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Thresholding Approach for Automatic Detection of Pseudomonas aeruginosa Biofilms from Fluorescence in situ Hybridization Images

Authors: Zonglin Yang, Tatsuya Akiyama, Kerry S. Williamson, Michael J. Franklin, Thiruvarangan Ramaraj

Abstract:

Pseudomonas aeruginosa is an opportunistic pathogen that forms surface-associated microbial communities (biofilms) on artificial implant devices and on human tissue. Biofilm infections are difficult to treat with antibiotics, in part, because the bacteria in biofilms are physiologically heterogeneous. One measure of biological heterogeneity in a population of cells is to quantify the cellular concentrations of ribosomes, which can be probed with fluorescently labeled nucleic acids. The fluorescent signal intensity following fluorescence in situ hybridization (FISH) analysis correlates to the cellular level of ribosomes. The goals here are to provide computationally and statistically robust approaches to automatically quantify cellular heterogeneity in biofilms from a large library of epifluorescent microscopy FISH images. In this work, the initial steps were developed toward these goals by developing an automated biofilm detection approach for use with FISH images. The approach allows rapid identification of biofilm regions from FISH images that are counterstained with fluorescent dyes. This methodology provides advances over other computational methods, allowing subtraction of spurious signals and non-biological fluorescent substrata. This method will be a robust and user-friendly approach which will enable users to semi-automatically detect biofilm boundaries and extract intensity values from fluorescent images for quantitative analysis of biofilm heterogeneity.

Keywords: Image informatics, Pseudomonas aeruginosa, biofilm, FISH, computer vision, data visualization.

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References:


[1] Flemming HC, Wingender J. 2010. The biofilm matrix. Nat Rev Microbiol 8:623-33.
[2] Hall-Stoodley L, Costerton JW, Stoodley P. 2004. Bacterial biofilms: from the natural environment to infectious diseases. Nat Rev Microbiol 2:95-108.
[3] Costerton JW, Stewart PS, Greenberg EP. 1999. Bacterial biofilms: a common cause of persistent infections. Science 284:1318-1322.
[4] Lyczak JB, Cannon CL, Pier GB. 2002. Lung infections associated with cystic fibrosis. Clin Microbiol Rev 15:194-222.
[5] Malhotra S, Hayes D, Jr., Wozniak DJ. 2019. Cystic Fibrosis and Pseudomonas aeruginosa: the Host-Microbe Interface. Clin Microbiol Rev 32.
[6] Stewart PS, Franklin MJ. 2008. Physiological heterogeneity in biofilms. Nat Rev Microbiol 6:199-210.
[7] Williamson KS, Richards LA, Perez-Osorio AC, Pitts B, McInnerney K, Stewart PS, Franklin MJ. 2012. Heterogeneity in Pseudomonas aeruginosa biofilms includes expression of ribosome hibernation factors in the antibiotic-tolerant subpopulation and hypoxia-induced stress response in the metabolically active population. J Bacteriol 194:2062-73.
[8] Akiyama T, Williamson KS, Schaefer R, Pratt S, Chang CB, Franklin MJ. 2017. Resuscitation of Pseudomonas aeruginosa from dormancy requires hibernation promoting factor (PA4463) for ribosome preservation. Proceedings of the National Academy of Sciences 114:3204-3209.
[9] Franklin MJ, Chang C, Akiyama T, Bothner B. 2015. New Technologies for Studying Biofilms. Microbiol Spectr 3.
[10] PĂ©rez-Osorio AC, Williamson KS, Franklin MJ. 2010. Heterogeneous rpoS and rhlR mRNA levels and 16S rRNA/rDNA (rRNA gene) ratios within Pseudomonas aeruginosa biofilms, sampled by laser capture microdissection. J Bacteriol 192:2991-3000.
[11] Deutscher MP. 2003. Degradation of stable RNA in bacteria. J Biol Chem 278:45041-4.
[12] Maki Y, Yoshida H, Wada A. 2000. Two proteins, YfiA and YhbH, associated with resting ribosomes in stationary phase Escherichia coli. Genes Cells 5:965-74.
[13] Hogardt M, Trebesius K, Geiger AM, Hornef M, Rosenecker J, Heesemann J. 2000. Specific and rapid detection by fluorescent in situ hybridization of bacteria in clinical samples obtained from cystic fibrosis patients. J Clin Microbiol 38:818-25.
[14] Lee N, Nielsen PH, Andreasen KH, Juretschko S, Nielsen JL, Schleifer KH, Wagner M. 1999. Combination of fluorescent in situ hybridization and microautoradiography-a new tool for structure-function analyses in microbial ecology. Appl Environ Microbiol 65:1289-97.
[15] Brileya KA, Camilleri LB, Fields MW. 2014. 3D-fluorescence in situ hybridization of intact, anaerobic biofilm. Methods Mol Biol 1151:189-97.
[16] Pretorius E, Page MJ, Engelbrecht L, Ellis GC, Kell DB. 2017. Substantial fibrin amyloidogenesis in type 2 diabetes assessed using amyloid-selective fluorescent stains. Cardiovasc Diabetol 16:141.
[17] Montgomery DC, Peck EA, Vining GG. 2012. Introduction to Linear Regression Analysis, 5th ed. Wiley, Hoboken.
[18] Otsu N. 1979. A Threshold Selection Method from Gray-Level Histograms, p 62-66, IEEE Transactions on Systems, Man, and Cybernetics, vol 9. IEEE.
[19] Canny J. 1986. A Computational Approach to Edge Detection, IEEE Transactions on Systems, Man, and Cybernetics, vol 9. IEEE.
[20] Rasband W. 2015. ImageJ, image processing and analysis in Java, p https://imagej.nih.gov/ij/.
[21] https://pypi.org/project/czifile/