Automated detection of apoptotic bodies and cells in label-free time-lapse high-throughput video microscopy using deep convolutional neural networks

Bioinformatics. 2023 Oct 3;39(10):btad584. doi: 10.1093/bioinformatics/btad584.

Abstract

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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Annexins
  • Extracellular Vesicles*
  • Humans
  • Microscopy, Video
  • Neural Networks, Computer*
  • Time-Lapse Imaging / methods

Substances

  • Annexins