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Centro de Investigaciones Biológicas (CIB-CSIC)
Autophagy in Development and Disease
Wimasis is wondeful since we no longer have to spend our valuable time counting dots inside cells!
Dr. Patricia Boya
Head of laboratory
Our lab uses cellular and animal models to understand the physiological roles of autophagy and its implications during disease.

Autophagy is an essential intracelullar degradation pathway that recycles cell components generating new building blocks and energy to maintain cellular homeostasis. Autophagy plays an important role in the response to nutrient starvation; the recycling of damaged organelles and is a survival mechanism under stress conditions. In addition, autophagy could as well participate in programmed cell death.

We are interested in the implication of autophagy during development and in the relationship of autophagy with basic processes such as proliferation, differentiation and cell death. Moreover we want to understand how autophagy deregulation may play a role in several pathological situations such as cancer and neurodegenerative conditions.

We have several projects with pharmaceutical companies to screen for new drugs that modulate autophagy with the aim to find new treatments for cancer, neurodegenerative diseases and other pathological conditions.

Wimasis has allowed us to speed up and standardize the process of autophagosome quantification in cells and tissues.
Products they are using
Aksaray University
Department of Biology, Faculty of Science and Letters
Our main scientific interest is to combine biology and nanotechnology, meeting in cancer studies using multifunctional nanoparticles. Wimasis enables us to measure both in vitro scratch assay and in-ovo CAM assay by giving results in a shorter time and more standardized manner. Initial and final scratch sizes are determined using the WimScratch analysis tool and the difference between the two is used to determine migration distance using ratio of scratch area to cell covered area.

We also frequently use CAM model in angiogenesis studies. Instead of using the macroscopic scoring method, thanks to WimCAM we can reliably measure many parameters such as vesseldensity, total vessel network length, total branching points, total nets, and segment properties, hidden details in the images obtained after the nanomaterials-biological system interaction in CAM assay, thus we can make comparisons between these measurements in a short time. It also allows seeing measurements on the processed image after all these analyzes and makes you evaluate measurement quality. By this way, we cannot only save time but also get more reliable morphological information than classical methods.

As it is known, this is crucial for a better understanding of the relationship between cells/tissues and nano-sized structures and for making way for successful clinical applications. Thanks to Wimasis’ online tools, it is now possible to make more informative analyzes about interactions between nanomaterials and biological systems.
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Technical University of Munich
Molecular Food Technology and Safety
At the chair of Molecular Nutritional Medicine we study the balance between energy intake and expenditure. Thermogenic brown adipocytes profoundly contribute to the latter by dissipating nutrient energy in the form of heat. Characteristic features of brown as compared to white adipocytes are the lower size and greater number of lipid droplets as well as the smaller overall cell size.

Wimasis has developed a custom solutiom to us to automatically determine adipocyte size and number in images of histological sections that saves us hours and hours of manual counting and measuring. Even better, in cooperation with Wimasis, we established an image analysis procedure enabling us to quantify lipid droplet size and number in images of cultured adipocytes. A task that is impossible to perform manually and far less efficient with off-the-shelf particle recognition software in our hands.

With Wimasis image analyses we do not only save a lot of time, but are even able to extract more quantitative information out of every image we take.
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