Development of tolerance is a well known pharmacological characteristic of opioids and a major clinical problem. In addition to the known neuronal mechanisms of opioid tolerance, activation of glia has emerged as a potentially significant new mechanism. We studied activation of microglia and astrocytes in morphine tolerance and opioid-induced hyperalgesia in rats using immunohistochemistry, flow cytometry and RNA sequencing in spinal- and supraspinal regions. Chronic morphine treatment that induced tolerance and hyperalgesia also increased immunoreactivity of spinal microglia in the dorsal and ventral horns. Flow cytometry demonstrated that morphine treatment increased the proportion of M2-polarized spinal microglia, but failed to impact the number or the proportion of M1-polarized microglia. In the transcriptome of microglial cells isolated from the spinal cord (SC), morphine treatment increased transcripts related to cell activation and defense response. In the studied brain regions, no activation of microglia or astrocytes was detected by immunohistochemistry, except for a decrease in the number of microglial cells in the substantia nigra. In flow cytometry, morphine caused a decrease in the number of microglial cells in the medulla, but otherwise no change was detected for the count or the proportion of M1- and M2-polarized microglia in the medulla or sensory cortex. No evidence for the activation of glia in the brain was seen. Our results suggest that glial activation associated with opioid tolerance and opioid-induced hyperalgesia occurs mainly at the spinal level. The transcriptome data suggest that the microglial activation pattern after chronic morphine treatment has similarities with that of neuropathic pain.
Intrastriatal administration of 6-hydroxydopamine (6-OHDA) induces partial degeneration of the nigrostriatal pathway, mimicking the pathology of Parkinson's disease (PD). Setting up the partial lesion model can be challenging because a number of experimental settings can be altered. This study compares seven experimental settings in a single study on d-amphetamine-induced rotations, tyrosine hydroxylase (TH)-positive neurites in the striatum, dopamine transporter (DAT)-positive neurites in the striatum, and TH-positive cells in the substantia nigra pars compacta (SNpc) in rats. Moreover, we validate a new algorithm for estimating the number of TH-positive cells. We show that the behavior and immunoreactivity vary greatly depending on the injection settings, and we categorize the lesions as progressive, stable, or regressive based on d-amphetamine-induced rotations. The rotation behavior correlated with the degree of the lesion, analyzed by immunohistochemistry; the largest lesions were in the progressive group, and the smallest lesions were in the regressive group. We establish a new low-dose partial 6-OHDA lesion model in which a total of 6 μg was distributed evenly to three sites in the striatum at a 10° angle. The administration of low-dose 6-OHDA produced stable and reliable rotation behavior and induced partial loss of striatal TH-positive and DAT-positive neurites and TH-positive cells in the SNpc. This model is highly suitable for neurorestoration studies in the search for new therapies for PD, and the new algorithm increases the efficacy for estimating the number of dopamine neurons. This study can be extremely useful for laboratories setting up the partial 6-OHDA model.
Astrocytes are involved in various brain pathologies including trauma, stroke, neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases, or chronic pain. Determining cell density in a complex tissue environment in microscopy images and elucidating the temporal characteristics of morphological and biochemical changes is essential to understand the role of astrocytes in physiological and pathological conditions. Nowadays, manual stereological cell counting or semi-automatic segmentation techniques are widely used for the quantitative analysis of microscopy images. Detecting astrocytes automatically is a highly challenging computational task, for which we currently lack efficient image analysis tools. We have developed a fast and fully automated software that assesses the number of astrocytes using Deep Convolutional Neural Networks (DCNN). The method highly outperforms state-of-the-art image analysis and machine learning methods and provides precision comparable to those of human experts. Additionally, the runtime of cell detection is significantly less than that of other three computational methods analysed, and it is faster than human observers by orders of magnitude. We applied our DCNN-based method to examine the number of astrocytes in different brain regions of rats with opioid-induced hyperalgesia/tolerance (OIH/OIT), as morphine tolerance is believed to activate glia. We have demonstrated a strong positive correlation between manual and DCNN-based quantification of astrocytes in rat brain.
In vitro humanized 3D microfluidic chip for testing personalized immunotherapeutics for head and neck cancer patients, Experimental Cell Research (2019), doi:
Astrocytes are involved in brain pathologies such as trauma or stroke, neurodegenerative disorders like Alzheimer's and Parkinson's disease, chronic pain, and many others. Determining cell density and timing of morphological and biochemical changes is important for a proper understanding of the role of astrocytes in physiological and pathological conditions. One of the most important of such analyses is astrocytes count within a complex tissue environment in microscopy images. The most widely used approaches for the quantification of microscopy images data are either manual stereological cell counting or semi-automatic segmentation techniques. Detecting astrocytes automatically is a highly challenging computational task, for which we currently lack efficient image analysis tools. In this study, we developed a fast and fully automated software that assesses the number of astrocytes using Deep Convolutional Neural Networks (DCNN). The method highly outperforms state-of-the-art image analysis and machine learning methods and provides detection accuracy and precision comparable to that of human experts. Additionally, the runtime of cell detection is significantly less than other three analyzed computational methods, and it is faster than human observers by orders of magnitude. We applied DCNN-based method to examine the number of astrocytes in different brain regions of rats with opioid-induced hyperalgesia/tolerance (OIH/OIT) as morphine tolerance is believed to activate glial cells in the brain. We observed strong positive correlation between manual cell detection and DCNN-based analysis method for counting astrocytes in the brains of experimental animals.
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