Novel approaches in function-driven single-cell genomics

Abstract

Deeper sequencing and improved bioinformatics in conjunction with single-cell and metagenomic approaches continue to illuminate undercharacterized environmental microbial communities. This has propelled the 'who is there, and what might they be doing' paradigm to the uncultivated and has already radically changed the topology of the tree of life and provided key insights into the microbial contribution to biogeochemistry. While characterization of 'who' based on marker genes can describe a large fraction of the community, answering 'what are they doing' remains the elusive pinnacle for microbiology. Function-driven single-cell genomics provides a solution by using a function-based screen to subsample complex microbial communities in a targeted manner for the isolation and genome sequencing of single cells. This enables single-cell sequencing to be focused on cells with specific phenotypic or metabolic characteristics of interest. Recovered genomes are conclusively implicated for both encoding and exhibiting the feature of interest, improving downstream annotation and revealing activity levels within that environment. This emerging approach has already improved our understanding of microbial community functioning and facilitated the experimental analysis of uncharacterized gene product space. Here we provide a comprehensive review of strategies that have been applied for function-driven single-cell genomics and the future directions we envision.

Keywords: function-driven sequencing; microbial dark matter; single-cell activity; single-cell genomics.

Figure 1.

Flow diagram of function-driven single-cell genomic pipeline. Top panel shows general workflow highlighting how function screen can aid in annotation and ecology of organisms recovered from original sample. Bottom panel highlights specific identification and isolation strategies. Throughput for flow cytometry and magnetic isolation is generally larger than micromanipulation and microfluidic systems.

Figure 2.

Labeling targets for function-based single-cell genomics exploiting activity-based profiling and incorporation approaches. (1) Extracellular integration, (2) extracellular recognition, (3) extracellular enzyme affinity, (4) intracellular receptor, (5) intracellular product, (6) intracellular enzymatic reaction, (7) intracellular integration, (8) isotope integration.

Figure 3.

Activity-based probe profiling strategies.