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Phasor Analysis for FLIM (Fluorescence Lifetime Imaging Microscopy)

The phasor analysis approach to analyze fluorescence lifetime does not require any fitting. Phasor FLIM (fluorescence lifetime imaging microscopy) provides a 2D graphical view of lifetime distributions. This graphical view enables any observer to distinguish and separate different lifetime populations within a FLIM image rapidly. The interpretation of phasor FLIM distributions is straightforward. Multiple molecular species are resolved within a single pixel, because every species has a specific phasor.

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How is the phasor plot generated?

When the data is acquired with Time Resolved Single Photon Counting (TCSPC) systems, the phasor FLIM distributions are derived from a Fourier transform (Digman et al., 2008). Each pixel in the image corresponds to a point in the phasor plot.

Rules: the algebra of phasors

There are only a few rules in the phasor approach and they enable a straightforward interpretation of the lifetime distribution. Three of the most important ones are shown below (E. Gratton, 2018).

1. Single exponential lifetimes lie on the universal circle line.

2. Multi-exponential lifetimes are found inside the universal circle and are a linear combination of their single exponential lifetime components.

3. The ratio of the linear combination for a multi-exponential lifetime (experimentally measured) determines the fraction of the components (f1 and f2) from their two single exponential lifetimes (τ1 and τ2).

Advantages of using phasors

Phasor FLIM is a very powerful analysis tool for molecular species separation and FRET analysis, in particular when the donor has a multi-exponential lifetime, something which is typical of CFP [cyan fluorescent protein] (Caiolfa et al., 2007). Moreover, phasor FLIM combined with STED, allows you to use less STED power to reach the same resolution (Lanzanò et al., 2015).

References and Additional Information

  1. M.A. Digman, V.R. Caiolfa, M. Zamai, E. Gratton, The Phasor Approach to Fluorescence Lifetime Imaging Analysis, Biophys. J. (2008) vol. 94, iss. 2, pp. L14–L16
  2. V.R. Caiolfa, M. Zamai, G. Malengo, A. Andolfo, C.D. Madsen, J. Sutin, M.A. Digman, E. Gratton, F. Blasi, N. Sidenius, Monomer–dimer dynamics and distribution of GPI-anchored uPAR are determined by cell surface protein assemblies, J. Cell Biol. (2007) vol. 179, iss. 5, pp. 1067-1082
  3. E. Hinde, K. Yokomori, K. Gaus, K.M. Hahn, E. Gratton, Fluctuation-based imaging of nuclear Rac1 activation by protein oligomerization, Sci. Rep. (2014), vol. 4, pp. 4219
  4. E. Hinde, M.A. Digman, C. Welch, K.M. Hahn, E. Gratton, Biosensor FRET detection by the phasor approach to fluorescence lifetime imaging microscopy (FLIM), Microsc. Res. Tech. (2012) vol. 75, iss. 3, pp. 271–281
  5. L. Albertazzi, D. Arosio, L. Marchetti, F. Ricci, F. Beltram, Quantitative FRET Analysis with the E0GFP‐mCherry Fluorescent Protein Pair, Photochem. Photobiol. (2009) vol. 85, iss. 1, pp. 287-97
  6. L. Lanzanò, I. Hernández, M. Castello, E. Gratton, A. Diaspro, G. Vicidomini, Encoding and decoding spatio-temporal information for super-resolution microscopy, Nat. Commun. (2015) vol. 6, p. 6701
  7. L. Wang, B. Chen, W. Yan, Z. Yang, X. Peng, D. Lin, X. Weng, T. Ye, J. Qu, Resolution improvement in STED super-resolution microscopy at low power using a phasor plot approach. Nanoscale. (2018), vol. 10 iss. 34, pp. 16252-16260
  8. J. Sosnik, L. Zheng, C.V. Rackauckas, M. Digman, E. Gratton, Q. Nie, T.F. Schilling, Noise modulation in retinoic acid signaling sharpens segmental boundaries of gene expression in the embryonic zebrafish hindbrain, eLife (2016) vol. 5, e14034
  9. E. Gratton, The Phasor approach: Application to FRET analysis and Tissue Autofluorescence, 13th LFD workshops 2018, 22- 26 October 2018, Laboratory for Fluorescence Dynamics (LFD), University of California, Irvine, USA
  10. E. Gratton, The Phasor approach: Application to FRET analysis and Tissue Autofluorescence, 6th European Workshop on Advanced Fluorescence Imaging and Dynamics, 10-14 December 2018, FAB LAB, Ludwig-Maximilians-Universität München, Munich, Germany