Ellis Jeremy E, Missan Dara S, Shabilla Matthew, Martinez Delyn, Fry Stephen E
Fry Laboratories, L.L.C., 15720 N. Greenway-Hayden Loop STE 3, Scottsdale, AZ 85260, United States.
Fry Laboratories, L.L.C., 15720 N. Greenway-Hayden Loop STE 3, Scottsdale, AZ 85260, United States.
J Microbiol Methods. 2017 Jul;138:12-19. doi: 10.1016/j.mimet.2016.09.012. Epub 2016 Sep 19.
Currently, there is a critical need to rapidly identify infectious organisms in clinical samples. Next-Generation Sequencing (NGS) could surmount the deficiencies of culture-based methods; however, there are no standardized, automated programs to process NGS data. To address this deficiency, we developed the Rapid Infectious Disease Identification (RIDI™) system. The system requires minimal guidance, which reduces operator errors. The system is compatible with the three major NGS platforms. It automatically interfaces with the sequencing system, detects their data format, configures the analysis type, applies appropriate quality control, and analyzes the results. Sequence information is characterized using both the NCBI database and RIDI™ specific databases. RIDI™ was designed to identify high probability sequence matches and more divergent matches that could represent different or novel species. We challenged the system using defined American Type Culture Collection (ATCC) reference standards of 27 species, both individually and in varying combinations. The system was able to rapidly detect known organisms in <12h with multi-sample throughput. The system accurately identifies 99.5% of the DNA sequence reads at the genus-level and 75.3% at the species-level in reference standards. It has a limit of detection of 146cells/ml in simulated clinical samples, and is also able to identify the components of polymicrobial samples with 16.9% discrepancy at the genus-level and 31.2% at the species-level. Thus, the system's effectiveness may exceed current methods, especially in situations where culture methods could produce false negatives or where rapid results would influence patient outcomes.
目前，临床样本中快速鉴定感染性生物体的需求极为迫切。新一代测序（NGS）技术可以克服基于培养方法的不足；然而，目前尚无标准化的自动化程序来处理NGS数据。为解决这一不足，我们开发了快速传染病鉴定（RIDI™）系统。该系统所需指导极少，从而减少了操作人员的错误。该系统与三大NGS平台兼容。它能自动与测序系统对接，检测其数据格式，配置分析类型，进行适当的质量控制，并分析结果。序列信息通过美国国立医学图书馆（NCBI）数据库和RIDI™特定数据库进行特征分析。RIDI™旨在识别高概率的序列匹配以及可能代表不同或新物种的更具差异的匹配。我们使用美国典型培养物保藏中心（ATCC）定义的27种参考标准菌株，单独或组合使用来测试该系统。该系统能够在<12小时内以多样本通量快速检测已知生物体。在参考标准中，该系统在属水平上能准确识别99.5%的DNA序列读数，在种水平上能准确识别75.3%的DNA序列读数。在模拟临床样本中，其检测限为146个细胞/毫升，并且还能够识别多微生物样本的成分，在属水平上差异为16.9%，在种水平上差异为31.2%。因此，该系统的有效性可能超过现有方法，特别是在培养方法可能产生假阴性结果或快速结果会影响患者治疗结果的情况下。
