ArticleThe Science of the total environment2023
Intensity of sample processing methods impacts wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes.
Article in The Science of the total environment, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 19 citations in OpenAlex.
- Optimization of a Semi-nested PCR Protocol for Amplifying the Entire Spike Protein Region to Identify SARS-CoV-2 Variants in Wastewater.Food and environmental virology · 2026Article
- SARS-CoV-2 wastewater genomic surveillance: approaches, challenges, and opportunities.Genome biology · 2026Review
- Recovering wastewater RNA for virome sequencing by systematically optimized tangential-flow ultrafiltration and Nanotrap microbiome particles.Applied and environmental microbiology · 2025Article
- Correlation of wastewater surveillance data with traditional influenza surveillance measures in Cook County, Illinois, October 2022-April 2023.The Science of the total environment · 2024Article
- Comparison of adsorption-extraction (AE) workflows for improved measurements of viral and bacterial nucleic acid in untreated wastewater.The Science of the total environment · 2024Article
- A narrative review of wastewater surveillance: pathogens of concern, applications, detection methods, and challenges.Frontiers in public health · 2024Review
- Comparative assessment of Nanotrap and polyethylene glycol-based virus concentration in wastewater samples.FEMS microbes · 2024Article
- Article
- Expanding a Wastewater-Based Surveillance Methodology for DNA Isolation from a Workflow Optimized for SARS-CoV-2 RNA Quantification.Journal of biomolecular techniques : JBT · 2023Article
- 20-Month monitoring of SARS-CoV-2 in wastewater of Curitiba, in Southern Brazil.Environmental science and pollution research international · 2023Article
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wastewater SARS-CoV-2 surveillance has been deployed since the beginning of the COVID-19 pandemic to monitor the dynamics in virus burden in local communities. Genomic surveillance of SARS-CoV-2 in wastewater, particularly efforts aimed at whole genome sequencing for variant tracking and identification, are still challenging due to low target concentration, complex microbial and chemical background, and lack of robust nucleic acid recovery experimental procedures. The intrinsic sample limitations are inherent to wastewater and are thus unavoidable. Here, we use a statistical approach that couples correlation analyses to a random forest-based machine learning algorithm to evaluate potentially important factors associated with wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes, with a specific focus on the breadth of genome coverage. We collected 182 composite and grab wastewater samples from the Chicago area between November 2020 to October 2021. Samples were processed using a mixture of processing methods reflecting different homogenization intensities (HA + Zymo beads, HA + glass beads, and Nanotrap), and were sequenced using one of the two library preparation kits (the Illumina COVIDseq kit and the QIAseq DIRECT kit). Technical factors evaluated using statistical and machine learning approaches include sample types, certain sample intrinsic features, and processing and sequencing methods. The results suggested that sample processing methods could be a predominant factor affecting sequencing outcomes, and library preparation kits was considered a minor factor. A synthetic SARS-CoV-2 RNA spike-in experiment was performed to validate the impact from processing methods and suggested that the intensity of the processing methods could lead to different RNA fragmentation patterns, which could also explain the observed inconsistency between qPCR quantification and sequencing outcomes. Overall, extra attention should be paid to wastewater sample processing (i.e., concentration and homogenization) for sufficient and good quality SARS-CoV-2 RNA for downstream sequencing.
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