Deep Learning and Single Cell Phenotyping for Rapid Antimicrobial Susceptibility Testing
Deep Learning and Single Cell Phenotyping for Rapid Antimicrobial Susceptibility Testing
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DOI：
10.1101/2022.12.08.22283219
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发表时间：
2022-12
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影响因子：
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通讯作者：
Aleksander Zagajewski;Piers Turner;Conor Feehily;Hafez El Sayyed;Monique Andersson;Lucinda Barrett;S. Oakley;Mathew Stracy;Derrick Crook;Christoffer Nellåker;N. Stoesser;A. Kapanidis
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文献类型：
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作者：
Aleksander Zagajewski;Piers Turner;Conor Feehily;Hafez El Sayyed;Monique Andersson;Lucinda Barrett;S. Oakley;Mathew Stracy;Derrick Crook;Christoffer Nellåker;N. Stoesser;A. Kapanidis
抗菌素耐药性（AMR）的上升是最大的公共卫生挑战之一，每年已造成多达120万人死亡，而且还在上升。目前的金标准抗菌药物敏感性试验（AST）是低通量的，可能需要长达48小时，对患者护理有影响。我们提出了一种新型快速AST的进展，该进展基于对与大肠杆菌抗菌药物敏感性直接相关的单细胞特异性表型的深度学习。我们的模型可以可靠地（80%的单细胞准确度）分类未处理和处理的易感细胞，包括一系列抗生素和表型-包括经过训练的人类观察者在视觉上没有区别的表型。将实验室参考敏感菌株训练的模型应用于临床分离的E。大肠杆菌与环丙沙星处理，我们证明我们的模型揭示了显着的差异（p<0.001）之间的耐药和敏感群体，在一个固定的治疗水平。相反，部署在用一系列环丙沙星浓度处理的细胞上，我们表明单细胞表型分析有可能提供与24小时生长AST测定相当的信息，但只需30分钟。
The rise of antimicrobial resistance (AMR) is one of the greatest public health challenges, already causing up to 1.2 million deaths annually and rising. Current gold-standard antimicrobial susceptibility tests (ASTs) are low-throughput and can take up to 48 hours, with implications for patient care. We present advances towards a novel, rapid AST, based on the deep-learning of single-cell specific phenotypes directly associated with antimicrobial susceptibility in Escherichia coli. Our models can reliably (80% single-cell accuracy) classify untreated and treated susceptible cells, across a range of antibiotics and phenotypes - including phenotypes not visually distinct to a trained, human observer. Applying models trained on lab-reference susceptible strains to clinical isolates of E. coli treated with ciprofloxacin, we demonstrate our models reveal significant (p<0.001) differences between resistant and susceptible populations, around a fixed treatment level. Conversely, deploying on cells treated with a range of ciprofloxacin concentrations, we show single-cell phenotyping has the potential to provide equivalent information to a 24-hour growth AST assay, but in as little as 30 minutes.