![]() This kinesin shares high amino acid identity and conservation within the ATP-hydrolyzing motor domain with minus end– and plus end–directed transport kinesins (Supplemental Figure S1, A and B). MCAK/Kif2C is a member of the kinesin-13 family of microtubule (MT)-depolymerizing kinesins. Thus, endogenous MCAK/Kif2C activity in normal cells is tuned to a mean level to achieve maximal suppression of chromosome instability. We also found that increasing WT MCAK/Kif2C protein levels over that of endogenous MCAK/Kif2C similarly increased chromosome instability. Using green fluorescent protein–FKBP-MCAK CRISPR cells we found that one deleterious hot-spot mutation increased chromosome instability in a wild-type (WT) background, suggesting that such mutants have the potential to promote tumor karyotype evolution. ![]() We found that a large proportion of these mutations adversely impact the motor. This allowed us to rapidly interrogate a number of MCAK/Kif2C motor domain mutations documented in the cancer database cBioPortal. We improved our workflow using CellProfiler to significantly speed up the imaging and analysis of transfected cells. We found that, despite their distinctly different activities, many mutations that impact transport kinesins also impair MCAK/Kif2C’s depolymerizing activity. This method allows us to score the impact of point mutations within the motor domain. We discuss how difficult real-world challenges faced by image informatics and personalized medicine are being tackled with open-source biomedical data and software.The microtubule (MT)-depolymerizing activity of MCAK/Kif2C can be quantified by expressing the motor in cultured cells and measuring tubulin fluorescence levels after enough hours have passed to allow tubulin autoregulation to proceed. Examples will be covered using existing open-source software tools such as ImageJ, CellProfiler, and IPython Notebook. We present high level discussions of sample preparation and image acquisition data formats storage and databases image processing computer vision and machine learning and visualization and interactive programming. This review targets biomedical scientists interested in getting started on tackling image analytics. Reliable data analytics products require as much automation as possible, which is a difficulty for data like histopathology and radiology images because we require highly trained expert physicians to interpret the information. One of the daunting challenges is how to effectively utilize medical image data in personalized medicine. Patient health data is computationally profiled against a large of pool of feature-rich data from other patients to ideally optimize how a physician chooses care. An area ready to take advantage of these developments is personalized medicine, the concept where the goal is tailor healthcare to the individual. There is a marked increase in image informatics applications as there have been simultaneous advances in imaging platforms, data availability due to social media, and big data analytics. Scenes, what an image contains, come from many imager devices such as consumer electronics, medical imaging systems, 3D laser scanners, microscopes, or satellites. Image informatics encompasses the concept of extracting and quantifying information contained in image data.
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