SynQuant: An Automatic Tool to Quantify Synapses from Microscopy Images

MotivationSynapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synapse imaging data, several issues prevent satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness for different neurites and synapses is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio (SNR) due to constraints of experiments and availability of sensitive antibodies. The combination of these issues makes the detection of synapses challenging and necessitates developing a new tool to accurately and reliably quantify synapses. ResultsWe present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. Through extensive experiments on both synthetic and real images in the presence of severe antibody diffusion, high heterogeneity, and large noise, our method was demonstrated to outperform peer specialized synapse detection tools as well as generic spot detection methods by a large margin. Finally, we show SynQuant reliably uncovers statistically significant differences between disease and control conditions in a neuron-astrocyte co-culture based model of Down Syndrome. AvailabilityThe Java source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/SynQuant. Contactyug@vt.edu