dna-virus-detection-system-based-on-rpa-crisprcas12a-spm-deep

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

We report a fast, easy-to-implement, highly sensitive, sequence-specific, and point-of-care (POC) DNA virus detection system, which combines recombinase ...

Tsinghua-Berkeley Shenzhen Institute

Tsinghua Shenzhen International Graduate School

Acute leukemia is a type of blood cancer with a high mortality rate. Current therapeutic methods include bone marrow transplantation, supportive therapy, and chemotherapy. Although a satisfactory remission of the disease can be achieved, the risk of recurrence is still high. Therefore, novel treatments are demanding. Chimeric antigen receptor-T (CAR-T) therapy has emerged as a promising approach to treating and curing acute leukemia. To harness the therapeutic potential of CAR-T cell therapy for blood diseases, reliable cell morphological identification is crucial. Nevertheless, the identification of CAR-T cells is a big challenge posed by their phenotypic similarity with other blood cells. To address this substantial clinical challenge, herein we first construct a CAR-T dataset with 500 original microscopy images after staining. Following that, we create a novel integrated model called RCMNet (ResNet18 with Convolutional Block Attention Module and Multi-Head Self-Attention) that combines the convolutional neural network (CNN) and Transformer. The model shows 99.63% top-1 accuracy on the public dataset. Compared with previous reports, our model obtains satisfactory results for image classification. Although testing on the CAR-T cell dataset, a decent performance is observed, which is attributed to the limited size of the dataset. Transfer learning is adapted for RCMNet and a maximum of 83.36% accuracy is achieved, which is higher than that of other state-of-the-art models. This study evaluates the effectiveness of RCMNet on a big public dataset and translates it to a clinical dataset for diagnostic applications.

RCMNet: A deep learning model assists CAR-T therapy for leukemia.

The outbreak of the monkeypox virus (MPXV) in non-endemic countries is an emerging global health threat and may have an economic impact if proactive actions are not taken. As shown by the COVID-19 pandemic, rapid, accurate, and cost-effective virus detection techniques play a pivotal role in disease diagnosis and control. Considering the sudden multicountry MPXV outbreak, a critical evaluation of the MPXV detection approaches would be a timely addition to the endeavors in progress for MPXV control and prevention. Herein, we evaluate the current MPXV detection methods, discuss their pros and cons, and provide recommended solutions to the problems. We review the traditional and emerging nucleic acid detection approaches, immunodiagnostics, whole-particle detection, and imaging-based MPXV detection techniques. The insights provided in this article will help researchers to develop novel techniques for the diagnosis of MPXV.

Current and Perspective Sensing Methods for Monkeypox Virus.

The global outbreak of the monkeypox virus (MPXV) highlights the need for rapid and cost-effective MPXV detection tools to effectively monitor and control the monkeypox disease. Herein, we demonstrated a portable CRISPR-Cas-based system for naked-eye detection of MPXV. The system harnesses the high selectivity of CRISPR-Cas12 and the isothermal nucleic acid amplification potential of recombinase polymerase amplification. It can detect both the current circulating MPXV clade and the original clades. We reached a limit of detection (LoD) of 22.4 aM (13.5 copies/µl) using a microtiter plate reader, while the visual LoD of the system is 75 aM (45 copies/µl) in a two-step assay, which is further reduced to 25 aM (15 copies/µl) in a one-pot system. We compared our results with quantitative polymerase chain reaction and obtained satisfactory consistency. For clinical application, we demonstrated a sensitive and precise visual detection method with attomolar sensitivity and a sample-to-answer time of 35 min.

CRISPR-Cas12-based field-deployable system for rapid detection of synthetic DNA sequence of the monkeypox virus genome.

A fast, easy-to-implement, highly sensitive, and point-of-care (POC) detection system for frog virus 3 (FV3) is proposed. Combining recombinase polymerase amplification (RPA) and CRISPR/Cas12a, a limit of detection (LoD) of 100 aM (60.2 copies/μL) is achieved by optimizing RPA primers and CRISPR RNAs (crRNAs). For POC detection, smartphone microscopy is implemented, and an LoD of 10 aM is achieved in 40 min. The proposed system detects four positive animal-derived samples with a quantitation cycle (Cq) value of quantitative PCR (qPCR) in the range of 13 to 32. In addition, deep learning models are deployed for binary classification (positive or negative samples) and multiclass classification (different concentrations of FV3 and negative samples), achieving 100 and 98.75% accuracy, respectively. Without temperature regulation and expensive equipment, the proposed RPA-CRISPR/Cas12a combined with smartphone readouts and artificial-intelligence-assisted classification showcases the great potential for FV3 detection, specifically POC detection of DNA virus.

Article	Year
RCMNet: A deep learning model assists CAR-T therapy for leukemia. Computers in biology and medicine\| PubMed ID: 36155267	2022
Current and Perspective Sensing Methods for Monkeypox Virus. Bioengineering (Basel, Switzerland)\| PubMed ID: 36290539	2022
CRISPR-Cas12-based field-deployable system for rapid detection of synthetic DNA sequence of the monkeypox virus genome. Journal of medical virology\| PubMed ID: 36478250	2023
Detection of Frog Virus 3 by Integrating RPA-CRISPR/Cas12a-SPM with Deep Learning. ACS omega\| PubMed ID: 37720737	2023

Zhengyang Lei

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