Benchmarking a massively parallel nucleic acid hybridization platform for monitoring biomarkers of public health significance in wastewater
Bibliographic record
Abstract
Abstract Nucleic acid amplification tests (NAATs) are exquisitely sensitive and specific, able to accurately and quickly monitor vanishingly small amounts of RNA or DNA in wastewater-based public health surveillance applications. Yet, multiplexing samples and target assays is difficult and the dependence of NAATs on enzymes increases false negative rates with inhibitor-rich environmental samples. The Nanostring nCounter system (NNS) is a nucleic acid hybridization platform with a capability for high multiplexing (upwards of 800 probes) that directly counts RNA or DNA biomarkers without reverse transcription or amplification. This study determines the feasibility of direct detection and quantification of genetic markers of public health significance in wastewater samples using NNS and compares the performance with gold-standard NAAT assays targeting antimicrobial resistance (AMR) DNA loci, 16S DNA, SARS-CoV-2 RNA, and the fecal content markers, Pepper mild mottle virus (PMMoV) RNA and crAssphage DNA. We demonstrate that the direct detection and quantification of high- and medium-copy markers, including PMMoV RNA, beta-lactamase, and carbapenemase DNA, in wastewater extracts is both feasible and accurate when benchmarked against gold-standard NAATs. Low-copy detection and monitoring daily trends of SARS-CoV-2 N RNA was achievable but was not as robust as NAATs. Due to the lower analytical sensitivity of NNS compared to NAATs and the requirement of a 18h hybridization step, NNS may not be suitable to provide early warning of incident cases to public health. However, as the scope of wastewater surveillance expands to monitor a broad range of nucleic acid-based biomarkers, the target and sample multiplexing capability of NNS, combined with reduced hands-on time and ease-of-analysis, are distinct advantages. Thus, NNS has a role to play in wastewater and environmental monitoring applications, especially for AMR surveillance. Highlights Nanostring nCounter system (NNS) benchmarked in wastewater surveillance (WWS). Markers of antimicrobial resistance, SARS-CoV-2, and feces directly measured by NNS. Strong concordance between NNS and nucleic acid amplification tests (NAATs). NNS has lower sensitivity compared to NAATs. Cost-effective, scalable multiplexing of NNS is an advantage in WWS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".