Hierarchical Amplification-Interlinked CRISPR-Cas14a Luminescent Biosensor Coupled with Portable Photonic Crystal
Lian Liu1, Mo Wu2, Xiaoming Sun3
1School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, P. R. China.
Abstract:
Mycoplasma pneumoniae pneumonia (MPP) is a respiratory infection that readily propagates within pediatric populations, and thus, the development of a robust assay approach for its timely diagnosis is of clinical importance. In this contribution, we build a hierarchical amplification-interlinked CRISPR-Cas14a luminescent biosensor coupled with portable photonic crystal biochip-boosted time-delayed signaling to fulfill this need. To first perform highly sensitive detection, an initial DNA walker module and a later rolling circle amplification module are integrated to construct a hierarchical amplification, which is then utilized to interlink CRISPR-Cas14a systems. To further improve applicability in complicated biosamples, a time-delayed signaling actualized by afterglow luminescence is introduced to circumvent background autoluminescence from biological media. After employing a photonic crystal self-assembled from polystyrene nanospheres to manufacture a portable biochip, the naturally attenuated afterglow luminescence is dramatically boosted. Leveraging these strategies, our biosensing platform achieves a limit of detection as low as 2.27 fM for the M. pneumoniae sequence while maintaining good specificity. Moreover, the luminescent biosensor permits precise analysis of targets in throat swab samples from a pediatric cohort (n = 250) comprising severe and mild MPP patients as well as healthy controls, based on which a convolutional neural network-implemented deep learning model is finally established for accurate automated disease stratification, thereby holding great potential as an efficient and promising diagnostic tool.
Insights
A new luminescent biosensor accurately detects Mycoplasma pneumoniae pneumonia (MPP) in children. This advanced diagnostic tool utilizes CRISPR-Cas14a technology for rapid and specific identification of this common respiratory infection.
Area of Science:
- Biomedical Engineering
- Molecular Diagnostics
- Infectious Disease Research
Background:
- Mycoplasma pneumoniae pneumonia (MPP) is a prevalent respiratory infection in pediatric populations.
- Timely and accurate diagnosis of MPP is crucial for effective clinical management.
- Existing diagnostic methods may face challenges with sensitivity, specificity, or complex biological samples.
Purpose of the Study:
- To develop a highly sensitive and specific luminescent biosensor for the detection of Mycoplasma pneumoniae.
- To create a portable diagnostic platform suitable for clinical application in pediatric settings.
- To establish an automated disease stratification method using deep learning for MPP patients.
Main Methods:
- Construction of a hierarchical amplification-interlinked CRISPR-Cas14a luminescent biosensor.
- Integration of DNA walker and rolling circle amplification modules for enhanced sensitivity.
- Utilization of time-delayed afterglow luminescence and photonic crystal biochips for signal amplification and background reduction.
- Application of a convolutional neural network (CNN) deep learning model for automated disease stratification.
Main Results:
- Achieved a limit of detection as low as 2.27 fM for the M. pneumoniae sequence with excellent specificity.
- Demonstrated precise analysis of M. pneumoniae targets in throat swab samples from a pediatric cohort (n=250).
- Developed a CNN model for accurate automated stratification of severe and mild MPP cases and healthy controls.
Conclusions:
- The developed biosensing platform offers a promising and efficient tool for the rapid diagnosis of MPP in children.
- The combination of advanced amplification, luminescent detection, and deep learning enables accurate disease stratification.
- This innovative approach holds significant potential for improving pediatric respiratory infection diagnostics.
