Universal high-throughput image quantification of subcellular structure dynamics and spatial distributions within cells
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Image analysis of subcellular structures and biological processes relies on specific, context-dependent pipelines, which are labor-intensive, constrained by the intricacies of the specific biological system, and inaccessible to broader applications. Here we introduce the application of dispersion indices, a statistical tool traditionally employed by economists, to analyze the spatial distribution and heterogeneity of subcellular structures. This computationally efficient high-throughput approach, termed GRID (Generalized Readout of Image Dispersion), is highly generalizable, compatible with open-source image analysis software, and adaptable to diverse biological scenarios. GRID readily quantifies diverse structures and processes to include autophagic puncta, mitochondrial clustering, and microtubule dynamics. Further, GRID is versatile, applicable to both 2D cell cultures and 3D multicellular organoids, and suitable for high-throughput screening and performance metric measurements, such as half-maximal effective concentration (EC50) values. The approach enables mechanistic analysis of critical subcellular structure processes of relevance for diseases ranging from metabolic and neuronal diseases to cancer as well as a first-pass screening method for identifying biologically active agents for drug discovery.
Statement of Significance
Current methods for image analysis in microscopy are tailored to specific biological contexts, which creates challenges in implementation and efficiency for researchers studying a diverse range of subcellular processes. Our application of dispersion indices, traditionally used in economics, offers a universal framework for high throughput quantification of biological structures, enabling easier and more consistent analysis across various biological contexts. By transforming pixel intensity and count into meaningful statistical measures, our method simplifies the quantification of subcellular structures such as autophagic puncta, microtubule dynamics, and mitochondrial clustering. This approach accelerates the quantitative analysis of sub-cellular processes in disease as well as imaged-based drug discovery.
Classification: Bioengineering, Cell Biology, Applied Biological Sciences
