Department of Biomedical Engineering Final Oral Examination Brandan Walters Morphometric Analysis to Characterize the Differentiation of Mesenchymal Stem Cells into Smooth Muscle Cells in Response to Biochemical and Mechanical Stimulation The morphology and biochemical
Department of Biomedical Engineering Final Oral Examination
Morphometric Analysis to Characterize the Differentiation of Mesenchymal Stem Cells
into Smooth Muscle Cells in Response to Biochemical and Mechanical Stimulation
The morphology and biochemical phenotype of cells are closely linked. This relationship is important in progenitor cell bioengineering, which generates functional, tissue-specific cells from uncommitted precursors. Advances in biofabrication have demonstrated that cell shape can regulate cell behavior and alter phenotype-specific functions. Establishing accessible and rigorous techniques for quantifying cell shape will therefore facilitate assessment of cellular responses to environmental stimuli, and will enable more comprehensive understanding of developmental, pathological, and regenerative processes. For progenitor cells being induced into specific lineages, this ability is valuable for validating the degree of differentiation and may lead to novel strategies for controlling cell phenotype.
In our approach, we used the differentiation of adult human mesenchymal stem cells (MSCs) into smooth muscle cells (SMCs) as a model system to investigate the relationship between cell shape and phenotype. These cell types are responsive to mechanical and biochemical stimuli and the shape of SMCs is a recognized marker of a differentiated state, providing a system in which morphological and biochemical phenotype are both understood and inducible. By applying exogenous stimuli, we changed cell shape and examined the corresponding cellular phenotype. In the first Aim, we applied stretch to MSCs on 2D collagen sheets to promote differentiation. Using mathematical shape factors, we quantified the morphological changes in response to defined stretch parameters. In the second Aim, we investigated the use of input energy as a means of controlling cell shape and corresponding differentiation. We examined how combinations of stretch parameters that produce equal energy input impacted morphology, and postulated that cell shape is a function of energy input. In the third Aim, we translated our method of quantifying shape factors into 3D culture, and validated the method by investigating the differentiation of MSCs into SMCs by mechanical and growth factor stimulation. We used relevant shape factors to quantify morphological differences and compared these changes to biochemical markers.
Our results demonstrate that mechanical stretch influences multiple aspects of MSC phenotype, including cell morphology. Shape factors described these changes objectively and quantitatively, and enabled the identification of relationships between SMC shape and differentiated state. Similar morphological responses could be induced using different combinations of stretch parameters that resulted in equal energy input. Cell shape followed a linear relationship with energy input despite the variance introduced by using MSCs from different patients. Only one SMC gene marker directly exhibited this relationship; however, partial least squares regression analysis revealed that other genes were also associated with shape factors. Translation of the shape quantification method into 3D collagen systems revealed that while the additional dimensionality hindered comparison of morphology between 2D and 3D samples, shape factor analysis was valid for relative studies within 3D systems. Differences in cell morphology caused by growth factors and mechanical stretch in 3D constructs were elucidated by shape analysis, and these phenotypic changes were corroborated through biochemical assays. Taken together, these results validate the use of cell shape as means of characterizing cell phenotype and the process of progenitor cell differentiation. The automated method we developed generates a robust set of morphological parameters that characterize the differentiation of MSCs into SMCs. This work has implications in our understanding of the relationship between cell morphology and phenotype, and may lead to new ways to control and improve differentiation efficiency in a variety of cell and tissue systems.
Chair: Dr. Jan Stegemann
(Tuesday) 10:00 am - 11:00 am
Lurie Engineering Center
GM Room, 4th Floor
Discovering Synergistic and Antagonistic Drug Combinations for Mycobacterium Tuberculosis With the rise of antibiotic resistance, treatments involving common frontline TB drugs have been rendered insufficient, especially in treating multidrug resistant (MDR)
Discovering Synergistic and Antagonistic Drug Combinations for Mycobacterium Tuberculosis
With the rise of antibiotic resistance, treatments involving common frontline TB drugs have been rendered insufficient, especially in treating multidrug resistant (MDR) and extremely drug resistant (XDR) strains of TB. As new anti-TB therapies are beginning to emerge, optimizing specific combinations of individual agents into effective and safe regimens remains a significant challenge. To address this problem, we have developed a in-silico computational tool—Inferring Drug Interactions using chemo-Genomics and Orthology for MTB (INDIGO-MTB). INDIGO-MTB leverages high resolution MTB transcriptomic profiles and TB drug interaction data to predict synergy/antagonism of TB drug regimens with high accuracy. INDIGO-MTB uses the random forests machine learning algorithm to train its predictive model, which can be used to make predictions for synergy of novel TB drug regimens. INDIGO-MTB predictions correlated well with corresponding in-vitro drug interaction validation scores. The in-vitro INDIGO-MTB interaction scores were also predictive of the percentage of patients with negative sputum cultures after 8 weeks in clinical trials for 58 TB drug regimens. We hope INDIGO-MTB can be used by clinicians and researchers to quickly assess the likelihood of success of new TB drug combinations using publicly available data.
Chair: Sriram Chandrasekaran, Ph.D.
(Tuesday) 12:00 pm - 1:00 pm
NCRC Building 100, Room 101
Department of Biomedical Engineering Master’s Thesis Defense Mingxiao Zhang Hardware Design, Integration and Optimization of a Virtual Reality Environment for Rodent Cortical Neuronal Recordings Understanding neural circuits often requires innovative and custom-designed behavioral
Department of Biomedical Engineering Master’s Thesis Defense
Hardware Design, Integration and Optimization of a Virtual Reality Environment for Rodent Cortical Neuronal Recordings
Understanding neural circuits often requires innovative and custom-designed behavioral setups. A head-fixed virtual reality (VR) system for rodents offers a number of advantages in terms of being able to investigate and manipulate neural circuits involved in both normal behaviors and in pathological brain states. A complete VR experimental system to investigate cortical neurons of rodents consists of three vital parts: 1) a hardware system with recording and reward functionality; 2) a software system to process, analyze and report the signals and also to control the logical flow of experiments; 3) a hardware-software interface to link these two systems. The current project focused on the design of the hardware interface, the optimization of the communication between the software system and multiple hardware systems, the locomotion-recording system, and the displacement/speed analysis from the locomotion-recording system. This functional VR system has enabled high resolution neural recordings in the study of precise behaviors.
Chair: Tim Bruns, PhD.
(Tuesday) 2:00 pm - 3:00 pm