An efficient single-cell transcriptomics workflow for microbial eukaryotes benchmarked on Giardia intestinalis cells

Abstract

Background: Most diversity in the eukaryotic tree of life is represented by microbial eukaryotes, which is a polyphyletic group also referred to as protists. Among the protists, currently sequenced genomes and transcriptomes give a biased view of the actual diversity. This biased view is partly caused by the scientific community, which has prioritized certain microbes of biomedical and agricultural importance. Additionally, some protists remain difficult to maintain in cultures, which further influences what has been studied. It is now possible to bypass the time-consuming process of cultivation and directly analyze the gene content of single protist cells. Single-cell genomics was used in the first experiments where individual protists cells were genomically explored. Unfortunately, single-cell genomics for protists is often associated with low genome recovery and the assembly process can be complicated because of repetitive intergenic regions. Sequencing repetitive sequences can be avoided if single-cell transcriptomics is used, which only targets the part of the genome that is transcribed. Results: In this study we test different modifications of Smart-seq2, a single-cell RNA sequencing protocol originally developed for mammalian cells, to establish a robust and more cost-efficient workflow for protists. The diplomonad Giardia intestinalis was used in all experiments and the available genome for this species allowed us to benchmark our results. We could observe increased transcript recovery when freeze-thaw cycles were added as an extra step to the Smart-seq2 protocol. Further we reduced the reaction volume and purified the amplified cDNA with alternative beads to test different cost-reducing changes of Smart-seq2. Neither improved the procedure, and reducing the volumes by half led to significantly fewer genes detected. We also added a 5′ biotin modification to our primers and reduced the concentration of oligo-dT, to potentially reduce generation of artifacts. Except adding freeze-thaw cycles and reducing the volume, no other modifications lead to a significant change in gene detection. Therefore, we suggest adding freeze-thaw cycles to Smart-seq2 when working with protists and further consider our other modification described to improve cost and time-efficiency. Conclusions: The presented single-cell RNA sequencing workflow represents an efficient method to explore the diversity and cell biology of individual protist cells.