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Live-cell imaging of invasion and intravasation in an artificial microvessel platform.

ABSTRACT: Methods to visualize metastasis exist, but additional tools to better define the biologic and physical processes underlying invasion and intravasation are still needed. One difficulty in studying metastasis stems from the complexity of the interface between the tumor microenvironment and the vascular system. Here, we report the development of an investigational platform that positions tumor cells next to an artificial vessel embedded in an extracellular matrix. On this platform, we used live-cell fluorescence microscopy to analyze the complex interplay between metastatic cancer cells and a functional artificial microvessel that was lined with endothelial cells. The platform recapitulated known interactions, and its use demonstrated the capabilities for a systematic study of novel physical and biologic parameters involved in invasion and intravasation. In summary, our work offers an important new tool to advance knowledge about metastasis and candidate antimetastatic therapies.

SUBMITTER: Wong AD

PROVIDER: S-EPMC4155070 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Live-cell imaging of invasion and intravasation in an artificial microvessel platform.

Wong Andrew D AD Searson Peter C PC

Cancer research 20140626 17

Methods to visualize metastasis exist, but additional tools to better define the biologic and physical processes underlying invasion and intravasation are still needed. One difficulty in studying metastasis stems from the complexity of the interface between the tumor microenvironment and the vascular system. Here, we report the development of an investigational platform that positions tumor cells next to an artificial vessel embedded in an extracellular matrix. On this platform, we used live-cel ...[more]

PMID: 24970480

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Project description:BackgroundAbout 30% of cell lines have been cellular cross-contaminated and misidentification, which can result in invalidated experimental results and unusable therapeutic products. Cell morphology under the microscope was observed routinely, and further DNA sequencing analysis was performed periodically to verify cell line identity, but the sequencing analysis was costly, time-consuming, and labor intensive. The purpose of this study was to construct a novel artificial intelligence (AI) technology for "cell face" recognition, in which can predict DNA-level identification labels only using cell images.MethodsSeven commonly used cell lines were cultured and co-cultured in pairs (totally 8 categories) to simulated the situation of pure and cross-contaminated cells. The microscopy images were obtained and labeled of cell types by the result of short tandem repeat profiling. About 2 million patch images were used for model training and testing. AlexNet was used to demonstrate the effectiveness of convolutional neural network (CNN) in cell classification. To further improve the feasibility of detecting cross-contamination, the bilinear network for fine-grained identification was constructed. The specificity, sensitivity, and accuracy of the model were tested separately by external validation. Finally, the cell semantic segmentation was conducted by DilatedNet.ResultsThe 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%.ConclusionsThe deep CNN model proposed in this study has the ability to recognize small differences in cell morphology, and achieved high classification accuracy.

Dataset Information

Live-cell imaging of invasion and intravasation in an artificial microvessel platform.

Publications

Live-cell imaging of invasion and intravasation in an artificial microvessel platform.

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