Synthetic biology-powered biosensors based on CRISPR/Cas mediated cascade signal amplification for precise RNA detection

Zhang, Chao; Zhang, Penghui; Ren, Hui; Jia, Pengpeng; Ji, Jingcheng; Cao, Lei; Yang, Peiwei; Li, Yuxin; Liu, Jie; Li, Zedong; You, Minli; Duan, Xili; Hu, J. F.; Xu, Feng

doi:10.1016/j.cej.2022.136864

Cited by 9 publications

(7 citation statements)

References 40 publications

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“…Significant advancements have been made in synthetic biology to manage pulmonary disorders [248]. These advances encompass various in vitro diagnostic platforms, including biosensing systems that leverage CRISPR/Cas technology and synthetic RNAs designed for the efficient identification of biomarkers linked to lung diseases [249]. Consequently, these developments hold promise for enhancing patient well-being and health outcomes.…”

Section: Synthetic Biology-based Diagnosticsmentioning

confidence: 99%

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Nanomaterials-assisted gene editing and synthetic biology for optimizing the treatment of pulmonary diseases

Lei,

Pan,

Shou

et al. 2024

J Nanobiotechnol

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The use of nanomaterials in gene editing and synthetic biology has emerged as a pivotal strategy in the pursuit of refined treatment methodologies for pulmonary disorders. This review discusses the utilization of nanomaterial-assisted gene editing tools and synthetic biology techniques to promote the development of more precise and efficient treatments for pulmonary diseases. First, we briefly outline the characterization of the respiratory system and succinctly describe the principal applications of diverse nanomaterials in lung ailment treatment. Second, we elaborate on gene-editing tools, their configurations, and assorted delivery methods, while delving into the present state of nanomaterial-facilitated gene-editing interventions for a spectrum of pulmonary diseases. Subsequently, we briefly expound on synthetic biology and its deployment in biomedicine, focusing on research advances in the diagnosis and treatment of pulmonary conditions against the backdrop of the coronavirus disease 2019 pandemic. Finally, we summarize the extant lacunae in current research and delineate prospects for advancement in this domain. This holistic approach augments the development of pioneering solutions in lung disease treatment, thereby endowing patients with more efficacious and personalized therapeutic alternatives.

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Section: Synthetic Biology-based Diagnosticsmentioning

confidence: 99%

“…Zhang et al employed a cell-free, synthetic biologydriven biosensing strategy known as SHARK [249]. This approach regulates cell-free enzyme synthesis by utilizing activated Cas13a profiling to amplify RNA signals efficiently and accurately.…”

Section: Synthetic Biology-based Diagnosticsmentioning

confidence: 99%

Nanomaterials-assisted gene editing and synthetic biology for optimizing the treatment of pulmonary diseases

Lei,

Pan,

Shou

et al. 2024

J Nanobiotechnol

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“…In this section, we will explain two examples of different electrical sensors to detect lung cancer cells. The first example is Zhang et al [ 112 ] developing a new biosensing strategy called SHARK (Synthetic Enzyme Shift RNA Signal Amplifier Related Cas13a Knockdown Reaction) for lung cancer detection. SHARK has broad compatibility and can be used as a portable SARS-CoV-2 biosensor with high sensitivity and selectivity, consistent with qRT-PCR results.…”

Section: Lab-on-a-chip In Cancer Detectionmentioning

confidence: 99%

Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis

Kokabi,

Tahir,

Singh

et al. 2023

Biosensors

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Cancer is a fatal disease and a significant cause of millions of deaths. Traditional methods for cancer detection often have limitations in identifying the disease in its early stages, and they can be expensive and time-consuming. Since cancer typically lacks symptoms and is often only detected at advanced stages, it is crucial to use affordable technologies that can provide quick results at the point of care for early diagnosis. Biosensors that target specific biomarkers associated with different types of cancer offer an alternative diagnostic approach at the point of care. Recent advancements in manufacturing and design technologies have enabled the miniaturization and cost reduction of point-of-care devices, making them practical for diagnosing various cancer diseases. Furthermore, machine learning (ML) algorithms have been employed to analyze sensor data and extract valuable information through the use of statistical techniques. In this review paper, we provide details on how various machine learning algorithms contribute to the ongoing development of advanced data processing techniques for biosensors, which are continually emerging. We also provide information on the various technologies used in point-of-care cancer diagnostic biosensors, along with a comparison of the performance of different ML algorithms and sensing modalities in terms of classification accuracy.

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“…Among various viral RNA detection methods, molecular diagnostic methods can detect viral RNA quickly and accurately with high sensitivity and specificity. [6,7] Reverse transcriptase polymerase chain reaction (RT-PCR) is considered the gold standard method, and several RT-PCR approaches for viral detection have been reported. [8][9][10] However, RT-PCR has a long turnaround time and requires an expensive thermal cycler, which is a bottleneck in resource-limited settings.…”

Section: Introductionmentioning

confidence: 99%

Pipette‐free field‐deployable molecular diagnostic kit for bimodal visual detection of infectious RNA viruses

Park,

Trinh,

Song

et al. 2024

Biotechnology Journal

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Here, we developed a field‐deployable molecular diagnostic kit for the detection of RNA viruses that operates in a pipette‐free manner. The kit is composed of acrylic sticks, PCR tubes, and palm‐sized three‐dimensional(3D)‐printed heaters operated by batteries. The kit performs RNA extraction, reverse transcriptase loop‐mediated isothermal amplification (RT‐LAMP), and visual detection in one kit. An acrylic stick was engraved with one shallow and one deep cylindrical chamber at each end for the insertion of an FTA card and ethidium homodimer‐1 (EthD‐1), respectively, to perform RNA extraction/purification and bimodal visual detection of the target amplicons. First, an intercalation of EthD‐1 into the target DNA initially produces fluorescence upon UV illumination. Next, the addition of a strong oxidant, in this case sodium (meta) periodate (NaIO4), produces intense aggregates in the presence of EthD‐1‐intercalated DNA, realized by electrostatic interaction. In the absence of the target amplicon, no fluorescence or aggregates are observed. Using this kit, two major infectious viruses—severe fever with thrombocytopenia syndrome virus (SFTSV) and severe acute respiratory syndrome coronavirus (SARS‐CoV‐2)—were successfully detected in 1 h, and the limits of detection (LOD) were approximately 1 virus μL−1 for SFTSV and 103 copies μL−1 for SARS‐CoV‐2 RNA. The introduced kit is portable, end‐user‐friendly, and can be operated in a pipette‐free manner, paving the way for simple and convenient virus detection in resource‐limited settings.

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Synthetic biology-powered biosensors based on CRISPR/Cas mediated cascade signal amplification for precise RNA detection

Cited by 9 publications

References 40 publications

Nanomaterials-assisted gene editing and synthetic biology for optimizing the treatment of pulmonary diseases

Nanomaterials-assisted gene editing and synthetic biology for optimizing the treatment of pulmonary diseases

Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis

Pipette‐free field‐deployable molecular diagnostic kit for bimodal visual detection of infectious RNA viruses

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