Applying Pattern Recognition to High-Resolution Images to Determine Cellular Signaling Status

Two frequently used tools to acquire high- resolution images of cells are scanning electron microscopy (SEM) and atomic force microscopy (AFM). The former provides a nanometer resolution view of cellular features rapidly and with high throughput, while the latter enables visualizing hydrated and liv...

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Bibliographic Details
Published in:	IEEE transactions on nanobioscience 2017-09, Vol.16 (6), p.438-446
Main Authors:	Lohrer, Michael F., Hanna, Darrin M., Liu, Yang, Wang, Kang-Hsin, Liu, Fu-Tong, Laurence, Ted A., Liu, Gang-Yu
Format:	Magazinearticle
Language:	eng
Subjects:	60 APPLIED LIFE SCIENCES Activation of mast cells Algorithms Animals Atomic force microscopy atomic force microscopy (AFM) Basophilic leukemia Biomembranes Cell Communication - physiology Cell Tracking - methods Cells, Cultured Cellular structure Data analysis Data processing Electron microscopy Eye Histograms Image acquisition Image classification Image Enhancement - methods Image resolution immunoglobulin E (IgE) Leukemia Leukemia, Basophilic, Acute - pathology Microscopes Microscopy, Atomic Force - methods Microscopy, Electron, Scanning Molecular Imaging - methods Nanobioscience Object recognition Pattern recognition Pattern Recognition, Automated - methods Rats Reproducibility of Results Scanning electron microscopy scanning electron microscopy (SEM) Sensitivity and Specificity Structural analysis Tumor Cells, Cultured
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Summary:	Two frequently used tools to acquire high- resolution images of cells are scanning electron microscopy (SEM) and atomic force microscopy (AFM). The former provides a nanometer resolution view of cellular features rapidly and with high throughput, while the latter enables visualizing hydrated and living cells. In current practice, these images are viewed by eye to determine cellular status, e.g., activated versus resting. Automatic and quantitative data analysis is lacking. This paper develops an algorithm of pattern recognition that works very effectively for AFM and SEM images. Using rat basophilic leukemia cells, our approach creates a support vector machine to automatically classify resting and activated cells. Ten-fold cross-validation with cells that are known to be activated or resting gives a good estimate of the generalized classification results. The pattern recognition of AFM images achieves 100% accuracy, while SEM reaches 95.4% for our images as well as images published in prior literature. This outcome suggests that our methodology could become an important and frequently used tool for researchers utilizing AFM and SEM for structural characterization as well as determining cellular signaling status and function.
ISSN:	1536-1241 1558-2639