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

Author

Listed:
  • Yu Miao

    (Tsinghua University)

  • Yuxiao Cheng

    (Tsinghua University)

  • Yushi Xia

    (Tsinghua University)

  • Yongzhen Hei

    (Tsinghua University)

  • Wenjuan Wang

    (Tsinghua University)

  • Qionghai Dai

    (Tsinghua University
    Tsinghua University (THUIBCS))

  • Jinli Suo

    (Tsinghua University
    Tsinghua University (THUIBCS)
    Shanghai Artificial Intelligence Laboratory)

  • Chunlai Chen

    (Tsinghua University)

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.

Suggested Citation

  • Yu Miao & Yuxiao Cheng & Yushi Xia & Yongzhen Hei & Wenjuan Wang & Qionghai Dai & Jinli Suo & Chunlai Chen, 2025. "Supervised multi-frame dual-channel denoising enables long-term single-molecule FRET under extremely low photon budget," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-024-54652-w
    DOI: 10.1038/s41467-024-54652-w
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