SynQuant: an automatic tool to quantify synapses from microscopy images.

MOTIVATION: Synapses 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 synaptic punctum imaging data, several issues are impeding 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 of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses. RESULTS: We 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. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real datasets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods. AVAILABILITY AND IMPLEMENTATION: Java source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/SynQuant. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Design and Use of de novo Cascades for the Biosynthesis of New Benzylisoquinoline Alkaloids.

The benzylisoquinoline alkaloids (BIAs) are an important group of secondary metabolites from higher plants and have been reported to show significant biological activities. The production of BIAs through synthetic biology approaches provides a higher-yielding strategy than traditional synthetic methods or isolation from plant material. However, the reconstruction of BIA pathways in microorganisms by combining heterologous enzymes can also give access to BIAs through cascade reactions. Most importantly, non-natural BIAs can be generated through such artificial pathways. In the current study, we describe the use of tyrosinases and decarboxylases and combine these with a transaminase enzyme and norcoclaurine synthase for the efficient synthesis of several BIAs, including six non-natural alkaloids, in cascades from l-tyrosine and analogues.