Fluorescence-based quantitative scratch wound healing assay demonstrating the role of MAPKAPK-2/3 in fibroblast migration

2009 ◽  
Vol 66 (12) ◽  
pp. 1041-1047 ◽  
Author(s):  
Manoj B. Menon ◽  
Natalia Ronkina ◽  
Jessica Schwermann ◽  
Alexey Kotlyarov ◽  
Matthias Gaestel
2020 ◽  
Author(s):  
Alejandra Suarez-Arnedo ◽  
Felipe Torres Figueroa ◽  
Camila Clavijo ◽  
Pablo Arbeláez ◽  
Juan C. Cruz ◽  
...  

AbstractIn vitro scratch wound healing assay, a simple and low-cost technique that works along with other image analysis tools, is one of the most widely used 2D methods to determine the cellular migration and proliferation in processes such as regeneration and disease. There are open-source programs such as imageJ to analyze images of in vitro scratch wound healing assays, but these tools require manual tuning of various parameters, which is time-consuming and limits image throughput. For that reason, we developed an optimized plugin for imageJ to automatically recognize the wound healing size, correct the average wound width by considering its inclination, and quantify other important parameters such as: area, wound area fraction, average wound width, and width deviation of the wound images obtained from a scratch/ wound healing assay. Our plugin is easy to install and can be used with different operating systems. It can be adapted to analyze both individual images and stacks. Additionally, it allows the analysis of images obtained from bright field, phase contrast, and fluorescence microscopes. In conclusion, this new imageJ plugin is a robust tool to automatically standardize and facilitate quantification of different in vitro wound parameters with high accuracy compared with other tools and manual identification.


2021 ◽  
Vol 36 (1) ◽  
pp. 226-233
Author(s):  
Rukiye Boran ◽  
Nurdan Sarac ◽  
Tuba Baygar ◽  
Aysel Ugur

The genus Hypericum sp. has a number of uses in traditional medicine like curing the burns, ulcers, haemorrhoids and wound healing. The species Hypericum lydium Boiss. (Hypericaceae), however, has not been known to have any properties related to the healing of injuries or antimicrobial working against the oral microorganisms. The present study was aimed to evaluate the efficiency of H. lydium in soft tissue healing and its capacity to prevent infections after dental extraction. H. lydium was extracted with ethanol and the obtained extract was tested for its inhibition ability on extracellular matrix-degrading enzymes; collagenase, hyaluronidase and elastase. To determine the cytotoxicity and wound healing capacity of the extract, MTT and in vitro scratch wound healing assay were performed using the NIH-3T3 fibroblast cells, respectively. Antimicrobial activity was investigated by microdilution method against oral pathogenic microorganisms. The highest enzyme inhibition activity was determined against elastase (80.27±0.1%). According to the cytotoxic activity results, the IC50 value of the H. lydium was found to be 82.20±4.05 μg/mL. Scratch wound healing assay of the extract exhibited a significant enhancement at 24 h with a closed wound area when compared with the control. The extract showed potent antimicrobial properties against oral pathogenic microorganisms. The results of the study revealed out that H. lydium can be considered as a natural compound for dental industry to improve soft tissue healing and to prevent the possible infections after dental extraction.


Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 911
Author(s):  
Entaz Bahar ◽  
Hyonok Yoon

The study of artificial neural networks (ANN) has undergone a tremendous revolution in recent years, boosted by deep learning tools. The presence of a greater number of learning tools and their applications, in particular, favors this revolution. However, there is a significant need to deal with the issue of implementing a systematic method during the development phase of the ANN to increase its performance. A multilayer feedforward neural network (FNN) was proposed in this paper to predict the cell migration assay on cisplatin-sensitive and cisplatin-resistant (CisR) ovarian cancer (OC) cell lines via scratch wound healing assay. An FNN training algorithm model was generated using the MATLAB fitting function in a MATLAB script to accomplish this task. The input parameters were types of cell lines, times, and wound area, and outputs were relative wound area, percentage of wound closure, and wound healing speed. In addition, we tested and compared the initial accuracy of various supervised learning classifier and support vector regression (SVR) algorithms. The proposed ANN model achieved good agreement with the experimental data and minimized error between the estimated and experimental values. The conclusions drawn demonstrate that the developed ANN model is a useful, accurate, fast, and inexpensive method to predict cancerous cell migration characteristics evaluated via scratch wound healing assay.


2019 ◽  
Vol 21 (Supplement_3) ◽  
pp. iii36-iii37
Author(s):  
M Ndiaye ◽  
C Rébé ◽  
A Ilie ◽  
L Ménégaut ◽  
T Pilot ◽  
...  

Abstract BACKGROUND Glioblastoma is the most common primary brain tumor. Its prognosis remains poor even with the standard treatment - the Stupp protocol.The classic Warburg effect in cancers leads to increased glycolysis which causes acidification of the tumor environment. This phenomenon may favor migration of tumor cells as already reported in pancreatic ductal adenocarcinoma. We therefore hypothesized that enhanced glycolysis in glioblastomas could favor the tumor cell migration. MATERIAL AND METHODS We measured glycolysis by the extracellular acidification rate (ECAR) of several human glioblastomas cell lines (LN229, LN18, T98-G, U87-MG, U373-MG, U118-MG) with the Seahorse Analyzer. To confirm these results, we also measured the intracellular cAMP rates using the Cayman’s Elisa kit and we analyzed by RT-PCR the expression of the main genes coding for enzymes involved in glycolysis in these glioblastomas cell lines. Cell migration was measured with a scratch wound healing assay during 24 hours. RESULTS U118-MG was the glioblastoma cell line with the highest glycolysis rate, the highest production of cAMP and showed a strong expression of glycolysis-associated genes. LN229 was the glioblastoma cell line with the less important glycolysis rate, the lower production of cAMP and showed a weaker expression of glycolysis-associated genes. According to the scratch wound healing assay, U118-MG cells showed a more important migration than LN229 cells at 24 hours. CONCLUSION Glycolysis may be an attractive target to prevent effectively tumor cell migration in glioblastomas. Coupling the evaluation of glycolysis with histomolecular characterization of glioblastomas, could help to identify patients to whom adjuvant therapies that inhibit glycolysis such as fenofibrate could be proposed.


Author(s):  
Gil Topman ◽  
Orna Sharabani-Yosef ◽  
Amit Gefen

A wound healing assay is simple but effective method to study cell migration in vitro. Cell migration in vitro was found to mimic migration in vivo to some extent [1,2]. In wound healing assays, a “wound” is created by either scraping or mechanically crushing cells in a monolayer, thereby forming a denuded area. Cells migrate into the denuded area to complete coverage, and thereby “heal” the wound. Micrographs at regular time intervals are captured during such experiments for analysis of the process of migration.


2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Fanxing Xu ◽  
Chenying Zhang ◽  
Dana T. Graves

Impaired diabetic wound healing constitutes a major health problem. The impaired healing is caused by complex factors such as abnormal keratinocyte and fibroblast migration, proliferation, differentiation, and apoptosis, abnormal macrophage polarization, impaired recruitment of mesenchymal stem cells (MSCs) and endothelial progenitor cells (EPCs), and decreased vascularization. Diabetes-enhanced and prolonged expression of TNF-αalso contributes to impaired healing. In this paper, we discuss the abnormal cell responses in diabetic wound healing and the contribution of TNF-α.


2011 ◽  
Vol 9 (4) ◽  
pp. 355-361 ◽  
Author(s):  
R Madhyastha ◽  
H Madhyastha ◽  
Y Nakajima ◽  
S Omura ◽  
M Maruyama

Sign in / Sign up

Export Citation Format

Share Document