生物样品的绝对定量提供了精确的数值表达水平,提高准确性,和稀有模板的性能。目前的方法,然而,面临挑战-流式细胞仪昂贵且复杂,而荧光成像,依靠软件或手动计数,既耗时又容易出错。提出了Deep-qGFP,用于绿色荧光蛋白(GFP)标记的微反应器的自动检测和分类的深度学习辅助管道,实现实时绝对量化。这种方法达到了96.23%的准确度,并使用标准的实验室荧光显微镜准确地测量微反应器的尺寸和占用状态,提供精确的模板浓度。Deep-qGFP显示出惊人的速度,在短短2.5秒内对10张图像中的2000多个微反应器进行量化,动态范围为56.52-1569.43拷贝µL-1。该方法展示了令人印象深刻的泛化能力,成功应用于各种GFP标记场景,包括基于液滴的,基于微孔,和基于琼脂糖的应用程序。值得注意的是,Deep-qGFP是第一个在液滴数字聚合酶链反应(PCR)中成功实现的一体化图像分析算法,微孔数字PCR,液滴单细胞测序,琼脂糖数字PCR,和细菌定量,不需要迁移学习,修改,或再培训。这使得Deep-qGFP易于应用于生物医学实验室,并具有更广泛的临床应用潜力。
Absolute quantification of biological samples provides precise numerical expression levels, enhancing accuracy, and performance for rare templates. Current methodologies, however, face challenges-flow cytometers are costly and complex, whereas fluorescence imaging, relying on software or manual counting, is time-consuming and error-prone. It is presented that Deep-qGFP, a deep learning-aided pipeline for the automated detection and classification of green fluorescent protein (GFP) labeled microreactors, enables real-time absolute quantification. This approach achieves an accuracy of 96.23% and accurately measures the sizes and occupancy status of microreactors using standard laboratory fluorescence microscopes, providing precise template concentrations. Deep-qGFP demonstrates remarkable speed, quantifying over 2000 microreactors across ten images in just 2.5 seconds, with a dynamic range of 56.52-1569.43 copies µL-1 . The method demonstrates impressive generalization capabilities, successfully applied to various GFP-labeling scenarios, including droplet-based, microwell-based, and agarose-based applications. Notably, Deep-qGFP is the first all-in-one image analysis algorithm successfully implemented in droplet digital polymerase chain reaction (PCR), microwell digital PCR, droplet single-cell sequencing, agarose digital PCR, and bacterial quantification, without requiring transfer learning, modifications, or retraining. This makes Deep-qGFP readily applicable in biomedical laboratories and holds potential for broader clinical applications.