Functional analysis and classification of phytoplankton based on data from an automated flow cytometer.

Analytical flow cytometry (FCM) is well suited for the analysis of phytoplankton communities in fresh and sea waters. The measurement of light scatter and autofluorescence properties of particles by FCM provides optical fingerprints, which enables different phytoplankton groups to be separated. A submersible version of the CytoSense flow cytometer (the CytoSub) has been designed for in situ autonomous sampling and analysis, making it possible to monitor phytoplankton at a short temporal scale and obtain accurate information about its dynamics. For data analysis, a manual clustering is usually performed a posteriori: data are displayed on histograms and scatterplots, and group discrimination is made by drawing and combining regions (gating). The purpose of this study is to provide greater objectivity in the data analysis by applying a nonmanual and consistent method to automatically discriminate clusters of particles. In other words, we seek for partitioning methods based on the optical fingerprints of each particle. As the CytoSense is able to record the full pulse shape for each variable, it quickly generates a large and complex dataset to analyze. The shape, length, and area of each curve were chosen as descriptors for the analysis. To test the developed method, numerical experiments were performed on simulated curves. Then, the method was applied and validated on phytoplankton cultures data. Promising results have been obtained with a mixture of various species whose optical fingerprints overlapped considerably and could not be accurately separated using manual gating.

Optimizing the setup of a flow cytometric cell sorter for efficient quantitative sorting of long filamentous cyanobacteria.

Heterogeneity within natural phytoplankton communities makes it very difficult to analyze parameters at the single-cell level. Flow cytometric sorting is therefore a useful tool in aquatic sciences, as it provides material for post-sort analysis and culturing. Sorting subpopulations from natural communities, however, often requires handling morphologically diverse and complex particles with various abundances. Long particles, such as filament-forming cyanobacteria (>100-µm long), prove very difficult to handle. These potentially toxic organisms are widespread in eutrophic systems and have important ecological consequences. Being able to sort filamentous cyanobacteria efficiently and as viable cells is therefore highly desirable when studying factors associated with their toxicity and occurrence. This unconventional sorting requires extensive user experience and special instrument setup. We have investigated the effect of hydrodynamic and electromechanical components of a flow cytometer, and sorting protocol on the quantitative sorting efficiency of these long particles using two filamentous cyanobacterial strains with average lengths of ∼100 and ∼300 µm. Sorting efficiency ranged from 9.4 to 96.0% and was significantly affected by filament length, sorting envelope, drop delay (dd), and for the long species also by tip size, but not by cycle time. Filaments survived sorting and were not damaged. The optimal settings found for the modular MoFlo® cell-sorter to sort the filaments were a 100-µm flow tip at 30 psi (207 kPa) with a three-droplet envelope in Enrich mode while using an extended analysis time of 17.6 µs and an intermediate plate charge and deflection percentage combination of 3,000 V/60%, combined with a dd 0 for the cultures with 100-µm filaments and dd +1 for the culture with 300-µm filaments. To the best of our knowledge, the filaments up to 1063.5 µm sorted in this study are the longest ever sorted.