A high-throughput, high-resolution imager for multi-fluosescence imaging of 3D and 2D cultured cells
96-well plates can be imaged in about 60 seconds and analyzed in about 30 seconds.
In addition, automatic capture of up to 200 plates per day is possible by connecting external devices such as plate stackers and incubators with a plate transfer robot.
3D cultured cells can be analyzed with Z-stacking imaging and focus composition functions. Even cells scattered in the height direction can be captured clearly.
Deep learning image processing enables "researcher's eye" segmentation of target cells. Accurate quantification is possible even for segmentation by cell morphology and high confluency, non-uniform images.
Fluorescent color and bright field are automatically and continuously captured.
5 types of fluorescent filters are available.
Respiratory diseases include a wide range of conditions, including
chronic progressive diseases and acute and severe diseases such as
the recent COVID-19 pneumonia. However, the development of
fundamental therapeutics in this field has been slow, partly because
animal models such as mice used for preclinical drug efficacy evaluation
often do not reflect human disease states. A typical example is
cystic fibrosis (CF), an autosomal recessive genetic disorder caused
by mutations in the cystic fibrosis transmembrane conductance
regulator (CFTR), a chloride ion channel.
In recent years, NK (Natural Killer) cells have been attracting attention in the fields of drug discovery development and cancer research. NK cells are a type of lymphocyte that works in innate immunity and play a major role in the removal of tumor cells and virus-infected cells.
By using Cell3iMager duos / duos2 and Deep Learning Plug-In, you will be able to perform a long-term Killing Assay. Since the imager uses a plate-fixed imaging method, clear imaging is possible even for suspended-cultured NK cells and 3D-cultured cancer cells.
With this study we conclude that Cell3iMager duos imaging technology in conjunction with deep learning is highly suited for delineating various biological processes using sympathetic neurons derived from hiPSC. Additionally, the platform can be used for identifying drug entities that could stimulate or block neuronal outgrowth. The analysis by deep learning accurately detects cells and is highly robust compared to traditional analyses even if there are differences in brightness or in the presence of debris.
