Rapid automated cell quantification on HIV microfluidic devices.

Area of Science:

• Biomedical Engineering
• Image Analysis
• Microfluidics

Background:

• Manual cell counting in lab-chip devices is time-consuming and prone to errors.
• High-throughput quantification of fluorescent cell images is crucial for various diagnostic applications.
• Existing methods lack efficiency and accuracy for complex multi-stained cell analysis.

Purpose of the Study:

• To develop and validate an automated image analysis method for segmenting and counting cells in microfluidic devices.
• To compare the performance of the automated method against manual counting.
• To address challenges in fluorescent lab-chip cell image analysis, including uneven backgrounds and overlapping cells.

Main Methods:

• Utilized MATLAB for image analysis techniques to segment and count cells.
• Applied the method to microfluidic devices capturing CD4(+) CD3(+) T lymphocytes from HIV-infected patient blood samples.
• Images were stained with DAPI, AF488-anti CD4, and AF647-anti CD3 for multi-color cell identification.

Main Results:

• The automated method demonstrated comparable accuracy to manual counting for small cell numbers.
• Automated counting was over 100 times faster than manual counting for multi-color stained cells, especially with >500 cells.
• The algorithm successfully handled uneven backgrounds, overlapping cells, and multi-stain detection.

Conclusions:

• Automated cell counting in microfluidic devices offers a faster, more accurate alternative to manual methods.
• This image analysis technique enhances efficiency for high-throughput cell quantification in lab-chip applications.
• The method is applicable to diverse fields, including HIV monitoring (CD4 counts), circulating tumor cell detection, and biosensor cell detection.