single-cell DNA sequencing data. This thesis also proposes two new phylogenetic inference methods for reconstructing the evolution of a tumor from the latest generation of high-throughput but ultra-low coverage single-cell DNA sequencing technologies. The first algorithm scales well with the size of the input and requires minimal preprocessing steps but focuses solely on reconstructing the evolutionary history of point mutations. The second algorithm more directly utilizes the strengths of these technologies, i.e., the uniformity of sequencing coverage, to infer a comprehensive tumor phylogeny that captures both point mutations and large amplifications and deletions of genomic regions. Finally, this thesis proposes a new method of B cell phylogenetic inference for single-cell RNA sequencing data. Single-cell RNA sequencing enables the complete characterization of the microevolutionary processes of B cells during an adaptive immune response, yielding features for somatic hypermutation (SHM) and class switch recombination (CSR). Both of these microevolutionary processes play a critical role in yielding an effective short and long-term immune response. To date, existing phylogenetic approaches to reconstruct B cell evolution have primarily focused only on reconstructing the SHM process. Here, we develop an algorithm that optimally reconstructs the evolutionary history of B cell clonal lineages undergoing both SHM and CSR from single-cell RNA sequencing data. This proposed algorithm holds the potential to further advance our understanding of vaccine responses, disease progression, and the identification of therapeutic antibodies. In summary, this thesis advances the field of molecular phylogenetics by developing specialized algorithms for studying microevolutionary processes in cellular populations. By leveraging the capabilities of single-cell sequencing technologies, these algorithms provide a deeper understanding of cancer progression and immune responses, ultimately advancing our biological knowledge of these highly complex systems and facilitating the design of more effective cancer treatments and immunization strategies."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/125548"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Leah Leslie Weber"],"dc:subject":["Tumor Evolution","Single-cell Sequencing","Phylogenetics","Somatic Hypermutation","Class Switch Recombination","Tumor Phylogeny Inference"],"dc:title":["Algorithms for single-cell phylogenetics to study tumor evolution and adaptive immune response"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}