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Abstract Fluorescence 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 termedflimGANE(fluorescencelifetimeimaging based onGenerativeAdversarialNetworkEstimation) 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 thatflimGANEprovides 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,flimGANEis particularly useful in fundamental biological research and clinical applications, where high-speed analysis is critical.
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Integrative visualization of the molecular structure of a cellular microdomain
Abstract An integrative approach to visualization is used to create a visual snapshot of the structural biology of the polar microdomain ofCaulobacter crescentus. The visualization is based on the current state of molecular and cellular knowledge of the microdomain and its cellular context. The collaborative process of researching and executing the visualization has identified aspects that are well determined and areas that require further study. The visualization is useful for dissemination, education, and outreach, and the study lays the groundwork for future 3D modeling and simulation of this well‐studied example of a cellular condensate.
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- Award ID(s):
- 1832184
- PAR ID:
- 10397220
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Protein Science
- Volume:
- 32
- Issue:
- 3
- ISSN:
- 0961-8368
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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