Journal of thrombosis and haemostasis : JTH · 2020
High-content, label-free analysis of proplatelet production from megakaryocytes.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАПоддержка правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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French Shauna L, Vijey Prakrith, Karhohs Kyle W, Wilkie Adrian R, Horin Lillian J, Ray Anjana, Posorske Benjamin, Carpenter Anne E, Machlus Kellie R, Italiano Joseph E
Аннотация
BACKGROUND: The mechanisms that regulate platelet biogenesis remain unclear; factors that trigger megakaryocytes (MKs) to initiate platelet production are poorly understood. Platelet formation begins with proplatelets, which are cellular extensions originating from the MK cell body.
OBJECTIVES: Proplatelet formation is an asynchronous and dynamic process that poses unique challenges for researchers to accurately capture and analyze. We have designed an open-source, high-content, high-throughput, label-free analysis platform.
METHODS: Phase-contrast images of live, primary MKs are captured over a 24-hour period. Pixel-based machine-learning classification done by ilastik generates probability maps of key cellular features (circular MKs and branching proplatelets), which are processed by a customized CellProfiler pipeline to identify and filter structures of interest based on morphology. A subsequent reinforcement classification, by CellProfiler Analyst, improves the detection of cellular structures.
RESULTS: This workflow yields the percent of proplatelet production, area, count of proplatelets and MKs, and other statistics including skeletonization information for measuring proplatelet branching and length. We propose using a combination of these analyzed metrics, in particular the area measurements of MKs and proplatelets, when assessing in vitro proplatelet production. Accuracy was validated against manually counted images and an existing algorithm. We then used the new platform to test compounds known to cause thrombocytopenia, including bromodomain inhibitors, and uncovered previously unrecognized effects of drugs on proplatelet formation, thus demonstrating the utility of our analysis platform.
CONCLUSION: This advance in creating unbiased data analysis will increase the scale and scope of proplatelet production studies and potentially serve as a valuable resource for investigating molecular mechanisms of thrombocytopenia.
OBJECTIVES: Proplatelet formation is an asynchronous and dynamic process that poses unique challenges for researchers to accurately capture and analyze. We have designed an open-source, high-content, high-throughput, label-free analysis platform.
METHODS: Phase-contrast images of live, primary MKs are captured over a 24-hour period. Pixel-based machine-learning classification done by ilastik generates probability maps of key cellular features (circular MKs and branching proplatelets), which are processed by a customized CellProfiler pipeline to identify and filter structures of interest based on morphology. A subsequent reinforcement classification, by CellProfiler Analyst, improves the detection of cellular structures.
RESULTS: This workflow yields the percent of proplatelet production, area, count of proplatelets and MKs, and other statistics including skeletonization information for measuring proplatelet branching and length. We propose using a combination of these analyzed metrics, in particular the area measurements of MKs and proplatelets, when assessing in vitro proplatelet production. Accuracy was validated against manually counted images and an existing algorithm. We then used the new platform to test compounds known to cause thrombocytopenia, including bromodomain inhibitors, and uncovered previously unrecognized effects of drugs on proplatelet formation, thus demonstrating the utility of our analysis platform.
CONCLUSION: This advance in creating unbiased data analysis will increase the scale and scope of proplatelet production studies and potentially serve as a valuable resource for investigating molecular mechanisms of thrombocytopenia.