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Wei Y, Abbasi SMT, Mehmood N, Li L, Qu F, Cheng G, Hu D, Ho YP, Yuan W, Ho HP. Deep-qGFP: A Generalist Deep Learning Assisted Pipeline for Accurate Quantification of Green Fluorescent Protein Labeled Biological Samples in Microreactors. Small Methods 2024; 8:e2301293. [PMID: 38010980 DOI: 10.1002/smtd.202301293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 11/12/2023] [Indexed: 11/29/2023]
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.
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Affiliation(s)
- Yuanyuan Wei
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
| | - Syed Muhammad Tariq Abbasi
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
| | - Nawaz Mehmood
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
| | - Luoquan Li
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
| | - Fuyang Qu
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
| | - Guangyao Cheng
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
| | - Dehua Hu
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
| | - Yi-Ping Ho
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
- Centre for Biomaterials, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
- Hong Kong Branch of CAS Center for Excellence in Animal Evolution and Genetics, Shatin, Hong Kong SAR, 999 077, China
- State Key Laboratory of Marine Pollution, City University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
| | - Wu Yuan
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
| | - Ho-Pui Ho
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, 999 077, China
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