FlowDiv: a new pipeline for analyzing flow cytometric diversity

Sebastián D Metz,María V Quiroga,André M Amado,Adrião D D Neto,Bruno M S Wanderley,Daniel S A Araújo,Fernando Unrein,Hugo Sarmento

doi:10.1186/s12859-019-2787-4

Abstract

BackgroundFlow cytometry (FCM) is one of the most commonly used technologies for analysis of numerous biological systems at the cellular level, from cancer cells to microbial communities. Its high potential and wide applicability led to the development of various analytical protocols, which are often not interchangeable between fields of expertise. Environmental science in particular faces difficulty in adapting to non-specific protocols, mainly because of the highly heterogeneous nature of environmental samples. This variety, although it is intrinsic to environmental studies, makes it difficult to adjust analytical protocols to maintain both mathematical formalism and comprehensible biological interpretations, principally for questions that rely on the evaluation of differences between cytograms, an approach also termed cytometric diversity. Despite the availability of promising bioinformatic tools conceived for or adapted to cytometric diversity, most of them still cannot deal with common technical issues such as the integration of differently acquired datasets, the optimal number of bins, and the effective correlation of bins to previously known cytometric populations.ResultsTo address these and other questions, we have developed flowDiv, an R language pipeline for analysis of environmental flow cytometry data. Here, we present the rationale for flowDiv and apply the method to a real dataset from 31 freshwater lakes in Patagonia, Argentina, to reveal significant aspects of their cytometric diversities.ConclusionsflowDiv provides a rather intuitive way of proceeding with FCM analysis, as it combines formal mathematical solutions and biological rationales in an intuitive framework specifically designed to explore cytometric diversity.

Highlights

To evaluate flowDiv, we analyzed bacterioplankton data from 31 lakes in Patagonia, Argentina, collected in the provinces of Chubut, Santa Cruz and Tierra del Fuego
Alpha diversity and evenness Principal components analysis (PCA) of cytometric indices revealed a smoothed separation pattern among the samples (Fig. 2a), suggesting that differences among waterbody trophic states could be associated with cytometric diversity, richness in particular
We note that pH, Diffuse Attenuation Coefficient (Kd) and dissolved organic carbon (DOC) are variables directly associated with the trophic status

Summary

Introduction

To evaluate flowDiv, we analyzed bacterioplankton data from 31 lakes in Patagonia, Argentina, collected in the provinces of Chubut, Santa Cruz and Tierra del Fuego. One of the greatest appeals of FCM stems from its rapid and reliable assessment of detailed information on single or multiple cells from any given cell population This versatility has led to its rapid adoption in different areas of expertise, resulting in a wide range of applications and the development of various specialized protocols for data analysis, which are usually not interchangeable. Environmental sciences in particular face difficulty in adapting non-specific protocols to their context, mainly because of the highly heterogeneous nature of environmental samples [4, 5] This heterogeneity is central to environmental studies, as it reveals much about the properties of any given community, for instance microbial communities [4, 5]. For this reason, the environmental FCM community has been directing efforts to developing methods focused on the depiction of this heterogeneity through cytograms, a concept presently explored under the closely related names of “cytometric pattern” [6], “cytometric fingerprint” [6] and “cytometric diversity” [7, 8]

Methods

Results

Conclusion