Analysis of gene expression levels in individual bacterial cells without image segmentation

• Published in: Biochemical and biophysical research communications 2012-05, Vol.421 (3), p.425-430
• Main Authors: Kwak, In Hae, Son, Minjun, Hagen, Stephen J.
• Format: Article
• Language: English
• Subjects: 60 APPLIED LIFE SCIENCES; ALGORITHMS; Bacteria; Bacteria - genetics; FLUORESCENCE; fluorescence microscopy; fluorescent proteins; gene expression; Gene Expression Regulation, Bacterial; GENES; Genes, Bacterial; Genes, Reporter; GFP; Green Fluorescent Proteins - analysis; Green Fluorescent Proteins - biosynthesis; Green Fluorescent Proteins - genetics; Image analysis; IMAGE PROCESSING; Image Processing, Computer-Assisted - methods; MICROSCOPY; Microscopy, Fluorescence; PEPTIDES; Phase contrast; Segmentation; Single cell; Software; SPATIAL RESOLUTION; Stochasticity; STREPTOCOCCUS; Streptococcus mutans - genetics
• ISSN: 0006-291X; 1090-2104
• DOI: 10.1016/j.bbrc.2012.03.117

[Display omitted] ► We present a method for extracting gene expression data from images of bacterial cells. ► The method does not employ cell segmentation and does not require high magnification. ► Fluorescence and phase contrast images of the cells are correlated through the physics of phase contrast. ► We demonstrate the method by characterizing noisy expression of comX in Streptococcus mutans. Studies of stochasticity in gene expression typically make use of fluorescent protein reporters, which permit the measurement of expression levels within individual cells by fluorescence microscopy. Analysis of such microscopy images is almost invariably based on a segmentation algorithm, where the image of a cell or cluster is analyzed mathematically to delineate individual cell boundaries. However segmentation can be ineffective for studying bacterial cells or clusters, especially at lower magnification, where outlines of individual cells are poorly resolved. Here we demonstrate an alternative method for analyzing such images without segmentation. The method employs a comparison between the pixel brightness in phase contrast vs fluorescence microscopy images. By fitting the correlation between phase contrast and fluorescence intensity to a physical model, we obtain well-defined estimates for the different levels of gene expression that are present in the cell or cluster. The method reveals the boundaries of the individual cells, even if the source images lack the resolution to show these boundaries clearly.