The information contained in a FLIM image can be easily handled when the phasor analysis approach is introduced. This method allows a graphical representation (phasor plot) of the lifetime distribution, making a FLIM image quicker to read. Indeed, the interpretation of the FLIM phasor plot is straightforward and the combination of FLIM with phasors enables the researcher to easily separate different lifetime populations within the same sample [1].

The possibility to discriminate between different fluorophores with different lifetimes is possible because every species is associated with a precise phasor (the so-called “fingerprint”). Using the geometrical working principle at the basis of the phasor-plot, different lifetime values (a cloud of points) are distributed over a semicircle where the “longest” lifetime decays are on the left side, while the “shortest” ones on the right side of the plot.

Moreover, if the lifetime cloud falls precisely on the semicircle, it means that the fluorescence signal can be described by a single exponential decay, while if the cloud falls within the semicircle area, the lifetime is a superposition of different values. Therefore, the FLIM-phasor approach is extremely straightforward for the visualization of fluorescence lifetime fingerprints in the sample under investigation [2].

FLIM LABS software allows the user to choose the desired phasor-plot representation, by giving access to a range of phasor segmentation techniques: for example, it is possible to opt for the simple coloring of the phasor cloud or by using a color gradient. FLIM LABS tools are very versatile and can be applied in different experimental contexts. Moreover, the automated machine learning algorithm can automatically identify phasor cluster, facilitating non-experienced users [3].


[1] Digman MA, Caiolfa VR, Zamai M, Gratton E, The phasor approach to fluorescence lifetime imaging analysis. Biophys J. 2008 Jan 15;94(2):L14-6.
doi: 10.1529/biophysj.107.120154
Epub 2007 Nov 2. PMID: 17981902; PMCID: PMC2157251

[2] Digmann MA, Gratton E, The phasor approach to fluorescence lifetime imaging: Exploiting phasor linear properties (2012)

[3] Vallmitjana A, Torrado B, Gratton E, Phasor-based image segmentation: machine learning clustering techniques. Biomed Opt Express.
2021 May 17;12(6):3410-3422.
doi: 10.1364/BOE.422766
PMID: 34221668; PMCID: PMC8221971