scholarly journals Heterogeneity in the Rat Brain Vasculature Revealed by Quantitative Confocal Analysis of Endothelial Barrier Antigen and P-Glycoprotein Expression

2011 ◽  
Vol 32 (1) ◽  
pp. 81-92 ◽  
Author(s):  
Bruno Saubaméa ◽  
Véronique Cochois-Guégan ◽  
Salvatore Cisternino ◽  
Jean-Michel Scherrmann
Keyword(s):  
Single Cell ◽  
Rat Brain ◽  
Confocal Imaging ◽  
Endothelial Barrier ◽  
Single Cell Level ◽  
Cell Level ◽  
Brain Vasculature ◽  
P Glycoprotein ◽  
Underlying Mechanisms ◽  
Endothelial Barrier Antigen

While phenotypic endothelial heterogeneity is well documented in peripheral organs, it is only now being explored in the brain. We used confocal imaging of thick sections of rat brain to qualitatively and quantitatively examine the expression of two key markers of the blood—brain barrier (BBB) in the rat, P-glycoprotein (P-gp), and endothelial barrier antigen (EBA). We found that these markers were not uniformly distributed throughout the whole vasculature of the cortex and hippocampus. P-glycoprotein displayed a gradient of expression from an almost undetectable level in large penetrating arterioles to a high and uniform level in capillaries and venules. While EBA was lacking in all cerebral arterioles, regardless of their size, its expression varied greatly among endothelial cells in capillaries and venules, yielding a striking mosaic pattern. A detailed quantitative analysis of the distribution of these markers at the single cell level in capillaries is provided. These results challenge the view of a uniform BBB and suggest that regulatory mechanisms might differentially modulate BBB features not only among arterioles/capillaries/venules but also at the single cell level within the capillaries. Hypotheses are made regarding the underlying mechanisms and physiopathological consequences of this heterogeneity.

Fluorescence Assay for Studying P-Glycoprotein Function at Single Cell Level

1996 ◽  
pp. 241-245
Author(s):  
László Homolya ◽  
Marianna Müller ◽  
Zsolt Holló ◽  
Balázs Sarkadi
Keyword(s):  
Single Cell ◽  
Fluorescence Assay ◽  
Single Cell Level ◽  
Cell Level ◽  
P Glycoprotein

scholarly journals FluidFM for single-cell biophysics

Nano Research ◽  
2021 ◽  
Author(s):  
Mi Li ◽  
Lianqing Liu ◽  
Tomaso Zambelli
Keyword(s):  
Single Cell ◽  
Three Dimensional ◽  
Cell Biophysics ◽  
Single Cell Level ◽  
Cell Level ◽  
Force Microscopy ◽  
Atomic Force ◽  
Underlying Mechanisms ◽  
Aqueous Conditions ◽  
Cell Force

AbstractFluidic force microscopy (FluidFM), which combines atomic force microscopy (AFM) with microchanneled cantilevers connected to a pressure controller, is a technique allowing the realization of force-sensitive nanopipette under aqueous conditions. FluidFM has unique advantages in simultaneous three-dimensional manipulations and mechanical measurements of biological specimens at the micro-/nanoscale. Over the past decade, FluidFM has shown its potential in biophysical assays particularly in the investigations at single-cell level, offering novel possibilities for discovering the underlying mechanisms guiding life activities. Here, we review the utilization of FluidFM to address biomechanical and biophysical issues in the life sciences. Firstly, the fundamentals of FluidFM are represented. Subsequently, the applications of FluidFM for biophysics at single-cell level are surveyed from several facets, including single-cell manipulations, single-cell force spectroscopy, and single-cell electrophysiology. Finally, the challenges and perspectives for future progressions are provided.

Expression of Mu opioid receptor mRNA in rat brain: An in situ hybridization study at the single cell level

1994 ◽  
Vol 345 (1) ◽  
pp. 46-68 ◽  
Author(s):  
Jill M. Delfs ◽  
Haeyoung Kong ◽  
Anton Mestek ◽  
Yan Chen ◽  
Lei Yu ◽  
...  
Keyword(s):  
In Situ Hybridization ◽  
Single Cell ◽  
Rat Brain ◽  
Mu Opioid Receptor ◽  
Single Cell Level ◽  
Hybridization Study ◽  
Cell Level ◽  
Mu Opioid ◽  
Receptor Mrna

An artificial intelligent platform for live cell identification and the detection of cross-contamination (Preprint)

2019 ◽  
Author(s):  
Ruixin Wang ◽  
Dongni Wang ◽  
Dekai Kang ◽  
Xusen Guo ◽  
Chong Guo ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Cell Morphology ◽  
Single Cell Level ◽  
Cross Contamination ◽  
Cell Level ◽  
Cell Identification ◽  
Pure Cell ◽  
Microscopy Images

BACKGROUND In vitro human cell line models have been widely used for biomedical research to predict clinical response, identify novel mechanisms and drug response. However, one-fifth to one-third of cell lines have been cross-contaminated, which can seriously result in invalidated experimental results, unusable therapeutic products and waste of research funding. Cell line misidentification and cross-contamination may occur at any time, but authenticating cell lines is infrequent performed because the recommended genetic approaches are usually require extensive expertise and may take a few days. Conversely, the observation of live-cell morphology is a direct and real-time technique. OBJECTIVE The purpose of this study was to construct a novel computer vision technology based on deep convolutional neural networks (CNN) for “cell face” recognition. This was aimed to improve cell identification efficiency and reduce the occurrence of cell-line cross contamination. METHODS Unstained optical microscopy images of cell lines were obtained for model training (about 334 thousand patch images), and testing (about 153 thousand patch images). The AI system first trained to recognize the pure cell morphology. In order to find the most appropriate CNN model，we explored the key image features in cell morphology classification tasks using the classical CNN model-Alexnet. After that, a preferred fine-grained recognition model BCNN was used for the cell type identification (seven classifications). Next, we simulated the situation of cell cross-contamination and mixed the cells in pairs at different ratios. The detection of the cross-contamination was divided into two levels, whether the cells are mixed and what the contaminating cell is. The specificity, sensitivity, and accuracy of the model were tested separately by external validation. Finally, the segmentation model DialedNet was used to present the classification results at the single cell level. RESULTS The cell texture and density were the influencing factors that can be better recognized by the bilinear convolutional neural network (BCNN) comparing to AlexNet. The BCNN achieved 99.5% accuracy in identifying seven pure cell lines and 86.3% accuracy for detecting cross-contamination (mixing two of the seven cell lines). DilatedNet was applied to the semantic segment for analyzing in single-cell level and achieved an accuracy of 98.2%. CONCLUSIONS This study successfully demonstrated that cell lines can be morphologically identified using deep learning models. Only light-microscopy images and no reagents are required, enabling most labs to routinely perform cell identification tests.

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