Maximizing microbiome resolution with single-cell genomics
- Reference number
- FFL24-0318
- Project leader
- Carroll, Laura
- Start and end dates
- 250801-300731
- Amount granted
- 15 000 000 SEK
- Administrative organization
- Umeå University
- Research area
- Life Sciences
Summary
Main objective: Bulk shotgun metagenomic sequencing (bulk metaG), which involves sequencing all DNA present in a sample, represents the current state-of-the-art method for querying microbiomes in their entirety. However, bulk metaG suffers from critical limitations, which hinder its application in industry and the clinic. My research group has recently developed a high-throughput, single-cell metagenomic sequencing (scMetaG) method, which can overcome the limitations of bulk metaG. However, existing bioinformatic tools are inappropriate for scMetaG and/or cannot scale to the massive amount of data generated by scMetaG. Here, I propose to develop the first-ever bioinformatic toolkit for end-to-end scMetaG data analysis. Condensed work plan: This project will facilitate the development of a bioinformatic toolkit for scMetaG data analysis which takes (i) short- and/or (ii) long-read sequencing data as input (Aim 1 and 2, respectively). To reduce costs and enable clinical/industrial adoption of scMetaG, I will develop algorithms to reduce the amount of sequencing required (Aim 3). Finally, to ensure rapid scMetaG data sharing and interoperability, I will develop new standards, including compressed file formats and a platform for data sharing (Aim 4). Expected results: Overall, this project will result in a complete, end-to-end scMetaG analysis and data sharing pipeline, which can be used by laboratories around the world to query microbiomes at maximum possible resolution.
Popular science description
Microbes are nearly everywhere, and they can have big impacts on human, animal, and environmental health. Laboratories with access to state-of-the-art equipment use a technique called bulk shotgun metagenomic sequencing (bulk metaG), which involves sequencing all DNA present in a sample to determine which microbes are present. Bulk metaG can give microbiome researchers a rough idea of which species are present in a sample and if any interesting genes are present (e.g., antimicrobial resistance [AMR] genes, which allow bacteria to survive in the presence of antibiotics). However, bulk metaG cannot differentiate closely related microbes from each other, and it can often not elucidate which species are carrying certain genes; for example, bulk metaG can tell that a bacterium in a clinical sample has an AMR gene, but it often can’t tell exactly which bacterium has the AMR gene (e.g., a pathogen that is making a patient sick, or a “good” bacterium that is part of one’s normal microbiome). To overcome these limitations, we have developed a novel method called single-cell metagenomic sequencing (scMetaG). Our scMetaG method can sequence the genomes of up to 1 million microbial cells in a single sample, allowing us to determine exactly which microbes are present and which genes they possess. However, because scMetaG is a novel method, there are no computational tools for analyzing the massive amounts of data it produces. In this project, my research group will develop a complete bioinformatic toolkit for scMetaG data analysis. This toolkit will be free and open-source, meaning that researchers and stakeholders around the world can use it to query microbiomes at the highest possible resolution. Overall, scMetaG methods developed here have the potential to completely transform microbiome research, putting Sweden at the forefront of a revolution in biology.