An Analysis of Errors and Discrepenices in Analyzing Single Cell RNA Sequence Data

Single-cell RNA sequencing (scRNA-seq) is an extremely vital sequencing technology that has enabled High-throughput mapping of cellular differentiation hierarchies. scRNA-seq has excellent sequencing potential with a wide range of applications beyond regular transcriptome profiling. scRNA-seq process involves analyzing data using 3' end counting technology, which involves sample composition and analytical processing including pre-processing, normalization, alignment and clustering. In order to accomplish this task bioinformaticians around the world have developed many computational tools. As of 2019, there exist 385 different tools that can be used to analyze scRNA-seq data, and that number is growing. Although this continuous addition of new features to single-cell data analysis confronts technical gaps with bulk RNA-seq, there have been very few attempts to standardize these practices. This study explores the various approaches to re-analyze previously published single cell RNA-cell sequencing data and discusses subsequent challenges to utilize publicly available data sets to conduct a multicenter study. Considering the differences in data publication formats, there are several methods that can be employed. 1) Analyzing BCL files 2) Analyzing FASTQ files 3) Analyzing matrix files 4) Analyzing Seurat or ScanPy objects. This thesis provides a concise overview of some of the steps, algorithms, and approaches that are currently used in the analysis of single-cell RNA-sequencing data, with an emphasis on recent developments. Hence, I propose that in order to develop reproducible algorithms and analysis software for scRNA-seq data sets, it is vital that standardization across all analysis platform exist and the software developers recognize and understand the computational challenges posed by the analysis tasks.Biology and Biochemistry, Department ofHonors Colleg

To examine the combinatorial therapeutic effects of EGFR inhibitor and mTORC2 inhibitor for treatment of pancreatic cancer

The role of protein expression and signaling pathways in cancer cells going under drug treatment has become apparent. It could yield significant insight into developing therapeutic response/ treatment of the diseases. A contributor of pancreatic cancer cell advancement and growth is the inflection of the protein expression and cell signaling pathway. This is a result of the mis-synchronization of protein kinase activity. Transformation of oncogenes can be caused due to mutations in kinase encoded genes. These genes are frequently found in cancer. Kinase inactivation occurs by the actions of these molecules as they are competing with ATP binding to the enzyme allosteric sites. Hence, they disturb cellular signaling pathways resulting in cancer cell growth and survival to deter. As a result, for this experiment, the is target mTORC2 pathway with inhibitors along with various EGFR inhibitors. MTT cell proliferation data represented with the Q curve shows a significant decrease in cell viability when treated with the combination of EGFR and mTORC1/mTORC2 inhibitors. The EGFR inhibitors cause a reduced downstream regulation of PI3K/Akt/mTOR signaling by inhibiting the EGFR phosphorylation. Afatinib is shows inhibitions of EGFR and HER2. Erlotinib and AZD9291 only inhibit EGFR.AZD8055 (mTORC1/mTORC2 inhibitor) that prevents the growth pancreatic cancer cells in combination with EGFR inhibitors (Erlotinib, Afatinib, AZD9291).Combinatorial treatment with EGFRi (Erlotinib, Afatinib, or AZD9291) and mTORi (AZD8055) improved anti-proliferative effects on BxPC-3 cell as compared to EGFRi treatment alone. This combination is shown to be synergetic by Chou and Talalay method, showing high synergetic drug combination at and above IC50. Furthermore, the relative importance of this study, it is important to look at the proteomic complexity and the respective modulating signaling networks. They are derived from alterations in the oncogenes. The therapeutic treatments and resistant mechanisms needed for these alterations have not been fully understood. This presents us with the critical requirement- to minimize the adverse effects of the therapeutic responses and increase drug resistance towards the cancer cells. Thus, the ultimate goal is to enhance treatment efficacy.Biology and Biochemistry, Department ofHonors Colleg

Defining function of wild-type and three patient-specific TP53 mutations in a zebrafish model of embryonal rhabdomyosarcoma

In embryonal rhabdomyosarcoma (ERMS) and generally in sarcomas, the role of wild-type and loss- or gain-of-function TP53 mutations remains largely undefined. Eliminating mutant or restoring wild-type p53 is challenging; nevertheless, understanding p53 variant effects on tumorigenesis remains central to realizing better treatment outcomes. In ERMS, >70% of patients retain wild-type TP53, yet mutations when present are associated with worse prognosis. Employing a kRASG12D-driven ERMS tumor model and tp53 null (tp53-/-) zebrafish, we define wild-type and patient-specific TP53 mutant effects on tumorigenesis. We demonstrate that tp53 is a major suppressor of tumorigenesis, where tp53 loss expands tumor initiation from <35% to >97% of animals. Characterizing three patient-specific alleles reveals that TP53C176F partially retains wild-type p53 apoptotic activity that can be exploited, whereas TP53P153Δ and TP53Y220C encode two structurally related proteins with gain-of-function effects that predispose to head musculature ERMS. TP53P153Δ unexpectedly also predisposes to hedgehog-expressing medulloblastomas in the kRASG12D-driven ERMS-model