Unsupervised clustering FLIM-phasor from multifunctionalized nanoparticles in living cancer cells

Area of Science:

• Biomedical Imaging
• Microscopy
• Computational Biology

Background:

• Fluorescence Lifetime Imaging Microscopy (FLIM) provides pixel-level molecular environment data.
• Manual FLIM analysis using phasor plots is prone to user bias and inconsistency.
• Automated segmentation is needed for objective and reproducible FLIM data analysis.

Purpose of the Study:

• To develop a groundbreaking algorithm for automated segmentation of FLIM images.
• To enhance the analysis of multifunctionalized nanoparticles within living cancer cells.
• To provide a more objective and reproducible approach to FLIM image analysis.

Main Methods:

• Developed a novel algorithm for automated FLIM image segmentation.
• Utilized clustering techniques to identify phasor-clusters in phasor plot space.
• Applied the algorithm to analyze nanoparticle effects on HeLa cell metabolism using NADH.

Main Results:

• The algorithm automates thresholding and segmentation, minimizing user bias.
• Achieved reproducible and consistent image segmentation for complex FLIM data.
• Demonstrated successful application in analyzing nanoparticle-induced metabolic changes in cancer cells.

Conclusions:

• The developed algorithm streamlines complex FLIM image segmentation.
• Presents a new, objective, and reproducible tool for precise biomedical imaging segmentation.
• Significantly reduces human error in FLIM image analysis.