@article {70198, title = {Redefining colocalization analysis with a novel Phasor Mixing Coefficient.}, journal = {J Cell Sci}, year = {2025}, month = {2025 Nov 24}, abstract = {

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.

}, 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 = {

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.

}, 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} } @article {69147, title = {Believing is seeing - the deceptive influence of bias in quantitative microscopy.}, journal = {Journal of Cell Science}, volume = {137}, year = {2024}, month = {2024 Jan 10}, abstract = {

The visual allure of microscopy makes it an intuitively powerful research tool. Intuition, however, can easily obscure or distort the reality of the information contained in an image. Common cognitive biases, combined with institutional pressures that reward positive research results, can quickly skew a microscopy project towards upholding, rather than rigorously challenging, a hypothesis. The impact of these biases on a variety of research topics is well known. What might be less appreciated are the many forms in which bias can permeate a microscopy experiment. Even well-intentioned researchers are susceptible to bias, which must therefore be actively recognized to be mitigated. Importantly, although image quantification has increasingly become an expectation, ostensibly to confront subtle biases, it is not a guarantee against bias and cannot alone shield an experiment from cognitive distortions. Here, we provide illustrative examples of the insidiously pervasive nature of bias in microscopy experiments - from initial experimental design to image acquisition, analysis and data interpretation. We then provide suggestions that can serve as guard rails against bias.

}, keywords = {Bias, Humans, Microscopy, Research Personnel}, issn = {1477-9137}, doi = {10.1242/jcs.261567}, author = {Lee, Rachel M and Eisenman, Leanna R and Khuon, Satya and Aaron, Jesse S and Chew, Teng-Leong} }