Image analysis for automatic segmentation of cytoplasms and classification of Rac1 activation

• Published in: Cytometry (New York, N.Y.) N.Y.), 2004-01, Vol.57A (1), p.22-33
• Main Authors: Lindblad, Joakim, Wählby, Carolina, Bengtsson, Ewert, Zaltsman, Alla
• Format: Article
• Language: English
• Subjects: Animals; automatic image analysis; Cell Membrane - metabolism; Cell Nucleus - drug effects; Cell Nucleus - metabolism; CHO Cells; classification; Cricetinae; Cricetulus; Cytoplasm - drug effects; Cytoplasm - metabolism; cytoplasm segmentation; Dose-Response Relationship, Drug; Green Fluorescent Proteins; Humans; Image Cytometry - methods; Image Enhancement - methods; Insulin - pharmacology; Insulin-Like Growth Factor I - pharmacology; intracellular translocation; Luminescent Proteins - genetics; Luminescent Proteins - metabolism; Rac; rac1 GTP-Binding Protein - biosynthesis; rac1 GTP-Binding Protein - classification; rac1 GTP-Binding Protein - genetics; Reproducibility of Results; ruffling; Transfection
• ISSN: 1552-4922; 1097-0320; 0196-4763; 1552-4930
• DOI: 10.1002/cyto.a.10107

Background Rac1 is a GTP‐binding molecule involved in a wide range of cellular processes. Using digital image analysis, agonist‐induced translocation of green fluorescent protein (GFP) Rac1 to the cellular membrane can be estimated quantitatively for individual cells. Methods A fully automatic image analysis method for cell segmentation, feature extraction, and classification of cells according to their activation, i.e., GFP‐Rac1 translocation and ruffle formation at stimuli, is described. Based on training data produced by visual annotation of four image series, a statistical classifier was created. Results The results of the automatic classification were compared with results from visual inspection of the same time sequences. The automatic classification differed from the visual classification at about the same level as visual classifications performed by two different skilled professionals differed from each other. Classification of a second image set, consisting of seven image series with different concentrations of agonist, showed that the classifier could detect an increased proportion of activated cells at increased agonist concentration. Conclusions Intracellular activities, such as ruffle formation, can be quantified by fully automatic image analysis, with an accuracy comparable to that achieved by visual inspection. This analysis can be done at a speed of hundreds of cells per second and without the subjectivity introduced by manual judgments. Cytometry Part A 57A:22–33, 2004. © 2003 Wiley‐Liss, Inc.