De novo assembled single-cell transcriptomes from aquatic phytoflagellates reveal metabolically distinct dormant cell types

Novel single-cell transcriptomics techniques are well established to decode cell fate and interactions in mammalian model organisms. Extrapolating these techniques to uncover metabolic dynamics in aquatic single-celled organisms holds huge potential, but evidence of their applicability to non-model, poorly understood microeukaryotes remains limited. Here, living Ochromonas triangulata cells from early and late growth phases were FACS-sorted based on food vacuole staining and chlorophyll signal, and single-cell transcriptomic libraries were prepared following the Smart-seq2 protocol. In total, 768 transcriptomes were sequenced using Illumina NovaSeq. Lacking a reference genome, transcriptomes were assembled de novo using Trinity and the resulting transcripts were annotated by BLASTing against the Swiss-Prot database. Following read mapping, differential expression was analysed using DESeq2, gene set enrichment using fgsea against MSigDB, and metabolic mapping using pathview against KEGG Orthology pathways. Clustering the read counts revealed the presence of two distinct transcriptional states corresponding to the growth phase as well as a third distinct cluster of cells made up of both early and late cells. Cells in this third cluster expressed fewer than 10% of the housekeeping genes. We hypothesise that this cluster represents encysted cells in dormancy justifying their presence in both early and late groups. Most differentially expressed genes were downregulated in the mixed dormant group. Pathways associated with glycolysis, carbon fixation, and ribosome-functioning were the most downregulated in the mixed dormant group. Additionally, ribosomal RNA detected in the transcriptome was used to assign taxonomy to both the single cell eukaryote and the associated prokaryotes, whether ingested or in metabolic cross-talk with, and downstream phylogenetic analysis was done. In conclusion, this study demonstrates the power of single cell transcriptomics for environmental applications where reference and annotation sources might be scarce.