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

Abstract

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.

Envisioning the Future of High-Throughput Biomedical Assays through the Convergence of AI and Droplet Microfluidics

“…Emerging AI methodologies are reshaping the paradigm for data processing, providing a means to adeptly manage the infl ux of valuable data derived from microfl uidic assays. The integration of AI and digital microfl uidics provides high accuracy of absolute quantifi cation, as well as a cost-eff ective solution for biomedical applications [10]. Moreover, contents of single cells from heterogeneous populations as well as analysis of single-cell genomes and transcriptomes by next-generation sequencing, and proteomes by nanofl ow liquid chromatography and tandem mass spectrometry can be realized by incorporating digital microfl uidics, laser cell lysis, and AI-driven image processing [11].…”

Auto-ICell: An Automated and Cost-Effective Integrated Droplet Microfluidic Platform for Real-Time Analysis of Single-Cell Morphology and Apoptosis

“…Alternative methods utilizing in-flow interrogation 6 , Raman spectroscopy 23 , mass spectrometry 24 , electrical impedance spectroscopy, and electrophysiological recording, require expensive and complex devices 7 and provide limited speed, portability, and operational simplicity. While deep-learning methods have emerged as powerful tools for automatic and sensitive analysis in the field of droplet microfluidics 25,26 , current automated methods have limited to study the physical mechanisms, such as coalescence, and sort droplets [27][28][29][30] .…”