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Updated: Jan 8, 2026

In Vitro Selection of Aptamers to Differentiate Infectious from Non-Infectious Viruses

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Published on: September 7, 2022

Systematic Evolution of Pattern Recognition Aptamers for Bacterial Identification.

Ying Xiang¹, Jing Chen¹, Jinnan Xuan²

¹Shanghai Key Laboratory of Green Chemistry and Chemical Processes, School of Chemistry and Molecular Engineering, Shanghai Frontiers Science Center of Genome Editing and Cell, East China Normal University, 500 Dongchuan Road, Shanghai 200241, P. R. China.

|December 17, 2025

Summary

This summary is machine-generated.

Researchers developed pattern recognition aptamers (PRAs) for rapid, high-precision target identification. This nucleic acid-based approach enables customized artificial receptor generation, achieving 98.5% accuracy in bacterial identification.

Keywords:

artificial neural network artificial receptor units bacterial identification pattern recognition aptamers systematic evolution

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Area of Science:

Biotechnology
Molecular Biology
Chemical Sensing

Background:

Natural pattern recognition systems inspire synthetic receptor development.
Current artificial receptor creation involves laborious trial-and-error with limited molecular choices.

Purpose of the Study:

To introduce pattern recognition aptamers (PRAs) as a novel class of artificial receptors.
To establish a systematic evolution method for high-precision, multi-target identification.

Main Methods:

Evolving single-stranded nucleic acid ligands (PRAs) using systematic exponential enrichment.
Utilizing a nucleic acid library for receptor generation.
Testing PRAs against common bacteria as model analytes.

Main Results:

Developed 9 PRAs capable of recognizing 15 different bacteria.
Achieved 98.5% accuracy in blinded bacterial identification after only 3 rounds of screening.
Demonstrated reliable generation of customized artificial receptors.

Conclusions:

PRAs offer a generalized pipeline for creating customized pattern recognition arrays.
This method overcomes limitations of traditional receptor development.
The approach supports rapidly increasing demands for precise molecular recognition applications.