Single-cell transcriptomics reveals functional insights into a non-model aquatic phytoflagellate and its metabolically linked bacterial community

Single-cell transcriptomics is a vital tool for unraveling metabolism and tissue diversity in model organisms. Its potential for elucidating the ecological roles of microeukaryotes, especially non-model ones, remains largely unexplored. This study employed the Smart-seq2 protocol on Ochromonas trian...

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Bibliographic Details
Published inbioRxiv
Main Authors Jeevannavar, Aditya, Florenza, Javier, Anna-Maria Divne, Tamminen, Manu, Bertilsson, Stefan
Format Paper
LanguageEnglish
Published Cold Spring Harbor Cold Spring Harbor Laboratory Press 28.12.2023
Subjects
Bacteria
Carbon dioxide
Carbon dioxide fixation
Gene mapping
Genomes
Homology
Metabolism
Metagenomics
Ochromonas triangulata
rRNA
Taxonomic revision
Transcription
Transcriptomics
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Summary:Single-cell transcriptomics is a vital tool for unraveling metabolism and tissue diversity in model organisms. Its potential for elucidating the ecological roles of microeukaryotes, especially non-model ones, remains largely unexplored. This study employed the Smart-seq2 protocol on Ochromonas triangulata, a microeukaryote lacking a reference genome, showcasing how transcriptional states align with growth phases. Unexpectedly, a third transcriptional state was identified, across both growth phases. Metabolic mapping revealed a down-regulation trend in path-ways associated with ribosome functioning, CO2 fixation, and carbohydrate catabolism from fast to slow growth to the third transcriptional state. Using carry-over rRNA reads, taxonomic identity of Ochromonas triangulata was re-confirmed and distinct bacterial communities associated with transcriptional states were identified. This study underscores single-cell transcriptomics as a powerful tool for characterizing metabolic states in microeukaryotes without a reference genome, offering in-sights into unknown physiological states and individual-level interactions with different bacterial taxa. This approach holds broad applicability for uncovering ecological roles, surpassing alternative methods like metagenomics or metatranscriptomics.Competing Interest StatementThe authors have declared no competing interest.Footnotes* New results in section titled: "Novel structural homology analysis pipeline adds annotation and upholds sequence homology-based inferences"; Figures revised; Cryptic population now called uncharacterised; overall manuscript made more concise* https://www.ebi.ac.uk/ena/browser/view/PRJEB60973
DOI:10.1101/2023.08.31.555713