Phylogenomic workflow for uncultivable microbial eukaryotes using single-cell RNA sequencing − A case study with planktonic ciliates (Ciliophora, Oligotrichea)

[Display omitted] •Twenty-eight new single-cell transcriptomes for Oligotrichea ciliates.•Phylogenomic workflow from single cell isolation to species tree inference.•Asteroid robustly infers species tree in the presence of paralogs and missing data. Phylogenetic analyses increasingly rely on genomic and transcriptomic data to produce better supported inferences on the evolutionary relationships among microbial eukaryotes. Such phylogenomic analyses, however, require robust workflows, bioinformatic expertise and computational power. Microbial eukaryotes pose additional challenges given the complexity of their genomes and the presence of non-target sequences (e.g., symbionts, prey) in data obtained from single cells of uncultivable lineages. To address these challenges, we developed a phylogenomic workflow based on single-cell RNA sequencing, integrating all essential steps from cell isolation to data curation and species tree inference. We assessed our workflow by using publicly available and newly generated transcriptomes (11 and 28, respectively) from the Oligotrichea, a diverse group of marine planktonic ciliates. This group’s phylogenetic relationships have been relatively well-studied based on ribosomal RNA gene markers, which we reconstructed by read mapping of transcriptome sequences and compared to our phylogenomic inferences. We also compared phylogenomic analyses based on single-copy protein-coding genes (well-curated orthologs) and multi-copy genes (including paralogs) by sequence concatenation and a coalescence approach (Asteroid), respectively. Finally, using subsets of up to 1,014 gene families (GFs), we assessed the influence of missing data in our phylogenomic inferences. All our analyses yielded similar results, and most inferred relationships were consistent and well-supported. Overall, we found that Asteroid provides robust support for species tree inferences, while simplifying curation steps, minimizing the effects of missing data and maximizing the number of GFs represented in the analyses. Our workflow can be adapted for phylogenomic analyses based on single-cell RNA sequencing of other uncultivable microbial eukaryotes.