PunctaFinder: An algorithm for automated spot detection in fluorescence microscopy images

• Published in: Molecular biology of the cell Vol. 35; no. 12; p. mr9
• Main Authors: Terpstra, Hanna M., Gómez-Sánchez, Rubén, Veldsink, Annemiek C., Otto, Tegan A., Veenhoff, Liesbeth M., Heinemann, Matthias
• Format: Journal Article
• Language: English
• Published: United States American Society for Cell Biology 01.12.2024; The American Society for Cell Biology
• Subjects: Algorithms; Biological Control of Cell Dimensions and Biophysical Properties; Cell research; Fluorescence microscopy; Humans; Image processing; Image Processing, Computer-Assisted - methods; Lipid Droplets - metabolism; Methods; Microscopy, Fluorescence - methods; Python (Programming language); Signal-To-Noise Ratio; Netherlands
• ISSN: 1059-1524; 1939-4586; 1939-4586
• DOI: 10.1091/mbc.E24-06-0254

Fluorescence microscopy has revolutionized biological research by enabling the visualization of subcellular structures at high resolution. With the increasing complexity and volume of microscopy data, there is a growing need for automated image analysis to ensure efficient and consistent interpretation. In this study, we introduce PunctaFinder, a novel Python-based algorithm designed to detect puncta, small bright spots, in raw fluorescence microscopy images without image denoising or signal enhancement steps. Furthermore, unlike other available spot detectors, PunctaFinder not only detects puncta, but also defines the cytoplasmic region, making it a valuable tool to quantify target molecule localization in cellular contexts. PunctaFinder is a widely applicable punctum detector and size estimator, as evidenced by its successful detection of Atg9-positive vesicles, lipid droplets, aggregates of a destabilized luciferase mutant, and the nuclear pore complex. Notably, PunctaFinder excels in detecting puncta in images with a relatively low resolution and signal-to-noise ratio, demonstrating its capability to identify dim puncta and puncta of dynamic target molecules. PunctaFinder reliably detects puncta in fluorescence microscopy images where automated analysis was not possible before, providing researchers with an efficient and robust method for punctum quantification in fluorescence microscopy images. Fluorescence microscopy datasets require automated quantification of subcellular structures. Existing detectors of such structures need high signal-to-noise ratios and resolution, and focus on specific puncta. PunctaFinder accurately identifies puncta in diverse contexts, even in low-resolution images with lower signal-to-noise ratios, based on raw pixel intensity values without image denoising or signal enhancement steps. It reliably detects structures like dynamic Atg9-positive vesicles. PunctaFinder enhances analysis efficiency and reproducibility, enabling effective study of subcellular structures in complex fluorescence microscopy datasets, providing a reliable, time-efficient method.