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Predicting Neuroinflammation in Morphine Tolerance for Tolerance Therapy from Immunostaining Images of Rat Spinal Cord.

Lin SL, Chang FL, Ho SY, Charoenkwan P, Wang KW, Huang HL - PLoS ONE (2015)

Bottom Line: Long-term morphine treatment leads to tolerance which attenuates analgesic effect and hampers clinical utilization.The experimental results suggest that neuroinflammation activity expresses more predominantly in astrocytes and microglia than in neuron cells.Based on neuroinflammation prediction, the proposed computer-aided image diagnosis system can greatly facilitate the development of tolerance therapy with anti-inflammatory drugs.

View Article: PubMed Central - PubMed

Affiliation: Department of Anesthesiology, Tri-Service General Hospital and National Defense Medical Center, Taipei, Taiwan.

ABSTRACT
Long-term morphine treatment leads to tolerance which attenuates analgesic effect and hampers clinical utilization. Recent studies have sought to reveal the mechanism of opioid receptors and neuroinflammation by observing morphological changes of cells in the rat spinal cord. This work proposes a high-content screening (HCS) based computational method, HCS-Morph, for predicting neuroinflammation in morphine tolerance to facilitate the development of tolerance therapy using immunostaining images for astrocytes, microglia, and neurons in the spinal cord. HCS-Morph first extracts numerous HCS-based features of cellular phenotypes. Next, an inheritable bi-objective genetic algorithm is used to identify a minimal set of features by maximizing the prediction accuracy of neuroinflammation. Finally, a mathematic model using a support vector machine with the identified features is established to predict drug-treated images to assess the effects of tolerance therapy. The dataset consists of 15 saline controls (1 μl/h), 15 morphine-tolerant rats (15 μg/h), and 10 rats receiving a co-infusion of morphine (15 μg/h) and gabapentin (15 μg/h, Sigma). The three individual models of astrocytes, microglia, and neurons for predicting neuroinflammation yielded respective Jackknife test accuracies of 96.67%, 90.00%, and 86.67% on the 30 rats, and respective independent test accuracies of 100%, 90%, and 60% on the 10 co-infused rats. The experimental results suggest that neuroinflammation activity expresses more predominantly in astrocytes and microglia than in neuron cells. The set of features for predicting neuroinflammation from images of astrocytes comprises mean cell intensity, total cell area, and second-order geometric moment (relating to cell distribution), relevant to cell communication, cell extension, and cell migration, respectively. The present investigation provides the first evidence for the role of gabapentin in the attenuation of morphine tolerance from phenotypic changes of astrocytes and microglia. Based on neuroinflammation prediction, the proposed computer-aided image diagnosis system can greatly facilitate the development of tolerance therapy with anti-inflammatory drugs.

No MeSH data available.


The statistics of M02 (left) and M20 (right) for the control and morphine-tolerance groups of astrocytes.The values of the morphine-tolerance group were significantly smaller than those of the control group for both M02 and M20.
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pone.0139806.g004: The statistics of M02 (left) and M20 (right) for the control and morphine-tolerance groups of astrocytes.The values of the morphine-tolerance group were significantly smaller than those of the control group for both M02 and M20.

Mentions: As shown in Table 1, of the five features, four are interpretable and one is computational. Four features were extracted from binary images, and one from gray images. All five features have high levels of prediction accuracy (i.e., greater than 86.67% with a mean accuracy of 92.67%). The interpretable feature Cell Distribution X/Y (0, 2) with P = 2.09E-09 has the best prediction accuracy of 96.67% for astrocytes. The Cell Distribution X/Y (i, j) feature is one type of image moments, called a geometric moment, computed from a weighted mean of the pixel intensity [23]. The equation for calculating the geometric moments Mij is as follows:Mij=∑x=1Nx∑y=1Ny​xiyjI(x,y)(1)where I(x, y) is the intensity of a pixel at the coordinate (x, y). For binary images, the intensity equals 0 or 1. The variables i and j are the moment orders. In this work, Nx = 800 and Ny = 600. In calculating the feature Cell Distribution X/Y (0, 2) (i.e., M02), i = 0 and j = 2. The center (M10/M00, M01/M00) of the fluorescence area was translated to the center of the image for translation normalization. The feature value was divided by a suitable scale factor relating to the total number of fluorescence pixels for scale normalization. This feature is highly correlated to the distribution of fluorescent pixels. Remarkably, the averaged Pearson’s correlation coefficient between M02 and M20 reaches 0.854. Considering the rotation invariants, the features M02 and M20 represent the same distribution of fluorescent pixels if the vertical and horizontal axes are exchanged. Fig 4 show the statistics of M02 and M20 for the control and morphine-tolerance groups of astrocytes. The values of the morphine-tolerance group were significantly smaller than those of the control group for both M02 and M20. The feature Cell Number has a P value of 0.080, suggesting that the cell numbers between control and morphine-tolerance groups were not significantly different. The decreased moments reveal that astrocytes in neuroinflammation would be aggregated compared with astrocytes in the control group with no neuroinflammation.


Predicting Neuroinflammation in Morphine Tolerance for Tolerance Therapy from Immunostaining Images of Rat Spinal Cord.

Lin SL, Chang FL, Ho SY, Charoenkwan P, Wang KW, Huang HL - PLoS ONE (2015)

The statistics of M02 (left) and M20 (right) for the control and morphine-tolerance groups of astrocytes.The values of the morphine-tolerance group were significantly smaller than those of the control group for both M02 and M20.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4593634&req=5

pone.0139806.g004: The statistics of M02 (left) and M20 (right) for the control and morphine-tolerance groups of astrocytes.The values of the morphine-tolerance group were significantly smaller than those of the control group for both M02 and M20.
Mentions: As shown in Table 1, of the five features, four are interpretable and one is computational. Four features were extracted from binary images, and one from gray images. All five features have high levels of prediction accuracy (i.e., greater than 86.67% with a mean accuracy of 92.67%). The interpretable feature Cell Distribution X/Y (0, 2) with P = 2.09E-09 has the best prediction accuracy of 96.67% for astrocytes. The Cell Distribution X/Y (i, j) feature is one type of image moments, called a geometric moment, computed from a weighted mean of the pixel intensity [23]. The equation for calculating the geometric moments Mij is as follows:Mij=∑x=1Nx∑y=1Ny​xiyjI(x,y)(1)where I(x, y) is the intensity of a pixel at the coordinate (x, y). For binary images, the intensity equals 0 or 1. The variables i and j are the moment orders. In this work, Nx = 800 and Ny = 600. In calculating the feature Cell Distribution X/Y (0, 2) (i.e., M02), i = 0 and j = 2. The center (M10/M00, M01/M00) of the fluorescence area was translated to the center of the image for translation normalization. The feature value was divided by a suitable scale factor relating to the total number of fluorescence pixels for scale normalization. This feature is highly correlated to the distribution of fluorescent pixels. Remarkably, the averaged Pearson’s correlation coefficient between M02 and M20 reaches 0.854. Considering the rotation invariants, the features M02 and M20 represent the same distribution of fluorescent pixels if the vertical and horizontal axes are exchanged. Fig 4 show the statistics of M02 and M20 for the control and morphine-tolerance groups of astrocytes. The values of the morphine-tolerance group were significantly smaller than those of the control group for both M02 and M20. The feature Cell Number has a P value of 0.080, suggesting that the cell numbers between control and morphine-tolerance groups were not significantly different. The decreased moments reveal that astrocytes in neuroinflammation would be aggregated compared with astrocytes in the control group with no neuroinflammation.

Bottom Line: Long-term morphine treatment leads to tolerance which attenuates analgesic effect and hampers clinical utilization.The experimental results suggest that neuroinflammation activity expresses more predominantly in astrocytes and microglia than in neuron cells.Based on neuroinflammation prediction, the proposed computer-aided image diagnosis system can greatly facilitate the development of tolerance therapy with anti-inflammatory drugs.

View Article: PubMed Central - PubMed

Affiliation: Department of Anesthesiology, Tri-Service General Hospital and National Defense Medical Center, Taipei, Taiwan.

ABSTRACT
Long-term morphine treatment leads to tolerance which attenuates analgesic effect and hampers clinical utilization. Recent studies have sought to reveal the mechanism of opioid receptors and neuroinflammation by observing morphological changes of cells in the rat spinal cord. This work proposes a high-content screening (HCS) based computational method, HCS-Morph, for predicting neuroinflammation in morphine tolerance to facilitate the development of tolerance therapy using immunostaining images for astrocytes, microglia, and neurons in the spinal cord. HCS-Morph first extracts numerous HCS-based features of cellular phenotypes. Next, an inheritable bi-objective genetic algorithm is used to identify a minimal set of features by maximizing the prediction accuracy of neuroinflammation. Finally, a mathematic model using a support vector machine with the identified features is established to predict drug-treated images to assess the effects of tolerance therapy. The dataset consists of 15 saline controls (1 μl/h), 15 morphine-tolerant rats (15 μg/h), and 10 rats receiving a co-infusion of morphine (15 μg/h) and gabapentin (15 μg/h, Sigma). The three individual models of astrocytes, microglia, and neurons for predicting neuroinflammation yielded respective Jackknife test accuracies of 96.67%, 90.00%, and 86.67% on the 30 rats, and respective independent test accuracies of 100%, 90%, and 60% on the 10 co-infused rats. The experimental results suggest that neuroinflammation activity expresses more predominantly in astrocytes and microglia than in neuron cells. The set of features for predicting neuroinflammation from images of astrocytes comprises mean cell intensity, total cell area, and second-order geometric moment (relating to cell distribution), relevant to cell communication, cell extension, and cell migration, respectively. The present investigation provides the first evidence for the role of gabapentin in the attenuation of morphine tolerance from phenotypic changes of astrocytes and microglia. Based on neuroinflammation prediction, the proposed computer-aided image diagnosis system can greatly facilitate the development of tolerance therapy with anti-inflammatory drugs.

No MeSH data available.