Optimizing Automated Detection for Cytoplasmic TDP25 Aggregates in Fluorescence Imaging

Protein aggregates are known to disrupt normal cellular functions and homeostasis, serving as key hallmarks of various neurodegenerative disorders, including Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis (ALS). Automated detection of cytoplasmic, disease-associated aggregates in fluorescence images is crucial for characterizing these aggregates and exploring potential strategies for their prevention. In this study, we demonstrate that removing background fluorescence and improving the brightness of aggregates using an automated aggregate detection algorithm significantly enhances the detection efficiency of cytoplasmic aggregates formed by the 25 kDa C-terminal fragment of ALS-associated TDP-43 (TDP25). A high signal-to-noise ratio can improve detection efficiency. Our findings contribute to the development of more effective detection methods for disease-associated aggregates of heterogeneous sizes and fluorescence intensities, which are typically challenging to identify automatically.