非模式植物鞭毛虫代谢和相关微生物组的细胞异质性

Aditya Jeevannavar, Javier Florenza, Anna-Maria Divne, Manu Tamminen, Stefan Bertilsson
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引用次数: 0

摘要

单细胞转录组学是揭示模式生物代谢和组织多样性的关键工具。它在阐明微真核生物,特别是非模式生物的生态作用方面的潜力,在很大程度上仍未得到探索。本研究对缺乏参考基因组的微真核生物三角Ochromonas triangulata采用了Smart-seq2方案,展示了转录状态如何与两个不同的生长阶段相一致:快速生长阶段和缓慢生长阶段。除了两个预期的表达簇,每一个都对应于一个生长阶段,第三个转录状态在两个生长阶段都被确定。代谢图谱显示,快生长期的光合活性高于慢生长期,而与核糖体功能、二氧化碳固定和碳水化合物分解代谢相关的途径在第三转录状态下呈下调趋势。此外,携带rRNA读取重述了目标的分类特性,同时揭示了与真核生物共培养的不同细菌群落,每个细菌群落都与不同的转录状态相关。这项研究强调了单细胞转录组学作为表征微真核生物代谢状态的有力工具,在没有参考基因组的情况下,为未知的生理状态和与不同细菌分类群的个体水平相互作用提供了见解。这种方法广泛适用于描述环境微真核生物的生态作用,无培养和无参考,超越其他方法,如宏基因组学或元转录组学。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cellular heterogeneity in metabolism and associated microbiome of a non-model phytoflagellate
Single-cell transcriptomics is a key tool for unravelling 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 two distinct growth phases: a fast-growing phase and a slow-growing phase. Besides the two expected expression clusters, each corresponding to either growth phase, a third transcriptional state was identified across both growth phases. Metabolic mapping revealed a boost of photosynthetic activity in the fast growth over the slow growth stage, as well as down-regulation trend in pathways associated with ribosome functioning, CO2 fixation, and carbohydrate catabolism characteristic of the third transcriptional state. In addition, carry-over rRNA reads recapitulated the taxonomic identity of the target while revealing distinct bacterial communities, in co-culture with the eukaryote, each associated with distinct transcriptional states. This study underscores single-cell transcriptomics as a powerful tool for characterizing metabolic states in microeukaryotes without a reference genome, offering insights into unknown physiological states and individual-level interactions with different bacterial taxa. This approach holds broad applicability to describe the ecological roles of environmental microeukaryotes, culture-free and reference-free, surpassing alternative methods like metagenomics or metatranscriptomics.
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