Mechanistically Guided Combinatorial Coding Based on Strand Displacement Probes for Multiplex Pathogen Variant Discrimination
Abstract
ABSTRACT Accurate and scalable discrimination of closely related genetic variants at single‐nucleotide resolution remains challenge in molecular diagnostics, particularly under multiplexed conditions. Herein, we present a mechanistically guided Color‐Coding Strategy for Multiple Variants Based on Single‐Mutation‐Responsive Strand‐Displacement Padlock Probes that enables combinatorial color‐coded identification of genetic variants with minimal parallel reactions. By integrating multiple isothermal amplification reactions ( n ) with multichannel strand displacement probes (m), the system establishes a theoretical n ‐dimensional coding framework capable of resolving up to variants in a single assay. Each variant is uniquely encoded by an “ n ‐color codon” and automatically decoded via a programmable algorithm. Using SARS‐CoV‐2 as a model, we identified up to 15 variants using only three reaction tubes across RNA, synthetic DNA, pseudovirus, and clinical nasopharyngeal swabs ( n = 76). In clinical evaluation, the assay achieved 100% positive agreement with RT‐qPCR for SARS‐CoV‐2‐positive specimens and successfully assigned variant identities to 72 of 76 samples (94.7%). The platform achieves a reduced reaction number, experimental complexity, and cost compared to conventional approaches. Owing to its modular and programmable design, this strategy is readily adaptable to emerging variants, demonstrating its potential for pathogen surveillance, and genetic variant analysis.
Article Details
Authors (11)
Yidan Tang
Key Laboratory of Electroanalytical Chemistry Changchun Institute of Applied Chemistry Chinese Academy of Sciences Changchun Jilin People's Republic of China
Huiying Lu
Key Laboratory of Electroanalytical Chemistry Changchun Institute of Applied Chemistry Chinese Academy of Sciences Changchun Jilin People's Republic of China
Li Dong
Yao Xiao
School of Chemistry and Chemical Engineering
Chunxu Yu
Rujian Zhao
Key Laboratory of Electroanalytical Chemistry Changchun Institute of Applied Chemistry Chinese Academy of Sciences Changchun Jilin People's Republic of China
Baiyang Lu
Key Laboratory of Electroanalytical Chemistry Changchun Institute of Applied Chemistry Chinese Academy of Sciences Changchun Jilin People's Republic of China
Lulu Guo
State Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology, School of Optoelectronic Science and Engineering, Key Laboratory of Opto-Electronic Information Acquisition and Manipulation of Ministry of Education, Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University
Yan Du
Shixing Tang
Institute for Global Health Southern Medical University Guangzhou Guangdong People's Republic of China
Bingling Li
Key Laboratory of Electroanalytical Chemistry Changchun Institute of Applied Chemistry Chinese Academy of Sciences Changchun Jilin People's Republic of China