Search

Competition among adult brain cells has not been extensively researched. To investigate whether healthy glia can outcompete diseased human glia in the adult forebrain, we engrafted wild-type (WT) human glial progenitor cells (hGPCs) produced from human embryonic stem cells into the striata of adult mice that had been neonatally chimerized with mutant Huntingtin (mHTT)-expressing hGPCs. The WT hGPCs outcompeted and ultimately eliminated their human Huntington's disease (HD) counterparts, repopulating the host striata with healthy glia. Single-cell RNA sequencing revealed that WT hGPCs acquired a YAP1/MYC/E2F-defined dominant competitor phenotype upon interaction with the host HD glia. WT hGPCs also outcompeted older resident isogenic WT cells that had been transplanted neonatally, suggesting that competitive success depended primarily on the relative ages of competing populations, rather than on the presence of mHTT. These data indicate that aged and diseased human glia may be broadly replaced in adult brain by younger healthy hGPCs, suggesting a therapeutic strategy for the replacement of aged and diseased human glia., (© 2023. The Author(s).)

Mitochondria are the main suppliers of energy for cells and their bioenergetic function is regulated by mitochondrial dynamics: the constant changes in mitochondria size, shape, and cristae morphology to secure cell homeostasis. Although changes in mitochondrial function are implicated in a wide range of diseases, our understanding is challenged by a lack of reliable ways to extract spatial features from the cristae, the detailed visualization of which requires electron microscopy (EM). Here, we present a semi-automatic method for the segmentation, 3D reconstruction, and shape analysis of mitochondria, cristae, and intracristal spaces based on 2D EM images of the murine hippocampus. We show that our method provides a more accurate characterization of mitochondrial ultrastructure in 3D than common 2D approaches and propose an operational index of mitochondria's internal organization. With an improved consistency of 3D shape analysis and a decrease in the workload needed for large-scale analysis, we speculate that this tool will help increase our understanding of mitochondrial dynamics in health and disease., (© 2024. The Author(s).)

Astroglial dysfunction contributes to the pathogenesis of Huntington's disease (HD), and glial replacement can ameliorate the disease course. To establish the topographic relationship of diseased astrocytes to medium spiny neuron (MSN) synapses in HD, we used 2-photon imaging to map the relationship of turboRFP-tagged striatal astrocytes and rabies-traced, EGFP-tagged coupled neuronal pairs in R6/2 HD and wild-type (WT) mice. The tagged, prospectively identified corticostriatal synapses were then studied by correlated light electron microscopy followed by serial block-face scanning EM, allowing nanometer-scale assessment of synaptic structure in 3D. By this means, we compared the astrocytic engagement of single striatal synapses in HD and WT brains. R6/2 HD astrocytes exhibited constricted domains, with significantly less coverage of mature dendritic spines than WT astrocytes, despite enhanced engagement of immature, thin spines. These data suggest that disease-dependent changes in the astroglial engagement and sequestration of MSN synapses enable the high synaptic and extrasynaptic levels of glutamate and K + that underlie striatal hyperexcitability in HD. As such, these data suggest that astrocytic structural pathology may causally contribute to the synaptic dysfunction and disease phenotype of those neurodegenerative disorders characterized by network overexcitation.

Brodmann Area 46 (BA46) has long been regarded as a hotspot of disease pathology in individuals with schizophrenia (SCH) and major depressive disorder (MDD). Pyramidal neurons in layer III of the Brodmann Area 46 (BA46) project to other cortical regions and play a fundamental role in corticocortical and thalamocortical circuits. The AutoCUTS-LM pipeline was used to study the 3-dimensional structural morphology and spatial organization of pyramidal cells. Using quantitative light microscopy, we used stereology to calculate the entire volume of layer III in BA46 and the total number and density of pyramidal cells. Volume tensors estimated by the planar rotator quantified the volume, shape, and nucleus displacement of pyramidal cells. All of these assessments were carried out in four groups of subjects: controls (C, n = 10), SCH (n = 10), MDD (n = 8), and suicide subjects with a history of depression (SU, n = 11). SCH subjects had a significantly lower somal volume, total number, and density of pyramidal neurons when compared to C and tended to show a volume reduction in layer III of BA46. When comparing MDD subjects with C, the measured parameters were inclined to follow SCH, although there was only a significant reduction in pyramidal total cell number. While no morphometric differences were observed between SU and MDD, SU had a significantly higher total number of pyramidal cells and nucleus displacement than SCH. Finally, no differences in the spatial organization of pyramidal cells were found among groups. These results suggest that despite significant morphological alterations in layer III of BA46, which may impair prefrontal connections in people with SCH and MDD, the spatial organization of pyramidal cells remains the same across the four groups and suggests no defects in neuronal migration. The increased understanding of pyramidal cell biology may provide the cellular basis for symptoms and neuroimaging observations in SCH and MDD patients., (© 2022. The Author(s).)

In this paper, we considered the representation power of local overlapping histograms for discrete binary signals. We give an algorithm that is linear in signal size and factorial in window size for producing the set of signals, which share a sequence of densely overlapping histograms, and we state the values for the sizes of the number of unique signals for a given set of histograms, as well as give bounds on the number of metameric classes, where a metameric class is a set of signals larger than one, which has the same set of densely overlapping histograms.

Techniques involving three-dimensional (3D) tissue structure reconstruction and analysis provide a better understanding of changes in molecules and function. We have developed AutoCUTS-LM, an automated system that allows the latest advances in 3D tissue reconstruction and cellular analysis developments using light microscopy on various tissues, including archived tissue. The workflow in this paper involved advanced tissue sampling methods of the human cerebral cortex, an automated serial section collection system, digital tissue library, cell detection using convolution neural network, 3D cell reconstruction, and advanced analysis. Our results demonstrated the detailed structure of pyramidal cells (number, volume, diameter, sphericity and orientation) and their 3D spatial organization are arranged in a columnar structure. The pipeline of these combined techniques provides a detailed analysis of tissues and cells in biology and pathology., (© 2021. The Author(s).)

Human taste perception is associated with the papillae on the tongue as they contain a large proportion of chemoreceptors for basic tastes and other chemosensation. Especially the density of fungiform papillae (FP) is considered as an index for responsiveness to oral chemosensory stimuli. The standard procedure for FP counting involves visual identification and manual counting of specific parts of the tongue by trained operators. This is a tedious task and automated image analysis methods are desirable. In this paper a machine learning image processing method based on a convolutional neural network is presented. This automated method was compared with three standard manual FP counting procedures using tongue pictures from 132 subjects. Automated FP counts, within the selected areas and the whole tongue, significantly correlated with the manual counting methods (all ρs ≥ 0.76). When comparing the images for gender and PROP status, the density of FP predicted from automated analysis was in good agreement with data from the manual counting methods, especially in the case of gender. Moreover, the present results reinforce the idea that caution should be applied in considering the relationship between FP density and PROP responsiveness since this relationship can be an oversimplification of the complexity of phenomena arising at the central and peripherical levels. Indeed, no significant correlations were found between FP and PROP bitterness ratings using the automated method for selected areas or the whole tongue. Besides providing estimates of the number of FP, the machine learning approach used a tongue coordinate system that normalizes the size and shape of an individual tongue and generated a heat map of the FP position and normalized area they cover. The present study demonstrated that the machine learning approach could provide similar estimates of FP on the tongue as compared to manual counting methods and provide estimates of more difficult-to-measure parameters, such as the papillae's areas and shape.

Imaging ultrastructures in cells using Focused Ion Beam Scanning Electron Microscope (FIB-SEM) yields section-by-section images at nano-resolution. Unfortunately, we observe that FIB-SEM often introduces sub-pixel drifts between sections, in the order of 2.5 nm. The accumulation of these drifts significantly skews distance measures and geometric structures, which standard image registration techniques fail to correct. We demonstrate that registration techniques based on mutual information and sum-of-squared-distances significantly underestimate the drift since they are agnostic to image content. For neuronal data at nano-resolution, we discovered that vesicles serve as a statistically simple geometric structure, making them well-suited for estimating the drift with sub-pixel accuracy. Here, we develop a statistical model of vesicle shapes for drift correction, demonstrate its superiority, and provide a self-contained freely available application for estimating and correcting drifted datasets with vesicles.

Background: Synchrotron X-ray computed tomography (SXCT) allows for three-dimensional imaging of objects at a very high resolution and in large field-of-view., Purpose: The aim of this study was to use SXCT imaging for morphological analysis of muscle tissue, in order to investigate whether the analysis reveals complementary information to two-dimensional microscopy., Methods: Three-dimensional SXCT images of muscle biopsies were taken from participants with cerebral palsy and from healthy controls. We designed morphological measures from the two-dimensional slices and three-dimensional volumes of the images and measured the muscle fibre organization, which we term orientation consistency., Results: The muscle fibre cross-sectional areas were significantly larger in healthy participants than in participants with cerebral palsy when carrying out the analysis in three dimensions. However, a similar analysis carried out in two dimensions revealed no patient group difference. The present study also showed that three-dimensional orientation consistency was significantly larger for healthy participants than for participants with cerebral palsy., Conclusion: Individuals with CP have smaller muscle fibres than healthy control individuals. We argue that morphometric measures of muscle fibres in two dimensions are generally trustworthy only if the fibres extend perpendicularly to the slice plane, and otherwise three-dimensional aspects should be considered. In addition, the muscle tissue of individuals with CP showed a decreased level of orientation consistency when compared to healthy control tissue. We suggest that the observed disorganization of the tissue may be induced by atrophy caused by physical inactivity and insufficient neural activation., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Vascular pathology is central to malaria pathogenesis and associated with severity of disease. We have previously documented shedding of the cerebral endothelial glycocalyx in experimental malaria and hypothesized that this action is implicated in the pathogenesis of cerebral malaria (CM). Quantification and characterization of the intraluminal vascular glycocalyx are technically challenging. Here, we used ferritin labeling, computerized image analysis, and biochemical characterization by using in vivo biotinylation and pull down. Image analysis divided mice with CM and uncomplicated malaria and uninfected control mice into 3 non-overlapping groups. Biochemical assessment of the luminal surface revealed malaria-induced alterations in all components of the glycocalyx in CM. This loss was mirrored in increases of the same components in peripheral blood samples. Corticosteroid treatment protected against CM, reduced inflammation, and prevented glycocalyx loss. Adjunctive antithrombin-3 also prevented glycocalyx loss and significantly reduced CM-associated mortality, as well as reduced local inflammation and prevented blood-brain barrier leakage. In contrast, inhibition of matrix metalloproteases with batimastat had limited effects on the glycocalyx and disease progression. Thus, glycocalyx loss may be associated with malaria pathogenesis and could be targeted by adjunctive treatment.-Hempel, C., Sporring, J., Kurtzhals, J. A. L. Experimental cerebral malaria is associated with profound loss of both glycan and protein components of the endothelial glycocalyx.

Diffeomorphic deformation is a popular choice in medical image registration. A fundamental property of diffeomorphisms is invertibility, implying that once the relation between two points A to B is found, then the relation B to A is given per definition. Consistency is a measure of a numerical algorithm's ability to mimic this invertibility, and achieving consistency has proven to be a challenge for many state-of-the-art algorithms. We present CDD (Collocation for Diffeomorphic Deformations), a numerical solution to diffeomorphic image registration, which solves for the Stationary Velocity Field (SVF) using an implicit A-stable collocation method. CDD guarantees the preservation of the diffeomorphic properties at all discrete points and is thereby consistent to machine precision. We compared CDD's collocation method with the following standard methods: Scaling and Squaring, Forward Euler, and Runge-Kutta 4, and found that CDD is up to 9 orders of magnitude more consistent. Finally, we evaluated CDD on a number of standard bench-mark data sets and compared the results with current state-of-the-art methods: SPM-DARTEL, Diffeomorphic Demons and SyN. We found that CDD outperforms state-of-the-art methods in consistency and delivers comparable or superior registration precision.

The purpose of this study was to evaluate bone-to-implant contact (BIC) in two-dimensional (2D) histology compared to high-resolution three-dimensional (3D) synchrotron radiation micro computed tomography (SR micro-CT). High spatial resolution, excellent signal-to-noise ratio, and contrast establish SR micro-CT as the leading imaging modality for hard X-ray microtomography. Using SR micro-CT at voxel size 5 μm in an experimental goat mandible model, no statistically significant difference was found between the different treatment modalities nor between recipient and reconstructed bone. The histological evaluation showed a statistically significant difference between BIC in reconstructed and recipient bone (p < 0.0001). Further, no statistically significant difference was found between the different treatment modalities which we found was due to large variation and subsequently due to low power. Comparing histology and SR micro-CT evaluation a bias of 5.2% was found in reconstructed area, and 15.3% in recipient bone. We conclude that for evaluation of BIC with histology and SR micro-CT, SR micro-CT cannot be proven more precise than histology for evaluation of BIC, however, with this SR micro-CT method, one histologic bone section is comparable to the 3D evaluation. Further, the two methods complement each other with knowledge on BIC in 2D and 3D., (Copyright © 2017 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.)

In this paper, we propose a multi-scale, multi-kernel shape, compactly supported kernel bundle framework for stationary velocity field-based image registration (Wendland kernel bundle stationary velocity field, wKB-SVF). We exploit the possibility of directly choosing kernels to construct a reproducing kernel Hilbert space (RKHS) instead of imposing it from a differential operator. The proposed framework allows us to minimize computational cost without sacrificing the theoretical foundations of SVF-based diffeomorphic registration. In order to recover deformations occurring at different scales, we use compactly supported Wendland kernels at multiple scales and orders to parameterize the velocity fields, and the framework allows simultaneous optimization over all scales. The performance of wKB-SVF is extensively compared to the 14 non-rigid registration algorithms presented in a recent comparison paper. On both MGH10 and CUMC12 datasets, the accuracy of wKB-SVF is improved when compared to other registration algorithms. In a disease-specific application for intra-subject registration, atrophy scores estimated using the proposed registration scheme separates the diagnostic groups of Alzheimer's and normal controls better than the state-of-the-art segmentation technique. Experimental results show that wKB-SVF is a robust, flexible registration framework that allows theoretically well-founded and computationally efficient multi-scale representation of deformations and is equally well-suited for both inter- and intra-subject image registration.

Obtaining regional volume changes from a deformation field is more precise when using simplex counting (SC) compared with Jacobian integration (JI) due to the numerics involved in the latter. Although SC has been proposed before, numerical properties underpinning the method and a thorough evaluation of the method against JI is missing in the literature. The contributions of this paper are: (a) we propose surface propagation (SP)-a simplification to SC that significantly reduces its computational complexity; (b) we will derive the orders of approximation of SP which can also be extended to SC. In the experiments, we will begin by empirically showing that SP is indeed nearly identical to SC, and that both methods are more stable than JI in presence of moderate to large deformation noise. Since SC and SP are identical, we consider SP as a representative of both the methods for a practical evaluation against JI. In a real application on Alzheimer's disease neuroimaging initiative data, we show the following: (a) SP produces whole brain and medial temporal lobe atrophy numbers that are significantly better than JI at separating between normal controls and Alzheimer's disease patients; (b) SP produces disease group atrophy differences comparable to or better than those obtained using FreeSurfer, demonstrating the validity of the obtained clinical results. Finally, in a reproducibility study, we show that the voxel-wise application of SP yields significantly lower variance when compared to JI.

We study the space of first order models of smooth frame fields using the method of moving frames. By exploiting the Maurer-Cartan matrix of connection forms we develop geometrical embeddings for frame fields which lie on spherical, ellipsoidal and generalized helicoid surfaces. We design methods for optimizing connection forms in local neighborhoods and apply these to a statistical analysis of heart fiber geometry, using diffusion magnetic resonance imaging. This application of moving frames corroborates and extends recent characterizations of muscle fiber orientation in the heart wall, but also provides for a rich geometrical interpretation. In particular, we can now obtain direct local measurements of the variation of the helix and transverse angles, of fiber fanning and twisting, and of the curvatures of the heart wall in which these fibers lie.

Many membrane proteins are not evenly distributed over the plasma membrane, but gathered in domains assumed to have a particular lipid composition. Using single molecule localization microscopy (SMLM) we have immunolocalized a glycosylphosphatidylinositol (GPI)-anchor protein that labels nanodomains in a specialized plant cell type, and compared the suitability of three methods to estimate their size. As conventional methods full width at half maximum (FWHM) and the full diameter (FWMin) of domains were used. A boundary detection method of the domain area (DA) was performed in order to take irregular shapes into account. In order to compare the influence of the chosen measurement methods, we have developed a MatLab program that allows for automated analysis of domain sizes from multiple SMLM images and provides the statistics of three key features of domains: FWHM and FWMin along their long and short axes as well as the DA, derived from the molecular density. Domains formed by the GPI-anchor protein are approximating elliptical shapes. Direct and indirect immunolabeling resulted in a statistically significant difference in apparent domain size, reflecting the fact that the secondary antibody molecules extend the uncertainty along the nanodomain border. FWMin values along the long and short axis give good estimates of regular, geometrically centred domain shapes, while the DA value matches regular as well as irregular shapes best, as derived from computer-generated, irregular point clusters., (© 2015 International Society for Advancement of Cytometry.)

The spatial interactions of synaptic vesicles in synapses were assessed after a detailed characterization of size, shape, and orientation of the synaptic vesicles. We hypothesized that shape and orientation of the synaptic vesicles are influenced by their movement toward the active zone causing deviations from spherical shape and systematic trends in their orientation. We studied three-dimensional representations of synapses obtained by manual annotation of focused ion beam scanning electron microscopy (FIB-SEM) images of male mouse brain. The configurations of synaptic vesicles were regarded as marked point patterns, where the points are the centers of the vesicles, and the mark of a vesicle is given by its size, shape, and orientation characteristics. Statistics for marked point processes were employed to study spatial interactions between vesicles. We found that the synaptic vesicles in excitatory synapses appeared to be of oblate ellipsoid shape and in inhibitory synapses appeared to be of cigar ellipsoid shape, and followed a systematic pattern regarding their orientation toward the active zone. Moreover, there was strong evidence of spatial alignment in the orientations of pairs of synaptic vesicles, and of repulsion between them only in excitatory synapses, beyond that caused by their physical extent.

The method of moving frames provides powerful geometrical tools for the analysis of smoothly varying frame fields. However, in the face of missing measurements, a reconstruction problem arises, one that is largely unexplored for 3D frame fields. Here we consider the particular example of reconstructing impaired cardiac diffusion magnetic resonance imaging (dMRI) data. We combine moving frame analysis with a diffusion inpainting scheme that incorporates rule-based priors. In contrast to previous reconstruction methods, this new approach uses comprehensive differential descriptors for cardiac fibers, and is able to fully recover their orientation. We demonstrate the superior performance of this approach in terms of error of fit when compared to alternate methods. We anticipate that these tools could find application in clinical settings, where damaged heart tissue needs to be replaced or repaired, and for generating dense fiber volumes in electromechanical modelling of the heart.

This paper presents a unifying approach for calculating a wide range of popular, but seemingly very different, similarity measures. Our domain is the registration of n-dimensional images sampled on a regular grid, and our approach is well suited for gradient-based optimization algorithms. Our approach is based on local intensity histograms and built upon the technique of Locally Orderless Images. Histograms by Locally Orderless Images are well posed and offer explicit control over the three inherent and unavoidable scales: the spatial resolution, intensity levels, and spatial extent of local histograms. Through Locally Orderless Images, we offer new insight into the relations between these scales. We demonstrate our unification by developing a Locally Orderless Registration algorithm for two quite different similarity measures, namely, Normalized Mutual Information and Sum of Squared Differences, and we compare these variations both theoretically and empirically. Finally, using our algorithm, we explain the empirically observed differences between two popular joint density estimation techniques used in registration: Parzen Windows and Generalized Partial Volume.

Accurate matching of cortical surfaces is necessary in many neuroscience applications. In this context diffeomorphisms are often sought, because they facilitate further statistical analysis and atlas building. Present methods for computing diffeomorphisms are based on optimizing flows or on inflating surfaces to a common template, but they are often computationally expensive. It typically takes several hours on a conventional desktop computer to match a single pair of cortical surfaces having a few hundred thousand vertices. We propose a very fast alternative based on an application of spectral graph theory on a novel association graph. Our symmetric approach can generate a diffeomorphic correspondence map within a few minutes on high-resolution meshes while avoiding the sign and multiplicity ambiguities of conventional spectral matching methods. The eigenfunctions are shared between surfaces and provide a smooth parameterization of surfaces. These properties are exploited to compute differentials on highly folded cortical surfaces. Diffeomorphisms can thus be verified and invalid surface folding detected. Our method is demonstrated to attain a vertex accuracy that is at least as good as that of FreeSurfer and Spherical Demons but in only a fraction of their processing time. As a practical experiment, we construct an unbiased atlas of cortical surfaces with a speed several orders of magnitude faster than current methods.