@article {70198,
	title = {Redefining colocalization analysis with a novel Phasor Mixing Coefficient.},
	journal = {J Cell Sci},
	year = {2025},
	month = {2025 Nov 24},
	abstract = {<p>The first step to probing any potential interaction between two biomolecules is to determine their spatial association. In other words, if two biomolecules localize similarly within a cell, then it is plausible they could interact. Traditionally, this is quantified through various colocalization metrics. These measures infer this association by estimating the degree to which fluorescent signals from each biomolecule overlap or correlate. However, these metrics are, at best, proxies, and they depend strongly on various experimental choices. Alternatively, here we define a new strategy which leverages multispectral imaging and phasor analysis, termed the Phasor Mixing Coefficient (PMC). PMC measures the precise mixing of fluorescent signals in each pixel. We demonstrate how PMC captures complex biological subtlety by offering two distinct values, a global measure of overall color mixing and the homogeneity thereof. We additionally show that PMC exhibits less sensitivity to signal-to-noise ratio, intensity threshold, and background signal compared to canonical methods. Moreover, this method provides a means to visualize color mixing at each pixel. We show that PMC offers users a nuanced and robust metric to quantify biological association.</p>
},
	issn = {1477-9137},
	doi = {10.1242/jcs.264388},
	author = {Puls, Owen F and Aaron, Jesse S and Quarles, Ellen K and Khuon, Satya and Eisenman, Leanna R and Kamaid, Andres and Malacrida, Leonel and Chew, Teng-Leong}
}
@article {70203,
	title = {Redefining colocalization analysis with a novel Phasor Mixing Coefficient.},
	journal = {J Cell Sci},
	year = {2025},
	month = {2025 Nov 24},
	abstract = {<p>The first step to probing any potential interaction between two biomolecules is to determine their spatial association. In other words, if two biomolecules localize similarly within a cell, then it is plausible they could interact. Traditionally, this is quantified through various colocalization metrics. These measures infer this association by estimating the degree to which fluorescent signals from each biomolecule overlap or correlate. However, these metrics are, at best, proxies, and they depend strongly on various experimental choices. Alternatively, here we define a new strategy which leverages multispectral imaging and phasor analysis, termed the Phasor Mixing Coefficient (PMC). PMC measures the precise mixing of fluorescent signals in each pixel. We demonstrate how PMC captures complex biological subtlety by offering two distinct values, a global measure of overall color mixing and the homogeneity thereof. We additionally show that PMC exhibits less sensitivity to signal-to-noise ratio, intensity threshold, and background signal compared to canonical methods. Moreover, this method provides a means to visualize color mixing at each pixel. We show that PMC offers users a nuanced and robust metric to quantify biological association.</p>
},
	issn = {1477-9137},
	doi = {10.1242/jcs.264388},
	url = {https://doi.org/10.1242/jcs.264388},
	author = {Puls, Owen F and Aaron, Jesse S and Quarles, Ellen K and Khuon, Satya and Eisenman, Leanna R and Kamaid, Andres and Malacrida, Leonel and Chew, Teng-Leong}
}
