Content area
Many of the cellular events that underlie neurodegenerative disease are best captured by continuously imaging live neurons over time. While the advent of robot-assisted microscopy has helped scale such longitudinal live microscopy to high-throughput regimes with the statistical power to detect transient events, time-intensive human annotation is still relied on for analyzing these experiments. We address this fundamental limitation of live microscopy with biomarker-optimized convolutional neural networks (BO-CNN): interpretable computer vision models trained directly on biosensor activity. We demonstrate the ability of BO-CNNs to detect cell death, a fundamental biological process that is typically measured by trained annotators. BO-CNNs detect cell death with super-human accuracy and speed by learning to identify important subcellular morphology associated with cell vitality, despite receiving no explicit supervision to rely on these features. Importantly, these models also uncover novel intranuclear morphology signal that is difficult to spot by eye and has not yet been linked to cell death, but reliably indicates death. BO-CNNs are broadly useful for analyzing any live microscopy and essential for interpreting high-throughput robotic-aided experiments.
Competing Interest Statement
The authors have declared no competing interest.