Fertilized eggs are crucial for the development of complex multicellular organisms because the process is tightly regulated by biological mechanisms. Effective cellular communication through signaling pathways plays a vital role in this context. When the activities of these signaling pathways are disrupted, distinct developmental abnormalities can be observed in the embryo.
In a recent publication in the esteemed journal Nature Methods, a team of researchers led by Professor Patrick Müller from the University of Konstanz introduced their innovative software called EmbryoNet. This software employs automated image analysis to detect and categorize defects that arise during the development of fish embryos. By determining the type of defect, researchers can infer which specific signaling pathway was disrupted in these embryos. The software’s exceptional speed and accuracy enable efficient investigation of various aspects, such as the mechanisms of drug actions, in high-throughput applications.
Artificial intelligence as a key component
Previously, the identification of underlying signaling mechanisms based on visible developmental defects required the expertise of specialists who had to meticulously examine a large number of embryos under a microscope. This laborious method was not only time-consuming but also prone to subjective interpretations due to the lack of standardized criteria.
“With the introduction of EmbryoNet, we have adopted a machine learning approach, leveraging a neural network trained on a vast dataset of over 2 million representative images of zebrafish embryos, enabling objective classification,” explains Matvey Safroshkin, one of the programmers behind EmbryoNet, alongside Hernán Morales-Naverrete. In addition to analyzing the visual data for classification, EmbryoNet incorporates temporal information about embryonic development and establishes connections between specific developmental defects and the corresponding disrupted signaling pathways.
More effective than humans
To evaluate the performance of their software, the researchers conducted a direct comparison between EmbryoNet and human experts. The task at hand involved matching previously unclassified images of zebrafish embryos with potential developmental defects. The competition included not only seasoned experts in the field of developmental biology but also groups of undergraduate students participating in a practical course.
“We included the data collected from the students in our study, which highlights the mutual benefits of current research and university education,” notes Müller. The findings of the study demonstrate that EmbryoNet exhibits remarkable reliability in identifying various signaling mutants in zebrafish. Moreover, the software outperformed its human counterparts, including the experienced experts, by being significantly faster and even more sensitive in detecting these defects.
Open-source and adaptable
In addition to its successful application in zebrafish, the researchers showcased the versatility of EmbryoNet by demonstrating its effectiveness in classifying other vertebrate species as well. “We were pleasantly surprised that with minimal adjustments, EmbryoNet could be trained to classify species that diverged from zebrafish hundreds of millions of years ago,” reveals Daniel Čapek, a developmental biologist and one of the authors of the study.
This remarkable adaptability of the open-source software, which is freely accessible and can be customized, holds immense potential in expediting the characterization of developmental mutants across a wide range of species. By facilitating broader investigations into the intricate mechanisms of embryonic development, EmbryoNet offers a valuable tool for accelerating scientific progress in the study of diverse organisms.
Source: University of Konstanz