Deep-learning-based segmentation of small extracellular vesicles in transmission electron microscopy images

Small extracellular vesicles (sEVs) are cell-derived vesicles of nanoscale size (~30-200 nm) that function as conveyors of information between cells, reflecting the cell of their origin and its physiological condition in their content. Valuable information on the shape and even on the composition of individual sEVs can be recorded using transmission electron microscopy (TEM). Unfortunately, sample preparation for TEM image acquisition is a complex procedure, which often leads to noisy images and renders automatic quantification of sEVs an extremely difficult task. We present a completely deep-learning-based pipeline for the segmentation of sEVs in TEM images. Our method applies a residual convolutional neural network to obtain fine masks and use the Radon transform for splitting clustered sEVs. Using three manually annotated datasets that cover a natural variability typical for sEV studies, we show that the proposed method outperforms two different state-of-the-art approaches in terms of detection and segmentation performance. Furthermore, the diameter and roundness of the segmented vesicles are estimated with an error of less than 10%, which supports the high potential of our method in biological applications.We want to acknowledge the support of NVIDIA Corporation with the donation of the Titan X (Pascal) GPU used for this research. This work was supported by the Spanish Ministry of Economy and Competitiveness (TEC2013-48552-C2-1-R, TEC2015-73064-EXP, TEC2016-78052-R) (EGM-AMB), a 2017 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation (EGM-AMB), and the Czech Science Foundation (GA17-05048S)(MM-PM) and (GJ17-11776Y) (AK-VP)

Deep learning-assisted high-throughput analysis of freeze-fracture replica images applied to glutamate receptors and calcium channels at hippocampal synapses

The molecular anatomy of synapses defines their characteristics in transmission and plasticity. Precise measurements of the number and distribution of synaptic proteins are important for our understanding of synapse heterogeneity within and between brain regions. Freeze–fracture replica immunogold electron microscopy enables us to analyze them quantitatively on a two-dimensional membrane surface. Here, we introduce Darea software, which utilizes deep learning for analysis of replica images and demonstrate its usefulness for quick measurements of the pre- and postsynaptic areas, density and distribution of gold particles at synapses in a reproducible manner. We used Darea for comparing glutamate receptor and calcium channel distributions between hippocampal CA3-CA1 spine synapses on apical and basal dendrites, which differ in signaling pathways involved in synaptic plasticity. We found that apical synapses express a higher density of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors and a stronger increase of AMPA receptors with synaptic size, while basal synapses show a larger increase in N-methyl-D-aspartate (NMDA) receptors with size. Interestingly, AMPA and NMDA receptors are segregated within postsynaptic sites and negatively correlated in density among both apical and basal synapses. In the presynaptic sites, Cav2.1 voltage-gated calcium channels show similar densities in apical and basal synapses with distributions consistent with an exclusion zone model of calcium channel-release site topography