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

Abstract

AbstractAbsolute 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.