High throughput single cell metagenomic sequencing with semi-permeable capsules: unraveling microbial diversity at the single-cell level in sewage and fecal microbiomes.

Meilee Ling, Judit Szarvas, Vaida Kurmauskaitė, Vaidotas Kiseliovas, Rapolas Žilionis, Baptiste Avot, Patrick Munk, Frank M Aarestrup
Author Information
  1. Meilee Ling: Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Kgs Lyngby, Denmark.
  2. Judit Szarvas: Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Kgs Lyngby, Denmark.
  3. Vaida Kurmauskaitė: Atrandi Biosciences, Vilnius, Lithuania.
  4. Vaidotas Kiseliovas: Atrandi Biosciences, Vilnius, Lithuania.
  5. Rapolas Žilionis: Atrandi Biosciences, Vilnius, Lithuania.
  6. Baptiste Avot: Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Kgs Lyngby, Denmark.
  7. Patrick Munk: Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Kgs Lyngby, Denmark.
  8. Frank M Aarestrup: Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Kgs Lyngby, Denmark.

Abstract

Single-cell sequencing may serve as a powerful complementary technique to shotgun metagenomics to study microbiomes. This emerging technology allows the separation of complex microbial communities into individual bacterial cells, enabling high-throughput sequencing of genetic material from thousands of singular bacterial cells in parallel. Here, we validated the use of microfluidics and semi-permeable capsules (SPCs) technology (Atrandi) to isolate individual bacterial cells from sewage and pig fecal samples. Our method involves extracting and amplifying single bacterial DNA within individual SPCs, followed by combinatorial split-and-pool single-amplified genome (SAG) barcoding and short-read sequencing. We tested two different sequencing approaches with different numbers of SPCs from the same sample for each sequencing run. Using a deep sequencing approach, we detected 1,796 and 1,220 SAGs, of which 576 and 599 were used for further analysis from one sewage and one fecal sample, respectively. In shallow sequencing data, we aimed for 10-times more cells and detected 12,731 and 17,909 SAGs, of which we used 2,456 and 1,599 for further analysis for sewage and fecal samples, respectively. Additionally, we identified the top 10 antimicrobial resistance genes (ARGs) in both sewage and feces samples and linked them to their individual host bacterial species.

Keywords

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Word Cloud

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