Quantifying the number and strength of synapses, and how these change over time is critical to understanding neuronal activity and plasticity. The dynamics of dendritic spines and the molecular composition of dendrites is strongly dependent on image analysis that can detect the position and quantity of labelled proteins and mRNAs of interest. Usually, light microscopy images are analyzed manually and involves switching between multiple software packages to locate spines, track dendrites, or identify mRNA puncta for each process step. State-of-the-art automated tools, while powerful, can often help only with selected parts of the data analysis pipeline, may be optimized for specific spatial resolutions, or lack the flexibility to customize the final outcome. To address these challenges, we present SpyDen. SpyDen is a Python package based upon three principles: i) ease of use for multi-task scenarios, ii) open-source accessibility and data export to a common, open data format, and iii) the ability to edit any software-generated annotation. Equipped with a graphical user interface and accompanied by video tutorials, SpyDen allows leveraging powerful algorithms for dendrite, synapse, and mRNA/protein analysis without any direct programming experience. To ensure its reliability, we benchmarked SpyDen results across diverse experimental conditions, validating its accuracy. These features make SpyDen a powerful, integrated platform for efficient and reproducible evaluation across neural imaging data.