Axon and myelin morphology in animal and human spinal cord

Characterizing precisely the microstructure of axons, their density, size and myelination is of interest for the neuroscientific community, for example to help maximize the outcome of studies on white matter (WM) pathologies of the spinal cord (SC). The existence of a comprehensive and structured database of axonal measurements in healthy and disease models could help the validation of results obtained by different researchers. The purpose of this article is to provide such a database of healthy SC WM, to discuss the potential sources of variability and to suggest avenues for robust and accurate quantification of axon morphometry based on novel acquisition and processing techniques. The article is organized in three sections. The first section reviews morphometric results across species according to range of densities and counts of myelinated axons, axon diameter and myelin thickness, and characteristics of unmyelinated axons in different regions. The second section discusses the sources of variability across studies, such as age, sex, spinal pathways, spinal levels, statistical power and terminology in regard to tracts and protocols. The third section presents new techniques and perspectives that could benefit histology studies. For example, coherent anti-stokes Raman spectroscopy (CARS) imaging can provide sub-micrometric resolution without the need for fixation and staining, while slide scanners and stitching algorithms can provide full cross-sectional area of SC. In combination with these acquisition techniques, automatic segmentation algorithms for delineating axons and myelin sheath can help provide large-scale statistics on axon morphometry

Digital electron microscopic examination of human sural nerve biopsies

Diabetic peripheral polyneuropathy is characterized by axonal degeneration and regeneration as well as by Schwann cell and microvascular changes. These changes have been described at both the light (LM) and the electron microscopic (EM) levels; however, EM has not been applied to large clinical trials. Our goal was to adapt the rigorous techniques used for quantifying human biopsies with LM image analysis to accommodate ultrastructural analyses. We applied digital image capture and analysis to the ultrastructural examination of axons in sural nerve biopsies from diabetic patients enrolled in a multicenter clinical trial. The selection of sural nerve biopsies was based on the quality of specimen fixation, absence of physical distortion, and nerve fascicle size (≥100 000; ≤425 000 µm 2 ). Thin sections were collected on formvar-coated slot grids, stabilized with carbon and scanned on a Phillips CM100 transmission electron microscope. Digital images were captured with a Kodak Megaplus 1.6 camera. A montage was constructed using software derived from aerial mapping applications, and this virtual image was viewed by EM readers. Computer-assisted analyses included identification and labeling of individual axons and axons within regenerating clusters. The average density of regenerating myelinated axon clusters per mm 2 was 65.8 ± 5.1, range of 0–412 ( n  = 193). These techniques increase the number of samples that may be analyzed by EM and extend the use of this technique to clinical trials using tissue biopsies as a primary endpoint.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72529/1/j.1085-9489.2003.03030.x.pd

Beyond imaging with coherent anti-Stokes Raman scattering microscopy

La microscopie optique permet de visualiser des échantillons biologiques avec une bonne sensibilité et une résolution spatiale élevée tout en interférant peu avec les échantillons. La microscopie par diffusion Raman cohérente (CARS) est une technique de microscopie non linéaire basée sur l’effet Raman qui a comme avantage de fournir un mécanisme de contraste endogène sensible aux vibrations moléculaires. La microscopie CARS est maintenant une modalité d’imagerie reconnue, en particulier pour les expériences in vivo, car elle élimine la nécessité d’utiliser des agents de contraste exogènes, et donc les problèmes liés à leur distribution, spécificité et caractère invasif. Cependant, il existe encore plusieurs obstacles à l’adoption à grande échelle de la microscopie CARS en biologie et en médecine : le coût et la complexité des systèmes actuels, les difficultés d’utilisation et d’entretient, la rigidité du mécanisme de contraste, la vitesse de syntonisation limitée et le faible nombre de méthodes d’analyse d’image adaptées. Cette thèse de doctorat vise à aller au-delà de certaines des limites actuelles de l’imagerie CARS dans l’espoir que cela encourage son adoption par un public plus large. Tout d’abord, nous avons introduit un nouveau système d’imagerie spectrale CARS ayant une vitesse de syntonisation de longueur d’onde beaucoup plus rapide que les autres techniques similaires. Ce système est basé sur un laser à fibre picoseconde synchronisé qui est à la fois robuste et portable. Il peut accéder à des lignes de vibration Raman sur une plage importante (2700–2950 cm-1) à des taux allant jusqu’à 10 000 points spectrales par seconde. Il est parfaitement adapté pour l’acquisition d’images spectrales dans les tissus épais. En second lieu, nous avons proposé une nouvelle méthode d’analyse d’images pour l’évaluation de la structure de la myéline dans des images de sections longitudinales de moelle épinière. Nous avons introduit un indicateur quantitatif sensible à l’organisation de la myéline et démontré comment il pourrait être utilisé pour étudier certaines pathologies. Enfin, nous avons développé une méthode automatisé pour la segmentation d’axones myélinisés dans des images CARS de coupes transversales de tissu nerveux. Cette méthode a été utilisée pour extraire des informations morphologique des fibres nerveuses dans des images CARS de grande échelle.Optical-based microscopy techniques can sample biological specimens using many contrast mechanisms providing good sensitivity and high spatial resolution while minimally interfering with the samples. Coherent anti-Stokes Raman scattering (CARS) microscopy is a nonlinear microscopy technique based on the Raman effect. It shares common characteristics of other optical microscopy modalities with the added benefit of providing an endogenous contrast mechanism sensitive to molecular vibrations. CARS is now recognized as a great imaging modality, especially for in vivo experiments since it eliminates the need for exogenous contrast agents, and hence problems related to the delivery, specificity, and invasiveness of those markers. However, there are still several obstacles preventing the wide-scale adoption of CARS in biology and medicine: cost and complexity of current systems as well as difficulty to operate and maintain them, lack of flexibility of the contrast mechanism, low tuning speed and finally, poor accessibility to adapted image analysis methods. This doctoral thesis strives to move beyond some of the current limitations of CARS imaging in the hope that it might encourage a wider adoption of CARS as a microscopy technique. First, we introduced a new CARS spectral imaging system with vibrational tuning speed many orders of magnitude faster than other narrowband techniques. The system presented in this original contribution is based on a synchronized picosecond fibre laser that is both robust and portable. It can access Raman lines over a significant portion of the highwavenumber region (2700–2950 cm-1) at rates of up to 10,000 spectral points per second and is perfectly suitable for the acquisition of CARS spectral images in thick tissue. Secondly, we proposed a new image analysis method for the assessment of myelin health in images of longitudinal sections of spinal cord. We introduced a metric sensitive to the organization/disorganization of the myelin structure and showed how it could be used to study pathologies such as multiple sclerosis. Finally, we have developped a fully automated segmentation method specifically designed for CARS images of transverse cross sections of nerve tissue.We used our method to extract nerve fibre morphology information from large scale CARS images

Automatic Axon and Myelin Segmentation of Microscopy Images and Morphometrics Extraction

Dans le système nerveux, la transmission des signaux électriques se fait par l’intermédiaire des axones de la matière blanche. La plupart de ces axones, aussi connus sous le nom de fibres nerveuses, sont entourés par la gaine de myéline. Le rôle principal de la gaine de myéline est d’accroître la vitesse de transmission du signal nerveux le long de l’axone, un élément crucial pour la communication sur de longues distances. Lors de pathologies démyélinisantes comme la sclérose en plaques, la gaine de myéline des axones du système nerveux central est attaquée par des cellules du système immunitaire. Ceci peut conduire à la dégénérescence de la myéline, qui peut se manifester de diverses façons : une perte du contenu en myéline, une diminution du nombre d’axones myélinisés ou même des dommages axonaux. La microscopie à haute résolution des tissus myélinisés offre l’avantage de pouvoir imager la microstructure du tissu au niveau cellulaire. L’extraction d’information quantitative sur la morphologie passe par la segmentation des axones et gaines de myélines composant le tissu sur les images microscopiques acquises. L’extraction de métriques morphologiques des fibres nerveuses à partir d’image microscopiques pourrait contribuer à plusieurs applications intéressantes : documentation de la morphométrie sur différentes espèces et tissus, étude des origines et effets des maladies démyélinisantes, et validation de nouveaux biomarqueurs d’Imagerie par Résonance Magnétique sensibles au contenu en myéline dans le tissu. L’objectif principal de ce projet de recherche est de concevoir, implémenter et valider un framework de segmentation automatique d’axones et de gaines de myéline sur des images microscopiques et d’en extraire des morphométriques pertinentes. Plusieurs approches de segmentation ont été explorées dans la littérature, mais la plupart ne sont pas totalement automatiques, sont conçues pour une modalité de microscopie spécifique, ou bien leur implémentation n’est pas publiquement disponible pour la communauté scientifique. Deux frameworks de segmentation ont été développés dans le cadre de ce projet : AxonSeg et AxonDeepSeg. Le framework AxonSeg (https://github.com/neuropoly/axonseg) se base sur une approche de traitement d’image classique pour la segmentation. Le pipeline de segmentation inclut une transformée de type extended-minima, un modèle d’analyse discriminante combinant des features de forme et d’intensité, un algorithme de détection de contours et un double algorithme de contours actifs. Le résultat de la segmentation est utilisé pour l’extraction de morphométriques. La validation du framework a été réalisée sur des échantillons de microscopie optique, microscopie électronique et microscopie Raman stimulée (CARS). Le framework AxonDeepSeg (https://github.com/neuropoly/axondeepseg) utilise plutôt une approche basée sur des réseaux neuronaux convolutifs. Un réseau convolutif a été conçu pour la segmentation sémantique des axones myélinisés. Un modèle de microscopie électronique à balayage (MEB) a été entraîné sur des échantillons de moelle épinière de rat et un modèle de microscopie électronique à transmission (MET) a été entraîné sur des échantillons de corps calleux de souris. Les deux modèles ont démontré une haute précision pixel par pixel sur les échantillons test (85% sur le MEB de rat, 81% sur le MEB d’humain, 95% sur le MET de souris, 84% sur le MET de macaque). On démontre également que les modèles entrainés sont robustes aux ajouts de bruit, au flou et aux changements d’intensité. Le modèle MEB de AxonDeepSeg a été utilisé pour segmenter une coupe transversale complète de moelle épinière de rat et les morphométriques extraites à partir des tracts de la matière blanche correspondaient bien aux tendances rapportées dans la littérature. AxonDeepSeg a démontré une plus grande précision au niveau de la segmentation lorsque comparé à AxonSeg. Les deux outils logiciels développés sont open source (licence MIT) et donc à disposition de la communauté scientifique. Des futures itérations sont prévues afin d’améliorer et d’étendre ce travail. Les objectifs à court terme sont l’entraînement de nouveaux modèles pour d’autres modalités de microscopie, l’entraînement sur des datasets plus larges afin d’améliorer la généralisation et la robustesse des modèles, et l’exploration de nouvelles architectures de réseaux neuronaux. De plus, les modèles de segmentations développés jusqu’à maintenant ont seulement été testés sur des images de tissus sains. Un développement futur important serait de tester la performance de ces modèles sur des échantillons démyélinisés.----------ABSTRACT In the nervous system, the transmission of electrical signals is ensured by the axons of the white matter. A large portion of these axons, also known as nerve fibers, is surrounded by a myelin sheath. The main role of the myelin sheath is to increase the transmission speed along the axons, which is crucial for long distance communication. In demyelinating diseases such as multiple sclerosis, the myelin sheath of the central nervous system is attacked by cells of the immune system. Myelin degeneration caused by such disorders can manifest itself in different ways at the microstructural level: loss of myelin content, decrease in the number of myelinated axons, or even axonal damage. High resolution microscopy of myelinated tissues can provide in-depth microstructural information about the tissue under study. Segmentation of the axon and myelin content of a microscopy image is a necessary step in order to extract quantitative morphological information from the tissue. Being able to extract morphometrics from the tissue would benefit several applications: document nerve morphometry across species or tissues, get a better understanding of the origins of demyelinating diseases, and validate novel magnetic resonance imaging biomarkers sensitive to myelin content. The main objective of this research project is to design, implement and validate an automatic axon and myelin segmentation framework for microscopy images and use it to extract relevant morphological metrics. Several segmentation approaches exist in the literature for similar applications, but most of them are not fully automatic, are designed to work on a specific microscopy modality and/or are not made available to the research community. Two segmentation frameworks were developed as part of this project: AxonSeg and AxonDeepSeg. The AxonSeg package (https://github.com/neuropoly/axonseg) uses a segmentation approach based on standard image processing. The segmentation pipeline includes an extendedminima transform, a discriminant analysis model based on shape and intensity features, an edge detection algorithm, and a double active contours step. The segmentation output is used to compute morphological metrics. Validation of the framework was performed on optical, electron and CARS microscopy. The AxonDeepSeg package (https://github.com/neuropoly/axondeepseg) uses a segmentation approach based on convolutional neural networks. A fully convolutional network architecture was designed for the semantic 3-class segmentation of myelinated axons. A scanning electron microscopy (SEM) model trained on rat spinal cord samples and a transmission electron microscopy (TEM) model trained on mice corpus callosum samples are presented. Both models presented high pixel-wise accuracy on test datasets (85% on rat SEM, 81% on human SEM, 95% on mice TEM and 84% on macaque TEM). We show that AxonDeepSeg models are robust to noise, blurring and intensity changes. AxonDeepSeg was used to segment a full rat spinal cord slice, and morphological metrics extracted from white matter tracks correlated well with the literature. The AxonDeepSeg framework presented a higher segmentation accuracy when compared to AxonSeg. Both AxonSeg and AxonDeepSeg are open source (MIT license) and thus freely available for use by the research community. Future iterations are planned to improve and extend this work. Training of new models for other microscopy modalities, training on larger datasets to improve generalization and robustness, and exploration of novel deep learning architectures are some of the short-term objectives. Moreover, the current segmentation models have only been tested on healthy tissues. Another important short-term objective would be to assess the performance of these models on demyelinated samples

Calibration of the stereological estimation of the number of myelinated axons in the rat sciatic nerve: a multicenter study.

Several sources of variability can affect stereological estimates. Here we measured the impact of potential sources of variability on numerical stereological estimates of myelinated axons in the adult rat sciatic nerve. Besides biological variation, parameters tested included two variations of stereological methods (unbiased counting frame versus 2D-disector), two sampling schemes (few large versus frequent small sampling boxes), and workstations with varying degrees of sophistication. All estimates were validated against exhaustive counts of the same nerve cross sections to obtain calibrated true numbers of myelinated axons (gold standard). In addition, we quantified errors in particle identification by comparing light microscopic and electron microscopic images of selected consecutive sections. Biological variation was 15.6%. There was no significant difference between the two stereological approaches or workstations used, but sampling schemes with few large samples yielded larger differences (20.7%±3.7% SEM) of estimates from true values, while frequent small samples showed significantly smaller differences (12.7%±1.9% SEM). Particle identification was accurate in 94% of cases (range: 89–98%). The most common identification error was due to profiles of Schwann cell nuclei mimicking profiles of small myelinated nerve fibers. We recommend sampling frequent small rather than few large areas, and conclude that workstations with basic stereological equipment are sufficient to obtain accurate estimates. Electron microscopic verification showed that particle misidentification had a surprisingly variable and large impact of up to 11%, corresponding to 2/3 of the biological variation (15.6%). Thus, errors in particle identification require further attention, and we provide a simple nerve fiber recognition test to assist investigators with self-testing and training

SuperCLEM: An accessible correlative light and electron microscopy approach for investigation of neurons and glia in vitro

The rapid evolution of super-resolution light microscopy has narrowed the gap between light and electron microscopy, allowing the imaging of molecules and cellular structures at high resolution within their normal cellular and tissue context. Multimodal imaging approaches such as correlative light electron microscopy (CLEM) combine these techniques to create a tool with unique imaging capacity. However, these approaches are typically reserved for specialists, and their application to the analysis of neural tissue is challenging. Here we present SuperCLEM, a relatively simple approach that combines super-resolution fluorescence light microscopy (FLM), 3D electron microscopy (3D-EM) and rendering into 3D models. We demonstrate our workflow using neuron-glia cultures from which we first acquire high-resolution fluorescent light images of myelinated axons. After resin embedding and re-identification of the region of interest, serially aligned EM sections are acquired and imaged using a serial block face scanning electron microscope (SBF-SEM). The FLM and 3D-EM data sets are then combined to render 3D models of the myelinated axons. Thus, the SuperCLEM imaging pipeline is a useful new tool for researchers pursuing similar questions in neuronal, as well as other complex tissue culture systems

Third Harmonic Generation: A Method for Visualizing Myelin in the Murine Cerebral Cortex

Here we present the use of Third Harmonic Generation (THG) for the label-free imaging of myelinated axons in the murine cerebral cortex. Myelin plays an important role in the processes of learning and disease. However, much of the myelin biology research thus far has focused on white matter tracts where myelin is more visible. Much is still unknown, particularly with regard to myelin in gray matter. First, we engage in THG microscopy using an optical parametric oscillator pumped by a titanium-sapphire laser to demonstrate the utility of the technique for imaging myelin in vivo. Second, we investigate the use of a custom built low-repetition rate laser to substantially increase THG signal. We characterize the improvements and limitations of this light source with regards to THG microscopy. Lastly, we demonstrate a method for the estimation of the g-ratio from THG images by the use of a Bayesian model. The g-ratio is an important physical property relating to the thickness of the myelin sheath; modulation in the g-ratio could give clues to its underlying function. THG microscopy is uniquely adept at providing the data necessary for a g-ratio estimation