Camera-based single-molecule techniques have emerged as crucial tools in revolutionizing the understanding of biochemical and cellular processes due to their ability to capture dynamic processes with high precision, high-throughput capabilities, and methodological maturity. However, the stringent requirement in photon number per frame and the limited number of photons emitted by each fluorophore before photobleaching pose a challenge to achieving both high temporal resolution and long observation times. In this work, we introduce MUFFLE, a supervised deep-learning denoising method that enables single-molecule FRET with up to 10-fold reduction in photon requirement per frame. In practice, MUFFLE extends the total number of observation frames by a factor of 10 or more, greatly relieving the trade-off between temporal resolution and observation length and allowing for long-term measurements even without the need for oxygen scavenging systems and triplet state quenchers.
© 2025. The Author(s).