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Software for Rapid Cell Count and Analysis

By Biotechdaily staff writers
Posted on 05 Apr 2004
New software is based on an innovative cluster analysis algorithm designed to provide more precise, accurate, and automated absolute cell counting and viability measurements.

Accuracy, reproducibility, and standardization are essential to successful outcomes in laboratories that perform cell count and viability assessments. Traditional methods for counting cells and assessing cell viability are time consuming. Moreover, they may produce variable results because of reliance on subjective visual discrimination of dye-stained cells using a hemacytometer and microscope.

The ViaCount Assay of Guava Technologies, Inc. (Hayward, CA, USA) is a simple, accurate, and reproducible test for absolute cell counting and viability measurements that overcomes many of the limitations of traditional methods. The assay reduces statistical variation by sampling large numbers of single cells, and miniaturizes operator variability by using fluorescence intensity measurements to categorize cells as live, dead, or apoptotic. Now, the system is enhanced with the Guava EasyFit software feature that automates and standardizes cell counting and viability assessments.

The software provides a computer-assisted clustering algorithm to group cellular distributions based on their positions in a 3D matrix. Cells are classified and counted based on three individual measurements of each cell, including the fluorescence intensity values for the live and dead cell based on the uptake of dyes in Guava's ViaCount solution.

"EasyFit provides users of our product with a means to operate the system without the need for manual adjustment of gating parameters,” said Keith Olson, director of product commercialization at Guava Technologies. The company is a leading provider of integrated microcytometry and cell counting systems for the life science, biotechnology, and pharmaceutical industries, as well as for use in clinical testing and blood processing and other areas where single cell counting is important.




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