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Protein Dynamics in Living Cells

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Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
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Updated: Dec 29, 2025

Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy

Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy

Published on: January 18, 2017

High-throughput, multi-parametric, and correlative fluorescence lifetime imaging.

Chetan Poudel¹, Ioanna Mela, Clemens F Kaminski

¹Department of Chemical Engineering and Biotechnology, Philippa Fawcett Drive, University of Cambridge, Cambridge CB3 0AS, United Kingdom.

Methods and Applications in Fluorescence

|February 7, 2020

Summary

This summary is machine-generated.

This review covers advancements in fluorescence lifetime imaging microscopy (FLIM), enhancing speed and resolution for biomedical research. New technologies enable high-throughput imaging and multi-modal data acquisition for deeper biological insights.

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Area of Science:

Biomedical Optics
Microscopy Techniques
Advanced Imaging

Background:

Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique for analyzing biological samples.
Traditional FLIM methods face limitations in speed and resolution, hindering high-content applications.
Recent technological progress is addressing these limitations, opening new research avenues.

Purpose of the Study:

To review recent advancements in FLIM technology.
To highlight methods enabling faster and higher-resolution measurements.
To discuss FLIM applications in biomedical research.

Main Methods:

Review of fast single-photon timing technologies.
Analysis of parallelized detection schemes for high-throughput imaging.
Appraisal of time-domain FLIM implementations.
Exploration of FLIM combined with other imaging modalities.

Main Results:

New FLIM technologies offer significantly improved speed and resolution.
Parallelized detection enables high-throughput and high-content imaging.
Multi-modal FLIM approaches capture comprehensive correlative data.
These advancements are particularly impactful for biomedical research.

Conclusions:

FLIM technology is rapidly evolving, offering unprecedented capabilities.
Challenges remain in further optimizing speed, resolution, and data analysis.
Future opportunities lie in integrating FLIM with other advanced techniques for multi-dimensional biological discovery.