Developing Spectral Phasor Analysis Software to Visualize Mitochondrial Metabolic Responses at the Pathway Level

Dogen Foster

Department of Physics, Miami University

B.S. in Physics and B.S. in Mathematics (2025)
Trumpet Player (Marching, Symphony, and Pep Bands)

Email: fosterds@miamioh.edu
Advisor: Paul Urayama (urayampk@miamioh.edu)

Abstract

A Python-based software tool was developed to analyze UV-induced autofluorescence spectra in cells, serving as a free and open-source replacement for the lab's current LabVIEW and MATLAB implementations. Built using tkinter for the GUI, plotly for visualization, and numpy and pandas for data processing, the software offers a user-friendly and modular platform for various analyses. Its primary function is spectral phasor analysis, which assigns complex numbers to spectra to distinguish between similar signals. In two- and three-component systems, the phasor's position relative to pure component phasors reveals the relative contributions of each component through geometric relationships. Additional features include calculations of means, widths, and spectral decomposition with respect to user-selected basis spectra.

NADH is the dominant source of the studied autofluorescence and plays a central role in mitochondrial metabolism through its redox cycling with NAD⁺. This central role in cellular respiration makes NADH fluorescence a valuable indicator of metabolic state. The spectral phasor approach is sensitive enough to detect subtle shifts in metabolism, enabling non-invasive monitoring of metabolic changes at a pathway level.

Background

Mitochondrial Metabolism

NADH and NAD⁺ play integral roles in mitochondrial function. Through their oxidation and reduction, these molecules carry electrons from one place to another, thus facilitating the transfer of energy. While NADH exhibits autofluorescence, NAD⁺ does not, and furthermore, the nature of NADH fluorescence changes as it binds to certain coenzymes. This makes the fluorescence from these molecules effective, real-time, non-invasive trackers of metabolic state. By examining the emission spectra of cells, we can gather information about the changes in state of these molecules and thus the health and metabolism of the cells.

Spectral Phasor Analysis

With Spectral Phasors, we are able to associate a single complex number with a given spectrum. The way this is done turns component information into spatial information. For a two-component system, our phasor lies on the line segment connecting the pure-component phasors, with the position on that line giving the composition of the phasor. For a three-component system, our phasor lies within the triangle bounded by our pure components. The contribution of each component in this case is the fraction covered by the triangle made by the phasor and the two other components.

Left: Phasor response to ethanol, then cyanideRight: Phasor response to D-glucose, then more D-glucose(Maltas et al., 2018)

Applications

Intensity of reflection and emission of normal and carcinogen transformed bronchial epithelial cells(Pitts et al., 2001)

Cancer

As they undergo different metabolic processes, subtly different spectra are produced by healthy versus cancerous cells. By applying this phasor approach, these different metabolic pathways can be distinguished. This, once turbid media issues are overcome, may enable the identification of cancerous tissue without the need for dyes or biopsies, offering a useful tool for non-invasive cancer diagnostics.

Mitochondrial Disease

Mitochondria, with their own genetic information, can be afflicted with various diseases affecting their function. The phasor approach again enables sensitive and noninvasive monitoring of mitochondrial redox states. This technique might provide a cheap and capable early diagnostic method for identifying mitochondrial dysfunction without requiring genetic sampling or testing.

NACE Career Readiness Competencies

Technology
- Used Python and key libraries to build an open-source, efficient tool for spectral analysis, replacing proprietary software.

Critical Thinking
- Applied spectral phasor analysis to interpret complex data, enabling logical and nuanced insights into cellular metabolism.

Career & Self-Development
- Showed initiative by learning new tools and creating a custom solution that improved lab capabilities.

Communication
- Designed a user-friendly, modular interface to clearly present complex analyses to researchers.

References

(1) Alturkistany, F. et al., ‘Fluorescence lifetime shifts of NAD(P)H during apoptosis measured by time‐resolved flow

cytometry’, Cytometry Part A, 95(1), pp. 70–79. doi:10.1002/cyto.a.23606.

(2) Maltas, J. et al, 'A metabolic interpretation for the response of cellular autofluorescence to chemical perturbations assessed using

spectral phasor analysis', RSC Advances, 8, pp. 41526–41535 (2018). doi:10.1039/c8ra07691j.

(3) Pitts et al., Autofluorescence characteristics of immortalized and carcinogen-transformed human bronchial epithelial cells, J

Biomedical Optics, 6(1), (2001). doi: 10.1117/1.1333057.

(4) Urayama, Paul, ‘Pathway-level sensing of NADH- and NADPH-linked metabolisms using cellular autofluorescence signals’,

CSIM Seminar, (2020).

Page updated

Report abuse