Supervised multi-frame dual-channel denoising enables long-term single-molecule FRET under extremely low photon budget

Yu Miao, Yuxiao Cheng, Yushi Xia, Yongzhen Hei, Wenjuan Wang, Qionghai Dai (), Jinli Suo () and Chunlai Chen ()
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Yu Miao: Tsinghua University
Yuxiao Cheng: Tsinghua University
Yushi Xia: Tsinghua University
Yongzhen Hei: Tsinghua University
Wenjuan Wang: Tsinghua University
Qionghai Dai: Tsinghua University
Jinli Suo: Tsinghua University
Chunlai Chen: Tsinghua University

Nature Communications, 2025, vol. 16, issue 1, 1-10

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-024-54652-w

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