Optimal inference of molecular interaction dynamics in FRET microscopy

Author:

Kamino Keita1234,Kadakia Nirag125ORCID,Avgidis Fotios6ORCID,Liu Zhe-Xuan7ORCID,Aoki Kazuhiro8910ORCID,Shimizu Thomas S.6ORCID,Emonet Thierry1211ORCID

Affiliation:

1. Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06511

2. Quantitative Biology Institute, Yale University, New Haven, CT 06511

3. Institute of Molecular Biology, Academia Sinica, Taipei 115, Taiwan

4. PRESTO, Japan Science and Technology Agency, Kawaguchi-shi, Saitama 332-0012, Japan

5. Swartz Foundation for Theoretical Neuroscience, Yale University, New Haven, CT 06511

6. AMOLF Institute, Amsterdam 1098 XG, Netherlands

7. Institute of Physics, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan

8. Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences, Okazaki, Aichi 444-8787, Japan

9. National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Aichi 444-8585, Japan

10. Department of Basic Biology, School of Life Science, SOKENDAI (The Graduate University for Advanced Studies), Okazaki, Aichi 240-0193, Japan

11. Department of Physics, Yale University, New Haven, CT 06511

Abstract

Intensity-based time-lapse fluorescence resonance energy transfer (FRET) microscopy has been a major tool for investigating cellular processes, converting otherwise unobservable molecular interactions into fluorescence time series. However, inferring the molecular interaction dynamics from the observables remains a challenging inverse problem, particularly when measurement noise and photobleaching are nonnegligible—a common situation in single-cell analysis. The conventional approach is to process the time-series data algebraically, but such methods inevitably accumulate the measurement noise and reduce the signal-to-noise ratio (SNR), limiting the scope of FRET microscopy. Here, we introduce an alternative probabilistic approach, B-FRET, generally applicable to standard 3-cube FRET-imaging data. Based on Bayesian filtering theory, B-FRET implements a statistically optimal way to infer molecular interactions and thus drastically improves the SNR. We validate B-FRET using simulated data and then apply it to real data, including the notoriously noisy in vivo FRET time series from individual bacterial cells to reveal signaling dynamics otherwise hidden in the noise.

Funder

HHS | NIH | National Institute of General Medical Sciences

MEXT | JST | Precursory Research for Embryonic Science and Technology

Swartz Foundation for Theoretical Neuroscience

HHS | NIH | National Institute of Neurological Disorders and Stroke

HHS | NIH | National Institute on Deafness and Other Communication Disorders

MEXT | Japan Society for the Promotion of Science

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

Reference77 articles.

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