ISME CommunicationsMicrobiologyPeer-reviewed journal article
A CRISPR-Cas9 assisted analysis of single-cell microbiomes for identifying rare bacterial taxa in phycospheres of diatoms
Ruben Schulte‐HilleniD, Jakob Karl GiesleriD, Thomas MöckiD, Nigel J. Belshaw, Uwe JohniD, Tilmann HarderiD, Nancy KühneiD, Stefan NeuhausiD, Sylke WohlrabiD
Abstract
Primary production in aquatic systems is governed by interactions between microalgae and their associated bacteria. Most of our knowledge about algal microbiomes stems from natural mixed communities or isolated algal monocultures, which therefore does neither address the role of genotypic diversity among the algal host cells nor do they reveal how this host diversity impacts the assembly process of associated bacteria. To overcome this knowledge gap, we developed a single-cell 16S sequencing approach in combination with CRISPR-Cas9 guided depletion of host 16S contaminations from the chloroplast. The validity of this novel method was tested by comparing bacterial communities of 144 single-cells across three genotypes of the Arctic marine diatom Thalassiosira gravida grown under different environmental conditions. From these, 62 single-cells were additionally sequenced after CRISPR-Cas9 treatment. Due to the improved sequencing depth, bacterial richness associated with individual diatom cells was increased by up to 56%. By applying this CRISPR-Cas9 treatment we not only revealed intraspecific host-genotype associations but also rare bacterial taxa that were not detected by standard 16S rRNA gene metabarcoding. Thus, the CRISPR-Cas9 assisted single-cell approach developed in this study advances our understanding on how the intraspecific diversity among algal hosts impacts the assembly process of their associated bacteria. This knowledge is essential to understand the co-evolution and adaptation of species in algal microbiomes.
- organism: Drosophila
- assay: metagenomics
- research focus: cell type annotation
- approach: experimental assay
- Citations
- 3
- FWCI
- 1.38
- Field percentile
- top 20%
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