The networks that control cell growth, differentiation, maintenance, repair, and many other functions of an adult organism, are essentially nonlinear types of process control. The genetic regulation mechanisms of cells are composed of many interacting complex, dynamical networks. This method should be able to help biologists identify the alterations of cellular mechanisms that allow drug candidates to change cell behavior and thereby improve the efficiency of drug discovery and treatment design.
The extracted cellular dynamics are highly reproducible and sensitive enough to detect subtle activity differences and identify mechanisms responding to selected perturbations. Experiments show that the proposed procedure achieves fast and robust image segmentation with sufficient accuracy. The proposed procedure contains two parts: (1) image processing, where the fluorescent images are processed to identify individual cells and allow their transcriptional activity levels to be quantified and (2) data representation, where the extracted time course data are summarized and represented in a way that facilitates efficient evaluation.
To fully realize these possibilities and explore their potential in biological and pharmaceutical applications, we introduce a new data processing procedure to extract information about the dynamics of cell processes based on this technology. This technology enhances our ability to discover treatment-induced regulatory mechanisms, temporally order their onsets and recognize their relationships. All rights reserved.High-content cell imaging based on fluorescent protein reporters has recently been used to track the transcriptional activities of multiple genes under different external stimuli for extended periods.
#TOPHAT METHOD CELLPROFILER SOFTWARE#
Human induced pluripotent stem cell Intracellular localization Membrane-trafficking Open-source image analysis software Parkinson disease Phagocytosis.Ĭopyright © 2021 Elsevier Inc. These present results suggest that this new analytical tool can be used as a supporting method for quantification of intracellular protein. Furthermore, Rab5 puncta of PARK8 patient iPSC-derived neurons exhibited distinct localization pattern relative to isogenic iPSC-derived neurons. On the other hands, at 14 days in vitro, the numbers of Rab5 puncta in PARK8 patient iPSC-derived neurons were lower than those of isogenic iPSC-derived neurons, but not Rab7 puncta. Interestingly, in long-term culture, we found that the numbers of Rab7 puncta in a single PARK8 patient iPSC-derived neurons were lower than that of control iPSC-derived neurons. These were able to successfully detect Rab5 puncta and Rab7 puncta in PARK8 patient iPSC-derived neurons. Finally, we developed new pipelines for analysis of disease phenotype using iPSCs from a patient with familial Parkinson's disease (PD), harboring the I2020T LRRK2 mutation (PARK8). In addition, when applied to quantitative measurement of phagocytosis, our pipelines clearly detected monocytic cell lines that had engulfed bioparticles. Using CellProfiler and ImageJ in combination, we confirmed that our pipelines were applicable both quantitatively and objectively to analysis of membrane trafficking of proteins such as Rab proteins and transferrin. We then constructed pipelines for measuring the distance from the center of the nucleus to allow investigation of the intracellular localization of Rab7 or transferrin. This revealed that scattered puncta of Rab7 and transferrin in neuroblastoma lines were clearly detectable by created analytical pipelines in CellProfiler. In the present study, to examine whether intracellular proteins can be discriminated using a combination of CellProfiler and ImageJ, we analyzed neuroblastoma and monocytic cell lines, and disease-specific induced pluripotent stem cell (iPSC)-derived neurons. Analytical pipeline, which is used for various analysis application, of CellProfiler, an open-source software for cell imaging analysis, is very important.
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