A Point-of-Care Device for Fully Automated, Fast and Sensitive Protein Quantification via qPCR.
A Point-of-Care Device for Fully Automated, Fast and Sensitive Protein Quantification via qPCR.
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DOI：
10.3390/bios12070537
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发表时间：
2022-07-19
期刊：
影响因子：
5.4
通讯作者：
Toumazou, Christofer
中科院分区：
文献类型：
--
作者：
Cavallo, Francesca Romana;Mirza, Khalid Baig;de Mateo, Sara;Miglietta, Luca;Rodriguez-Manzano, Jesus;Nikolic, Konstantin;Toumazou, Christofer
本文提出了一种使用短 DNA 适体进行蛋白质定量的全自动床旁设备，无需手动样品制备。该设备基于我们新颖的基于适体的方法与实时聚合酶链式反应 (qPCR) 相结合，我们采用该方法进行非常灵敏的蛋白质定量。通过 qPCR 进行的 DNA 扩增、传感和实时数据处理无缝集成到配备一次性试剂盒的护理点设备中，用于自动样品制备。该系统的模块化特性可以轻松组装、调整和扩展用于疾病诊断和个性化医疗应用的各种生物标记物。除了设备描述之外，我们还介绍了一种新算法，我们将其命名为 PeakFluo，用于执行蛋白质的自动实时定量。 PeakFluo 比市售 qPCR 机器的专有软件具有更好的线性度，并且可以及早检测扩增信号。此外，我们提出了一种除了定量读数之外使用所提出的设备的替代方法，它可以提供临床相关的建议。我们演示了在 qPCR 图像上训练的卷积神经网络算法如何将样本分类为高/低浓度类别。该方法可以帮助根据瘦素值对肥胖患者进行分类，以优化临床环境中的减肥疗法。
This paper presents a fully automated point-of-care device for protein quantification using short-DNA aptamers, where no manual sample preparation is needed. The device is based on our novel aptamer-based methodology combined with real-time polymerase chain reaction (qPCR), which we employ for very sensitive protein quantification. DNA amplification through qPCR, sensing and real-time data processing are seamlessly integrated into a point-of-care device equipped with a disposable cartridge for automated sample preparation. The system’s modular nature allows for easy assembly, adjustment and expansion towards a variety of biomarkers for applications in disease diagnostics and personalised medicine. Alongside the device description, we also present a new algorithm, which we named PeakFluo, to perform automated and real-time quantification of proteins. PeakFluo achieves better linearity than proprietary software from a commercially available qPCR machine, and it allows for early detection of the amplification signal. Additionally, we propose an alternative way to use the proposed device beyond the quantitative reading, which can provide clinically relevant advice. We demonstrate how a convolutional neural network algorithm trained on qPCR images can classify samples into high/low concentration classes. This method can help classify obese patients from their leptin values to optimise weight loss therapies in clinical settings.
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