High-throughput screening of high Monascus pigment-producing strain based on digital image processing

Area of Science:

• Microbiology
• Biotechnology
• Computational Biology

Background:

• Efficient screening of microbial strains is crucial for optimizing the production of valuable compounds like pigments and statins.
• Traditional methods for mold strain screening can be time-consuming and labor-intensive, limiting throughput.
• Automated approaches are needed to accelerate the identification of high-performing microbial strains.

Purpose of the Study:

• To develop and validate a novel, automated method for screening mold strains based on image processing and Support Vector Machine (SVM) analysis.
• To quantify the morphological characteristics of Monascus colonies and correlate them with pigment production capability.
• To establish a rapid and effective strategy for microbial strain improvement.

Main Methods:

• Image processing techniques were employed to quantify the morphological characteristics of Monascus colonies.
• Support Vector Machine (SVM) was utilized to establish the association between colony morphology and pigment production.
• A high-throughput screening strategy was developed and validated against traditional methods.

Main Results:

• The automated screening strategy achieved an accuracy of 80.6%, comparable to existing microplate (81.1%) and flask (85.4%) methods.
• Screening of 500 colonies was completed in just 20-30 minutes, representing a significant increase in speed.
• The method successfully identified 13 high-producing strains, with the best strain showing a 2.8-fold increase in pigment and a 1.9-fold increase in lovastatin production.

Conclusions:

• The proposed automated method offers an effective and promising approach for mold strain screening and improvement.
• This image processing and SVM-based strategy significantly enhances the efficiency and speed of identifying superior microbial strains.
• The developed method has the potential to accelerate biotechnological advancements in compound production through optimized strain selection.