bioRxivMicrobiologyPreprint
Integrating microscopy and transcriptomics from individual uncultured eukaryotic plankton
Catherine GattiD, Yike XieiD, Kanu WahiiD, Emma M. V. JohanssoniD, Fabio ZaniniiD
Abstract
Eukaryotic plankton comprises organisms as diverse as diatoms and pelagic larvae, covering a wide spectrum of shapes, molecular compositions, and ecological functions. Plankton research is often approached using either optical methods, especially for taxonomic purposes, or genomics, which excels at describing the biochemistry of microbial communities. This technological dichotomy hampers efforts to link the morpho-optical properties of each species with its genetic and biomolecular makeup, leading to fragmented information and limited reproducibility. Methods to simultaneously acquire multimodal, i.e. optical and genetic, information on planktonic organisms would provide a connection between organismal appearance and function, improve taxonomic prediction, and strengthen ecological analysis. Here we present Ukiyo-e-Seq, an approach to generate paired optical and transcriptomic data from individual eukaryotic plankton. We performed Ukiyo-e-Seq on 66 microscopic organisms from Coogee, NSW, Australia and assembled transcriptomic contigs using a merge-split strategy. While overall phylogenetic heterogeneity spanned hundreds of taxa, diversity in individual wells was low, enabling accurate classification of both microbial plankton and marine larvae. We then combined Ukiyo-e-Seq with AlphaFold 3, a protein language model, and could confidently infer (i) the joint structure and interactions of 34 photosynthesis proteins from a single Chaetoceros diatom, and (ii) the cellular and developmental functions of novel proteins highly expressed in one trout larva. In summary, Ukiyo-e-Seq is a precise tool to connect morphological and genetic information of eukaryotic plankton.
- assay: imaging
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: protein structure prediction
- research focus: gene regulatory network inference
- approach: experimental assay
- approach: deep learning
- Citations
- 0
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