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Published on: June 14, 2013
Automated image analysis for quantitative fluorescence in situ hybridization with environmental samples
Zhi Zhou1, Marie Noëlle Pons, Lutgarde Raskin
1Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
This study developed an automated quantitative fluorescence in situ hybridization (FISH) method to accurately analyze microbial communities in complex environmental samples like manure and soil. The new automated FISH technique overcomes challenges with cell aggregates and background noise, providing reliable results comparable to manual counts.
Area of Science:
- Environmental microbiology
- Molecular biology
- Biotechnology
Background:
- Fluorescence in situ hybridization (FISH) is crucial for analyzing microbial communities in environmental samples.
- Complex samples like manure and soil present challenges for FISH due to cell aggregates and variable background fluorescence.
- Existing FISH methods often lack robustness and automation for quantitative analysis of such samples.
Purpose of the Study:
- To develop a robust and automated quantitative FISH method for analyzing microbial populations in complex environmental samples.
- To optimize sample dispersion and image analysis to overcome common FISH limitations.
- To establish a reliable classification system for FISH signals in challenging matrices.
Main Methods:
- Optimized sample dispersion techniques to minimize microbial cell aggregate interference.
- Developed an automated image analysis program using Visilog software for cell detection and fluorescence intensity extraction.
- Employed fuzzy c-means clustering for signal classification, distinguishing target (positive) from nontarget (negative) cells.
- Validated automated classification with manual quality control and counting.
Main Results:
- The automated FISH method achieved high agreement with manual counts for both bacterial (16S rRNA probe) and archaeal (16S rRNA probe) detection in swine manure and soil samples.
- Successfully quantified microbial populations, with bacterial detection ranging from 50.4% to 72.1% and archaeal detection from 2.5% to 21.6% across samples.
- Demonstrated the method's effectiveness in overcoming issues of cell aggregation and non-uniform background fluorescence.
Conclusions:
- The developed automated quantitative FISH method provides a robust and efficient tool for microbial community analysis in complex environmental samples.
- This automated approach significantly facilitates the quantitative analysis of FISH images, improving accuracy and reproducibility.
- The method holds promise for broader application in environmental microbiology research and monitoring.
