Motivation: Automated profiling of cell–cell interactions from high-throughput time-lapse imaging microscopy data of cells in nanowell grids (TIMING) has led to fundamental insights into cell–cell interactions in immunotherapy. This application note aims to enable widespread adoption of TIMING by (i) enabling the computations to occur on a desktop computer with a graphical processing unit instead of a server; (ii) enabling image acquisition and analysis to occur in the laboratory avoiding network data transfers to/from a server and (iii) providing a comprehensive graphical user interface. Results: On a desktop computer, TIMING 2.0 takes 5 s/block/image frame, four times faster than our previous method on the same computer, and twice as fast as our previous method (TIMING) running on a Dell PowerEdge server. The cell segmentation accuracy (f-number = 0.993) is superior to our previous method (f-number1⁄4 0.821). A graphical user interface provides the ability to inspect the video analysis results, make corrective edits efficiently (one-click editing of an entire nanowell video sequence in 5–10 s) and display a summary of the cell killing efficacy measurements. Availability and implementation: Open source Python software (GPL v3 license), instruction manual, sample data and sample results are included with the Supplement (https://github.com/ RoysamLab/TIMING2). Contact: nvaradar@central.uh.edu or broysam@central.uh.edu Supplementary information: Supplementary data are available at Bioinformatics online.
分享
引用
