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Application of non-negative matrix factorization to multispectral FLIM data analysis.

Paritosh Pande1, Brian E Applegate, Javier A Jo

  • 1Department of Biomedical Engineering, 5018 Emerging Technology Building, Texas A&M University, College Station, Texas - 77843, USA.

Biomedical Optics Express
|October 2, 2012
PubMed
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Non-negative matrix factorization (NMF) simplifies multispectral fluorescence lifetime imaging microscopy (FLIM) analysis. This method extracts fluorophore abundances, offering more intuitive features than traditional intensity and lifetime values.

Area of Science:

  • Biophotonics
  • Data Analysis
  • Microscopy

Background:

  • Interpreting fluorescence lifetime imaging microscopy (FLIM) data is challenging due to multiple fluorophores and multispectral complexity.
  • Current methods rely on subjective pixel-wise comparisons of intensity and lifetime values against known fluorophores.

Purpose of the Study:

  • To apply Non-negative Matrix Factorization (NMF) to multispectral time-domain FLIM data.
  • To develop a new set of FLIM features based on the relative abundance of constituent fluorophores.
  • To improve the interpretability and objectivity of FLIM data analysis.

Main Methods:

  • Utilized Non-negative Matrix Factorization (NMF), a multivariate data analysis technique.
  • Applied NMF to raw multispectral time-domain FLIM intensity data.
Keywords:
(070.5010) Pattern recognition(170.1580) Chemometrics(170.1610) Clinical applications(170.6920) Time-resolved imaging(170.6935) Tissue characterization(300.2530) Fluorescence, laser-induced

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  • Validated the NMF approach on simulated and experimental FLIM data.

    • Main Results:

    • NMF successfully extracted relative abundance features for constituent fluorophores from multispectral FLIM data.
    • The NMF-derived features are more intuitive and easier to interpret than standard intensity and lifetime values.
    • The NMF method is not limited by the number of fluorophores or their decay dynamics and bypasses the need for time deconvolution.

    Conclusions:

    • NMF provides a robust and efficient method for analyzing complex multispectral FLIM data.
    • The derived abundance features offer enhanced interpretability for FLIM imaging.
    • NMF-based FLIM analysis demonstrates potential for improved discrimination of biological samples, such as atherosclerotic plaques.