DOI: 10.5061/dryad.vmcvdnctx
title: A high-throughput assay for quantifying phenotypic traits of microalgae

description: High-throughput methods for phenotyping microalgae are in demand across a
 variety of research and commercial purposes. Many microalgae can be
 readily cultivated in multi-well plates for experimental studies which can
 reduce overall costs, while measuring traits from low volume samples can
 reduce handling. Here we develop a high-throughput quantitative phenotypic
 assay (QPA) that can be used to phenotype microalgae grown in multi-well
 plates. The QPA integrates 10 low-volume, relatively high-throughput trait
 measurements (growth rate, cell size, granularity, chlorophyll a, neutral
 lipid content, silicification, reactive oxygen species accumulation, and
 photophysiology parameters: ETRmax, Ik, and alpha) into one workflow. We
 demonstrate the utility of the QPA on Thalassiosira spp., a cosmopolitan
 marine diatom, phenotyping six strains in a standard nutrient rich
 environment (f/2 media) using the full 10-trait assay. The multivariate
 phenotypes of strains can be simplified into two dimensions using
 principal component analysis, generating a trait-scape. We determine that
 traits show a consistent pattern when grown in small volume compared to
 more typical large volumes. The QPA can thus be used for quantifying
 traits across different growth environments without requiring exhaustive
 large-scale culturing experiments, which facilitates experiments on trait
 plasticity. We confirm that this assay can be used to phenotype newly
 isolated diatom strains within 4 weeks of isolation. The QPA described
 here is highly amenable to customisation for other traits or unicellular
 taxa and provides a framework for designing high-throughput experiments.
 This method will have applications in experimental evolution, modelling,
 and for commercial applications where screening of phytoplankton traits is
 of high importance.
description: These data were collected and processed according to methods
 outlined in Argyle et al. 2021. Some key details are outlined below but
 see the publication for full details. <strong>Data
 Processing</strong>
 Trait
 data files Flow cytometry trait measures
 (Size, Granularity, Chl a, Lipids, Silicification) are all the median
 values as calculated by the instrument (CytoFlex LX). Growth rate was
 calculated from the mean in vivo fluorescence measures made using the
 TECAN Infinite M1000 Pro plate reader (i.e. mean of multiple measurements
 made across each well of the plate). ROS fluorescence was also calculated
 based on the mean fluorescence from the plate reader. Photophysiology
 traits were generated using the WATER-PAM and represent one-time
 measurements, not a median or average. For all trait
 data files, approxiate spherical size was calculated from the median
 forward scatter from the flow cytometer using the equation Size = (FSC + 275,549)/83,539. The data shown is the final
 calculated size in µm.<br> Other trait values were divided by spherical size to account for size-related effects on trait values. These traits were: Granularity, Chl a, Lipids, ROS, and Silicification. Therefore the data shown is the final value after this correction. Silicification was also corrected against population growth rate (the median was divided by growth rate over the 24 hours of the assay) prior to cell size correction. Correlation matrix data These were generated from trait data using the 'corrplot' package in R. For figure 3A and 3B, the corresponding trait data was taken from Figure 2A and B.
description: The data is organised according to the experiments outlined in
 Argyle et al. 2021. There are no missing values. Datasets are organised
 according to their use in figures 2-6 in the paper. Please see ReadMe file
 for further information regarding datasheet setup.