ArticleNature communications2025
Supervised multi-frame dual-channel denoising enables long-term single-molecule FRET under extremely low photon budget.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
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