Monitoring receptor-ligand interactions using fluorescence lifetime FRET imaging via deep learning (Conference Presentation)

Margarida Barroso; Alena Rudkouskaya; Jason T. Smith; Xavier Intes

doi:10.1117/12.2546342

9 March 2020 Monitoring receptor-ligand interactions using fluorescence lifetime FRET imaging via deep learning (Conference Presentation)

Margarida Barroso, Alena Rudkouskaya, Jason T. Smith, Xavier Intes

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Proceedings Volume 11244, Multiphoton Microscopy in the Biomedical Sciences XX; 1124419 (2020) https://doi.org/10.1117/12.2546342
Event: SPIE BiOS, 2020, San Francisco, California, United States

Abstract

Quantification of ligand-receptor engagement in human breast cancer cells and tumor xenografts has been performed using fluorescence lifetime Forster resonance energy transfer (FLI-FRET) imaging at multiscale, from in vitro microscopy to in vivo macroscopy and across visible to near-infrared wavelengths. We have developed a 3D convolutional neural network architecture, named FLI-Network (FLI-Net), to process fluorescence lifetime decays acquired by either Time-Correlated Single-Photon Counting (TCSCP)- or gated ICCD- based instruments. FLI-FRET ability to measure target engagement across different imaging platforms as well as post-processing analysis approaches can find numerous applications in pre-clinical drug delivery and targeted therapy assessment and optimization.

Conference Presentation

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Margarida Barroso, Alena Rudkouskaya, Jason T. Smith, and Xavier Intes "Monitoring receptor-ligand interactions using fluorescence lifetime FRET imaging via deep learning (Conference Presentation)", Proc. SPIE 11244, Multiphoton Microscopy in the Biomedical Sciences XX, 1124419 (9 March 2020); https://doi.org/10.1117/12.2546342

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KEYWORDS

Luminescence

Fluorescence resonance energy transfer

Near infrared

Visible radiation

3D acquisition

3D image processing

3D metrology

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