After a myocardial infarction, the boundary between the injured, hypoxic tissue and the adjacent viable, normoxic tissue, known as the border zone, is characterized by an oxygen gradient. Yet, the impact of an oxygen gradient on cardiac tissue function is poorly understood, largely due to limitations of existing experimental models. Here, we engineered a microphysiological system to controllably expose engineered cardiac tissue to an oxygen gradient that mimics the border zone and measured the effects of the gradient on electromechanical function and the transcriptome. The gradient delayed calcium release, reuptake, and propagation; decreased diastolic and peak systolic stress; and increased expression of inflammatory cascades that are hallmarks of myocardial infarction. These changes were distinct from those observed in tissues exposed to uniform normoxia or hypoxia, demonstrating distinct regulation of cardiac tissue phenotypes by an oxygen gradient. Our border-zone-on-a-chip model advances functional and mechanistic insight into oxygen-dependent cardiac tissue pathophysiology.
Recent surges in large-scale mass spectrometry (MS)-based proteomics studies demand a concurrent rise in methods to facilitate reliable and reproducible data analysis. Quantification of proteins in MS analysis can be affected by variations in technical factors such as sample preparation and data acquisition conditions leading to batch effects, which adds to noise in the data set. This may in turn affect the effectiveness of any biological conclusions derived from the data. Here we present Batch-effect Identification, Representation, and Correction of Heterogeneous data (BIRCH), a workflow for analysis and correction of batch effect through an automated, versatile, and easy to use web-based tool with the goal of eliminating technical variation. BIRCH also supports diagnosis of the data to check for the presence of batch effects, feasibility of batch correction, and imputation to deal with missing values in the data set. To illustrate the relevance of the tool, we explore two case studies, including an iPSC-derived cell study and a Covid vaccine study to show different context-specific use cases. Ultimately this tool can be used as an extremely powerful approach for eliminating technical bias while retaining biological bias, toward understanding disease mechanisms and potential therapeutics.
The phenotype and severity of cardiovascular diseases, such as a thoracic aortic aneurysm and dissection and atherosclerosis, differ between females and males. Notably, coronary artery dissection risk increases four‐fold in women during late pregnancy and postpartum, suggesting that female sex hormones such as estrogen may play a role in vascular pathophysiology, yet are considered protective under most other circumstances. This paradox underscores the fact that the effect of estrogen on the vasculature is not fully understood at a molecular level, and most previous work in this area has been done using immortalized cell lines or animal models, which may not accurately model human responses. Furthermore, examining these effects in genetic diseases such as Marfan syndrome (MFS) allows us to view these effects in an aneurysm phenotypic model. Marfan syndrome is a connective tissue disorder caused by a mutation in the fibrillin‐1 gene. Individuals typically develop aortic root aneurysms with aortic disruption and rupture that remains a leading cause of death. Here, we used Healthy and MFS iPSC‐derived vascular smooth muscle cells (iVSMCs) to examine the effects of estrogen. Healthy and MFS IPSC’s were differentiated into iVSMCs for 21 days then treated with estrogen for 72hrs. Immunofluorescence of smooth muscle markers and contractility assays were conducted to confirm a contractile vascular smooth muscle phenotype. A proteomic profile was established to evaluate molecular differences. Peptide data was collected using a Thermo Fusion Lumos Orbitrap mass spectrometer. Data were analyzed using the DIA‐Neural networks software. We showed expression of smooth muscle markers and contractile response to stimuli. MFS iVSMCs showed decreased contractility response and a decrease in FBN1 expression compared to healthy control (MFS iVSMC vs Control iVMSC of‐1.4). The proteomic profile revealed an upregulation in proteins related to elastin fiber formation and cell adhesion in both Healthy and Marfan iVSMCs following estrogen treatment. FBN1 expression increased in response to estrogen in both Healthy and Marfan iVSMC. As well as increases in Lysyl Oxidase and TGFB2 expression in MFS iVSMC following treatment. We also see a downregulation of collagen expression and RNA synthesis in healthy iVSMCs. These findings will allow us to gain a greater understanding of the complex interaction estrogen has on the vasculature and to determine their role in cardiovascular disease risk.
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