Antimicrobial resistance (AMR) occurs when bacteria and other pathogenic microorganisms evolve to survive the antibiotics designed to kill them. These microorganisms develop AMR primarily by acquiring antibiotic resistance genes (ARGs) that enable them to thrive even upon antibiotic exposure. Limiting the spread of antibiotic-resistant bacteria has rapidly become a pressing global public health challenge, particularly as AMR-related infection rates have increased 20% since 2020 (CDC 2024).

AMR surveillance is a critical tool to assess the burden of resistant pathogens and subsequently develop policies to prevent and control infections (WHO 2024; National Academies of Sciences, Palmer, and Buckley 2021). Effective surveillance requires accurate, sensitive monitoring of numerous pathogen-related targets like ARGs across a wide range of crops, livestock, wastewater, and other environmental samples. Currently, over 1,000 types of ARGs have been identified that confer resistance to the hundreds of available antibiotics. As new ARGs continue to be identified, comprehensive AMR surveillance grows both increasingly essential and more challenging.

Though there are numerous methods of ARG detection, qPCR has gained popularity for its speed, ease, and sensitive quantification of environmental samples (Abramova, Berendonk, and Bengtsson-Palme 2023; Takara Bio Blog Team 2024). However, scientists using qPCR for AMR surveillance struggle with its limitations, including low throughput, intensive labor demands, and high scale-up costs. In response to the growing need for routine quantification of large numbers of ARGs, many AMR surveillance organizations have turned to high-throughput qPCR technology like the SmartChip ND Real-Time PCR System.

The SmartChip system has long been an established mainstay of AMR surveillance. The SmartChip system was utilized in 75% of antibiotic resistance research publications in the first decade after its launch (Waseem et al. 2019), and is still recognized as one of the most popular, scalable, and affordable technologies for high-throughput PCR (Delannoy et al. 2022). With a proven record of quantifying ARGs across multiple target gene categories and sample types—including soil, water, sediment, manure, lettuce, fish, and sludge (Antibiotic Resistance Genes 2020), the SmartChip system has recently expanded to enabling environmental surveillance on a national scale, as AMR researchers monitored wastewater treatment plant effluents across Wales (Knight et al. 2024). The SmartChip system is currently the only high-throughput PCR technology of its kind that combines a decade of dependable performance with the flexible assay formats that AMR surveillance requires. Resistomap, a leader in antibiotic resistance monitoring, regularly employs the SmartChip system in a variety of AMR-related projects, from tracking antibiotics impact on dairy farms to wastewater management consulting (Somers 2022).

With features ideally suited for high-throughput monitoring (Table 1), the SmartChip ND Real-Time PCR System offers a time-tested, trusted solution for large-scale AMR surveillance.

ARG detection workflow

ARG detection assays for AMR surveillance using the SmartChip ND Real-Time PCR System follow a simple workflow with ~30 min of hands-on time and less than 3 hr of total processing time per 5,184 reactions.

Samples are first collected and processed to extract and purify sample DNA. The DNA is then combined with assay mix and dispensed into 5,184-well SmartChip MyDesign Chips. Once the chips are loaded into the SmartChip ND Real-Time PCR Cycler, the instrument amplifies sample, detects target genes, and prepares data for analysis (Figure 2).

Precise detection across a range of sample concentrations

To validate the range of SmartChip ND Real-Time PCR System detection, custom double-stranded DNA samples were acquired from GenScript Biotech, diluted twofold to produce a concentration range from 10 to 1,280 copies/reaction, and amplified over 40 cycles. The 96 primers, from Integrated DNA Technologies, used in this assay were carefully selected to correspond to common ARG targets (Stedtfeld et al. 2018). The DNA samples were prepared and run using the Takara Bio SmartChip TB Green Gene Expression Master Mix, the SmartChip ND system, and the SmartChip MyDesign Kit with a layout of 96 assays x 54 samples.

Upon analysis, the SmartChip ND Real‑Time RT‑PCR System showed efficient detection of all gene targets in this study from 10 to 1,280 copies/reaction (Figure 3), demonstrating robust analytical measurement range.

Accurate ARG target specificity

The target specificity of the SmartChip ND Real‑Time PCR System was demonstrated using SmartChip qPCR software and the previously referenced double-stranded DNA samples. Analysis of sample melt curves showed single peaks for each gene (Figure 4), indicating significant amplification of target sequences without corresponding amplification of non-target sequences that could result in cross-reaction (false-positive results) or interference (false-negative results).

High sensitivity for low-abundance gene targets

Sensitivity for the SmartChip ND system was measured by determining the analytical limit of detection (LoD), defined as the lowest detectable concentration of DNA at which approximately 95% of all true positive replicates test positive. Using a limiting dilution of known concentration for 12 ARG targets, the analytical LoD was estimated to be 20 copies/reaction (Table 2).

The SmartChip system, with an established decade-long track record of enabling AMR research, provides an ideal solution for large-scale AMR surveillance. Capable of detecting ARG targets across a robust concentration range (10 to 1,280 copies/reaction), amplifying targets with reliable specificity, and identifying low-abundance ARGs with high sensitivity (LoD of 20 copies/reaction), the SmartChip ND Real-Time PCR System delivers high-quality data at a high-throughput capacity. The SmartChip ND system's nanoliter-scale reaction volumes and automated dispensing/cycling steps reduce labor and cost requirements when scaling up, while maintaining a rapid 3 hr workflow with only 30 min of hands-on time. Finally, flexible assay configuration for each run, user-friendly software, and low data storage allow for easy accommodation of new ARG targets and simple data analysis, enabling effective deployment of the SmartChip ND Real-Time PCR System for AMR surveillance at any scale. 

Primers

Assay primers were ordered as custom DNA oligos from Integrated DNA Technologies. Primer sequences were obtained from publications targeting major antibiotic classes (e.g., Aminoglycoside, Beta Lactam, Quinolone, Phenicol, Tetracycline, Trimethoprim, and Vancomycin) related to antimicrobial resistance (Stedtfeld et al. 2018). Assays were diluted to 300 nM for each reaction. Of 96 assays, 12 were used to determine an analytical LoD.

Nucleic acid

25 custom DNA templates were designed as a positive control by Takara Bio USA and synthesized by GenScript Biotech. Control DNA was pooled together, then diluted twofold to produce a range of concentrations from 10 copies/reaction to 1,280 copies/reaction and amplified for 40 cycles. For every target, there were 12 no template controls (NTC) and 72 positive controls for LoD testing. For linear dilution response testing, there were 6 replicates per assay.

Assays

Assays were performed following the documented user manual protocol for the SmartChip TB Green Gene Expression Master Mix (Cat. # 640210) and run on the SmartChip ND Real-Time PCR System. For this assay, 100nl reactions were run in 150nl-well chips. Samples and assays were dispensed into the chips using the SmartChip ND dispenser. Once the samples and assays were dispensed, the SmartChip ND Real-Time PCR Cycler was used for thermal cycling, imaging, and analysis.

Interpretation of assay results

When all controls exhibit the expected performance for LOD studies, the following criteria were used to interpret the sample results:

Positive control shows positive growth curves for target genes in at least 68/72 replicates, with expected Ct values <33.5.

NTC shows no growth curves for all targets.

Abramova, A., Berendonk, T. U. & Bengtsson-Palme, J. A global baseline for qPCR-determined antimicrobial resistance gene prevalence across environments. Environ. Int. 178, 108084 (2023).

Antibiotic Resistance Genes. Tak. Bio 37–41 (2020). https://www.takarabio.com/learning-centers/automation-systems/smartchip-real-time-pcr-system-introduction/smartchip-real-time-pcr-system-applications/antibiotic-resistance-genes

CDC. Antimicrobial Resistance Facts. Antimicrob. Resist. (2024). https://www.cdc.gov/antimicrobial-resistance/data-research/facts-stats/index.html

Delannoy, S. et al. High Throughput Screening of Antimicrobial Resistance Genes in Gram-Negative Seafood Bacteria. Microorganisms 10, 1–14 (2022).

Knight, M. E. et al. National-scale antimicrobial resistance surveillance in wastewater: A comparative analysis of HT qPCR and metagenomic approaches. Water Res. 262, 121989 (2024).

National Academies of Sciences, E. and M. H. and M. D. B. on P. H. and P. H. P. C. on the L.-T. H. and E. E. of A. R. in the U. S., Palmer, G. H. & Buckley, G. J. (ed.). Strengthening Surveillance. Combat. Antimicrob. Resist. Prot. Miracle Mod. Med. 1–366 (2021). doi:10.17226/26350

Somers, J. Real-Time PCR Aids in Fight Against AMR - Future of Personal Health. (2022). https://www.futureofpersonalhealth.com/antibiotic-resistance/new-tools-in-the-fight-against-antimicrobial-resistance/

Stedtfeld, R. D. et al. Primer set 2.0 for highly parallel qPCR array targeting antibiotic resistance genes and mobile genetic elements. FEMS Microbiol. Ecol. 94, fiy130 (2018).

Takara Bio Blog Team. Shifting perspective on methods of antimicrobial resistance detection. BioView Blog (2024). https://www.takarabio.com/about/bioview-blog/automation/methods-of-detection-for-amr-surveillance

Waseem, H. et al. Contributions and Challenges of High Throughput qPCR for Determining Antimicrobial Resistance in the Environment: A Critical Review. Mol. 24, 163 (2019).

WHO. Global Antimicrobial Resistance and Use Surveillance System (GLASS). Initiatives (2024). https://www.who.int/initiatives/glass