Date of Graduation


Document Type


Degree Name

Bachelor of Science in Biomedical Engineering

Degree Level



Biomedical Engineering


Rao, Raj


As of 2017, vascular diseases contributed to 23.1% of all deaths in America. To address the need for more effective and sustainable treatment options for these ailments, stem cell differentiation and implantation has emerged as a viable alternative to standard bypass and graft insertions. A completely autologous treatment can be achieved by extracting adult stem cells, differentiating them into vascular smooth muscle cells (VSMCs), and then reimplanting these cells at the affected tissue site. This study aims to investigate the efficiency of the VSMC differentiation from human mesenchymal stem cells (hMSCs) by comparing 4 cell lines of untreated hMSCs with 3 cell lines of hMSCs that have been grown in two differentiation factors–platelet derived growth factor (PDGF) and transforming growth factor ��-1 (TGF��-1)–in order to mature into VSMCs. The cell lines will be evaluated based on variations in RNA expression. Total RNA will be isolated from the cell lines and subsequently sequenced. Raw data will be analyzed using bioinformatic techniques to determine which genes are transcribed significantly differently between hMSCs and hMSC-derived VSMCs. Total RNA is being sequenced so that the transcription rates of all genes may be compared between cell lines; however, expected outcomes of known hMSC and VSMC markers would include increased transcription of CD 29/44/73/105 and decreased transcription of MYH11, ACTA2, and TAGLN in hMSCs. Converse results would be expected in the hMSC-derived VSMCs. Elucidating the specific variations in transcription levels between hMSCs and hMSC-derived VSMCs will lead to the development of standardized chromatin immunoprecipitation sequencing (ChIP-Seq) assays that can determine if post-treatment hMSCs have been successfully differentiated into VSMCs, leading to accurate autologous stem cell treatments to various cardiovascular diseases.


Total RNA Sequencing, gene markers, gene expression


Data incomplete due to COVID-19 pandemic.