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Peer-reviewedCC BY 4.0Methods and applications in fluorescence

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

  • Dora-Luz Flores1,2
  • Esperanza Guerra-Rosas1,2
  • Balam Benítez-Mata3,2
  • Gustavo Hirata4
  • Michelle Digman3
4 affiliations
  1. 1Facultad de Ingeniería, Arquitectura y Diseño, Universidad Autónoma de Baja California, Ensenada, Baja California 22860, Mexico
  2. 2These authors contributed equally to this work.
  3. 3Department of Biomedical Engineering, University of California Irvine, Irvine, CA 92697, United States of America
  4. 4Centro de Nanociencias y Nanotecnología, Ensenada, Baja California 22860, Mexico
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Abstract

In the present work, we developed a data-driven algorithm for the automated segmentation of fluorescence lifetime imaging microscopy (FLIM) images, enhancing the analysis of multifunctionalized nanoparticles (NPs) within living cancer cells. FLIM, a powerful microscopy technique, generates images that capture the fluorescence lifetime across a sample at the pixel level, revealing critical details about the molecular environment. Traditionally, FLIM image analysis has relied on manual segmentation with the phasor plot approach, a graphical representation of FLIM data in a G and S coordinate system, which is susceptible to user bias and inconsistency. Here, we present an automated and free of user-biased thresholding and segmentation algorithm that streamlines with clustering techniques to automatically identify phasor-clusters in the phasor plot space, reducing user dependency and providing a reproducible strategy under tested conditions image segmentation. We demonstrate its application in the context of FLIM images displaying a map of intensity heterogeneity where functionalized NPs affect the cellular metabolism of HeLa cells, reported by NADH, a bright and a dim fluorescent source, respectively, both of biological relevance. This algorithm provides a transparent and reproducible approach for FLIM image analysis, showing good agreement with expert-defined segmentation under sufficient contrast conditions, while presenting limitations in low-contrast or noisy regimes.

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Cite

BibTeXRIS
BibTeX
@article{flores2026unsupervised,
  title = {{Unsupervised clustering FLIM-phasor from multifunctionalized nanoparticles in living cancer cells}},
  author = {Dora-Luz Flores and Esperanza Guerra-Rosas and Balam Benítez-Mata and Gustavo Hirata and Michelle Digman},
  journal = {Methods and applications in fluorescence},
  year = {2026},
  month = jul,
  doi = {10.1088/2050-6120/ae7fed},
  url = {https://doi.org/10.1088/2050-6120/ae7fed},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Unsupervised clustering FLIM-phasor from multifunctionalized nanoparticles in living cancer cells
AU  - Dora-Luz Flores
AU  - Esperanza Guerra-Rosas
AU  - Balam Benítez-Mata
AU  - Gustavo Hirata
AU  - Michelle Digman
PY  - 2026
DA  - 2026/07/01
JO  - Methods and applications in fluorescence
DO  - 10.1088/2050-6120/ae7fed
UR  - https://doi.org/10.1088/2050-6120/ae7fed
AB  - In the present work, we developed a data-driven algorithm for the automated segmentation of fluorescence lifetime imaging microscopy (FLIM) images, enhancing the analysis of multifunctionalized nanoparticles (NPs) within living cancer cells. FLIM, a powerful microscopy technique, generates images that capture the fluorescence lifetime across a sample at the pixel level, revealing critical details about the molecular environment. Traditionally, FLIM image analysis has relied on manual segmentation with the phasor plot approach, a graphical representation of FLIM data in a G and S coordinate system, which is susceptible to user bias and inconsistency. Here, we present an automated and free of user-biased thresholding and segmentation algorithm that streamlines with clustering techniques to automatically identify phasor-clusters in the phasor plot space, reducing user dependency and providing a reproducible strategy under tested conditions image segmentation. We demonstrate its application in the context of FLIM images displaying a map of intensity heterogeneity where functionalized NPs affect the cellular metabolism of HeLa cells, reported by NADH, a bright and a dim fluorescent source, respectively, both of biological relevance. This algorithm provides a transparent and reproducible approach for FLIM image analysis, showing good agreement with expert-defined segmentation under sufficient contrast conditions, while presenting limitations in low-contrast or noisy regimes.
SN  - 2050-6120
ER  -