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Vyshenska D, Sampara P, Singh K, Tomatsu A, Kauffman WB, Nuccio EE, Blazewicz SJ, Pett-Ridge J, Louie KB, Varghese N, Kellom M, Clum A, Riley R, Roux S, Eloe-Fadrosh EA, Ziels RM, Malmstrom RR. A standardized quantitative analysis strategy for stable isotope probing metagenomics. mSystems 2023; 8:e0128022. [PMID: 37377419 PMCID: PMC10469821 DOI: 10.1128/msystems.01280-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 04/19/2023] [Indexed: 06/29/2023] Open
Abstract
Stable isotope probing (SIP) facilitates culture-independent identification of active microbial populations within complex ecosystems through isotopic enrichment of nucleic acids. Many DNA-SIP studies rely on 16S rRNA gene sequences to identify active taxa, but connecting these sequences to specific bacterial genomes is often challenging. Here, we describe a standardized laboratory and analysis framework to quantify isotopic enrichment on a per-genome basis using shotgun metagenomics instead of 16S rRNA gene sequencing. To develop this framework, we explored various sample processing and analysis approaches using a designed microbiome where the identity of labeled genomes and their level of isotopic enrichment were experimentally controlled. With this ground truth dataset, we empirically assessed the accuracy of different analytical models for identifying active taxa and examined how sequencing depth impacts the detection of isotopically labeled genomes. We also demonstrate that using synthetic DNA internal standards to measure absolute genome abundances in SIP density fractions improves estimates of isotopic enrichment. In addition, our study illustrates the utility of internal standards to reveal anomalies in sample handling that could negatively impact SIP metagenomic analyses if left undetected. Finally, we present SIPmg, an R package to facilitate the estimation of absolute abundances and perform statistical analyses for identifying labeled genomes within SIP metagenomic data. This experimentally validated analysis framework strengthens the foundation of DNA-SIP metagenomics as a tool for accurately measuring the in situ activity of environmental microbial populations and assessing their genomic potential. IMPORTANCE Answering the questions, "who is eating what?" and "who is active?" within complex microbial communities is paramount for our ability to model, predict, and modulate microbiomes for improved human and planetary health. These questions can be pursued using stable isotope probing to track the incorporation of labeled compounds into cellular DNA during microbial growth. However, with traditional stable isotope methods, it is challenging to establish links between an active microorganism's taxonomic identity and genome composition while providing quantitative estimates of the microorganism's isotope incorporation rate. Here, we report an experimental and analytical workflow that lays the foundation for improved detection of metabolically active microorganisms and better quantitative estimates of genome-resolved isotope incorporation, which can be used to further refine ecosystem-scale models for carbon and nutrient fluxes within microbiomes.
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Affiliation(s)
- Dariia Vyshenska
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Pranav Sampara
- Department of Civil Engineering, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Kanwar Singh
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Andy Tomatsu
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - W. Berkeley Kauffman
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Erin E. Nuccio
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California, USA
| | - Steven J. Blazewicz
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California, USA
| | - Jennifer Pett-Ridge
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California, USA
- Life & Environmental Sciences Department, University of California Merced, Merced, California, USA
| | - Katherine B. Louie
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Neha Varghese
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Matthew Kellom
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Alicia Clum
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Robert Riley
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Simon Roux
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Emiley A. Eloe-Fadrosh
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Ryan M. Ziels
- Department of Civil Engineering, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Rex R. Malmstrom
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA
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Iwaszkiewicz-Eggebrecht E, Granqvist E, Buczek M, Prus M, Kudlicka J, Roslin T, Tack AJ, Andersson AF, Miraldo A, Ronquist F, Łukasik P. Optimizing insect metabarcoding using replicated mock communities. Methods Ecol Evol 2023; 14:1130-1146. [PMID: 37876735 PMCID: PMC10593422 DOI: 10.1111/2041-210x.14073] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 01/20/2023] [Indexed: 10/26/2023]
Abstract
1: Metabarcoding (high-throughput sequencing of marker gene amplicons) has emerged as a promising and cost-effective method for characterizing insect community samples. Yet, the methodology varies greatly among studies and its performance has not been systematically evaluated to date. In particular, it is unclear how accurately metabarcoding can resolve species communities in terms of presence-absence, abundances, and biomass. 2: Here we use mock community experiments and a simple probabilistic model to evaluate the effect of different DNA extraction protocols on metabarcoding performance. Specifically, we ask four questions: (Q1) How consistent are the recovered community profiles across replicate mock communities?; (Q2) How does the choice of lysis buffer affect the recovery of the original community?; (Q3) How are community estimates affected by differing lysis times and homogenization?; and (Q4) Is it possible to obtain adequate species abundance estimates through the use of biological spike-ins? 3: We show that estimates are quite variable across community replicates. In general, a mild lysis protocol is better at reconstructing species lists and approximate counts, while homogenization is better at retrieving biomass composition. Small insects are more likely to be detected in lysates, while some tough species require homogenization to be detected. Results are less consistent across biological replicates for lysates than for homogenates. Some species are associated with strong PCR amplification bias, which complicates the reconstruction of species counts. Yet, with adequate spike-in data, species abundance can be determined with roughly 40% standard error for homogenates, and with roughly 50% standard error for lysates, under ideal conditions. In the latter case, however, this often requires species-specific reference data, while spike-in data generalizes better across species for homogenates. 4: We conclude that a non-destructive, mild lysis approach shows the highest promise for presence/absence description of the community, while also allowing future morphological or molecular work on the material. However, homogenization protocols perform better for characterizing community composition, in particular in terms of biomass.
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Affiliation(s)
| | - Emma Granqvist
- Department of Bioinformatics and Genetics, Swedish Museum of Natural History, Box 50007, SE-104 05 Stockholm, Sweden
| | - Mateusz Buczek
- Institute of Environmental Sciences, Faculty of Biology, Jagiellonian University, ul. Gronostajowa 7, 30-387 Kraków, Poland
| | - Monika Prus
- Institute of Environmental Sciences, Faculty of Biology, Jagiellonian University, ul. Gronostajowa 7, 30-387 Kraków, Poland
| | - Jan Kudlicka
- Department of Data Science and Analytics, BI Norwegian Business School, NO-0442 Oslo, Norway
| | - Tomas Roslin
- Department of Ecology; Box 7044, Swedish University of Agricultural Sciences, SE-750 07 Uppsala, Sweden
| | - Ayco J.M. Tack
- Department of Ecology, Environment and Plant Sciences, Stockholm University, SE-114 18 Stockholm, Sweden
| | - Anders F. Andersson
- KTH Royal Institute of Technology, Science for Life Laboratory, Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, Stockholm, Sweden
| | - Andreia Miraldo
- Department of Bioinformatics and Genetics, Swedish Museum of Natural History, Box 50007, SE-104 05 Stockholm, Sweden
| | - Fredrik Ronquist
- Department of Bioinformatics and Genetics, Swedish Museum of Natural History, Box 50007, SE-104 05 Stockholm, Sweden
| | - Piotr Łukasik
- Department of Bioinformatics and Genetics, Swedish Museum of Natural History, Box 50007, SE-104 05 Stockholm, Sweden
- Institute of Environmental Sciences, Faculty of Biology, Jagiellonian University, ul. Gronostajowa 7, 30-387 Kraków, Poland
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Abstract
The sequencing of the transcriptome of single cells, or single-cell RNA-sequencing, has now become the dominant technology for the identification of novel cell types in heterogeneous cell populations or for the study of stochastic gene expression. In recent years, various experimental methods and computational tools for analysing single-cell RNA-sequencing data have been proposed. However, most of them are tailored to different experimental designs or biological questions, and in many cases, their performance has not been benchmarked yet, thus increasing the difficulty for a researcher to choose the optimal single-cell transcriptome sequencing (scRNA-seq) experiment and analysis workflow. In this review, we aim to provide an overview of the current available experimental and computational methods developed to handle single-cell RNA-sequencing data and, based on their peculiarities, we suggest possible analysis frameworks depending on specific experimental designs. Together, we propose an evaluation of challenges and open questions and future perspectives in the field. In particular, we go through the different steps of scRNA-seq experimental protocols such as cell isolation, messenger RNA capture, reverse transcription, amplification and use of quantitative standards such as spike-ins and Unique Molecular Identifiers (UMIs). We then analyse the current methodological challenges related to preprocessing, alignment, quantification, normalization, batch effect correction and methods to control for confounding effects.
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