1 Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
2 Image and Data Analysis Core, Harvard Medical School, Boston, MA, USA.
SLAS Technol. 2017 Dec;22(6):662-674. doi: 10.1177/2472630317727721. Epub 2017 Aug 24.
Antibiotic resistance is compromising our ability to treat bacterial infections. Clinical microbiology laboratories guide appropriate treatment through antimicrobial susceptibility testing (AST) of patient bacterial isolates. However, increasingly, pathogens are developing resistance to a broad range of antimicrobials, requiring AST of alternative agents for which no commercially available testing methods are available. Therefore, there exists a significant AST testing gap in which current methodologies cannot adequately address the need for rapid results in the face of unpredictable susceptibility profiles. To address this gap, we developed a multicomponent, microscopy-based AST (MAST) platform capable of AST determinations after only a 2 h incubation. MAST consists of a solid-phase microwell growth surface in a 384-well plate format, inkjet printing-based application of both antimicrobials and bacteria at any desired concentrations, automated microscopic imaging of bacterial replication, and a deep learning approach for automated image classification and determination of antimicrobial minimal inhibitory concentrations (MICs). In evaluating a susceptible strain set, 95.8% were within ±1 and 99.4% were within ±2, twofold dilutions, respectively, of reference broth microdilution MIC values. Most (98.3%) of the results were in categorical agreement. We conclude that MAST offers promise for rapid, accurate, and flexible AST to help address the antimicrobial testing gap.
抗生素耐药性正在削弱我们治疗细菌感染的能力。临床微生物学实验室通过对患者细菌分离物进行抗菌药物敏感性测试（AST）来指导合理的治疗。然而，越来越多的病原体对广泛的抗菌药物产生了耐药性，这就需要对替代药物进行 AST 检测，但目前还没有商业上可用的检测方法。因此，存在着一个显著的 AST 检测缺口，现有的方法无法在面对不可预测的药敏谱时提供快速结果。为了解决这一缺口，我们开发了一种多组分、基于显微镜的 AST（MAST）平台，仅需孵育 2 小时即可进行 AST 测定。MAST 由一个固相微孔生长表面组成，采用 384 孔板格式，基于喷墨打印技术可将抗菌药物和细菌以任意所需浓度应用于微孔中，自动对细菌复制进行显微镜成像，并采用深度学习方法对图像进行自动分类和抗菌药物最小抑菌浓度（MIC）的测定。在评估敏感菌株集时，95.8%的菌株在参考肉汤微量稀释 MIC 值的 ±1 倍稀释范围内，99.4%的菌株在 ±2 倍稀释范围内。大多数（98.3%）结果在类别上是一致的。我们得出结论，MAST 有望实现快速、准确和灵活的 AST，以帮助解决抗菌药物检测缺口问题。
