Effective Rapid Fluorescence Lifetime Imaging of the Brain: A Novel Approach Using Upconversion Photoluminescence Lifetime Based on Gate-Width Acquisition.

Nano Lett

Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.

Published: November 2024

AI Article Synopsis

  • The study presents a new data acquisition method that uses three adjustable gates to improve the resolution of rapid lifetime imaging in upconversion photoluminescence, addressing limitations of existing techniques.
  • This method allows for the visualization of capillary networks and pH mapping in mouse brain vasculature by utilizing enhanced brightness from specific nanoparticles.
  • Results show a high accuracy in fluorescence duration measurements, making it a fast and effective solution for multiplex brain imaging compared to traditional methods.

Article Abstract

The rapid lifetime imaging of upconversion photoluminescence is becoming increasingly popular in biosensing, anticounterfeiting, optical thermometry, and multiplex imaging. However, existing Rapid Lifetime Determination (RLD) techniques are limited in their ability to integrate contiguous, overlapping, and discrete windows into a single measurement, hindering accurate fluorescence lifetime retrieval. This study introduces a new data acquisition method using three adjustable gates in a single measurement to enhance resolution. We apply this method in rapid upconversion fluorescence lifetime imaging to visualize capillary networks and map pH levels based on intensity and lifetime differences in mouse brain vasculature. By enhancing brightness using NaYbF4@NaYF4,Er,Tm@NaYF4 nanoparticles, we achieve effective brain imaging. Monte Carlo simulations demonstrate a relative standard deviation of less than 0.4% for fluorescence durations spanning from 1 to 20 ns. This method provides a fast, high-contrast solution for multiplex brain imaging, addressing the limitations of slow data collection and poor accuracy in existing RLD techniques.

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Source
http://dx.doi.org/10.1021/acs.nanolett.4c03516DOI Listing

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