EPBscore: a Novel Method for Computer-Assisted Analysis of Axonal Structure and Dynamics

Live brain imaging at cellular and synaptic resolution has dramatically improved our understanding of its organization and plasticity. A major issue in this field is the lack of automated tools to reliably analyze synaptic structures without heavily depending on the user’s subjectivity. As time-lapse imaging experiments can produce vast amounts of data, the tediousness of extracting key structural features of neurites such as the number, location and size of synaptic contacts through image analysis is a major bottleneck to perform large-scale studies. Unbiased quantitative tracking of large populations of synaptic sites is still challenging (Helmstaedter et al. 2011), despite the importance of understanding the principles of synaptic organization, connectivity and plasticity. This task is especially complex for axons and their boutons (Brown et al. 2011). Canonical cortical axons have a relatively simple structure, consisting of thin axonal shafts with uniform diameters (~100– 1000 nm, depending on axon type and species (Faisal et al. 2005)). En Passant Boutons (EPBs), varicosities distributed along axons, are the main class of cortical presynaptic structures. Since EPBs are small (~1 μm) and thei r densi ty on unmyel inated f ibers low (~0.1 μm), EPBs are sparsely located along axons. They vary greatly in diameter, ranging from the axonal diameter to 30–60 times larger (De Paola et al. 2006; Grillo et al. 2013). Unfortunately, current methods to identify and track axonal boutons are still largely manual (Canty and De Paola 2011), thus prone to be userdependent and non-rigorous, a recognized problem in the synaptic structural plasticity field (Brown et al. 2011; Holtmaat et al. 2009). This has slowed down our understanding of axon biology, especially regarding the organization and plasticity of synaptic boutons. To address these issues, we have developed a semiautomated reconstruction software named EPBscore that can automatically trace axons and measure synaptic bouton size (i.e., strength) and dynamics from timelapse in vivo imaging experiments. EPBscore has been designed and validated to process data generated with cytosolic fluorescent proteins such as Green Fluorescent Protein (GFP) and therefore is compatible with a wide variety of both in vitro and in vivo imaging experiments. Given a set of thresholds, EPBscore automatically and reproducibly tracks boutons and their sizes from time-lapse image stacks. EPBscore has been recently used in experiments that led to the discovery of altered synaptic dynamics in the aged brain (Grillo et al. 2013), S. Song and F. W. Grillo contributed equally to this work.