De novo assembled single-cell transcriptomes from aquatic phytoflagellates reveal a metabolically distinct cell population

Abstract

Single-cell transcriptomics have rapidly become a standard tool for decoding cell iden­tity, fate, and interactions in mammalian model organisms. Adopting these techniques to uncover functional dynamics in aquatic single-celled organisms holds huge potential, but evidence of their applicability to non-model, poorly understood microeukaryotes remains limited. In the present study, live Ochromonas triangulata cells from fast and slow growth phases were FACS-sorted based on food vacuole staining and chlorophyll fluorescence, and single-cell transcriptomic libraries were prepared following the Smart-seq2 protocol. In total, 744 transcriptomes were Illumina sequenced. Lacking a reference genome, transcriptomes were assembled de novo using Trinity and resulting transcripts were annotated by BLAST using the Swiss-Prot database. Following read mapping, differential gene expression was evaluated using DESeq2 and metabolic maps were generated based on pathways from the KEGG Orthology database. Clustering the read counts revealed the identity of the two expected transcriptional states correspond­ing to each growth phase as well as a third distinct cluster of cells present in both growth phases. This cryptic group showed extensive downregulation of genes in pathways as­sociated with ribosome-functioning, CO₂ fixation and core carbohydrate catabolism such as glycolysis, P oxidation of fatty acids, and tricarboxylic acid cycle. Nevertheless, the biological underpinnings of this cluster, which would have remained unnoticed in an integrated approach, could not be clarified. Additionally, the possibility of using carry-over rRNA reads for taxonomic annotation was tested, which revealed specific individual-level bacterial communities associated with members within each cluster, as well as verified identification of 88% of the cells as O. triangulata. In conclusion, we demonstrate the power of single cell transcriptomics for metabolic mapping of micro­eukaryotes for which genomic reference resources might be limited, and thereby high­light its potential as a tool to gain access to microeukaryote dynamics and diversity in natural communities.