Characterizing the movement and behavior of biomolecules in single cells quantitatively is essential to understanding fundamental biological mechanisms. One approach to studying such dynamics is tagging and imaging molecules of interest. RNA fluorescent in situ hybridization (RNA-FISH) is a frequently used technique for visualizing RNA in fixed cells using fluorescent probes. Automated processing of the resulting images is essential for large datasets that may include many different targets and time points. Here we demonstrate using our data set of 1555 simulated and 869 experimentally obtained images that our MATLAB based RNA-FISH image processing pipeline, TrueSpot, is a useful tool for automatically detecting the 3D locations of RNA transcripts at single molecule resolution in an RNA-FISH image stack. In particular, this tool is effective for facilitating quantitative analyses of FISH data such as determining the colocalization of multiple transcripts or the relative amount of RNA in various subcellular compartments. TrueSpot also performs well on images with immunofluorescent (IF) and GFP tagged protein targets that form clusters. Overall, we show that our 3D spot detection approach substantially outperforms current 2D spot detection algorithms. TrueSpot is capable of performing analysis for a variety of imaging-based approaches to studying biomolecular dynamics, which can aid in endeavors such as advancing understanding of eukaryotic transcription regulation mechanisms by studying the quantities and behaviors of transcripts, regulatory RNA, and other targets at discrete time points. This repository contains snapshots of the GitHub repositories containing the TrueSpot code and benchmarking data.