Noise-Corrected Principal Component Analysis of fluorescence lifetime imaging data

Alix Le Marois*, Simon Labouesse, Klaus Suhling, Rainer Heintzmann

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)


Fluorescence Lifetime Imaging (FLIM) is an attractive microscopy method in the life sciences, yielding information on the sample otherwise unavailable through intensity-based techniques. A novel Noise-Corrected Principal Component Analysis (NC-PCA) method for time-domain FLIM data is presented here. The presence and distribution of distinct microenvironments are identified at lower photon counts than previously reported, without requiring prior knowledge of their number or of the dye's decay kinetics. A noise correction based on the Poisson statistics inherent to Time-Correlated Single Photon Counting is incorporated. The approach is validated using simulated data, and further applied to experimental FLIM data of HeLa cells stained with membrane dye di-4-ANEPPDHQ. Two distinct lipid phases were resolved in the cell membranes, and the modification of the order parameters of the plasma membrane during cholesterol depletion was also detected. (Figure presented.) Noise-corrected Principal Component Analysis of FLIM data resolves distinct microenvironments in cell membranes of live HeLa cells.

Original languageEnglish
Pages (from-to)1124-1133
Number of pages10
JournalJournal of biophotonics
Issue number9
Publication statusPublished - 1 Sept 2017


  • Confocal fluorescence microscopy
  • data processing
  • fluorescence lifetime imaging
  • global analysis
  • live cell imaging
  • Poisson noise correction


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