1. High‐content, label‐free analysis of proplatelet production from megakaryocytes
- Author
-
Shauna L. French, Adrian R. Wilkie, Benjamin Posorske, Prakrith Vijey, Anjana Ray, Lillian J. Horin, Kyle W. Karhohs, Joseph E. Italiano, Anne E. Carpenter, and Kellie R. Machlus
- Subjects
Blood Platelets ,Computer science ,Hematology ,Computational biology ,030204 cardiovascular system & hematology ,Thrombocytopenia ,Article ,Skeletonization ,Thrombopoiesis ,03 medical and health sciences ,Statistical classification ,0302 clinical medicine ,Open source ,Platelet production ,Humans ,Platelet formation ,Megakaryocytes ,Cells, Cultured ,Label free - Abstract
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 that originate from the MK cell body. Proplatelet formation is a highly dynamic and asynchronous process which poses unique challenges for researchers to accurately capture and analyze. We have designed an open-source, high-content, high-throughput, label-free analysis platform. 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 then processed by a customized CellProfiler pipeline to identify and filter structures of interest based on morphologic parameters. CellProfiler Analyst, provides a final, supervised, machine learning classification to bolster accurate identification of cellular structures. This entire workflow yields the percent of proplatelet production, area, and count of proplatelets and MKs, as well as 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. 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. more...
- Published
- 2020
- Full Text
- View/download PDF