scholarly journals Deep learning enables rapid and robust analysis of fluorescence lifetime imaging in photon-starved conditions

2020 ◽  
Author(s):  
Yuan-I Chen ◽  
Yin-Jui Chang ◽  
Shih-Chu Liao ◽  
Trung Duc Nguyen ◽  
Jianchen Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fluorescence Lifetime ◽  
Resonance Energy ◽  
Excitation Power ◽  
Least Square ◽  
Biological Research ◽  
Fluorescence Lifetime Imaging ◽  
Accurate Analysis ◽  
Lifetime Imaging ◽  
Cellular Environment

AbstractFluorescence lifetime imaging microscopy (FLIM) is a powerful tool to quantify molecular compositions and study the molecular states in the complex cellular environment as the lifetime readings are not biased by the fluorophore concentration or the excitation power. However, the current methods to generate FLIM images are either computationally intensive or unreliable when the number of photons acquired at each pixel is low. Here we introduce a new deep learning-based method termed flimGANE (fluorescence lifetime imaging based on Generative Adversarial Network Estimation) that can rapidly generate accurate and high-quality FLIM images even in the photon-starved conditions. We demonstrated our model is not only 258 times faster than the most popular time-domain least-square estimation (TD_LSE) method but also provide more accurate analysis in barcode identification, cellular structure visualization, Förster resonance energy transfer characterization, and metabolic state analysis. With its advantages in speed and reliability, flimGANE is particularly useful in fundamental biological research and clinical applications, where ultrafast analysis is critical.

scholarly journals Generative adversarial network enables rapid and robust fluorescence lifetime image analysis in live cells

2022 ◽  
Vol 5 (1) ◽  
Author(s):  
Yuan-I Chen ◽  
Yin-Jui Chang ◽  
Shih-Chu Liao ◽  
Trung Duc Nguyen ◽  
Jianchen Yang ◽  
...  
Keyword(s):  
Fluorescence Lifetime ◽  
High Speed ◽  
Resonance Energy ◽  
Likelihood Estimation ◽  
Live Cells ◽  
Biological Research ◽  
Fluorescence Lifetime Imaging ◽  
Accurate Analysis ◽  
Generative Adversarial Network ◽  
Cellular Environment

AbstractFluorescence lifetime imaging microscopy (FLIM) is a powerful tool to quantify molecular compositions and study molecular states in complex cellular environment as the lifetime readings are not biased by fluorophore concentration or excitation power. However, the current methods to generate FLIM images are either computationally intensive or unreliable when the number of photons acquired at each pixel is low. Here we introduce a new deep learning-based method termed flimGANE (fluorescence lifetime imaging based on Generative Adversarial Network Estimation) that can rapidly generate accurate and high-quality FLIM images even in the photon-starved conditions. We demonstrated our model is up to 2,800 times faster than the gold standard time-domain maximum likelihood estimation (TD_MLE) and that flimGANE provides a more accurate analysis of low-photon-count histograms in barcode identification, cellular structure visualization, Förster resonance energy transfer characterization, and metabolic state analysis in live cells. With its advantages in speed and reliability, flimGANE is particularly useful in fundamental biological research and clinical applications, where high-speed analysis is critical.

Automated multiwell fluorescence lifetime imaging for Förster resonance energy transfer assays and high content analysis

2015 ◽  
Vol 7 (10) ◽  
pp. 4071-4089 ◽  
Author(s):  
Douglas J. Kelly ◽  
Sean C. Warren ◽  
Dominic Alibhai ◽  
Sunil Kumar ◽  
Yuriy Alexandrov ◽  
...  
Keyword(s):  
Content Analysis ◽  
Energy Transfer ◽  
Fluorescence Lifetime ◽  
Resonance Energy Transfer ◽  
Resonance Energy ◽  
Förster Resonance Energy Transfer ◽  
Fluorescence Lifetime Imaging ◽  
Lifetime Imaging ◽  
High Content Analysis ◽  
Förster Resonance

An HCA-FLIM instrument is presented alongside exemplar oligomerisation, intermolecular and intramolecular FRET assays that require robust measurement of small lifetime changes.

Deep learning for quantitative bi-exponential fluorescence lifetime imaging (Conference Presentation)

2019 ◽  
Author(s):  
Jason T. Smith ◽  
Ruoyang Yao ◽  
Sez-Jade Chen ◽  
Nattawut Sinsuebphon ◽  
Alena Rudkouskaya ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fluorescence Lifetime ◽  
Fluorescence Lifetime Imaging ◽  
Lifetime Imaging

scholarly journals Screening for protein-protein interactions using Förster resonance energy transfer (FRET) and fluorescence lifetime imaging microscopy (FLIM)

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Anca Margineanu ◽  
Jia Jia Chan ◽  
Douglas J. Kelly ◽  
Sean C. Warren ◽  
Delphine Flatters ◽  
...  
Keyword(s):  
Energy Transfer ◽  
Fluorescence Lifetime ◽  
Protein Interactions ◽  
Resonance Energy Transfer ◽  
Resonance Energy ◽  
Forster Resonance Energy Transfer ◽  
Förster Resonance Energy Transfer ◽  
Fluorescence Lifetime Imaging ◽  
Lifetime Imaging ◽  
Förster Resonance

Abstract We present a high content multiwell plate cell-based assay approach to quantify protein interactions directly in cells using Förster resonance energy transfer (FRET) read out by automated fluorescence lifetime imaging (FLIM). Automated FLIM is implemented using wide-field time-gated detection, typically requiring only 10 s per field of view (FOV). Averaging over biological, thermal and shot noise with 100’s to 1000’s of FOV enables unbiased quantitative analysis with high statistical power. Plotting average donor lifetime vs. acceptor/donor intensity ratio clearly identifies protein interactions and fitting to double exponential donor decay models provides estimates of interacting population fractions that, with calibrated donor and acceptor fluorescence intensities, can yield dissociation constants. We demonstrate the application to identify binding partners of MST1 kinase and estimate interaction strength among the members of the RASSF protein family, which have important roles in apoptosis via the Hippo signalling pathway. K D values broadly agree with published biochemical measurements.

scholarly journals A biosensor of local kinesin activity reveals roles of PKC and EB1 in KIF17 activation

2013 ◽  
Vol 203 (3) ◽  
pp. 445-455 ◽  
Author(s):  
Cedric Espenel ◽  
Bipul R. Acharya ◽  
Geri Kreitzer
Keyword(s):  
Energy Transfer ◽  
Fluorescence Lifetime ◽  
Single Cells ◽  
Resonance Energy Transfer ◽  
Resonance Energy ◽  
Epithelial Differentiation ◽  
Fluorescence Lifetime Imaging ◽  
Lifetime Data ◽  
Lifetime Imaging ◽  
Phasor Plot

We showed previously that the kinesin-2 motor KIF17 regulates microtubule (MT) dynamics and organization to promote epithelial differentiation. How KIF17 activity is regulated during this process remains unclear. Several kinesins, including KIF17, adopt compact and extended conformations that reflect autoinhibited and active states, respectively. We designed biosensors of KIF17 to monitor its activity directly in single cells using fluorescence lifetime imaging to detect Förster resonance energy transfer. Lifetime data are mapped on a phasor plot, allowing us to resolve populations of active and inactive motors in individual cells. Using this biosensor, we demonstrate that PKC contributes to the activation of KIF17 and that this is required for KIF17 to stabilize MTs in epithelia. Furthermore, we show that EB1 recruits KIF17 to dynamic MTs, enabling its accumulation at MT ends and thus promoting MT stabilization at discrete cellular domains.

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