Using machine learning and liquid crystal droplets to identify and quantify endotoxins from different bacterial species
Using machine learning and liquid crystal droplets to identify and quantify endotoxins from different bacterial species
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DOI：
10.1039/d0an02220a
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发表时间：
2021-02-21
期刊：
影响因子：
4.2
通讯作者：
Zavala, Victor M.
中科院分区：
文献类型：
--
作者：
Jiang, Shengli;Noh, JungHyun;Zavala, Victor M.
细菌内毒素的检测和定量在一系列与健康相关的环境中是重要的，包括在治疗性蛋白质和疫苗的药物制造期间。在这里，我们结合联合收割机的实验测量的基础上，液晶液滴和机器学习方法，以表明它是可能的分类细菌来源（大肠杆菌，铜绿假单胞菌，沙门氏菌）和量化浓度的内毒素来自所有三种细菌物种存在于水溶液中。该方法使用流式细胞术以高通量方式定量内毒素引发的β 4-氰基-4 ' -戊基联苯的微米大小液滴的内部排序的变化。内部有序的变化改变了液晶液滴的侧向散射（SSC，大角度）和前向散射（FSC，小角度）的强度。卷积神经网络（Endonet）使用流式细胞术生成的大数据集进行训练，并显示直接从FSC/SSC散点图预测内毒素来源和浓度。通过使用显着性图，我们揭示了EndoNet如何捕获散射场的细微差异，以实现细菌源的分类和内毒素浓度的定量，范围跨越8个数量级（0.01 pg mL（-1）至1 μ g mL（-1））。我们将EndoNet检测到的内毒素细菌来源的散射场变化归因于来自三种细菌的内毒素的脂质A结构域的不同分子结构。总的来说，我们得出结论，液晶液滴和EndoNet的组合提供了一种有前途的内毒素分析方法的基础，该方法不需要使用复杂的生物衍生试剂（例如，鲎变形细胞裂解物）。
Detection and quantification of bacterial endotoxins is important in a range of health-related contexts, including during pharmaceutical manufacturing of therapeutic proteins and vaccines. Here we combine experimental measurements based on nematic liquid crystalline droplets and machine learning methods to show that it is possible to classify bacterial sources (Escherichia coli, Pseudomonas aeruginosa, Salmonella minnesota) and quantify concentration of endotoxin derived from all three bacterial species present in aqueous solution. The approach uses flow cytometry to quantify, in a high-throughput manner, changes in the internal ordering of micrometer-sized droplets of nematic 4-cyano-4 ' -pentylbiphenyl triggered by the endotoxins. The changes in internal ordering alter the intensities of light side-scattered (SSC, large-angle) and forward-scattered (FSC, small-angle) by the liquid crystal droplets. A convolutional neural network (Endonet) is trained using the large data sets generated by flow cytometry and shown to predict endotoxin source and concentration directly from the FSC/SSC scatter plots. By using saliency maps, we reveal how EndoNet captures subtle differences in scatter fields to enable classification of bacterial source and quantification of endotoxin concentration over a range that spans eight orders of magnitude (0.01 pg mL(-1) to 1 mu g mL(-1)). We attribute changes in scatter fields with bacterial origin of endotoxin, as detected by EndoNet, to the distinct molecular structures of the lipid A domains of the endotoxins derived from the three bacteria. Overall, we conclude that the combination of liquid crystal droplets and EndoNet provides the basis of a promising analytical approach for endotoxins that does not require use of complex biologically-derived reagents (e.g., Limulus amoebocyte lysate).