MOTIVATION: Reliable label-free methods are needed for detecting and profiling apoptotic events in time-lapse cell-cell interaction assays. Prior studies relied on fluorescent markers of apoptosis, e.g. Annexin-V, that provide an inconsistent and late indication of apoptotic onset for human melanoma cells. Our motivation is to improve the detection of apoptosis by directly detecting apoptotic bodies in a label-free manner. RESULTS: Our trained ResNet50 network identified nanowells containing apoptotic bodies with 92% accuracy and predicted the onset of apoptosis with an error of one frame (5 min/frame). Our apoptotic body segmentation yielded an IoU accuracy of 75%, allowing associative identification of apoptotic cells. Our method detected apoptosis events, 70% of which were not detected by Annexin-V staining. AVAILABILITY AND IMPLEMENTATION: Open-source code and sample data provided at https://github.com/kwu14victor/ApoBDproject.

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-number = 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). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.