TitleFunctional quality control for human blood-based gene expression products
NameFrahm, Stephanie A. (author), Brooks, Andrew I (chair), Tischfield, Jay A (internal member), Matise, Tara C (internal member), Rutgers University, Graduate School - New Brunswick,
SubjectMicrobiology and Molecular Genetics,
DescriptionReliable and robust gene expression data generated by microarrays or quantitative real-time polymerase chain reaction (qPCR) is directly dependent upon use of high-quality input material, namely high caliber cDNA generated from intact, non-degraded RNA. Due to its labile nature, the integrity of RNA can be jeopardized at multiple points during sample collection, extraction, and storage, adversely affecting downstream gene expression data interpretation and discovery. Accurately assessing RNA and cDNA quality prior to gene expression analysis proves to be a critical step requiring a highly sensitive and specific quality control method. Existing industry-standard RNA quality control techniques rely on microcapillary electrophoresis, which provides an analytical assessment of ribosomal RNA (rRNA) integrity. While providing a gross evaluation of total RNA quality using rRNA as a surrogate, these methods fail to adequately predict the downstream functional performance of messenger RNA (mRNA), the class of RNA used to study gene expression. Conversely, real-time qPCR offers a sensitive functional quality control tool for evaluating mRNA integrity and predicting future performance on gene expression platforms by directly evaluating cDNA functionality. Design of tissue-specific assay panels targeting a select set of genes provides a focused and versatile solution, which is lacking in broad-spectrum microcapillary electrophoresis analysis methods. Furthermore, qPCR assays offer high-throughput laboratories and biorepositories an efficient and automatable quality control screening method. By taking advantage of regional degradation patterns of RNA, class prediction algorithms can be developed for individual assays to measure RNA quality as a function of the magnitude of deviation from an expected gene expression value, or CT value. By determining the degree of shift from an expected CT value for each assay in the panel, individual algorithm outputs can be collectively evaluated to determine an overall RNA quality score, allowing researchers to properly weight or exclude subpar samples from analysis. The following work describes the ongoing development of a novel functional quality control method for RNA samples extracted from human whole blood, consisting of a custom gene expression assay panel and complementary class prediction algorithms.
NoteIncludes bibliographical references
Noteby Stephanie A. Frahm
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.