Optimization of the 16S rRNA sequencing analysis pipeline for studying in vitro communities of gut commensals

Arianna I Celis; Andrés Aranda-Díaz; Rebecca Culver; Katherine Xue; David Relman; Handuo Shi; Kerwyn Casey Huang

doi:10.1016/j.isci.2022.103907

Optimization of the 16S rRNA sequencing analysis pipeline for studying in vitro communities of gut commensals

iScience. 2022 Feb 11;25(4):103907. doi: 10.1016/j.isci.2022.103907. eCollection 2022 Apr 15.

Authors

Arianna I Celis^{1

2

3}, Andrés Aranda-Díaz¹, Rebecca Culver⁴, Katherine Xue^{2

3}, David Relman^{2

3

5}, Handuo Shi^{1

3}, Kerwyn Casey Huang^{1

3

5}

Affiliations

¹ Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
² Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
³ Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA.
⁴ Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
⁵ Chan Zuckerberg Biohub, San Francisco, CA 94158, USA.

Abstract

While microbial communities inhabit a wide variety of complex natural environments, in vitro culturing enables highly controlled conditions and high-throughput interrogation for generating mechanistic insights. In vitro assemblies of gut commensals have recently been introduced as models for the intestinal microbiota, which plays fundamental roles in host health. However, a protocol for 16S rRNA sequencing and analysis of in vitro samples that optimizes financial cost, time/effort, and accuracy/reproducibility has yet to be established. Here, we systematically identify protocol elements that have significant impact, introduce bias, and/or can be simplified. Our results indicate that community diversity and composition are generally unaffected by substantial protocol streamlining. Additionally, we demonstrate that a strictly aerobic halophile is an effective spike-in for estimating absolute abundances in communities of anaerobic gut commensals. This time- and money-saving protocol should accelerate discovery by increasing 16S rRNA data reliability and comparability and through the incorporation of absolute abundance estimates.

Keywords: Methodology in biological sciences; Microbiology.

Abstract

Grants and funding