Getting more from cell size distributions: establishing more accurate biovolumes by estimating viable cell populations.

Abstract

Current approaches for cell size distribution modeling are attempting to describe the behavior of the entire distribution with respect to time. Although some advances have been made in this area, the modeling process requires a large number of culture-specific parameters and an a priori assumption of the distribution nature (Poisson, Gaussian, etc.). In this work, we propose a deconvolution of the distribution into size ranges and an iterative regression process with respect to a single culture variable, such as viability. Following this approach, two example applications are outlined using data collected with a Coulter Counter Multisizer. In the first, traditional biovolume measurements are corrected to account for the noneven distribution of nonviable cells. These corrections amount to an average increase of 7-65% in the calculated biovolume from 24 to 72 h postinfection and are expected to aid in the development of a new basis for nutrient consumption postinfection. In the second example, viability is predicted from the cell size distribution using both linear and exponential regressions. Differences between predicted and measured viabilities were found to be normally distributed with means of 0.4% and 0% as well as standard deviations of 7.6% and 8.1% for linear and exponential regression, respectively. Although only viability relationships were tested, our approach yielded significant results for both applications, allowing the possibility for further development.