Automatic Microfluidic Harmonized RAA-CRISPR Diagnostic System for Rapid and Accurate Identification of Bacterial

Xinran Xiang1,2, Xiaoqing Ren3, Qianyu Wen4

  • 1Fujian Key Laboratory of Aptamers Technology, Fuzhou General Clinical Medical School (the 900th Hospital), Fujian Medical University, Fuzhou 350001, China.

Analytical Chemistry
|April 10, 2024
PubMed

Insights

This study presents a rapid, all-in-one RAA-CRISPR diagnostic system on a microfluidic chip for detecting respiratory pathogens. It enables quick and accurate identification of 12 common bacteria from clinical samples.

Area of Science:

  • Biotechnology
  • Microbiology
  • Medical Diagnostics

Background:

  • Bacterial respiratory tract infections (RTIs) are a major global health concern, causing significant morbidity and mortality.
  • Current diagnostic methods for respiratory pathogens often lack the speed and sensitivity required for timely clinical intervention.

Purpose of the Study:

  • To develop a rapid, sensitive, and high-throughput diagnostic system for the simultaneous detection of multiple respiratory bacterial pathogens.
  • To overcome the limitations of conventional two-step CRISPR-mediated detection systems.

Main Methods:

  • Integration of a recombinase-aided amplification (RAA) with a clustered regularly interspaced short palindromic repeats (CRISPR) system on a centrifugal microfluidic chip.
  • Development of an all-in-one RAA-CRISPR assay for enhanced bacterial detection.
  • Inclusion of Chelex-100 for simplified sample pretreatment, enabling a sample-to-answer workflow.

Main Results:

  • The RAA-CRISPR system demonstrated enhanced accuracy and sensitivity compared to conventional methods.
  • The microfluidic chip facilitated reduced sample consumption and increased detection throughput for simultaneous pathogen analysis.
  • The system successfully detected 12 common respiratory bacteria in 60 clinical samples with high accuracy.

Conclusions:

  • The developed centrifugal microfluidic RAA-CRISPR system provides a rapid, reliable, and efficient platform for diagnosing bacterial respiratory infections.
  • This technology has the potential to significantly improve clinical decision-making and patient treatment outcomes by enabling timely diagnosis.