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

BoutonNet: an automatic method to detect anterogradely labeled presynaptic boutons in brain tissue sections

Neurons emit axons, which form synapses, the fundamental unit of the nervous system. Neuroscientists use genetic anterograde tracing methods to label the synaptic output of specific neuronal subpopulations, but the resulting data sets are too large for manual analysis, and current automated methods have significant limitations in cost and quality. In this paper, we describe a pipeline optimized to identify anterogradely labeled presynaptic boutons in brain tissue sections. Our histologic pipeline labels boutons with high sensitivity and low background. To automatically detect labeled boutons in slide-scanned tissue sections, we developed BoutonNet. This detector uses a two-step approach: an intensity-based method proposes possible boutons, which are checked by a neural network-based confirmation step. BoutonNet was compared to expert annotation on a separate validation data set and achieved a result within human inter-rater variance. This open-source technique will allow quantitative analysis of the fundamental unit of the brain on a whole-brain scale.

In vivo imaging of injured cortical axons reveals a rapid onset form of Wallerian degeneration

Background

Despite the widespread occurrence of axon and synaptic loss in the injured and diseased nervous system, the cellular and molecular mechanisms of these key degenerative processes remain incompletely understood. Wallerian degeneration (WD) is a tightly regulated form of axon loss after injury, which has been intensively studied in large myelinated fibre tracts of the spinal cord, optic nerve and peripheral nervous system (PNS). Fewer studies, however, have focused on WD in the complex neuronal circuits of the mammalian brain, and these were mainly based on conventional endpoint histological methods. Post-mortem analysis, however, cannot capture the exact sequence of events nor can it evaluate the influence of elaborated arborisation and synaptic architecture on the degeneration process, due to the non-synchronous and variable nature of WD across individual axons.

Results

To gain a comprehensive picture of the spatiotemporal dynamics and synaptic mechanisms of WD in the nervous system, we identify the factors that regulate WD within the mouse cerebral cortex. We combined single-axon-resolution multiphoton imaging with laser microsurgery through a cranial window and a fluorescent membrane reporter. Longitudinal imaging of > 150 individually injured excitatory cortical axons revealed a threshold length below which injured axons consistently underwent a rapid-onset form of WD (roWD). roWD started on average 20 times earlier and was executed 3 times slower than WD described in other regions of the nervous system. Cortical axon WD and roWD were dependent on synaptic density, but independent of axon complexity. Finally, pharmacological and genetic manipulations showed that a nicotinamide adenine dinucleotide (NAD⁺)-dependent pathway could delay cortical roWD independent of transcription in the damaged neurons, demonstrating further conservation of the molecular mechanisms controlling WD in different areas of the mammalian nervous system.

Conclusions

Our data illustrate how in vivo time-lapse imaging can provide new insights into the spatiotemporal dynamics and synaptic mechanisms of axon loss and assess therapeutic interventions in the injured mammalian brain.

Presynaptic Boutons That Contain Mitochondria Are More Stable

The addition and removal of presynaptic terminals reconfigures neuronal circuits of the mammalian neocortex, but little is known about how this presynaptic structural plasticity is controlled. Since mitochondria can regulate presynaptic function, we investigated whether the presence of axonal mitochondria relates to the structural plasticity of presynaptic boutons in mouse neocortex. We found that the overall density of axonal mitochondria did not appear to influence the loss and gain of boutons. However, positioning of mitochondria at individual presynaptic sites did relate to increased stability of those boutons. In line with this, synaptic localization of mitochondria increased as boutons aged and showed differing patterns of localization at en passant and terminaux boutons. These results suggest that mitochondria accumulate locally at boutons over time to increase bouton stability.

DeepBouton: Automated Identification of Single-Neuron Axonal Boutons at the Brain-Wide Scale

Fine morphological reconstruction of individual neurons across the entire brain is essential for mapping brain circuits. Inference of presynaptic axonal boutons, as a key part of single-neuron fine reconstruction, is critical for interpreting the patterns of neural circuit wiring schemes. However, automated bouton identification remains challenging for current neuron reconstruction tools, as they focus mainly on neurite skeleton drawing and have difficulties accurately quantifying bouton morphology. Here, we developed an automated method for recognizing single-neuron axonal boutons in whole-brain fluorescence microscopy datasets. The method is based on deep convolutional neural networks and density-peak clustering. High-dimensional feature representations of bouton morphology can be learned adaptively through convolutional networks and used for bouton recognition and subtype classification. We demonstrate that the approach is effective for detecting single-neuron boutons at the brain-wide scale for both long-range pyramidal projection neurons and local interneurons.

Computer assisted detection of axonal bouton structural plasticity in in vivo time-lapse images

The ability to measure minute structural changes in neural circuits is essential for long-term in vivo imaging studies. Here, we propose a methodology for detection and measurement of structural changes in axonal boutons imaged with time-lapse two-photon laser scanning microscopy (2PLSM). Correlative 2PLSM and 3D electron microscopy (EM) analysis, performed in mouse barrel cortex, showed that the proposed method has low fractions of false positive/negative bouton detections (2/0 out of 18), and that 2PLSM-based bouton weights are correlated with their volumes measured in EM (r = 0.93). Next, the method was applied to a set of axons imaged in quick succession to characterize measurement uncertainty. The results were used to construct a statistical model in which bouton addition, elimination, and size changes are described probabilistically, rather than being treated as deterministic events. Finally, we demonstrate that the model can be used to quantify significant structural changes in boutons in long-term imaging experiments.

Detection of axonal synapses in 3D two-photon images

Studies of structural plasticity in the brain often require the detection and analysis of axonal synapses (boutons). To date, bouton detection has been largely manual or semi-automated, relying on a step that traces the axons before detection the boutons. If tracing the axon fails, the accuracy of bouton detection is compromised. In this paper, we propose a new algorithm that does not require tracing the axon to detect axonal boutons in 3D two-photon images taken from the mouse cortex. To find the most appropriate techniques for this task, we compared several well-known algorithms for interest point detection and feature descriptor generation. The final algorithm proposed has the following main steps: (1) a Laplacian of Gaussian (LoG) based feature enhancement module to accentuate the appearance of boutons; (2) a Speeded Up Robust Features (SURF) interest point detector to find candidate locations for feature extraction; (3) non-maximum suppression to eliminate candidates that were detected more than once in the same local region; (4) generation of feature descriptors based on Gabor filters; (5) a Support Vector Machine (SVM) classifier, trained on features from labelled data, and was used to distinguish between bouton and non-bouton candidates. We found that our method achieved a Recall of 95%, Precision of 76%, and F1 score of 84% within a new dataset that we make available for accessing bouton detection. On average, Recall and F1 score were significantly better than the current state-of-the-art method, while Precision was not significantly different. In conclusion, in this article we demonstrate that our approach, which is independent of axon tracing, can detect boutons to a high level of accuracy, and improves on the detection performance of existing approaches. The data and code (with an easy to use GUI) used in this article are available from open source repositories.