A deep learning approach to identify and segment alpha‑smooth muscle actin stress fiber positive cells

Cardiac fibrosis is a pathological process characterized by excessive tissue deposition, matrix remodeling, and tissue stiffening, which eventually leads to organ failure. On a cellular level, the development of fibrosis is associated with the activation of cardiac fibroblasts into myofibroblasts, a highly contractile and secretory phenotype. Myofibroblasts are commonly identified in vitro by the de novo assembly of alpha‑smooth muscle actin stress fibers; however, there are few methods to automate stress fiber identification, which can lead to subjectivity and tedium in the process. To address this limitation, we present a computer vision model to classify and segment cells containing alpha‑smooth muscle actin stress fibers into 2 classes (α‑SMA SF + and α‑SMA SF‑), with a high degree of accuracy (cell accuracy: 77%, F1 score 0.79). The model combines standard image processing methods with deep learning techniques to achieve semantic segmentation of the different cell phenotypes. We apply this model to cardiac fibroblasts cultured on hyaluronic acid‑based hydrogels of various moduli to induce alpha‑smooth muscle actin stress fiber formation. The model successfully predicts the same trends in stress fiber identification as obtained with a manual analysis. Taken together, this work demonstrates a process to automate stress fiber identification in in vitro fibrotic models, thereby increasing reproducibility in fibroblast phenotypic characterization.This research was supported by the Burroughs Wellcome Fund (CASI-1015895, A.M.R) and by the National Sci- ence Foundation (NSF MRSEC DMR-1720595 A.M.R). The authors acknowledge the use of shared research facili- ties supported in part by the Texas Materials Institute, the Center for Dynamics and Control of Materials: an NSF MRSEC (DMR-1720595), and the NSF National Nanotechnology Coordinated Infrastructure (ECCS-1542159). We also acknowledge the use of shared facilities in the UT Proteomics Facility (CPRIT RP110782). Finally, we also acknowledge the Texas Advanced Computing Center for their high performance computing resources.Center for Dynamics and Control of Material

Genetic Control of Radical Crosslinking in a Semi-Synthetic Hydrogel

Enhancing materials with the qualities of living systems, including sensing, computation, and adaptation, is an important challenge in designing next-generation technologies. Living materials address this challenge by incorporating live cells as actuating components that control material function. For abiotic materials, this requires new methods that couple genetic and metabolic processes to material properties. Toward this goal, we demonstrate that extracellular electron transfer (EET) from Shewanella oneidensis can be leveraged to control radical cross-linking of a methacrylate-functionalized hyaluronic acid hydrogel. Cross-linking rates and hydrogel mechanics, specifically storage modulus, were dependent on various chemical and biological factors, including S. oneidensis genotype. Bacteria remained viable and metabolically active in the networks for a least 1 week, while cell tracking revealed that EET genes also encode control over hydrogel microstructure. Moreover, construction of an inducible gene circuit allowed transcriptional control of storage modulus and cross-linking rate via the tailored expression of a key electron transfer protein, MtrC. Finally, we quantitatively modeled hydrogel stiffness as a function of steady-state mtrC expression and generalized this result by demonstrating the strong relationship between relative gene expression and material properties. This general mechanism for radical cross-linking provides a foundation for programming the form and function of synthetic materials through genetic control over extracellular electron transfer.Center for Dynamics and Control of Material

Understanding the fibroblast to myofibroblast activation process using dynamic hydrogels and machine learning

Cardiac fibrosis is a pathological process characterized by excessive tissue deposition, matrix remodeling, and tissue stiffening. On a cellular level, fibrosis is associated with myofibroblast persistence. Myofibroblasts are a highly secretory and contractile phenotype that arise from resident cardiac fibroblasts, among other cell types, after an initiating injury. Of specific interest is the relationship between environmental stiffness and the myofibroblast activation process. Excess ECM produced by myofibroblasts significantly increases the stiffness of the surrounding tissue. Interestingly, increasing tissue stiffness is also a known stimulus of fibroblast activation, initiating a positive feedback loop ultimately resulting in fibrosis. To better understand this process, the work began with the development of a dynamic stiffening 2D hydrogel model, which was shown to mechanically control myofibroblast activation. A machine learning model was then trained to identify and segment activated myofibroblasts from a population of cultured cells. Next, in order to better understand the activation process, we focused on individual cells, rather than population averages. We then fully characterized cell size and shape and used the measured features to create a model to predict activation state with high accuracy. Further, we used these features and other deep learning techniques to create a continuous classification system that more accurately captures the natural progression. Next, to study the behavior of these cell types in a more natural system, we moved to 3D cell culture. Hydrogels were designed to isolate the effects of environmental stiffness and degradability, and their effects on cell ECM secretion and contractility were quantified. Lastly, we combined our 3D cell culture system with collagen to create an IPN to promote the vasculogenesis of iPSC-EPs where we once again saw that hydrogel stiffness and degradability had a significant effect of the viability and behavior of this cell type.Chemical Engineerin

Immunomodulatory functions of human mesenchymal stromal cells are enhanced when cultured on HEP/COL multilayers supplemented with interferon-gamma

Human mesenchymal stromal cells (hMSCs) are multipotent cells that have been proposed for cell therapies due to their immunosuppressive capacity that can be enhanced in the presence of interferon-gamma (IFN-γ). In this study, multilayers of heparin (HEP) and collagen (COL) (HEP/COL) were used as a bioactive surface to enhance the immunomodulatory activity of hMSCs using soluble IFN-γ. Multilayers were formed, via layer-by-layer assembly, varying the final layer between COL and HEP and supplemented with IFN-γ in the culture medium. We evaluated the viability, adhesion, real-time growth, differentiation, and immunomodulatory activity of hMSCs on (HEP/COL) multilayers. HMSCs viability, adhesion, and growth were superior when cultured on (HEP/COL) multilayers compared to tissue culture plastic. We also confirmed that hMSCs osteogenic and adipogenic differentiation remained unaffected when cultured in (HEP/COL) multilayers in the presence of IFN-γ. We measured the immunomodulatory activity of hMSCs by measuring the level of indoleamine 2,3-dioxygenase (IDO) expression. IDO expression was higher on (HEP/COL) multilayers treated with IFN-γ. Lastly, we evaluated the suppression of peripheral blood mononuclear cell (PBMC) proliferation when co-cultured with hMSCs on (HEP/COL) multilayers with IFN-γ. hMSCs cultured in (HEP/COL) multilayers in the presence of soluble IFN-γ have a greater capacity to suppress PBMC proliferation. Altogether, (HEP/COL) multilayers with IFN-γ in culture medium provides a potent means of enhancing and sustaining immunomodulatory activity to control hMSCs immunomodulation

CB2: a cannabinoid receptor with an identity crisis

CB2 was first considered to be the ‘peripheral cannabinoid receptor’. This title was bestowed based on its abundant expression in the immune system and presumed absence from the central nervous system. However, multiple recent reports question the absence of CB2 from the central nervous system. For example, it is now well accepted that CB2 is expressed in brain microglia during neuroinflammation. However, the extent of CB2 expression in neurons has remained controversial. There have been studies claiming either extreme-its complete absence to its widespread expression-as well as everything in between. This review will discuss the reported tissue distribution of CB2 with a focus on CB2 in neurons, particularly those in the central nervous system as well as the implications of that presence. As CB2 is an attractive therapeutic target for pain management and immune system modulation without overt psychoactivity, defining the extent of its presence in neurons will have a significant impact on drug discovery. Our recommendation is to encourage cautious interpretation of data that have been presented for and against CB2's presence in neurons and to encourage continued rigorous study