Automated recognition system to classify subcellular protein localizations in images of different cell lines acquired by different imaging systems

Abstract

Systemic analysis of subcellular protein localization (location proteomics) provides clues for understanding gene functions and physiological condition of the cells. However, recognition of cell images of subcellular structures highly depends on experience and becomes the rate-limiting step when classifying subcellular protein localization. Several research groups have extracted specific numerical features for the recognition of subcellular protein localization, but these recognition systems are restricted to images of single particular cell line acquired by one specific imaging system and not applied to recognize a range of cell image sources. In this study, we establish a single system for automated subcellular structure recognition to identify cell images from various sources. Two different sources of cell images, 317 Vero (http://gfp-cdna.embl.de) and 875 CHO cell images of subcellular structures, were used to train and test the system. When the system was trained by a single source of images, the recognition rate is high and specific to the trained source. The system trained by the CHO cell images gave high average recognition accuracy for CHO cells of 96%, but this was reduced to 46% with Vero images. When we trained the system using a mixture of CHO and Vero cell images, an average accuracy of recognition reached 86.6% for both CHO and Vero cell images. The system can reject images with low confidence and identify the cell images correctly recognized to avoid manual reconfirmation. In summary, we have established a single system that can recognize subcellular protein localizations from two different sources for location-proteomic studies. studies.

Silica biotemplating by self‐assembling peptides via serine residues activated by the peptide amino terminal group

“…The use of computer vision and classification techniques is not new in microscopy research and has performed well in many situations (Chen et al,2006b; Jalba et al,2004; Ranzato et al,2007; Tsai et al,2008; Wu et al,2008). There have been many attempts to automate pollen grain identification in microscopic images by computer algorithms, but there is no inexpensive, complete, and automated imaging process.…”