Joenid Colón-Mateo is a rising senior at the University of Puerto Rico at Cayey, majoring in Biology. She is collaborating in Dr. Murat Guvendiren’s lab, which studies the stem cells response to hydrogels with spatiotemporal properties. After graduating, Joenid plans to pursue a PhD in biomedical engineering.
Patterning Surfaces for Inducing Cardiomyocyte Alignment
Joenid A. Colón-Mateo, Andrea N. Plaza-Castro, Christian Tessman
Wrinkles are a property found in many biological tissues. Scientists have tried to mimic these surface patterns to understand how cells mechanically interact with their microenvironment. Controlled surface patterns on gels have been shown to affect cell alignment, morphology, gene regulation, and differentiation. Here, wrinkle patterns were fabricated on polydimethylsiloxane (PDMS) substrates to regulate human cardiomyocyte (hCM) alignment, which is important for proper tissue function. PDMS sheets were subject to ultraviolet and ozone (UVO) treatment, with an initial strain of 20%, to form a thin film surface with a higher Young’s modulus than the bulk. Exposure time was modified to determine its effect on wrinkle wavelength, amplitude and film thickness. Analysis of microscope images of the PDMS sheets showed that wrinkle wavelength and amplitude increased linearly with UVO exposure time, and that critical strain decreased linearly with time. The effect of wrinkling on hCM nuclei alignment was also investigated by culturing hCMs on flat and patterned PDMS sheets. Analysis of microscope images of the hCMs showed the average direction of nuclei alignment was similar for both topographical conditions: 84.7±48.0 degrees for flat and 88.1±13.3 degrees for patterned on day 4. However, the standard deviation of nuclei alignment on flat substrates was approximately three times greater than for patterned substrates. This indicates more uniform cellular nuclei alignment on patterned substrates. Development of materials that can mimic surface topography of tissues promises a greater understanding of the morphological response of cells leading to more diverse biomedical applications.
Katie Driscoll is a rising junior at Rowan University studying Biomedical Engineering with minors in Arabic, History, and Chemistry, and concentrations in the Honors College and Global Health. She (clearly) has a lot of interests and isn’t sure what she wants to do in the future but will likely be in school for a very long time. This summer she is very excited to be working on identifying scaling relationships of particular genes from cancer patients in the Discher Lab.
Scaling Relationships and Patient Prognosis for COL1A1 and ACTA2 in Human Urinary Tract Cancers
Bulk mRNA-seq data obtained from The Cancer Genome Atlas (TCGA) can be used to elucidate gene expression and gene scaling relationships across cancers in order to identify common genes and groups of genes that predict significant survival changes in a cohort of patients. In this analysis, gene scaling relationships among two fibrosis-associated genes of interest (COL1A1 and ACTA2) and four urinary tract cancers were assessed for survival trends across data sets. Bladder cancer (BLCA), Kidney Chromophobe Cancer (KICH), Kidney Clear Cell Carcinoma (KIRC), and Kidney Papillary Cell Carcinoma (KIRP) were chosen for this analysis because they represent a common system and each cancer expressed gene scaling for each gene of interest. For COL1A1, a gene associated with fibrosis and positive survival trends in other cancers1, KIRC and KIRP showed significantly decreased survival of high expressors of the gene and none of the cohorts showed improved survival. Further, in ACTA2, a gene associated with cancer metastasis and fibrosis, only KIRP and BLCA showed significantly reduced survival with KICH indicating non-significant positive survival with high ACTA2 expression. A significant finding in this analysis is that COL5A1, COL6A3, and COL1A2 not only scaled with COL1A1 in all four cancers, but also predicted significantly poor survival with elevated expression in two of the four urinary cancers. These genes are associated with tumor growth and poor prognosis in both urinary cancers and cancers from other systems2,3,4,5. Furthermore, PLN and CNN1 scaled with ACTA2 across all four cancers and indicated negative survival in two of the four. CNN1 has been identified as a possible oncogene in bladder cancer related to poor survival outcomes and PLN has not been previously explored in the context of cancer prognosis6. Taken together, these genes have potential as targets for gene therapies and as prognostic biomarkers. Since many of these genes indicate fibrosis, emerging ultrasound technology could be used as a non-invasive detection tool for urinary cancers7.
Vashisth, M., Cho, S., Irianto, J., Xia, Y., Wang M., Hayes B., Jafarpour, F., Wells, R., Liu, A., Discher, D. (2020). Scaling concepts in ‘omics: nuclear lamin-B scales with tumor growth and predicts poor prognosis, whereas fibrosis can be pro-survival [unpublished manuscript]. Physical Science Oncology Center at Penn, University of Pennsylvania.
Di, Y., Chen, D., Yu, W., & Yan, L. (2019, 1 28). Bladder cancer stage-associated hub genes revealed by WGCNA co-expression network analysis. Hereditas, 156(1), 7.
Kang, C., Wang, J., Axell-House, D., Soni, P., Chu, M.-L., Chipitsyna, G., . . . Arafat, H. (n.d.). 2013 SSAT PLENARY PRESENTATION Clinical Significance of Serum COL6A3 in Pancreatic Ductal Adenocarcinoma.
Li, J., Ding, Y., & Li, A. (2016, 11 29). Identification of COL1A1 and COL1A2 as candidate prognostic factors in gastric cancer. World Journal of Surgical Oncology, 14(1), 297.
Liu, W., Wei, H., Gao, Z., Chen, G., Liu, Y., Gao, X., . . . Xiao, J. (2018, 7 30). COL5A1 may contribute the metastasis of lung adenocarcinoma. Gene, 665, 57-66.
Liu, Y., Wu, X., Wang, G., Hu, S., Zhang, Y., & Zhao, S. (2019, 1 1). CALD1, CNN1, and TAGLN identified as potential prognostic molecular markers of bladder cancer by bioinformatics analysis. Medicine, 98(2), e13847.
Correas, J. M., Anglicheau, D., Joly, D., Gennisson, J. L., Tanter, M., & Hélénon, O. (2016). Ultrasound-based imaging methods of the kidney-recent developments. Kidney international, 90(6), 1199–1210. https://doi.org/10.1016/j.kint.2016.06.042
Phillips, J., Pavlovich, C., Walther, M., Ried, T., & Linehan, W. (2001). The genetic basis of renal epithelial tumors: Advances in research and its impact on prognosis and therapy. Current Opinion in Urology, 11(5), 463-469.
Takahashi, M., Rhodes, D., Furge, K., Kanayama, H., Kagawa, S., Haab, B., & Teh, B. (2001, 8 14). Gene expression profiling of clear cell renal cell carcinoma: Gene identification and prognostic classification. Proceedings of the National Academy of Sciences of the United States of America, 98(17), 9754-9759.
Bruce Enzmann is a rising junior at Johns Hopkins University, majoring in Materials Science & Engineering with a concentration in biomaterials. This summer, Bruce is working in Dr. Jason Burdick’s Polymeric Biomaterials Laboratory with Dr. Claudia Loebel to analyze patterns of nascent matrix with respect to properties of three-dimensional hydrogels. After graduation, Bruce plans to pursue a PhD in biomedical engineering to develop translational regenerative medicine.
Image Analysis to Examine Spatial Properties of the Pericellular Matrix within 3D Hydrogels
Biomaterials, such as hydrogels, can be engineered with biophysical cues that enable the study of three-dimensional microenvironments that simulate aspects of native extracellular matrix and modulate cellular functions such as differentiation and matrix deposition. Recent data showed that the accumulation of deposited matrix in the pericellular region influences the interactions between cells and their engineered hydrogel environment; however, little is known about the spatial localization and density of newly secreted (nascent) matrix at the cell-hydrogel interface. Using a metabolic labeling technique, we fluorescently labeled nascent proteins deposited by bovine chondrocytes within 7 days upon encapsulation in covalently crosslinked 5 kPa and 20 kPa hyaluronic acid hydrogels. To examine spatial properties of these nascent proteins, we used ImageJ to generate nascent protein intensity profiles and developed new analysis tools to quantify nascent protein area and average intensity. Our results show significant increases in nascent protein area and intensity around chondrocytes embedded within 5 kPa hydrogels compared to 20 kPa hydrogels. These findings suggest that secreted matrix within 5 kPa hydrogels distributes further into the hydrogel, whereas the more densely crosslinked 20 kPa hydrogels restrict nascent matrix distribution. Moreover, lower nascent protein average intensity and area within 20 kPa hydrogels indicate that densely crosslinked hydrogels reduce nascent protein deposition. Ongoing work is analyzing the effect of culture time and local mechanical properties on nascent matrix deposition and distribution. We anticipate that these results have implications on hydrogel design for applications in tissue engineering and regenerative medicine.
Samantha Hall (Sam) is a rising senior at Bryn Mawr College majoring in Mathematics. Sam is an Accelerated Masters student enrolled at the University of Pennsylvania pursuing an MSE in Systems Engineering. She is from Downingtown, a small suburban town outside of Philadelphia. This summer, Sam is part of Dr. Ravi Radhakrishnan’s lab, which is developing a multiscale computational model for targeting drug-filled nanoparticles to the inflamed lung regions to combat Acute Respiratory Distress Syndrome (ARDS), which manifests in a majority of COVID-19 patients with severe symptoms.
Towards a Multiscale Computational Model of the Human Complement System for Predicting Immune Response in COVID-19 Patients
Virion envelope flexibility and receptor spatial arrangement impacts immune modulation, recruitment, and internalization. Given the pandemic’s topical nature, it is advantageous to investigate the mechanics of virion elicited immune response, and how it manifests in patients under pulmonary duress. The majority of COVID-19 deaths occur in patients who have Acute Respiratory Distress Syndrome (ARDS), an acute, diffuse, inflammatory lung injury caused by a variety of insults, most commonly pneumonia, sepsis, trauma, and COVID-19. ARDS affects 200,000 patients each year in the US, has a 40% mortality rate, and occurs in 25% of hospitalized COVID-19-infected patients, yet there are currently no FDA-approved drugs for ARDS. Inflammatory conditions resulting from ARDS are caused by the interaction between the virus and immune cells, namely neutrophils and macrophages. These signaling interactions are primarily mediated via the complement pathway, a part of the immune system that enhances the ability of antibodies to clear pathogens, playing a role in inflammation, host defense, and signaling adaptive immunity. In this project, using methods of systems biology, we look towards signaling models of the complement system to compartmentally understand this complex system. COPASI is a computer software that creates and solves mathematical models, encoding differential equations of biological processes, and was a part of the methodology behind this research. A previously existing computational model involving the enhancement and suppression mechanisms that regulate complement activity provided a template model in Systems Biology Markup Language (SBML) format, a standard form of systems biology XML codes (Liu et al, 2011). Plots were produced of complement regulation with inhibitors under infection inflammation conditions in terms of mediator protein deposition, and of positive feedback amplification of neutrophil activation. Results focused on three aspects: validation of existing data, exploring the amplification modules of the complement pathway, and characterizing emergent properties such as bistable switches regulating the complement cascade. The broader question to be explored in next steps involves how this modeled mechanism would work on the viral surface. Because the complement cascade occurs on the surface of the virus during neutrophil interaction, we believe that the mechanics of the virus, whether it is crystalline or noncrystalline (COVID-19 is noncrystalline), in conjunction with the spatial arrangement of the cascade proteins determine this behavior. Future work involving spatial and stochastic models will involve looking at mechanical and spatio-temporal criterion in virion interaction.