Researchers led by Prof. Zhu Ling from Hefei Institutes of Physical Science, Chinese Academy of Sciences, have developed a digital Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) hydrogel fluorescence-enhancing microsphere sensing system (CrisprDEM) that enables amplification-free relative quantification of nucleic acids.
The study was published in Analytical Chemistry.
CRISPR/Cas12a-based molecular diagnostics are considered a promising next-generation approach for nucleic acid detection due to their high specificity and programmable recognition. However, current amplification-free CRISPR assays still suffer from limited sensitivity and a lack of accurate quantification. These limitations hinder their application in areas such as early infectious disease screening and cancer liquid biopsy.
In this study, the system addresses these limitations by combining signal enrichment with digital imaging and automated data analysis in a single workflow.
Unlike conventional homogeneous CRISPR detection methods, the system uses hydrogel microspheres as confined microreactors. The spatial confinement effect significantly concentrates fluorescent signals generated by CRISPR/Cas12a collateral cleavage activity, resulting in strong signal enhancement. A microfluidic chip was further designed to distribute microspheres into a monolayer, reducing signal overlap and aggregation and ensuring stable imaging.
For quantitative analysis, the team introduced an unsupervised K-means clustering algorithm to automatically distinguish positive and negative microspheres. Based on this, a new metric termed the Positive Bead Ratio was established, enabling robust relative quantification of target nucleic acids without pre-amplification.
The system provides a rapid and quantitative approach for detecting low-abundance nucleic acids, with applications in early pathogen screening and point-of-care diagnostics.
Workflow Diagram of CrisprDEM: Digital Hydrogel Microsphere CRISPR/Cas12a for Amplification-Free Nucleic Acid Assay (Image by ZHU Cancan)
