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A high-throughput assay for quantifying phenotypic traits of microalgae

Cite this dataset

Argyle, Phoebe et al. (2022). A high-throughput assay for quantifying phenotypic traits of microalgae [Dataset]. Dryad.


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.


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.

Data Processing

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.

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.

Usage notes

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.


Gordon and Betty Moore Foundation, Award: MMI 7397