Dispersion indices for universal quantification of fluorescently-labelled subcellular structure spatial distributions.

In biology, accurate and robust quantification of biological images is critical for understanding distribution patterns and heterogeneity of subcellular structures within a cell. While various methods tailored to specific biological contexts have been employed for image analysis, there is a need for versatile approaches that transcend the constraints imposed by the intricacies of different biological systems. Here we report the application of dispersion indices - a statistical concept widely used to measure the income distribution within a population by economists - as a powerful and agnostic tool for quantifying biological images, which offers distinct advantages over traditional methods. In our approach, we substitute pixel intensity for income and number of pixels for population. We demonstrate the utility of dispersion indices in quantifying autophagic puncta, mitochondrial clustering, and microtubule dynamics, all of which are key measures relevant for maladies ranging from metabolic and neuronal diseases to cancer. Further, we show utility in 2D cell cultures and a 3D multicellular midbrain culture as well as measurement of a performance metric such as a half maximal effective concentration value (EC50).