To the Editor: Infectious diseases remain a significant threat to global public health, underscoring the urgent need for rapid and precise pathogen detection methods.1 Metagenomic next-generation sequencing (mNGS) offers a comprehensive solution for pathogen identification; however, its clinical application is often hindered by high costs, prolonged processing time, and interference from human background DNA. Targeted next-generation sequencing (tNGS), which utilizes multiplex polymerase chain reaction (PCR)—a widely favored method for its simplicity, speed, and cost-effectiveness—or hybrid capture to enrich clinically relevant pathogens, presents a promising alternative by minimizing the human host’s background and data burden.2,3 Despite these benefits, there is a notable lack of systematic performance evaluations among commercially available multiplex PCR-based tNGS platforms. Hence, we present a concise evaluation of three prevalent pathogen-targeted next-generation sequencing (ptNGS) protocols (Genskey Medical Technology Co., Ltd. GK, Beijing, China, Sansure Biotech Inc. SS, Changsha, China and Hugobiotech Co., Ltd. HG, Beijing, China) and propose practical recommendations for clinical implementation. This study was approved by West China Hospital, Sichuan University (No. 2023-(2)), and the institutional review board waived the requirement for written informed consent. Pathogen-targeted panels based on multiplex PCR generally encompass 200 to 300 pathogens, including viruses, bacteria, fungi, and specialized species, and often incorporating genes related to drug resistance and virulence Figure 1A. Drawing on extensive mNGS experience, these panels can detect over 90% of clinically relevant pathogens across various sample types. We used standardized reference materials containing ten pathogens at 100 CFU/mL Supplementary Table 1, https://links.lww.com/CM9/C830 and 100 clinical samples with confirmed mNGS results to assess the performance of three ptNGS protocols (GK, SS, and HG) Figure 1B. Each kit utilized ultra-multiplex PCR for simultaneous DNA/RNA detection, evaluating metrics such as detection limit, processing time, dimer formation rate, and diagnostic agreement. Sample preparation followed type-specific protocols: dilution for viscous samples, centrifugation for liquids, and mechanical grinding for tissues. Nucleic acid extraction adhered to manufacturer instructions, with GK using automated magnetic beads, SS employing manual column-based extraction with internal controls, and HG utilizing manual magnetic beads. Library construction involved two-step PCR amplification with purification, followed by sequencing on MGI (MGI Tech Co., Ltd., Shenzhen, China) or Illumina (Illumina, Inc., San Diego, California, USA) platforms and customized bioinformatic analysis Figure 1C. The detailed information of sample pre-processing and nucleic acid extraction, library construction, quality control, sequencing, processing of raw data, pathogen determination, and statistical analysis is presented in Supplementary Methods and Supplementary Tables 2 and 3, https://links.lww.com/CM9/C830.Figure 1: (A) Pathogen-targeted panels of GK, SS, and HG, which are established based on pathogen profiles derived from tens of thousands of samples tested, as well as the panel design ideas, final target composition, and a comparison between mNGS and ptNGS. (B) Overall experimental design ideas, including analytical performance validation of ptNGS-based workflow using reference discs and clinical performance validation of 100 retrospective samples. (C) Summary diagram of the experimental process for ptNGS assays. 1st PCR stands for amplicon enrichment. 2nd PCR stands for adapter-mediated PCR. (D) Sample types and pathogen distribution of the clinical retrospective samples. ROC curves showing the diagnostic value of mNGS and tNGS methods in detecting pathogens in 100 retrospective samples. Comparison of retrospective sample testing and mNGS results by ptNGS methods of GK, SS, and HG. ACC: Accuracy; GK: GensKey; HG: Hugobiotech; LOD: Limit of detection; mNGS: Metagenomic next-generation sequencing; PCR: Polymerase chain reaction; ptNGS: Pathogen-targeted next-generation sequencing; QC: Quality control; ROC: Receiver operating characteristic; SEN: Sensitivity; SPE: Specificity; SS: Sansure Biotech; TAT: Turn-around time; UDG: Uracil-DNA glycosylase.Compared to mNGS, the three ptNGS protocols significantly enhanced operational efficiency, reducing turnaround time to 8.3–13 hours (about a 50% decrease) and requiring only 1 million reads per sample, versus 20 million for mNGS, leading to substantial cost savings. All kits successfully detected all reference pathogens at 100 CFU/mL, though performance varied. Library quality assessments highlighted crucial differences: GK outperformed with 89.6% effective fragments and only 8.1% dimers, thanks to optimized buffer incubation; SS delivered solid results (78.2% effective fragments, 13.1% dimers) using two-step bead purification; while HG had higher dimer rates (48.2%) despite enzymatic treatment, impacting sequencing efficiency. Pathogen detection patterns differed, with GK excelling in bacterial targets, SS in fungal, and HG in DNA viruses. All protocols maintained perfect reproducibility across batches, with GK showing the most consistent performance Supplementary Tables 4 and 5 and Supplementary Figures 1–5, https://links.lww.com/CM9/C830. These results establish that effective dimer control through purification optimization is crucial for sequencing quality and detection sensitivity, particularly for low-abundance pathogens. Clinical performance and diagnostic accuracy: In a clinical validation study involving 100 retrospective samples from seven different types, including bronchoalveolar lavage fluid (BALF), sputum, cerebrospinal fluid (CSF), and blood, ptNGS showed high agreement with mNGS, achieving an overall sensitivity of 92.6%, specificity of 94.7%, and accuracy of 93.0%, confirming its reliability in real-world clinical scenarios Figure 1D and Supplementary Tables 6–8, https://links.lww.com/CM9/C830. A detailed analysis by protocol revealed that both GK and SS achieved perfect specificity (100%), while HG reported one false positive, likely due to procedural contamination. In terms of sensitivity, GK and SS performed comparably well, whereas HG exhibited slightly lower detection efficiency, particularly for certain bacterial targets. Most false-negative cases were linked to either suboptimal nucleic acid extraction or extremely low pathogen biomass near the detection threshold Figure 1D. Importantly, inter-batch reproducibility remained excellent, with 100% agreement across all triplicate experiments at both 100 and 200 CFU/mL, highlighting the robustness of ptNGS technology. Practical recommendations for performance optimization: Based on our comparative analysis, we offer the following evidence-based recommendations to enhance ptNGS performance in clinical practice. For wet-lab processes, nucleic acid extraction methods should be customized according to sample type: centrifugation is advised for whole blood and urine to effectively enrich pathogens, while grinding intensity should be adjusted for viral samples to avoid nucleic acid degradation. The number of cycles in the first-round PCR should be carefully optimized to strike a balance between library concentration and non-specific amplification. Purification remains critical for eliminating dimers, with buffer incubation proving most effective. Quality control (QC) measures should be applied at both nucleic acid and library stages, though first PCR QC may be omitted Supplementary Tables 9 and 10, https://links.lww.com/CM9/C830. In dry-lab workflows, establishing rigorous bioinformatics thresholds is vital. We suggest setting pathogen-specific cut-offs based on no-template control read counts, typically using a 5- to 10-fold difference threshold to minimize false positives. Normalizing clean reads to a fixed value enables cross-sample comparability. Contamination control measures should include physical laboratory separation, regular sterilization, and specific techniques like oil sealing and immediate cap replacement to reduce aerosol-mediated contamination. The use of unique barcodes effectively reduces index hopping, while incorporating multiple primer pairs for critical pathogens enhances detection reliability Supplementary Tables 9 and 10, https://links.lww.com/CM9/C830. Currently, much of the process remains manual. Drawing on our prior research, we propose integrating the two-step amplification into a single-step or single-tube method, which could substantially reduce cross-contamination. Moreover, full automation could be achieved using an unattended automated nucleic acid extractor and a microfluidic lab-on-chip for library construction. By minimizing reagent use and employing precise droplet pipetting technology, we can further reduce the generation of aerosols and the risk of contamination.4 This represents the focus of our future optimization efforts. In summary, our systematic evaluation confirms that ptNGS is a rapid, cost-effective, and accurate diagnostic tool that effectively complements mNGS in clinical microbiology. Importantly, our findings highlight that protocol-specific optimizations, particularly in dimer removal, contamination control, and primer design, significantly impact detection performance. Acknowledgments We express our gratitude to Genskey Medical Technology Co., Ltd. (Beijing, China), Sansure Biotech Inc. (Changsha, China), and Hugobiotech Co., Ltd. (Beijing, China) for their testing support and technical assistance provided for this experiment. Funding This work was funded by the Ministry of Science and Technology of China (Nos. 2023YFC2412900 and 2022YFB3205604) and the Fundamental Research Funds for the Central Universities (No. ZYGX2022YGRH002). Conflicts of interest None.
Hu et al. (Tue,) studied this question.