Bacterial Diseases Unit, Sciensano, Brussels, Belgium.
Bruker Daltonic GmbH, Bremen, Germany.
J Clin Microbiol. 2022 Jul 20;60(7):e0032522. doi: 10.1128/jcm.00325-22. Epub 2022 Jun 14.
Fourier transform infrared (FT-IR) spectroscopy (IR Biotyper; Bruker) allows highly discriminatory fingerprinting of closely related bacterial strains. In this study, FT-IR spectroscopy-based capsular typing of Streptococcus pneumoniae was validated as a rapid, cost-effective, and medium-throughput alternative to the classical phenotypic techniques. A training set of 233 strains was defined, comprising 34 different serotypes and including all 24 vaccine types (VTs) and 10 non-vaccine types (NVTs). The acquired spectra were used to (i) create a dendrogram where strains clustered together according to their serotypes and (ii) train an artificial neural network (ANN) model to predict unknown pneumococcal serotypes. During validation using 153 additional strains, we reached 98.0% accuracy for determining serotypes represented in the training set. Next, the performance of the IR Biotyper was assessed using 124 strains representing 59 non-training set serotypes. In this setting, 42 of 59 serotypes (71.1%) could be accurately categorized as being non-training set serotypes. Furthermore, it was observed that comparability of spectra was affected by the source of the Columbia medium used to grow the pneumococci and that this complicated the robustness and standardization potential of FT-IR spectroscopy. A rigorous laboratory workflow in combination with specific ANN models that account for environmental noise parameters can be applied to overcome this issue in the near future. The IR Biotyper has the potential to be used as a fast, cost-effective, and accurate phenotypic serotyping tool for S. pneumoniae.
傅里叶变换红外(FT-IR)光谱(IR Biotyper;Bruker)可高度区分密切相关的细菌菌株指纹图谱。在这项研究中,FT-IR 光谱基于荚膜分型肺炎链球菌被验证为一种快速、具有成本效益且高通量的替代经典表型技术。定义了一个包含 233 株的训练集,包括 34 个不同的血清型,包括所有 24 种疫苗型(VTs)和 10 种非疫苗型(NVTs)。获得的光谱用于 (i) 创建一个 dendrogram,其中菌株根据其血清型聚类在一起,以及 (ii) 训练人工神经网络(ANN)模型来预测未知的肺炎链球菌血清型。在使用 153 株额外菌株进行验证期间,我们达到了 98.0%的准确性,可确定训练集中存在的血清型。接下来,使用代表 59 个非训练集血清型的 124 株菌株评估了 IR Biotyper 的性能。在这种情况下,59 个血清型中的 42 个(71.1%)可以准确地归类为非训练集血清型。此外,观察到光谱的可比性受到用于生长肺炎球菌的哥伦比亚培养基的来源的影响,这使得 FT-IR 光谱的稳健性和标准化潜力复杂化。在不久的将来,可以应用严格的实验室工作流程以及考虑环境噪声参数的特定 ANN 模型来克服此问题。IR Biotyper 有可能成为一种快速、具有成本效益且准确的肺炎链球菌表型血清分型工具。