Skew-Laplace and Cell-Size Distribution in Microbial Axenic Cultures: Statistical Assessment and Biological Interpretation

Olga Julià,Josep Vives-Rego,Jaume Vidal-Mas,Nicolai S Panikov

doi:10.1155/2010/191585

Abstract

We report a skew-Laplace statistical analysis of both flow cytometry scatters and cell size from microbial strains primarily grown in batch cultures, others in chemostat cultures and bacterial aquatic populations. Cytometry scatters best fit the skew-Laplace distribution while cell size as assessed by an electronic particle analyzer exhibited a moderate fitting. Unlike the cultures, the aquatic bacterial communities clearly do not fit to a skew-Laplace distribution. Due to its versatile nature, the skew-Laplace distribution approach offers an easy, efficient, and powerful tool for distribution of frequency analysis in tandem with the flow cytometric cell sorting.

Highlights

We report a skew-Laplace statistical analysis of both flow cytometry scatters and cell size from microbial strains primarily grown in batch cultures, others in chemostat cultures and bacterial aquatic populations
Bacterial growth has been intensively studied during the last century, and the understanding of bacterial cultures has increased from decade to decade
Flow cytometry cell-size estimates are based on the intensity of forward light scatter (FS), which is preferred to 90◦ scatter or side light scatter (SS) do to its high signal intensity and its insensitivity to sub-cellular structure—conventionally described as “granulosity.” FS is generally assumed to be proportional to bacterial size (Christensen et al [9], Juliaet al. [10], Koch et al [11], Lopez-Amoros et al [12]), this relationship between particle size and FS is not monotonic as it is affected by cell structure and chemical composition (Shapiro [6])

Summary

Introduction

Bacterial growth has been intensively studied during the last century, and the understanding of bacterial cultures has increased from decade to decade. Flow cytometry combines direct and rapid assays to determine the number, cell-size distribution and other biochemical information regarding individual cells (Robinson [5], Shapiro [6], Vives-Rego et al [7]). This makes it attractive for studying heterogeneous bacterial populations (Davey and Kell, [8], Vives-Rego et al [7]). The normal distribution ( called the Gaussian or the bell curve) remains the most commonly encountered distribution in nature and statistics due to the central limit theorem: every variable that can be modeled as a sum of many small independent variables should be normal

Methods

Results

Conclusion