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SAM-dPCR: Accurate and Generalist Nuclei Acid Quantification Leveraging the Zero-Shot Segment Anything Model

  • Yuanyuan Wei
  • , Shanhang Luo
  • , Changran Xu
  • , Yingqi Fu
  • , Yi Zhang
  • , Fuyang Qu
  • , Guoxun Zhang
  • , Yi-Ping Ho
  • , Ho-Pui Ho*
  • , Wu Yuan*
  • *Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

12 Downloads (CityUHK Scholars)

Abstract

Digital PCR (dPCR) has transformed nucleic acid diagnostics by enabling the absolute quantification of rare mutations and target sequences. However, traditional dPCR detection methods, such as those involving flow cytometry and fluorescence imaging, may face challenges due to high costs, complexity, limited accuracy, and slow processing speeds. In this study, SAM-dPCR is introduced, a training-free open-source bioanalysis paradigm that offers swift and precise absolute quantification of biological samples. SAM-dPCR leverages the robustness of the zero-shot Segment Anything Model (SAM) to achieve rapid processing times (<4 seconds) with an accuracy exceeding 97.10%. This method has been extensively validated across diverse samples and reactor morphologies, demonstrating its broad applicability. Utilizing standard laboratory fluorescence microscopes, SAM-dPCR can measure nucleic acid template concentrations ranging from 0.154 copies µL−1 to 1.295 × 103 copies µL−1 for droplet dPCR and 0.160 × 103 to 3.629 × 103 copies µL−1 for microwell dPCR. Experimental validation shows a strong linear relationship (r2 > 0.96) between expected and determined sample concentrations. SAM-dPCR offers high accuracy, accessibility, and the ability to address bioanalytical needs in resource-limited settings, as it does not rely on hand-crafted “ground truth” data. © 2024 The Author(s). Advanced Science published by Wiley-VCH GmbH.
Original languageEnglish
Article number2406797
JournalAdvanced Science
Volume12
Issue number7
Online published27 Dec 2024
DOIs
Publication statusPublished - 17 Feb 2025
Externally publishedYes

Funding

The authors were grateful to the funding support from the Hong Kong Research Grants Council (project reference: GRF14216222, GRF14203821, GRF14204621, GRF14207121, GRF14207920, GRF14207419, GRF14203919, GRF14219922, N_CUHK407/16), the Marine Conservation Enhancement Fund (MCEF20108_L02), the Science, Technology, and Innovation Commission of Shenzhen Municipality (SGDX20220530111005039) and the Innovation and Technology Commission (project reference: GHX-004-18SZ). The authors would like to acknowledge Prof. Mingli You (School of Life Science and Technology, Xi'an Jiaotong University), Mr. Yucheng Wu and Dr. Mingkun Xu (Guangdong Institute of Intelligence Science and Technology, Zhuhai), Dr. Ronjie Zhao and Dr. Meng Yan (State Key Laboratory of Marine Pollution, City University of Hong Kong, Hong Kong, China), Dr. Guangyao Cheng, Ms. Tianle Wang, Mr. Syed Muhammad Tariq Abbasi, Mr. Minqing Zhang, Mr. Chenglang Yuan, Ms. Qingyue Dong, Mr. Shirui Zhao, Ms. Khadija BIBI, and Ms. Syeda Aimen Abbasi (Department of Biomedical Engineering, The Chinese University of Hong Kong) for their support in the project development.

Research Keywords

  • deep-learning
  • digital PCR
  • droplet microfluidics
  • nucleic acid quantification
  • segment anything model

Publisher's Copyright Statement

  • This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

RGC Funding Information

  • RGC-funded

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