Two New FRET Imaging Measures: Linearly Proportional to and Highly Contrasting the Fraction of Active Molecules

Yamao, Masataka; Aoki, Kazuhiro; Yukinawa, Naoto; Ishii, Shin; Naoki, Honda

doi:10.1371/journal.pone.0164254

Cited by 6 publications

(4 citation statements)

References 39 publications

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“…Similarly, HS-AFM imaging of actin networks can be combined with imaging using Förster (or fluorescence) resonance energy transfer (FRET)-based biosensors. For example, the spatiotemporal activity of signaling molecules such as Rac1 and Cdc42, which are Rho GTPases responsible for cell motility, can be live-imaged using FRET-based biosensors 16 . Such hybrid AMF imaging with FRET-based biosensors should improve our understanding of how intracellular signaling controls cellular morphology via the reorganization of the actin network.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-guided reconstruction of cytoskeleton network from Live-cell AFM Images

Ju,

Skibbe,

Fukui

et al. 2024

Preprint

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How actin filaments (F-actins) are dynamically reorganized in motile cells at the level of individual filaments is an open question. To this end, we developed a high-speed atomic force microscopy (HS-AFM) to live-imagine intracellular dynamics of the individual F-actins. However, noise and low resolution made it difficult to fully recognize individual F-actins in the HS-AFM images. To tackle this problem, we developed a new machine learning method that quantitatively recognizes individual F-actins. The method estimates F-actin orientation from the image while improving the resolution. We found that F-actins were oriented at ±35° toward the membrane in the lamellipodia, which is consistent with Arp2/3 complex-induced branching. Furthermore, in the cell cortex our results showed non-random orientation at four specific angles, suggesting a new mechanism for F-actin organization demonstrating the potential of our newly developed method to fundamentally improve our understanding of the structural dynamics of F-actin networks.

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Section: Introductionmentioning

confidence: 99%

Machine learning-guided reconstruction of cytoskeleton network from Live-cell AFM Images

Ju,

Skibbe,

Fukui

et al. 2024

Preprint

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“…Numerous methods to analyze intensity-based time-series FRET data have been proposed ( 6 – 8 , 45 – 50 ). Qualitative methods, such as simple ratiometry ( 45 ), neglect spectral crosstalk and photobleaching.…”

mentioning

confidence: 99%

Optimal inference of molecular interaction dynamics in FRET microscopy

Kamino

Kadakia

Avgidis

et al. 2023

Proc. Natl. Acad. Sci. U.S.A.

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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.

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“…2A). Since the FRET-based chimeric molecular sensors were not bound by common Förster distance rules [31][32][33] , the combined fluorescent donor and acceptor molecules emitted visible or near-infrared light with a large apparent Stokes shift at the point of spectroscopic resolution irrespective of the target 34,35 . This enables researchers to select appropriate FRET pair combinations adjusted for their equipment mounting unique settings.…”

mentioning

confidence: 99%

Live imaging of apoptotic signaling flow using tunable combinatorial FRET-based bioprobes for cell population analysis of caspase cascades

Suzuki

Shindo

Yamanaka

et al. 2022

Sci Rep

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Understanding cellular signaling flow is required to comprehend living organisms. Various live cell imaging tools have been developed but challenges remain due to complex cross-talk between pathways and response heterogeneities among cells. We have focused on multiplex live cell imaging for statistical analysis to address the difficulties and developed simple multiple fluorescence imaging system to quantify cell signaling at single-cell resolution using Förster Resonance Energy Transfer (FRET)-based chimeric molecular sensors comprised of fluorescent proteins and dyes. The dye-fluorescent protein conjugate is robust for a wide selection of combinations, facilitating rearrangement for coordinating emission profile of molecular sensors to adjust for visualization conditions, target phenomena, and simultaneous use. As the molecular sensor could exhibit highly sensitive in detection for protease activity, we customized molecular sensor of caspase-9 and combine the established sensor for caspase-3 to validate the system by observation of caspase-9 and -3 dynamics simultaneously, key signaling flow of apoptosis. We found cumulative caspase-9 activity rather than reaction rate inversely regulated caspase-3 execution times for apoptotic cell death. Imaging-derived statistics were thus applied to discern the dominating aspects of apoptotic signaling unavailable by common live cell imaging and proteomics protein analysis. Adopted to various visualization targets, the technique can discriminate between rivalling explanations and should help unravel other protease involved signaling pathways.

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Two New FRET Imaging Measures: Linearly Proportional to and Highly Contrasting the Fraction of Active Molecules

Cited by 6 publications

References 39 publications

Machine learning-guided reconstruction of cytoskeleton network from Live-cell AFM Images

Machine learning-guided reconstruction of cytoskeleton network from Live-cell AFM Images

Optimal inference of molecular interaction dynamics in FRET microscopy

Live imaging of apoptotic signaling flow using tunable combinatorial FRET-based bioprobes for cell population analysis of caspase cascades

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