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Absolute quantification of biological samples entails determining expressionlevels in precise numerical copies, offering enhanced accuracy and superiorperformance for rare templates. However, existing methodologies suffer fromsignificant limitations: flow cytometers are both costly and intricate, whilefluorescence imaging relying on software tools or manual counting istime-consuming and prone to inaccuracies. In this study, we have devised acomprehensive deep-learning-enabled pipeline that enables the automatedsegmentation and classification of GFP (green fluorescent protein)-labeledmicroreactors, facilitating real-time absolute quantification. Our findingsdemonstrate the efficacy of this technique in accurately predicting the sizesand occupancy status of microreactors using standard laboratory fluorescencemicroscopes, thereby providing precise measurements of template concentrations.Notably, our approach exhibits an analysis speed of quantifying over 2,000microreactors (across 10 images) within remarkably 2.5 seconds, and a dynamicrange spanning from 56.52 to 1569.43 copies per micron-liter. Furthermore, ourDeep-dGFP algorithm showcases remarkable generalization capabilities, as it canbe directly applied to various GFP-labeling scenarios, including droplet-based,microwell-based, and agarose-based biological applications. To the best of ourknowledge, this represents the first successful implementation of an all-in-oneimage analysis algorithm in droplet digital PCR (polymerase chain reaction),microwell digital PCR, droplet single-cell sequencing, agarose digital PCR, andbacterial quantification, without necessitating any transfer learning steps,modifications, or retraining procedures. We firmly believe that our Deep-dGFPtechnique will be readily embraced by biomedical laboratories and holdspotential for further development in related clinical applications.

Deep Learning Approach for Large-Scale, Real-Time Quantification of Green Fluorescent Protein-Labeled Biological Samples in Microreactors