Deep-qGFP: A Generalist Deep Learning Assisted Pipeline for Accurate Quantification of Green Fluorescent Protein Labeled Biological Samples in Microreactors

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 mu L-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. Unlocking precision in biological sample quantification, Deep-qGFP, an innovative deep learning-aided paradigm, redefines GFP-labeled microreactor analysis. Its real-time, automated detection and classification capabilities shatter conventional method limitations. With its seamless integration into common fluorescence microscopes and various microreactor formats, Deep-qGFP is poised to optimize laboratory practices, transforming the way GFP-labeled microreactor analysis is conducted.image

Automated summary

The study presents a deep learning-aided algorithm called Deep-qGFP for real-time absolute quantification. It enables automated detection and classification of fluorescent-labeled microreactors, accurately measuring their sizes and occupancy status. Deep-qGFP achieves high accuracy and remarkable speed, making it applicable to various GFP-labeling scenarios. It can be seamlessly integrated into common fluorescence microscopes and different microreactor formats, optimizing laboratory practices and potentially transforming GFP-labeled microreactor analysis.