Spheroids of Endothelial Cells and Vascular Smooth Muscle Cells Promote Cell Migration in Hyaluronic Acid and Fibrinogen Composite Hydrogels

Zuo, Xingang; Zhang, Haolan; Zhou, Tong; Duan, Yuanyuan; Shou, Hao; Yu, Shan Ping; Gao, Changyou

doi:10.34133/2020/8970480

Cited by 19 publications

(16 citation statements)

References 80 publications

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“…We also confirmed that VSMC spheroid morphologies with DMSO are mostly spherical (Figs. 2 C, S1 C), as previously shown in other studies 35 , 36 . Inhibition of FAK phosphorylation significantly disrupted the spheroid formation of both human (Fig.…”

Section: Resultssupporting

confidence: 88%

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A machine learning pipeline revealing heterogeneous responses to drug perturbations on vascular smooth muscle cell spheroid morphology and formation

Vaidyanathan

Wang

Krajnik

et al. 2021

Sci Rep

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Machine learning approaches have shown great promise in biology and medicine discovering hidden information to further understand complex biological and pathological processes. In this study, we developed a deep learning-based machine learning algorithm to meaningfully process image data and facilitate studies in vascular biology and pathology. Vascular injury and atherosclerosis are characterized by neointima formation caused by the aberrant accumulation and proliferation of vascular smooth muscle cells (VSMCs) within the vessel wall. Understanding how to control VSMC behaviors would promote the development of therapeutic targets to treat vascular diseases. However, the response to drug treatments among VSMCs with the same diseased vascular condition is often heterogeneous. Here, to identify the heterogeneous responses of drug treatments, we created an in vitro experimental model system using VSMC spheroids and developed a machine learning-based computational method called HETEROID (heterogeneous spheroid). First, we established a VSMC spheroid model that mimics neointima-like formation and the structure of arteries. Then, to identify the morphological subpopulations of drug-treated VSMC spheroids, we used a machine learning framework that combines deep learning-based spheroid segmentation and morphological clustering analysis. Our machine learning approach successfully showed that FAK, Rac, Rho, and Cdc42 inhibitors differentially affect spheroid morphology, suggesting that multiple drug responses of VSMC spheroid formation exist. Overall, our HETEROID pipeline enables detailed quantitative drug characterization of morphological changes in neointima formation, that occurs in vivo, by single-spheroid analysis.

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Section: Resultssupporting

confidence: 88%

“…In this study, we created VSMC spheroids , which are spherical cellular aggregates consisting of multiple layers of VSMCs 35 , 36 , using a hanging drop culture technique 37 , 38 (Fig. 1 A).…”

Section: Introductionmentioning

confidence: 99%

A machine learning pipeline revealing heterogeneous responses to drug perturbations on vascular smooth muscle cell spheroid morphology and formation

Vaidyanathan

Wang

Krajnik

et al. 2021

Sci Rep

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show abstract

“…We first confirmed that VSMC spheroid morphologies with DMSO are mostly spherical (Fig. 2C, left panel) as previously shown in other studies [33,34]. FAK activity (FAK phosphorylation at Tyr 397) in both human VSMCs ( Fig.…”

Section: Effect Of Fak Rac Rho or Cdc42 Inhibition On The Process supporting

confidence: 90%

“…In this study, therefore, to test our primary hypothesis that FAK and its downstream targets (Rac, Rho, and Cdc42) are required during the process of spheroid formation, we used VSMC spheroids as a test platform. To do this, we created VSMC spheroids in vitro, which are spherical cellular aggregates consisting of multiple layers of VSMC [33,34], using a hanging drop culture technique [35,36]. This platform can mimic a VSMC-rich neointima in the vessel wall similar to animal models that simulate human pathologies such as restenosis and atherosclerosis.…”

Section: Fak Controls Cellular Processes Including Cell Adhesion and mentioning

confidence: 99%

Machine learning reveals heterogeneous responses to FAK, Rac, Rho, and Cdc42 inhibition on vascular smooth muscle cell spheroid formation and morphology

Vaidyanathan

Wang

Krajnik

et al. 2020

Preprint

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Hyper proliferation of vascular smooth muscle cells (VSMCs) contributes to neointima formation in atherosclerosis and the response to vascular injury. Understanding how to control VSMC proliferation would advance the effort to treat vascular disease. Drug responses are often different among patients with the same vascular disease condition, making it difficult to cater patientspecific treatments (existence of heterogeneity). Thus, we examined variations in response to drug treatments using VSMC spheroids that mimic vascular disease condition in vivo. FAK and its downstream Rho GTPases (Rac, Rho, and Cdc42) control cell-cell contact and play a key role in vascular pathology. Here, we tested the importance of FAK and Rho GTPases in spheroid formation. VSMC spheroids were made using a hanging-drop method with either inhibitors or vehicle control. Changes in morphology were used as a key indicator of spheroid response to drug treatment. A machine learning (ML) image segmentation (VGG16-U-net) was used to segment the spheroid images. After the morphological features were extracted from the segmented images, a two-level cluster framework was used to cluster VSMC spheroids into different morphologies. We found that FAK and Rho GTPases are required for normal spheroid formation. Next, we analyzed the various morphologies of disrupted spheroids resulting from drug treatment using our ML pipeline. The first-level clustering analysis showed the presence of 4 clusters of spheroids with rounded and disrupted morphologies. The subsequent second-level clustering analysis identified the presence of four distinct morphological clusters among these disrupted spheroids, and they exhibited differential responses to FAK and Rho GTPases inhibition. Particularly, we found FAK and Rho specific morphological phenotypes, thus suggesting that there may be two distinct pathways governing VSMC spheroid formation.Collectively, we revealed there are significant heterogeneities in the drug responses of spheroid formation, which were overlooked in previous analyses. This is our first step towards developing the ML-based method that can be used to assess the effects of different drugs on the VSMC spheroid model for better characterization of pathologic progression of vascular disease.

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“…In contrast, traditional in vitro models in 2D, such as scratch/wound healing assays [26], Boyden chamber/trans-well assays [18], stencil assays [27], and tube formation assays [28] are highly controllable, but poorly represent 3D multicellular organization of angiogenesis. Some 3D models including bead assays with spheroids composed of ECs and mural cells have been used [29,30], showing 3D sprouting phenotypes, yet the model has limited imaging capacity inside the spheroids and the coverage was not fully achieved. Brudno et al employed ECs and pericytes in the bead assays and showed EC sprouting and pericyte migration with nice imaging modalities [31], yet this model barely recapitulated perfusable 3D blood vessels with functional lumens.…”

Section: Discussionmentioning

confidence: 99%

An organotypicin vitromodel of matured blood vessels

Lee

2020

Preprint

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Angiogenesis is a physiological process in which brand-new blood vessels are formed from pre-existing blood vessels. The angiogenic processes are achieved by multiple steps, including angiogenic vascular sprouting, lumen formation, mural cell (e.g., smooth muscle cells) recruitment, and vessel stabilization by the mural cell coverage of the neovessels. Especially, mural cell recruitment to and coverage of the newly formed endothelium is a fundamental process to provide fully matured, functional blood vessels. Although investigation of the mural cell interactions with endothelial cells is crucial not only for better understanding of vascular physiology, but also for treating numerous vascular diseases, there has been a lack of three-dimensional (3D) in vitro models that recapitulate spontaneous processes of the vascular maturation. In this study, we describe an organotypic in vitro model that represents multi-step, spontaneous vascular maturation processes, which includes angiogenic vessel sprouting, smooth muscle cell (SMC) recruitment, and the SMC coverage of the neovessels. Using the system, we could spatiotemporally control vessel sprouting and vessel stabilization/maturation; and revealed an optimal condition that could reconstitute SMC-covered, matured blood vessels in 3D in vitro. We may provide a new platform for future mechanism studies of vascular interactions to mural cells and vessel maturation; and for pre-clinical screening and validation of therapeutic agent candidates for treating vascular diseases.

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Spheroids of Endothelial Cells and Vascular Smooth Muscle Cells Promote Cell Migration in Hyaluronic Acid and Fibrinogen Composite Hydrogels

Cited by 19 publications

References 80 publications

A machine learning pipeline revealing heterogeneous responses to drug perturbations on vascular smooth muscle cell spheroid morphology and formation

A machine learning pipeline revealing heterogeneous responses to drug perturbations on vascular smooth muscle cell spheroid morphology and formation

Machine learning reveals heterogeneous responses to FAK, Rac, Rho, and Cdc42 inhibition on vascular smooth muscle cell spheroid formation and morphology

An organotypicin vitromodel of matured blood vessels

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