Stiffness of polyelectrolyte multilayer film influences endothelial function of endothelial cell monolayer

Chang, Hao; He, Zhimin; Mi, Hu; Chen, Jia-Yan; Li, Bochao; Ren, Keke; Martins, M. Cristina L.; Barbosa, Mário A.; Ji, Jian

doi:10.1016/j.colsurfb.2016.11.012

Cited by 28 publications

(19 citation statements)

References 52 publications

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“…Endothelial cells (HMEC) were also investigated in view of further development of co‐cultures with cancer cells. Similarly to CB74 cells (Figure a), they demonstrated a slight improvement of viability only with the lowest GnP concentrations (GnP1 and GnP5) (Figure S3d,e), contrary to Chang et al's () work that showed that endothelial cells were able to adhere and proliferate atop LbL films composed of PLL and HA, whatever their stiffness in the range of (200–430 kPa).…”

Section: Discussionmentioning

confidence: 57%

“…Interestingly, the association of HA and PLL was mainly studied as LbL films models (Abdelkebir et al, ; Picart et al, ). For example, (HA/PLL) LbL films were designed for the culture of endothelial cells and showed a better proliferation when the films were mechanically favorable (Chang et al, ). Recently, Madaboosi et al () illustrated that the thicker a HA/PLL multilayer film, the more cell repellent it is, regarding various type of cells from various tissues (fibroblasts, stem cells, from kidney, bone, and ovary).…”

Section: Discussionmentioning

confidence: 99%

“…Interestingly, the association of HA and PLL was mainly studied as LbL films models (Abdelkebir et al, 2011;Picart et al, 2001). For example, (HA/PLL) LbL films were designed for the culture of endothelial cells and showed a better proliferation when the films were mechanically favorable (Chang et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

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Hyaluronan‐based hydrogels as versatile tumor‐like models: Tunable ECM and stiffness with genipin‐crosslinking

Bonnesœur

Morin-Grognet

Thoumire

et al. 2020

J Biomedical Materials Res

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Three‐dimensional (3D) biomimetic cell culture platforms offer more realistic microenvironments that cells naturally experience in vivo. We developed a tunable hyaluronan‐based hydrogels that could easily be modified to mimic healthy or malignant extracellular matrices (ECMs). For that, we pre‐functionalized our hydrogels with an adhesive polypeptide (poly‐l‐lysine, PLL) or ECM proteins (type III and type IV collagens), naturally present in tumorous tissues, and next, we tuned their stiffness by crosslinking with gradual concentrations of genipin (GnP). Then, we thoroughly characterized our substrates before testing them with glioblastoma and breast cancer cells, and thereafter with endothelial cells. Overall, our hydrogels exhibited (a) increasing stiffness with GnP concentration for every pre‐functionalization and (b) efficient enzyme resistance with PLL treatment, and also with type IV collagen but to a lesser extent. While PLL‐treated hydrogels were not favorable to the culture of any glioblastoma cell lines, they enhanced the proliferation of breast cancer cells in a stiffness‐dependent manner. Contrary to type III collagen, type IV collagen pre‐treated hydrogels supported the proliferation of glioblastoma cells. The as‐desired HA‐based 3D tumor‐like models we developed may provide a useful platform for the study of various cancer cells by simply tuning their biochemical composition and their mechanical properties.

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Section: Discussionmentioning

confidence: 57%

Section: Discussionmentioning

confidence: 99%

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Hyaluronan‐based hydrogels as versatile tumor‐like models: Tunable ECM and stiffness with genipin‐crosslinking

Bonnesœur

Morin-Grognet

Thoumire

et al. 2020

J Biomedical Materials Res

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“…The cell resistance of the PCUL1‐PO samples, regardless of the VEGF surface density [Figure (A)], could be assigned to the low stiffness of the bulk material that possessed the lowest hard segment content (29.6%) among the three PCUL x ‐PO materials ( x = 1, 2, and 3, Table ). Previous studies have demonstrated that soft materials inhibit EC adhesion and proliferation, and grafting with growth factors cannot change the stiffness‐dependent manner of endothelial function . Both PCUL2‐PO and PCUL3‐PO possessed similar hard segment contents (39.7% vs. 38.9%, Table ), but the latter possessed much more PEG content (11.2% vs. 31.1%, Table ).…”

Section: Discussionmentioning

confidence: 95%

Toward endothelialization via vascular endothelial growth factor immobilization on cell‐repelling functional polyurethanes

Liao

Yang

et al. 2018

J Biomed Mater Res

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We screened a family of nonspecific cell-repelling polyurethanes (PUs) whose backbones are attached with epoxy group-terminated polyethylene glycol (PEG) side chains. Water incubation of the PU films (with 9.2-31.1 wt % PEG) caused a surface enrichment of PEG chains where vascular endothelial growth factor (VEGF) was grafted by forming secondary amine linkages between VEGF molecules and the PEG spacer. These linkages are still ionizable similar to original primary amines in VEGF, thereby retaining the original charge distribution on VEGF macromolecules. This charge conservation together with PEG steric repulsion helped to preserve VEGF conformation and bioactivity. The PU substrates with suitable hard segments contents and VEGF surface densities can selectively induce endothelial cells (ECs) adhesion and proliferation toward endothelialization. Moreover, the PU substrates, even grafted with fibrinogen (Fg), cannot trigger platelet adhesion and deformation, suggesting an inactive conformation of the grafted Fg. Thus enough antithrombogenicity of the PU substrates could be expected before full endothelialization. These PU materials might be applied onto the lumens of vascular grafts, potentially stimulating luminal endothelialization in vivo.

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“…For single-cell segmentation, culture density is commonly restricted to avoid cell junctions, unless special markers such as CD31 for endothelial cells are used. [4] In addition, good image quality, particularly low background noise, is also highly desired when using automatic DOI: 10.1002/adts.202000172 binarization with a defined threshold of pixel intensity. Paradoxically, the fluorescence decay always brings obvious background noise, and thus precise cell segmentation still relies on many manual operations.…”

Section: Introductionmentioning

confidence: 99%

Deep Mining of Subtle Differences in Cell Morphology via Deep Learning

Xue

Wang

Ren

et al. 2020

Advcd Theory and Sims

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Cell morphology analysis is crucial in life science. Accurate determination of differences in cell morphology is of great significance in understanding cell states under different conditions. However, conventional approaches for morphology analysis are constrained in efficiency or accuracy under many circumstances. Thus, an efficient and reliable method to profile morphological differences is needed. In this study, deep learning is used in cell image analysis to demonstrate its ability to find non‐apparent cell morphology differences. The convolutional neural network can accurately classify cell images from substrates of different stiffness that are undistinguishable to the human eye or conventional statistical methods. Moreover, with analysis of feature maps, and the assistance of a fully connected neural network and a random forest classifier, morphological information in images is systematically proved as the main basis of classification for the deep learning model. The above results indicate that deep learning is valuable for the in‐depth analysis of morphology to better understand subtle changes in cells, which can provide people with deeper insights into cell biology.

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Stiffness of polyelectrolyte multilayer film influences endothelial function of endothelial cell monolayer

Cited by 28 publications

References 52 publications

Hyaluronan‐based hydrogels as versatile tumor‐like models: Tunable ECM and stiffness with genipin‐crosslinking

Hyaluronan‐based hydrogels as versatile tumor‐like models: Tunable ECM and stiffness with genipin‐crosslinking

Toward endothelialization via vascular endothelial growth factor immobilization on cell‐repelling functional polyurethanes

Deep Mining of Subtle Differences in Cell Morphology via Deep Learning

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