PhasorPy: A Python library for phasor analysis

Time-resolved (FLIM) and hyperspectral imaging (HSI) have become paramount in biomedical science. The power of the combination between traditional imaging and spectroscopy opens the possibility to address information inaccessible before. For bioimaging analysis of these data, the Phasor plots are a tool revolutionizing the field because of their straightforward approach. Thus it is becoming a key player in democratizing access to FLIM and HSI

PhasorPy library is based on SimFCS, a software developed by Enrico Gratton at the Laboratory for Fluorescence Dynamic, University of California, Irvine. It is intended to adapt from the SimFCS the FLIM and HSI modules that enable handling traditional (fitting) and Phasor-based analysis to an open-source code and, most importantly, supported by a community that will guarantee its sustainability.

Documentation

Phasor Analysis

Considering an hyperspectral image, the fluorescence spectra at each pixel can be transformed in phasor coordinates (G (λ)) and (S (λ)) as described in the following equations. I(λ) represent the intensity at every wavelength (channel), n is the number of the harmonic and λ i the initial wavelength. The, x and y coordinates are plotted in the spectral phasor plot.

The position for every pixel in the spectral phasor plot can be defined by the phase angle and the modulus (M) given the coordinates G and S.

The angular position in the spectral phasor plot relates to the center of mass of the emission spectrum and the modulus depends on the spectrum’s full width at the half maximum (FWHM). For instance, if the spectrum is broad its location should be close to the center. Otherwise, if there is a red shift in the spectrum, its location will move counterclockwise toward increasing angle from position (1, 0). Spectral phasors have the same vector properties as lifetime phasors. A detailed description of the spectral phasor plot properties can be found in Malacrida et al. 1.

Installation

  pip install PhasorPy
  conda install PhasorPy

Demo

Phasor and Pseudocolor representation

Obtain the phasor plot. From the average intensity image users can obtain the cutoff intensity in orther to clean up the background. Having a much cleaer phasor representation.

Its also allows users to get the pseudocolor RGB image rom the phasor, with three components.

Phasor plot

This funtionality allows users to obtain one or many phasors in the same plot.

Phasor components determination

To obtain the component percentage between two components and visualize its histogram.

Authors

• @bschuty

License

MIT

Contributing

Contributions are always very well welcome. The PhasorPy library intends to create an open-source and collaborative community between spectroscopy and fluorescence microscopy users with the same functionalities as SimFCS but accessible and self-sustainable in the long term as other Python libraries and communities.

References

[1] Malacrida, L., Gratton, E. & Jameson, D. M. Model-free methods to study membrane environmental probes: A comparison of the spectral phasor and generalized polarization approaches. Methods Appl. Fluoresc. 3, 047001 (2015).

Used By

This project is used:

• Advance Bioimaging Unit at Institt Pasteur Montevideo and Universidad de la República