2,625 research outputs found
VIPAR, a quantitative approach to 3D histopathology applied to lymphatic malformations.
BACKGROUND: Lack of investigatory and diagnostic tools has been a major contributing factor to the failure to mechanistically understand lymphedema and other lymphatic disorders in order to develop effective drug and surgical therapies. One difficulty has been understanding the true changes in lymph vessel pathology from standard 2D tissue sections. METHODS: VIPAR (volume information-based histopathological analysis by 3D reconstruction and data extraction), a light-sheet microscopy-based approach for the analysis of tissue biopsies, is based on digital reconstruction and visualization of microscopic image stacks. VIPAR allows semiautomated segmentation of the vasculature and subsequent nonbiased extraction of characteristic vessel shape and connectivity parameters. We applied VIPAR to analyze biopsies from healthy lymphedematous and lymphangiomatous skin. RESULTS: Digital 3D reconstruction provided a directly visually interpretable, comprehensive representation of the lymphatic and blood vessels in the analyzed tissue volumes. The most conspicuous features were disrupted lymphatic vessels in lymphedematous skin and a hyperplasia (4.36-fold lymphatic vessel volume increase) in the lymphangiomatous skin. Both abnormalities were detected by the connectivity analysis based on extracted vessel shape and structure data. The quantitative evaluation of extracted data revealed a significant reduction of lymphatic segment length (51.3% and 54.2%) and straightness (89.2% and 83.7%) for lymphedematous and lymphangiomatous skin, respectively. Blood vessel length was significantly increased in the lymphangiomatous sample (239.3%). CONCLUSION: VIPAR is a volume-based tissue reconstruction data extraction and analysis approach that successfully distinguished healthy from lymphedematous and lymphangiomatous skin. Its application is not limited to the vascular systems or skin. FUNDING: Max Planck Society, DFG (SFB 656), and Cells-in-Motion Cluster of Excellence EXC 1003
Recommended from our members
Identifying specific prefrontal neurons that contribute to autism-associated abnormalities in physiology and social behavior.
Functional imaging and gene expression studies both implicate the medial prefrontal cortex (mPFC), particularly deep-layer projection neurons, as a potential locus for autism pathology. Here, we explored how specific deep-layer prefrontal neurons contribute to abnormal physiology and behavior in mouse models of autism. First, we find that across three etiologically distinct models-in utero valproic acid (VPA) exposure, CNTNAP2 knockout and FMR1 knockout-layer 5 subcortically projecting (SC) neurons consistently exhibit reduced input resistance and action potential firing. To explore how altered SC neuron physiology might impact behavior, we took advantage of the fact that in deep layers of the mPFC, dopamine D2 receptors (D2Rs) are mainly expressed by SC neurons, and used D2-Cre mice to label D2R+ neurons for calcium imaging or optogenetics. We found that social exploration preferentially recruits mPFC D2R+ cells, but that this recruitment is attenuated in VPA-exposed mice. Stimulating mPFC D2R+ neurons disrupts normal social interaction. Conversely, inhibiting these cells enhances social behavior in VPA-exposed mice. Importantly, this effect was not reproduced by nonspecifically inhibiting mPFC neurons in VPA-exposed mice, or by inhibiting D2R+ neurons in wild-type mice. These findings suggest that multiple forms of autism may alter the physiology of specific deep-layer prefrontal neurons that project to subcortical targets. Furthermore, a highly overlapping population-prefrontal D2R+ neurons-plays an important role in both normal and abnormal social behavior, such that targeting these cells can elicit potentially therapeutic effects
Telomere disruption results in non-random formation of de novo dicentric chromosomes involving acrocentric human chromosomes
Copyright: © 2010 Stimpson et al.Genome rearrangement often produces chromosomes with two centromeres (dicentrics) that are inherently unstable because of bridge formation and breakage during cell division. However, mammalian dicentrics, and particularly those in humans, can be quite stable, usually because one centromere is functionally silenced. Molecular mechanisms of centromere inactivation are poorly understood since there are few systems to experimentally create dicentric human chromosomes. Here, we describe a human cell culture model that enriches for de novo dicentrics. We demonstrate that transient disruption of human telomere structure non-randomly produces dicentric fusions involving acrocentric chromosomes. The induced dicentrics vary in structure near fusion breakpoints and like naturally-occurring dicentrics, exhibit various inter-centromeric distances. Many functional dicentrics persist for months after formation. Even those with distantly spaced centromeres remain functionally dicentric for 20 cell generations. Other dicentrics within the population reflect centromere inactivation. In some cases, centromere inactivation occurs by an apparently epigenetic mechanism. In other dicentrics, the size of the alpha-satellite DNA array associated with CENP-A is reduced compared to the same array before dicentric formation. Extrachromosomal fragments that contained CENP-A often appear in the same cells as dicentrics. Some of these fragments are derived from the same alpha-satellite DNA array as inactivated centromeres. Our results indicate that dicentric human chromosomes undergo alternative fates after formation. Many retain two active centromeres and are stable through multiple cell divisions. Others undergo centromere inactivation. This event occurs within a broad temporal window and can involve deletion of chromatin that marks the locus as a site for CENP-A maintenance/replenishment.This work was supported by the Tumorzentrum Heidelberg/Mannheim grant (D.10026941)and by March of Dimes Research Foundation grant #1-FY06-377 and NIH R01 GM069514
Stride-level analysis of mouse open field behavior using deep-learning-based pose estimation.
Gait and posture are often perturbed in many neurological, neuromuscular, and neuropsychiatric conditions. Rodents provide a tractable model for elucidating disease mechanisms and interventions. Here, we develop a neural-network-based assay that adopts the commonly used open field apparatus for mouse gait and posture analysis. We quantitate both with high precision across 62 strains of mice. We characterize four mutants with known gait deficits and demonstrate that multiple autism spectrum disorder (ASD) models show gait and posture deficits, implying this is a general feature of ASD. Mouse gait and posture measures are highly heritable and fall into three distinct classes. We conduct a genome-wide association study to define the genetic architecture of stride-level mouse movement in the open field. We provide a method for gait and posture extraction from the open field and one of the largest laboratory mouse gait and posture data resources for the research community
Cortical control of forelimb movement
Cortical control of movement is mediated by wide-spread projections impacting many nervous system regions in a top-down manner. Although much knowledge about cortical circuitry has been accumulated from local cortical microcircuits, cortico-cortical and cortico-subcortical networks, how cortex communicates to regions closer to motor execution, including the brainstem, is less well understood.
In this dissertation, we investigate the organization of cortico-medulla projections and their roles in controlling forelimb movement. We focus on anatomical and functional relationships between cortex and lateral rostral medulla (LatRM), a region in caudal brainstem which is shown to be key in the control of forelimb movement. Our findings reveal the precise anatomical and functional organization between different cortical regions and matched postsynaptic neurons in the caudal brainstem, tuned to different phases of one carefully orchestrated behavior, which advance the our knowledge on circuit mechanisms involved in the control of body movements, and unravel the logic of how the top-level control region in the mammalian nervous system – the cortex – intersects with a high degree of specificity with command centers in the brainstem and beyond
Mechanisms of Experience-dependent Prevention of Plasticity in Visual Circuits
Development of brain function is instructed by both genetically-determined processes (nature) and environmental stimuli (nurture). The relative importance of nature and nurture is a major question in developmental neurobiology. In this dissertation, I investigated the role of visual experience in the development and plasticity of the visual pathway. Each neuron that receives visual input responds to a specific area of the visual field- their receptive field (RF). Developmental refinement reduces RF size and underlies visual acuity, which is important for survival. By rearing Syrian hamsters (Mesocricetus auratus) in constant darkness (dark rearing, DR) from birth, I investigated the role of visual experience in RF refinement and plasticity. Previous work in this lab has shown that developmental refinement of RFs occurs in the absence of visual experience in the superior colliculus (SC), but that RFs unrefine and thus enlarge in adulthood during chronic DR. Using an in vivo electrophysiological approach, I show that, contrary to a widely held view, visual experience is not necessary for refinement of RFs in primary visual cortex (V1). In both SC and V1, RFs refine by postnatal day (P) 60, but enlarge by P90 with chronic DR. One week of visual experience was sufficient to prevent RF enlargement in SC and V1. How normal sensory experience prevents plasticity in mature circuits is not well understood. Using an in vitro electrophysiological approach, I demonstrated that GABAergic inhibition is reduced in DR SC, which in turn affects short-term (but not long-term) synaptic plasticity. The level of GABABR-mediated short-term synaptic depression (STD) that occurs during high-frequency afferent stimulation, such as occurs during vision, is reduced by DR. Using a computational model of RF size, I propose that, in addition to the effect of reduced inhibition, reduced STD of excitation could contribute to enlarged RFs. This work provides insight into mechanisms of development and plasticity of the nervous system. How plasticity is restricted in mature circuits is of fundamental importance in neuroscience and could instruct therapies to prevent maladaptive plasticity in disease and to enhance recovery of function in adults
Plasticity in eye movement control
The cerebellum plays an important role in the recalibration and adaptive adjustment of
movements, as well as learning new motor skills and motor related associations. In this thesis,
we investigated the mechanisms underlying cerebellar motor learning. To obtain a better
understanding, in how the cerebellum processes and stores information, we used specific
perturbations that affected the information processing of the cerebellum. Signal transduction
pathways were affected that were considered important for cerebellar motor learning by using
genetic tools (transgenic mice) and the application of antibodies.
Alterations in cerebellar
motor learning were studied by monitoring the oculomotor system of these transgenic and
treated mice
GC/MS-Based Urinary Metabolomics Reveals Systematic Differences in Metabolism and Ethanol Response between Sprague–Dawley and Wistar Rats
Metabolic differences of experimental animals contribute to pharmacological variations. Sprague–Dawley (SD) and Wistar rats are commonly used experimental rats with similar genetic background, and considered interchangeable in practical researches. In this study, we present the urinary metabolomics results, based on gas chromatography coupled to mass spectrometry (GC/MS), which reveal the systematic metabolic differences between SD and Wistar rats under different perturbations such as fasting, feeding, and consecutive acute ethanol interventions. The different metabotypes between the two strains of rats involve a number of metabolic pathways and symbiotic gut microflora. SD rats exhibited higher individualized metabolic variations in the fasting and feeding states, and a stronger ability to recover from an altered metabolic profile with less hepatic injury from the consecutive ethanol exposure, as compared to Wistar rats. In summary, the GC/MS-based urinary metabolomics studies demonstrated an intrinsic metabolic difference between SD and Wistar rats, which warrants consideration in experimental design using these animal strains
Zika Virus Disrupts Phospho-TBK1 Localization and Mitosis in Human Neuroepithelial Stem Cells and Radial Glia
Graphical Abstract Highlights d Derivation of human neocortical and spinal cord neuroepithelial stem (NES) cells d Zika virus (ZIKV) infects NES cells and radial glia, impairing mitosis and survival d ZIKV induces mitochondrial sequestration of centrosomal phospho-TBK1 d Nucleoside analogs inhibit ZIKV replication, protecting NES cells from cell death In Brief Onorati et al. establish neuroepithelial stem (NES) cells as a model for studying human neurodevelopment and ZIKV-induced microcephaly. Together with analyses in human brain slices and microcephalic human fetal tissue, they find that ZIKV predominantly infects NES and radial glial cells, reveal a pivotal role for pTBK1, and find that nucleoside analogs inhibit ZIKV replication, protecting NES cells from cell death
Mass spectral imaging of clinical samples using deep learning
A better interpretation of tumour heterogeneity and variability is vital for the improvement of novel diagnostic techniques and personalized cancer treatments. Tumour tissue heterogeneity is characterized by biochemical heterogeneity, which can be investigated by unsupervised metabolomics.
Mass Spectrometry Imaging (MSI) combined with Machine Learning techniques have generated increasing interest as analytical and diagnostic tools for the analysis of spatial molecular patterns in tissue samples. Considering the high complexity of data produced by the application of MSI, which can consist of many thousands of spectral peaks, statistical analysis and in particular machine learning and deep learning have been investigated as novel approaches to deduce the relationships between the measured molecular patterns and the local structural and biological properties of the tissues.
Machine learning have historically been divided into two main categories: Supervised and Unsupervised learning. In MSI, supervised learning methods may be used to segment tissues into histologically relevant areas e.g. the classification of tissue regions in H&E (Haemotoxylin and Eosin) stained samples. Initial classification by an expert histopathologist, through visual inspection enables the development of univariate or multivariate models, based on tissue regions that have significantly up/down-regulated ions. However, complex data may result in underdetermined models, and alternative methods that can cope with high dimensionality and noisy data are required.
Here, we describe, apply, and test a novel diagnostic procedure built using a combination of MSI and deep learning with the objective of delineating and identifying biochemical differences between cancerous and non-cancerous tissue in metastatic liver cancer and epithelial ovarian cancer. The workflow investigates the robustness of single (1D) to multidimensional (3D) tumour analyses and also highlights possible biomarkers which are not accessible from classical visual analysis of the H&E images. The identification of key molecular markers may provide a deeper understanding of tumour heterogeneity and potential targets for intervention.Open Acces
- …