Apoptotic cell death is a crucial mechanism that contributes to tissue homeostasis and prevents the onset of several diseases. However, this phenomenon is challenging to identify within microscopy movies that can encompass thousands of cells. Led by Santiago Gonzalez, the recent study carried at the IRB in Bellinzona introduces ADeS, an innovative approach based on artificial intelligence for the automatic detection of apoptotic cells in microscopy movies. ADeS not only ensures an accurate quantification of this dynamic process, but also reduces processing time, delivering results comparable to those of imaging experts. Developed by Dr. Alain Pulfer and Dr. Diego Pizzagalli, ADeS was recentlly published in the eLife Journal, creating new avenues in cell death research.
Bellinzona, April 17, 2024 – Intravital microscopy has revolutionized live cell imaging by allowing the study of spatial-temporal cell dynamics in living animals. However, the complexity of the data generated by this technology has limited the development of effective computational tools to identify and quantify cell processes. Amongst them, apoptosis is a crucial form of regulated cell death involved in tissue homeostasis and host defense. Live-cell imaging enabled the study of apoptosis at the cellular level, enhancing our understanding of its spatial-temporal regulation. However, at present, no computational method can deliver robust detection of apoptosis in microscopy time-lapses.
To overcome this limitation, we developed ADeS, a deep learning-based apoptosis detection system that employs the principle of activity recognition. We trained ADeS on extensive datasets containing more than 10,000 apoptotic instances collected both in vitro and in vivo, achieving a classification accuracy above 98% and outperforming state-of-the-art solutions. ADeS is the first method capable of detecting the location and duration of multiple apoptotic events in full microscopy time-lapses, surpassing human performance in the same task. We demonstrated the effectiveness and robustness of ADeS across various imaging modalities, cell types, and staining techniques.
Finally, we employed ADeS to quantify cell survival in vitro and tissue damage in vivo, demonstrating its potential application in toxicity assays, treatment evaluation, and inflammatory dynamics. Our findings suggest that ADeS is a valuable tool for the accurate detection and quantification of apoptosis in live-cell imaging and, in particular, intravital microscopy data, providing insights into the complex spatial-temporal regulation of this process.
Link to the scientific article
A. ADeS input consists of single channel 2D microscopy videos (x,y,t).
B. Given the coordinates of the ROI at time t, ADeS extracts a series of snapshots ranging from t-n to t+n. A deep learning network classifies the sequence either as non-apoptotic (0) or apoptotic (1).
C. The readout of ADeS consist of bounding boxes and associated probabilities, which can generate a probability map of apoptotic events.
