Scientists at the National University of Singapore (NUS) have demonstrated that deep learning, specifically utilizing convolutional neural networks (CNNs), allows for more accurate detection of the dynamics of individual molecules with less data compared to traditional evaluation methods. The researchers applied CNNs to analyze the movement of single molecules within cells, artificial systems, and small animals. This innovative approach is expected to enhance the efficiency of single-molecule measurements in complex systems, making them more accessible to a broader scientific community.
Examining a single molecule serves as the smallest observable unit in biological systems, offering valuable insights into the functioning and interactions of biological systems, paving the way for targeted therapeutic approaches to various disorders. Among the efficient techniques for observing single molecules, fluorescence spectroscopy stands out due to its specificity, allowing the observation of labeled molecules with a robust signal. For over five decades, Fluorescence Correlation Spectroscopy (FCS) has been a prominent method in this field, delivering highly accurate and precise measurements of molecular mobility and interactions.
Imaging FCS, an advanced iteration of Fluorescence Correlation Spectroscopy (FCS), enhances its capability to characterize mobilities, concentrations, and interactions across entire images. While Imaging FCS proves powerful, it comes with limitations, primarily the extensive data requirements leading to slow evaluations due to demanding computational processing. In response to this challenge, a research team led by Professor Thorsten Wohland and Adrian Röllin from NUS's Departments of Biological Sciences and Chemistry, and Statistics and Data Science respectively, utilized deep learning techniques to achieve comparable results with traditional methods while reducing data requirements to approximately 5 MB per measurement. The team, including Dr. Wai Hon Tang and Mr. Shao Ren Sim, developed two CNNs, namely FCSNet and ImFCSNet, tailored for this approach. Convolutional Neural Networks (CNNs), a subset of deep learning algorithms, excel in visual data analysis, utilizing multiple layers of specialized filters to identify specific elements such as edges, textures, and colors within an image.
