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Development of Computational Tools for Single-Cell Discovery

DePasquale, Erica
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1614021318421845

Abstract Details

2020, PhD, University of Cincinnati, Medicine: Biomedical Informatics.
Hematopoiesis is a complex developmental process that requires the coordinated regulation of gene programs to ensure proper lineage specification. Dysregulation in this system can lead to hematological malignancies, which represent 10 percent of all cancer diagnoses. Our team has been on the forefront of research in hematopoiesis, with the discovery of new mixed-lineage intermediates and the development of novel methods for characterizing myeloid progenitors from single-cell RNA-sequencing (scRNA-seq) data. While dozens of transcriptionally distinct cell states have been identified from single-cell profiling experiments in hematopoiesis, it is unknown which specific populations of cells give rise to different hematopoietic malignancies. Our preliminary data suggest distinct mixed-lineage progenitors may be implicated in the development of many subtypes of acute myeloid leukemia (AML) that differ depending on the underlying spectrum of mutations. While these results are exciting, we still lack sufficiently rigorous computational tools to identify the precise cells that give rise to leukemias. In particular, there are three computational challenges: 1) detecting and removing technical artifacts in scRNA-seq data, 2) identifying known and novel mixed-lineage states, and 3) aligning and comparing disease transcription to healthy expression. Incomplete understanding of progenitor specification and heterogeneity is an enduring issue. While several tools for lineage analysis now exist, no methods can effectively identify known or novel mixed-lineages and transitional cell states. A further complication in the identification of mixed-lineage states is the presence of multiplet captures, two or more cells sequenced together with the same barcode identifier. Current multiplet detection techniques identify problematic captures through transcriptional similarity to synthetic doublets, which can remove bipotential and multi-lineage progenitors whose expression appears to be a blend of multiple cell states. Erroneous removal of these cells not only hinders research on hematopoietic processes and diseases characterized by improper hematopoiesis, but affects research concerning any process involving differentiation. Comparison of cell types from a disease state to equivalent cells in a healthy control is the basis for biomedical research. Several approaches have been recently developed to align transcriptionally similar cells, though these tools are neither appropriate for application to heavily perturbed disease datasets, such as AML, nor designed to elucidate significant global, regional, and local differences among cell states at the pathway and network level. An improved understanding of multi-lineage cells may shed light on which populations give rise to AML and other hematological malignancies. Though the medical focus of this research is AML, the development of new computational tools for the processing and analysis of scRNA-seq data comprises the bulk of the work. The research presented in the dissertation shows how the creation of bioinformatics tools — DoubletDecon, cellHarmony, and sc-Hrodinger — to overcome challenges in scRNA-seq data analysis allows for the characterization of the heterogeneity in mixed-lineage progenitor states that potentially underlie hematological malignancies without the noise generated by the inclusion of technical artifacts.
Nathan Salomonis, M.D. (Committee Chair)
H. Leighton Grimes, Ph.D. (Committee Member)
Jaroslaw Meller, Ph.D. (Committee Member)
Krishna Roskin, PhD (Committee Member)
339 p.
Bioinformatics
Bioinformatics; Single-cell; Doublet; Cluster matching; Trajectory inference; AML

Recommended Citations

Citations

  • DePasquale, E. (2020). Development of Computational Tools for Single-Cell Discovery [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1614021318421845

    APA Style (7th edition)

  • DePasquale, Erica. Development of Computational Tools for Single-Cell Discovery. 2020. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1614021318421845.

    MLA Style (8th edition)

  • DePasquale, Erica. "Development of Computational Tools for Single-Cell Discovery." Doctoral dissertation, University of Cincinnati, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1614021318421845

    Chicago Manual of Style (17th edition)

ucin1614021318421845
721
© 2020, some rights reserved.Creative Commons License Development of Computational Tools for Single-Cell Discovery by Erica DePasquale is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.