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深度学习和单细胞表型化用于快速检测大肠杆菌中的抗菌素敏感性

Alexander Zagajewski^1,2, Piers Turner^1,2, Conor Feehily³

¹Department of Physics, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK.

Communications biology

|November 14, 2023

概括

抗菌素耐药性 (AMR) 是一个重大威胁. 这项研究使用单个细菌细胞的深度学习在短短30分钟内预测抗生素敏感性,为传统方法提供更快的替代方案.

科学领域:

微生物学微生物学
计算生物学计算生物学
医学诊断医学诊断医学诊断

背景情况:

抗微生物药物耐药性 (AMR) 是一个重大的全球卫生危机.
目前的抗微生物敏感性测试 (AST) 方法需要18-24小时才能获得结果.
快速诊断对于有效的AMR治疗至关重要.

研究的目的:

开发一种新的,快速的抗微生物敏感性测试 (AST) 方法.
利用单细胞形态学的深度学习来预测细菌对抗生素的反应.
证明这种方法在快速临床决策方面的潜力.

主要方法:

应用深度学习模型来分析大肠杆菌的单细胞形态表型.
在四种抗生素中分类未经处理和经过抗生素治疗的细菌细胞.
相关的观察到的表型变化与已确立的临床敏感度断点.

主要成果:

在对易受感染的大肠杆菌细胞进行分类时,获得了80%的单细胞准确性.
在用西普洛克萨治疗的细菌群体中发现了显著的形态差异.
证明30分钟内表型变化与临床敏感性相关.
在短短30分钟内显示出与基于增长的AST相当的信息的潜力.

结论:

单细胞形态表型与深度学习相结合,提供了快速的AST方法.
这种方法有可能显著减少细菌感染诊断的周转时间.
更快的AST结果可以导致更及时,更有效的抗微生物治疗策略.

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