{"id":{"repo_id":"milano","oai_identifier":"oai:air.unimi.it:2434/1128003"},"canonical_url":"https://search.dev.ndltd.org/etd/milano/oai:air.unimi.it:2434/1128003","repository":{"repo_id":"milano","name":"Università degli Studi di Milano","base_url":"https://air.unimi.it/oai/request"},"display":{"title":"MITO: ROBUST INFERENCE OF MITOCHONDRIAL PHYLOGENIES AND CLONES","abstract":"Somatic evolution, the process by which cells acquire genetic and epigenetic changes throughout an individual’s lifetime, underlies both normal development and diseases like cancer. Single-cell lineage tracing (scLT) has emerged as a powerful approach to study these cellular dynamics, especially in primary tissues. In this scenario, mtDNA variants (MT-SNVs) have gained special attention recently, due to their low-profiling costs and compatibility with other informative cell-state modalities. This thesis introduces MiTo, an novel toolkit for MT-SNV-based scLT. MiTo provides an integrated pipelines for flexible preprocessing of scLT data, lineage inference, and interactive exploration of MT-SNV-derived phylogenies and clonal structures. MiTo integrates seamlessly with popular single-cell analysis libraries, filling a significant gap in the scLT community. To benchmark MiTo, we generated a new single-cell multi-modal dataset, with simultaneous and longitudinal profiling of gene expression, expressed MT-SNVs, and lentiviral barcode labels. We used this dataset to benchmark several tasks in MT-SNVs based scLT (MT-scLT), demonstrating superior performance of MiTo compared to state-of-the-art tools. Here, we found that informative MT-SNVs spaces may include even rare (i.e., 0.02-0.03 allelic frequency in at least 2 cells, and mean 1.2 1.5 alternative UMIs) detection events, but that statistically sound MT-SNVs genotyping methods are needed to handle the intrinsic noise of single-cell measurement, especially in high-clonal-complexity scenarios. Moreover, we show that MT-SNVs-based-phylogenies (MT-phylogenies) exhibit remarkable robustness to noise, even though the constrained number of available characters limits their resolution. Finally, by tracing clonally-enriched MT-SNVs, we show that multiple sub-clonal lineages within individual clones participate to the metastatic dissemination in Breast Cancer xenografts, implying stronger lineage-dependency of the metastatic phenotype in Breast Cancer that previously thought. In summary, this work highlights opportunities and limitations of MT-scLT, providing novel data analysis tools and benchmarking datasets, and demonstrating the power of scLT to investigate complex cellular dynamics in somatic evolution.","abstract_html":"Somatic evolution, the process by which cells acquire genetic and epigenetic changes throughout an individual’s lifetime, underlies both normal development and diseases like cancer. Single-cell lineage tracing (scLT) has emerged as a powerful approach to study these cellular dynamics, especially in primary tissues. In this scenario, mtDNA variants (MT-SNVs) have gained special attention recently, due to their low-profiling costs and compatibility with other informative cell-state modalities. This thesis introduces MiTo, an novel toolkit for MT-SNV-based scLT. MiTo provides an integrated pipelines for flexible preprocessing of scLT data, lineage inference, and interactive exploration of MT-SNV-derived phylogenies and clonal structures. MiTo integrates seamlessly with popular single-cell analysis libraries, filling a significant gap in the scLT community. To benchmark MiTo, we generated a new single-cell multi-modal dataset, with simultaneous and longitudinal profiling of gene expression, expressed MT-SNVs, and lentiviral barcode labels. We used this dataset to benchmark several tasks in MT-SNVs based scLT (MT-scLT), demonstrating superior performance of MiTo compared to state-of-the-art tools. Here, we found that informative MT-SNVs spaces may include even rare (i.e., 0.02-0.03 allelic frequency in at least 2 cells, and mean 1.2 1.5 alternative UMIs) detection events, but that statistically sound MT-SNVs genotyping methods are needed to handle the intrinsic noise of single-cell measurement, especially in high-clonal-complexity scenarios. Moreover, we show that MT-SNVs-based-phylogenies (MT-phylogenies) exhibit remarkable robustness to noise, even though the constrained number of available characters limits their resolution. Finally, by tracing clonally-enriched MT-SNVs, we show that multiple sub-clonal lineages within individual clones participate to the metastatic dissemination in Breast Cancer xenografts, implying stronger lineage-dependency of the metastatic phenotype in Breast Cancer that previously thought. In summary, this work highlights opportunities and limitations of MT-scLT, providing novel data analysis tools and benchmarking datasets, and demonstrating the power of scLT to investigate complex cellular dynamics in somatic evolution.","abstract_has_math":false,"creators":["COSSA, ANDREA"],"institution":"Università degli Studi di Milano","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["tutor: P. G. Pelicci ; internal advisor: M. Schaefer ; co-supervisor: A. Tirelli ; phd coordinator: D. Pasini","Schaefer","Martin; Schiebinger","Geoffrey;","A. 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Single-cell lineage tracing (scLT) has emerged as a powerful approach to study these cellular dynamics, especially in primary tissues. In this scenario, mtDNA variants (MT-SNVs) have gained special attention recently, due to their low-profiling costs and compatibility with other informative cell-state modalities. This thesis introduces MiTo, an novel toolkit for MT-SNV-based scLT. MiTo provides an integrated pipelines for flexible preprocessing of scLT data, lineage inference, and interactive exploration of MT-SNV-derived phylogenies and clonal structures. MiTo integrates seamlessly with popular single-cell analysis libraries, filling a significant gap in the scLT community. To benchmark MiTo, we generated a new single-cell multi-modal dataset, with simultaneous and longitudinal profiling of gene expression, expressed MT-SNVs, and lentiviral barcode labels. We used this dataset to benchmark several tasks in MT-SNVs based scLT (MT-scLT), demonstrating superior performance of MiTo compared to state-of-the-art tools. Here, we found that informative MT-SNVs spaces may include even rare (i.e., 0.02-0.03 allelic frequency in at least 2 cells, and mean 1.2 1.5 alternative UMIs) detection events, but that statistically sound MT-SNVs genotyping methods are needed to handle the intrinsic noise of single-cell measurement, especially in high-clonal-complexity scenarios. Moreover, we show that MT-SNVs-based-phylogenies (MT-phylogenies) exhibit remarkable robustness to noise, even though the constrained number of available characters limits their resolution. Finally, by tracing clonally-enriched MT-SNVs, we show that multiple sub-clonal lineages within individual clones participate to the metastatic dissemination in Breast Cancer xenografts, implying stronger lineage-dependency of the metastatic phenotype in Breast Cancer that previously thought. In summary, this work highlights opportunities and limitations of MT-scLT, providing novel data analysis tools and benchmarking datasets, and demonstrating the power of scLT to investigate complex cellular dynamics in somatic evolution."]},{"key":"dc:title","label":"Title","values":["MITO: ROBUST INFERENCE OF MITOCHONDRIAL PHYLOGENIES AND CLONES"]}]}],"canonical_facts":{"dc:contributor":["tutor: P. G. Pelicci ; internal advisor: M. Schaefer ; co-supervisor: A. Tirelli ; phd coordinator: D. Pasini","Schaefer","Martin; Schiebinger","Geoffrey;","A. Cossa","PELICCI, PIER GIUSEPPE","PASINI, DIEGO"],"dc:creator":["COSSA, ANDREA"],"dc:date":["2025-01-21"],"dc:description":["Somatic evolution, the process by which cells acquire genetic and epigenetic changes throughout an individual’s lifetime, underlies both normal development and diseases like cancer. Single-cell lineage tracing (scLT) has emerged as a powerful approach to study these cellular dynamics, especially in primary tissues. In this scenario, mtDNA variants (MT-SNVs) have gained special attention recently, due to their low-profiling costs and compatibility with other informative cell-state modalities. This thesis introduces MiTo, an novel toolkit for MT-SNV-based scLT. MiTo provides an integrated pipelines for flexible preprocessing of scLT data, lineage inference, and interactive exploration of MT-SNV-derived phylogenies and clonal structures. MiTo integrates seamlessly with popular single-cell analysis libraries, filling a significant gap in the scLT community. To benchmark MiTo, we generated a new single-cell multi-modal dataset, with simultaneous and longitudinal profiling of gene expression, expressed MT-SNVs, and lentiviral barcode labels. We used this dataset to benchmark several tasks in MT-SNVs based scLT (MT-scLT), demonstrating superior performance of MiTo compared to state-of-the-art tools. Here, we found that informative MT-SNVs spaces may include even rare (i.e., 0.02-0.03 allelic frequency in at least 2 cells, and mean 1.2 1.5 alternative UMIs) detection events, but that statistically sound MT-SNVs genotyping methods are needed to handle the intrinsic noise of single-cell measurement, especially in high-clonal-complexity scenarios. Moreover, we show that MT-SNVs-based-phylogenies (MT-phylogenies) exhibit remarkable robustness to noise, even though the constrained number of available characters limits their resolution. Finally, by tracing clonally-enriched MT-SNVs, we show that multiple sub-clonal lineages within individual clones participate to the metastatic dissemination in Breast Cancer xenografts, implying stronger lineage-dependency of the metastatic phenotype in Breast Cancer that previously thought. In summary, this work highlights opportunities and limitations of MT-scLT, providing novel data analysis tools and benchmarking datasets, and demonstrating the power of scLT to investigate complex cellular dynamics in somatic evolution."],"dc:identifier":["https://hdl.handle.net/2434/1128003","http://dx.doi.org/10.13130/cossa-andrea_phd2025-01-21","10.13130/cossa-andrea_phd2025-01-21"],"dc:language":["eng"],"dc:publisher":["Università degli Studi di Milano","place:Istituto Europeo di Oncologia"],"dc:relation":["numberofpages:117","alleditors:Schaefer, Martin; Schiebinger, Geoffrey;"],"dc:rights":["info:eu-repo/semantics/openAccess"],"dc:subject":["Mitochondria","Lineage Tracing","Cancer Evolution","Settore MED/04 - Patologia Generale","Settore MEDS-04/A - Anatomia patologica"],"dc:title":["MITO: ROBUST INFERENCE OF MITOCHONDRIAL PHYLOGENIES AND CLONES"],"dc:type":["info:eu-repo/semantics/doctoralThesis"]},"updated_at":"2026-07-27T20:19:03Z"}