Using
a Novel Multiplexed Algal Cytological Imaging
(MACI) Assay and Machine Learning as a Way to Characterize Complex
Phenotypes in Plant-Type Organisms
posted on 2024-03-06, 17:06authored byEric Ostovich, Rebecca Klaper
High-throughput phenotypic profiling
assays, popular for their
ability to characterize alternations in single-cell morphological
feature data, have been useful in recent years for predicting cellular
targets and mechanisms of action (MoAs) for different chemicals and
novel drugs. However, this approach has not been extensively used
in environmental toxicology due to the lack of studies and established
methods for performing this kind of assay in environmentally relevant
species. Here, we developed a multiplexed algal cytological imaging
(MACI) assay, based on the subcellular structures of the unicellular
microalgae, Raphidocelis subcapitata, a toxicology and ecological model species. Several different herbicides
and antibiotics with unique MoAs were exposed to R.
subcapitata cells, and MACI was used to characterize
cellular impacts by measuring subtle changes in their morphological
features, including metrics of area, shape, quantity, fluorescence
intensity, and granularity of individual subcellular components. This
study demonstrates that MACI offers a quick and effective framework
for characterizing complex phenotypic responses to environmental chemicals
that can be used for determining their MoAs and identifying their
cellular targets in plant-type organisms.