Software for quantification of labeled bacteria from digital microscope images by automated image analysis

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• Accurate bacterial cell quantification is crucial for various biological and medical research fields.
• Manual enumeration of bacterial cells from digital microscopy images is time-consuming and prone to error.
• Existing automated methods may lack comprehensive features for diverse imaging techniques.

Purpose of the Study:

• To develop and validate CellC, an automated image analysis software for bacterial cell quantification.
• To enable automated enumeration and morphological analysis of bacterial cells from digital microscopy images.
• To provide a user-friendly and efficient tool for researchers working with bacterial imaging data.

Main Methods:

• Development of CellC software with a graphical user interface.
• Validation of CellC using digital microscope images, including DAPI and FISH.
• Comparison of CellC's automated counts with manual enumeration.
• Quantitative estimation of bacterial cell morphology using CellC.

Main Results:

• CellC enables automated enumeration of bacterial cells from various image types.
• The software provides quantitative estimates of bacterial cell morphology.
• Validation demonstrated a high correlation (r > 0.98) between CellC counts and manual enumeration.
• CellC facilitates sequential analysis of multiple images without user intervention.

Conclusions:

• CellC is a validated and accurate software tool for automated bacterial cell quantification.
• The software offers an efficient and user-friendly solution for analyzing digital microscopy images.
• CellC's ability to quantify cell morphology and handle diverse image types enhances its utility in research.
• The freely available and modifiable nature of CellC promotes its adoption and further development.