Construction of a rat spinal cord atlas of axon morphometry.

Atlases of the central nervous system are essential for understanding the pathophysiology of neurological diseases, which remains one of the greatest challenges in neuroscience research today. These atlases provide insight into the underlying white matter microstructure and have been created from a variety of animal models, including rats. Although existing atlases of the rat spinal cord provide some details of axon microstructure, there is currently no histological dataset that quantifies axon morphometry exhaustively in the entire spinal cord. In this study, we created the first comprehensive rat spinal cord atlas of the white matter microstructure with quantifiable axon and myelin morphometrics. Using full-slice scanning electron microscopy images and state-of-the-art segmentation algorithms, we generated an atlas of microstructural metrics such as axon diameter, axonal density and g-ratio. After registering the Watson spinal cord white matter atlas to our template, we computed statistics across metrics, spinal levels and tracts. We notably found that g-ratio is relatively constant, whereas axon diameter showed the greatest variation. The atlas, data and full analysis code are freely available at: https://github.com/neuropoly/atlas-rat.

Axons morphometry in the human spinal cord.

Due to the technical challenges of large-scale microscopy and analysis, to date only limited knowledge has been made available about axon morphometry (diameter, shape, myelin thickness, volume fraction), thereby limiting our understanding of neuronal microstructure and slowing down research on neurodegenerative pathologies. This study addresses this knowledge gap by establishing a state-of-the-art acquisition and analysis framework for mapping axon morphometry, and providing the first comprehensive mapping of axon morphometry in the human spinal cord. We dissected, fixed and stained a human spinal cord with osmium tetroxide, and used a scanning electron microscope to image the entirety of 23 axial slices, covering C1 to L5 spinal levels. An automatic method based on deep learning was then used to segment each axon and myelin sheath to produce maps of axon morphometry. These maps were then registered to a standard spinal cord magnetic resonance imaging (MRI) template. Between 500,000 (lumbar) and 1 million (cervical) myelinated axons were segmented at each level of this human spinal cord. Morphometric features show a large disparity between tracts, but high right-left symmetry. Our results suggest a modality-based organization of the dorsal column in the human, as it has been observed in the rat. The generated axon morphometry template is publicly available at https://osf.io/8k7jr/ and could be used as a reference for quantitative MRI studies. The proposed framework for axon morphometry mapping could be extended to other parts of the central or peripheral nervous system that exhibit coherently-oriented axons.

Histology-informed automatic parcellation of white matter tracts in the rat spinal cord.

The white matter is organized into "tracts" or "bundles," which connect different parts of the central nervous system. Knowing where these tracts are located in each individual is important for understanding the cause of potential sensorial, motor or cognitive deficits and for developing appropriate treatments. Traditionally, tracts are found using tracer injection, which is a difficult, slow and poorly scalable technique. However, axon populations from a given tract exhibit specific characteristics in terms of morphometrics and myelination. Hence, the delineation of tracts could, in principle, be done based on their morphometry. The objective of this study was to generate automatic parcellation of the rat spinal white matter tracts using the manifold information from scanning electron microscopy images of the entire spinal cord. The axon morphometrics (axon density, axon diameter, myelin thickness and g-ratio) were computed pixelwise following automatic axon segmentation using AxonSeg. The parcellation was based on an agglomerative clustering algorithm to group the tracts. Results show that axon morphometrics provide sufficient information to automatically identify some white matter tracts in the spinal cord, however, not all tracts were correctly identified. Future developments of microstructure quantitative MRI even bring hope for a personalized clustering of white matter tracts in each individual patient. The generated atlas and the associated code can be found at https://github.com/neuropoly/tract-clustering.

Hydrogel Mechanics Influence the Growth and Development of Embedded Brain Organoids.

Brain organoids are three-dimensional, tissue-engineered neural models derived from induced pluripotent stem cells that enable studies of neurodevelopmental and disease processes. Mechanical properties of the microenvironment are known to be critical parameters in tissue engineering, but the mechanical consequences of the encapsulating matrix on brain organoid growth and development remain undefined. Here, Matrigel was modified with an interpenetrating network (IPN) of alginate, to tune the mechanical properties of the encapsulating matrix. Brain organoids grown in IPNs were viable, with the characteristic formation of neuroepithelial buds. However, organoid growth was significantly restricted in the stiffest matrix tested. Moreover, stiffer matrixes skewed cell populations toward mature neuronal phenotypes, with fewer and smaller neural rosettes. These findings demonstrate that the mechanics of the culture environment are important parameters in brain organoid development and show that the self-organizing capacity and subsequent architecture of brain organoids can be modulated by forces arising from growth-induced compression of the surrounding matrix. This study therefore suggests that carefully designing the mechanical properties of organoid encapsulation materials is a potential strategy to direct organoid growth and maturation toward desired structures.

Midbrain organoids with an SNCA gene triplication model key features of synucleinopathy.

SNCA, the first gene associated with Parkinson's disease, encodes the α-synuclein protein, the predominant component within pathological inclusions termed Lewy bodies. The presence of Lewy bodies is one of the classical hallmarks found in the brain of patients with Parkinson's disease, and Lewy bodies have also been observed in patients with other synucleinopathies. However, the study of α-synuclein pathology in cells has relied largely on two-dimensional culture models, which typically lack the cellular diversity and complex spatial environment found in the brain. Here, to address this gap, we use three-dimensional midbrain organoids, differentiated from human-induced pluripotent stem cells derived from patients carrying a triplication of the SNCA gene and from CRISPR/Cas9 corrected isogenic control iPSCs. These human midbrain organoids recapitulate key features of α-synuclein pathology observed in the brains of patients with synucleinopathies. In particular, we find that SNCA triplication human midbrain organoids express elevated levels of α-synuclein and exhibit an age-dependent increase in α-synuclein aggregation, manifested by the presence of both oligomeric and phosphorylated forms of α-synuclein. These phosphorylated α-synuclein aggregates were found in both neurons and glial cells and their time-dependent accumulation correlated with a selective reduction in dopaminergic neuron numbers. Thus, human midbrain organoids from patients carrying SNCA gene multiplication can reliably model key pathological features of Parkinson's disease and provide a powerful system to study the pathogenesis of synucleinopathies.