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Description
Induced pluripotent stem cells (iPSCs) are an intriguing approach for neurological disease modeling, because neural lineage-specific cell types that retain the donors' complex genetics can be established in vitro. The statistical power of these iPSC-based models, however, is dependent on accurate diagnoses of the somatic cell donors; unfortunately, many neurodegenerative

Induced pluripotent stem cells (iPSCs) are an intriguing approach for neurological disease modeling, because neural lineage-specific cell types that retain the donors' complex genetics can be established in vitro. The statistical power of these iPSC-based models, however, is dependent on accurate diagnoses of the somatic cell donors; unfortunately, many neurodegenerative diseases are commonly misdiagnosed in live human subjects. Postmortem histopathological examination of a donor's brain, combined with premortem clinical criteria, is often the most robust approach to correctly classify an individual as a disease-specific case or unaffected control. We describe the establishment of primary dermal fibroblasts cells lines from 28 autopsy donors. These fibroblasts were used to examine the proliferative effects of establishment protocol, tissue amount, biopsy site, and donor age. As proof-of-principle, iPSCs were generated from fibroblasts from a 75-year-old male, whole body donor, defined as an unaffected neurological control by both clinical and histopathological criteria. To our knowledge, this is the first study describing autopsy donor-derived somatic cells being used for iPSC generation and subsequent neural differentiation. This unique approach also enables us to compare iPSC-derived cell cultures to endogenous tissues from the same donor. We utilized RNA sequencing (RNA-Seq) to evaluate the transcriptional progression of in vitro-differentiated neural cells (over a timecourse of 0, 35, 70, 105 and 140 days), and compared this with donor-identical temporal lobe tissue. We observed in vitro progression towards the reference brain tissue, supported by (i) a significant increasing monotonic correlation between the days of our timecourse and the number of actively transcribed protein-coding genes and long intergenic non-coding RNAs (lincRNAs) (P < 0.05), consistent with the transcriptional complexity of the brain, (ii) an increase in CpG methylation after neural differentiation that resembled the epigenomic signature of the endogenous tissue, and (iii) a significant decreasing monotonic correlation between the days of our timecourse and the percent of in vitro to brain-tissue differences (P < 0.05) for tissue-specific protein-coding genes and all putative lincRNAs. These studies support the utility of autopsy donors' somatic cells for iPSC-based neurological disease models, and provide evidence that in vitro neural differentiation can result in physiologically progression.
ContributorsHjelm, Brooke E (Author) / Craig, David W. (Thesis advisor) / Wilson-Rawls, Norma J. (Thesis advisor) / Huentelman, Matthew J. (Committee member) / Mason, Hugh S. (Committee member) / Kusumi, Kenro (Committee member) / Arizona State University (Publisher)
Created2013
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Description
The technology expansion seen in the last decade for genomics research has permitted the generation of large-scale data sources pertaining to molecular biological assays, genomics, proteomics, transcriptomics and other modern omics catalogs. New methods to analyze, integrate and visualize these data types are essential to unveil relevant disease mechanisms. Towards

The technology expansion seen in the last decade for genomics research has permitted the generation of large-scale data sources pertaining to molecular biological assays, genomics, proteomics, transcriptomics and other modern omics catalogs. New methods to analyze, integrate and visualize these data types are essential to unveil relevant disease mechanisms. Towards these objectives, this research focuses on data integration within two scenarios: (1) transcriptomic, proteomic and functional information and (2) real-time sensor-based measurements motivated by single-cell technology. To assess relationships between protein abundance, transcriptomic and functional data, a nonlinear model was explored at static and temporal levels. The successful integration of these heterogeneous data sources through the stochastic gradient boosted tree approach and its improved predictability are some highlights of this work. Through the development of an innovative validation subroutine based on a permutation approach and the use of external information (i.e., operons), lack of a priori knowledge for undetected proteins was overcome. The integrative methodologies allowed for the identification of undetected proteins for Desulfovibrio vulgaris and Shewanella oneidensis for further biological exploration in laboratories towards finding functional relationships. In an effort to better understand diseases such as cancer at different developmental stages, the Microscale Life Science Center headquartered at the Arizona State University is pursuing single-cell studies by developing novel technologies. This research arranged and applied a statistical framework that tackled the following challenges: random noise, heterogeneous dynamic systems with multiple states, and understanding cell behavior within and across different Barrett's esophageal epithelial cell lines using oxygen consumption curves. These curves were characterized with good empirical fit using nonlinear models with simple structures which allowed extraction of a large number of features. Application of a supervised classification model to these features and the integration of experimental factors allowed for identification of subtle patterns among different cell types visualized through multidimensional scaling. Motivated by the challenges of analyzing real-time measurements, we further explored a unique two-dimensional representation of multiple time series using a wavelet approach which showcased promising results towards less complex approximations. Also, the benefits of external information were explored to improve the image representation.
ContributorsTorres Garcia, Wandaliz (Author) / Meldrum, Deirdre R. (Thesis advisor) / Runger, George C. (Thesis advisor) / Gel, Esma S. (Committee member) / Li, Jing (Committee member) / Zhang, Weiwen (Committee member) / Arizona State University (Publisher)
Created2011
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Description
In species with highly heteromorphic sex chromosomes, the degradation of one of the sex chromosomes can result in unequal gene expression between the sexes (e.g., between XX females and XY males) and between the sex chromosomes and the autosomes. Dosage compensation is a process whereby genes on the sex chromosomes

In species with highly heteromorphic sex chromosomes, the degradation of one of the sex chromosomes can result in unequal gene expression between the sexes (e.g., between XX females and XY males) and between the sex chromosomes and the autosomes. Dosage compensation is a process whereby genes on the sex chromosomes achieve equal gene expression which prevents deleterious side effects from having too much or too little expression of genes on sex chromsomes. The green anole is part of a group of species that recently underwent an adaptive radiation. The green anole has XX/XY sex determination, but the content of the X chromosome and its evolution have not been described. Given its status as a model species, better understanding the green anole genome could reveal insights into other species. Genomic analyses are crucial for a comprehensive picture of sex chromosome differentiation and dosage compensation, in addition to understanding speciation.

In order to address this, multiple comparative genomics and bioinformatics analyses were conducted to elucidate patterns of evolution in the green anole and across multiple anole species. Comparative genomics analyses were used to infer additional X-linked loci in the green anole, RNAseq data from male and female samples were anayzed to quantify patterns of sex-biased gene expression across the genome, and the extent of dosage compensation on the anole X chromosome was characterized, providing evidence that the sex chromosomes in the green anole are dosage compensated.

In addition, X-linked genes have a lower ratio of nonsynonymous to synonymous substitution rates than the autosomes when compared to other Anolis species, and pairwise rates of evolution in genes across the anole genome were analyzed. To conduct this analysis a new pipeline was created for filtering alignments and performing batch calculations for whole genome coding sequences. This pipeline has been made publicly available.
ContributorsRupp, Shawn Michael (Author) / Wilson Sayres, Melissa A (Thesis advisor) / Kusumi, Kenro (Committee member) / DeNardo, Dale (Committee member) / Arizona State University (Publisher)
Created2016
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Description
Rapid advancements in genomic technologies have increased our understanding of rare human disease. Generation of multiple types of biological data including genetic variation from genome or exome, expression from transcriptome, methylation patterns from epigenome, protein complexity from proteome and metabolite information from metabolome is feasible. "Omics" tools provide comprehensive view

Rapid advancements in genomic technologies have increased our understanding of rare human disease. Generation of multiple types of biological data including genetic variation from genome or exome, expression from transcriptome, methylation patterns from epigenome, protein complexity from proteome and metabolite information from metabolome is feasible. "Omics" tools provide comprehensive view into biological mechanisms that impact disease trait and risk. In spite of available data types and ability to collect them simultaneously from patients, researchers still rely on their independent analysis. Combining information from multiple biological data can reduce missing information, increase confidence in single data findings, and provide a more complete view of genotype-phenotype correlations. Although rare disease genetics has been greatly improved by exome sequencing, a substantial portion of clinical patients remain undiagnosed. Multiple frameworks for integrative analysis of genomic and transcriptomic data are presented with focus on identifying functional genetic variations in patients with undiagnosed, rare childhood conditions. Direct quantitation of X inactivation ratio was developed from genomic and transcriptomic data using allele specific expression and segregation analysis to determine magnitude and inheritance mode of X inactivation. This approach was applied in two families revealing non-random X inactivation in female patients. Expression based analysis of X inactivation showed high correlation with standard clinical assay. These findings improved understanding of molecular mechanisms underlying X-linked disorders. In addition multivariate outlier analysis of gene and exon level data from RNA-seq using Mahalanobis distance, and its integration of distance scores with genomic data found genotype-phenotype correlations in variant prioritization process in 25 families. Mahalanobis distance scores revealed variants with large transcriptional impact in patients. In this dataset, frameshift variants were more likely result in outlier expression signatures than other types of functional variants. Integration of outlier estimates with genetic variants corroborated previously identified, presumed causal variants and highlighted new candidate in previously un-diagnosed case. Integrative genomic approaches in easily attainable tissue will facilitate the search for biomarkers that impact disease trait, uncover pharmacogenomics targets, provide novel insight into molecular underpinnings of un-characterized conditions, and help improve analytical approaches that use large datasets.
ContributorsSzelinger, Szabolcs (Author) / Craig, David W. (Thesis advisor) / Kusumi, Kenro (Thesis advisor) / Narayan, Vinodh (Committee member) / Rosenberg, Michael S. (Committee member) / Huentelman, Matthew J (Committee member) / Arizona State University (Publisher)
Created2015