Open Access Article
This Open Access Article is licensed under a Creative Commons Attribution-Non Commercial 3.0 Unported Licence

CRISPR–Cas based platforms for RNA detection: fundamentals and applications

Mahsa Bagi, Sina Jamalzadegan, Anastasiia Steksova and Qingshan Wei*
Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27605, USA. E-mail: qwei3@ncsu.edu

Received 9th June 2025 , Accepted 21st July 2025

First published on 21st July 2025


Abstract

The detection of RNA biomarkers is crucial for diagnosing many urgent diseases such as infections and cancer. Conventional RNA detection techniques such as RT-PCR, LAMP, and microarrays are effective, but often face limitations in terms of speed, sensitivity, and equipment demands. In recent years, CRISPR/Cas systems have emerged as versatile platforms for RNA detection, which offer high specificity, programmability, and adaptability across a wide range of diagnostic applications. This review first categorizes different CRISPR-based RNA detection systems according to the CRISPR effectors employed, including Cas13, Cas12, Cas14, Cas9, and newly characterized enzymes such as Cas7–11 and Cas10, detailing their mechanisms of target recognition, cleavage activity, and signal generation. The CRISPR detection platforms are coupled with or without pre-amplification steps to meet the different sensitivity needs. Preamplification-based systems integrate CRISPR with methods like RT-PCR and isothermal amplification to enhance sensitivity. In parallel, preamplification-free strategies, such as split-crRNA or split-activator systems, are gaining attention for their balanced assay performance and simplicity, which are especially attractive for point-of-care (POC) settings. Then, the diagnostic applications of these technologies are explored across two major domains: infectious disease detection and cancer biomarker identification via miRNAs, demonstrating the clinical potential of CRISPR-based RNA detection platforms. In addition, we explore ongoing challenges such as improving sensitivity in amplification-free formats, and developing field-deployable, cost-effective systems. The review concludes by outlining emerging trends and future directions in CRISPR-based RNA diagnostics, emphasizing their transformative potential in clinical settings.


Introduction

Ribonucleic acid (RNA), known as the intermediate molecule between DNA and protein, serves as a key nucleic acid for tracking cell performance.1 RNA detection is a versatile tool for monitoring cell status,2 detection of infectious diseases,3 as well as for diagnosis of cancer4 and neurodegenerative5 diseases by tracking gene expression. It can also be used to evaluate the efficiency of drugs designed to target specific molecular pathways by monitoring RNA molecules, and to confirm the success rate of CRISPR–Cas systems in achieving functional gene editing.

To date, a wide range of RNA detection techniques have been developed to target various RNA types, including microRNAs (miRNAs) and viral RNAs, across both in vitro and in vivo environments. Among the most commonly used methods are reverse transcription polymerase chain reaction (RT-PCR),6 quantitative reverse transcription PCR (RT-qPCR),7 northern blotting,8 RNA sequencing (RNA-Seq),9 in situ hybridization (ISH),10 and microarray11 technologies. Each of these approaches offers distinct advantages, yet they also present notable limitations. Specifically, many of these methods require expensive instrumentation, non-portable setups, labor-intensive protocols, and the expertise of trained laboratory personnel.12 To overcome these challenges, there is a growing need to develop RNA detection strategies that are compatible with portable, cost-effective, fast-response, and user-friendly devices. These innovations are particularly valuable for critical biomedical and biological applications, including the early diagnosis of viral infections, transcriptomic profiling in cancer and neurodegenerative diseases, and the monitoring of gene expression dynamics in cellular therapies and drug delivery systems. Consequently, advancing rapid and cost-effective RNA detection technologies remains a crucial objective to enhance the accessibility, timeliness, and impact of RNA-based diagnostics and therapeutic monitoring.

Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) enzymes, first identified in bacteria in 1987,13 represent a natural defense mechanism against invading genetic elements such as plasmids and phages.14 The transformative potential of the CRISPR–Cas system for genome editing was first demonstrated in 2012, when researchers successfully repurposed it for precise and programmable DNA cleavage.14 Later, around 2016–2017, specific Cas enzymes, particularly Cas13,15 were shown to possess high efficacy for nucleic acid detection, enabling the direct targeting of RNA molecules and the development of novel RNA-based diagnostic technologies. Compared to conventional RNA detection methods, CRISPR/Cas-based platforms offer rapid, highly sensitive, and highly specific detection that can be integrated into portable biosensor and point-of-care (POC) assay formats.16–18 Several types of Cas enzymes have been employed for RNA detection depending on whether the strategy relies on direct or indirect detection, pre-amplification-based or amplification-free approaches, and the choice of readout techniques, including fluorescent and non-fluorescent modalities.19

Several previous reviews have discussed the applicability of CRISPR-based diagnostic assays for detecting RNA nucleic acids.20–25 However, many existing works consider RNA detection within the broader context of nucleic acid diagnostics, which, while informative, may not fully elaborate on the unique challenges and strategies pertinent to the diverse array of RNA species, ranging from messenger RNAs (mRNAs), miRNAs, long non-coding RNAs (lncRNAs), to viral RNAs.21,26 From an experimental perspective, it is also critical to comprehensively discuss both preamplification-free and preamplification-based CRISPR mechanisms for RNA detection. This distinction is crucial for better understanding their respective advantages, limitations, and compatibility with different RNA targets, yet previous reviews often treat these strategies in isolation.25 In addition, it is important to highlight that CRISPR platforms for RNA detection are relevant not only for pathogenic RNAs, such as viral genomes, but also for non-pathogenic host RNAs that play key roles in complex diseases like cancer and neurodegeneration. Previous reviews, tend to concentrate on pathogen-oriented detection and may overlook other applications.21,26 Even the more targeted reviews27 that emphasize Cas13 and modified Cas9 systems for RNA imaging and detection, may not comprehensively address the full range of emerging RNA-targeting Cas effectors or offer a systematic comparison of their molecular mechanisms and application scopes across all RNA classes. As such, there is a need for a timely review that not only introduces CRISPR-based RNA detection as a transformative approach but also systematically categorizes strategies by Cas enzyme and RNA target, delves into their core biological mechanisms and comparative performance, and finally broadly surveys their applications and challenges, thereby bridging fundamental science with real-world utility.

In this review, we first provide an overview of RNA detection and its significance in modern diagnostics, outlining traditional RNA detection methods along with their inherent limitations (Fig. 1). We then introduce CRISPR-based RNA detection as a promising and transformative approach that addresses many of these challenges. Following this, we categorize the various CRISPR-based RNA detection strategies, including platforms built upon various Cas enzymes. For each platform, we comprehensively explore the fundamental biological mechanisms involved, distinguishing between preamplification-based and amplification-free detection strategies, and we summarize their unique advantages and disadvantages. Next, we highlight recent applications of CRISPR-based RNA detection across a wide range of fields, from infectious disease diagnostics to cancer biomarker detection, and from neurodegenerative disease monitoring to agricultural and environmental surveillance. Finally, we prospect the current challenges that still limit the widespread adoption of these platforms in real-world applications, considering critical dimensions such as sensitivity, specificity, scalability, and regulatory pathways. We also propose our future perspectives to advance CRISPR/Cas-based RNA detection technologies and bring them closer to their full clinical and environmental potential. To summarize, Sections 1–5 detail various CRISPR–Cas based detection platforms and their characteristics, while Sections 6–8 explore their diverse diagnostic and monitoring applications across different domains.


image file: d5cc03257a-f1.tif
Fig. 1 RNA detection with various CRISPR–Cas systems and their applications in detecting viral RNA and cancer-related RNA biomarkers.

1. RNA detection using CRISPR–Cas13

The general idea behind CRISPR–Cas diagnostics is based on the activation of the Cas protein upon binding to a specific target. The activation of Cas enzymes will then lead to different types of cleavage behaviors, which are eventually translated into different signal generation for detection. Unlike Cas9 enzymes, which was originally characterized for its site-specific (cis) cleavage activity (Fig. 2a) and widely adopted for genome editing applications, Cas12 and Cas13 enzymes are distinguished by their robust trans-cleavage activities, a unique feature that has been strategically utilized for nucleic acid detection in molecular diagnostics.23 The phenomenon of trans-cleavage, also known as collateral cleavage, is the core mechanism enabling signal amplification in several leading CRISPR-based diagnostic platforms. Depending on the Cas enzyme involved, the molecular basis and substrate specificity of this non-specific nuclease activity differ. For Cas12 enzymes, such as Cas12a and Cas12b classes, the guide RNA-directed recognition of a specific DNA target activates the enzyme's single RuvC nuclease domain. This activation not only results in cleavage of the intended double-stranded DNA (dsDNA) target (cis-cleavage) but also unleashes an indiscriminate nuclease activity (trans-cleavage) that degrades surrounding, non-target single-stranded DNA (ssDNA) molecules28 (Fig. 2b). Also, Cas14 enzymes, are RNA-guided nucleases that specifically recognize ssDNA targets, and upon binding, activate a collateral cleavage mechanism that indiscriminately degrades nearby non-target ssDNA molecules.29
image file: d5cc03257a-f2.tif
Fig. 2 Detection mechanisms of different Cas effectors. (a) Schematic illustration of Cas 9-based cis-cleavage for dsDNA target. (b) Schematic of Cas12-induced cis-cleavage and trans-cleavage (non-target cleavage) mechanism for dsDNA target detection. (c) Schematic of Cas13-assisted cis-cleavage and trans-cleavage (non-target cleavage) for ssRNA target detection.17

In contrast, the Cas13 family exclusively targets RNA. Upon recognition of its specific single-stranded RNA (ssRNA) target, Cas13's two HEPN (higher eukaryotes and prokaryotes nucleotide-binding) domains become catalytically active, triggering promiscuous degradation of surrounding non-target ssRNA (Fig. 2c). This trans-cleavage property is utilized in diagnostic platforms by introducing engineered reporter RNA molecules, the cleavage of which produces a detectable signal, such as fluorescence or a colorimetric change.28 Cas13-based diagnostics first emerged with the SHERLOCK system developed by Zhang's group in 2017 and have since evolved significantly, with numerous amplification strategies developed to improve sensitivity and adaptability for different applications.30

1.1. Preamplification-based RNA detection. While Cas13 itself offers a powerful mechanism for signal generation, many detection platforms incorporate a nucleic acid preamplification step to improve sensitivity, especially when working with low-abundance RNA targets. Preamplification not only increases the quantity of target nucleic acids but also facilitates easier activation of Cas13 enzymes by generating more substrates. One such approach is SATCAS, developed by Ting Wang et al. in 2024,31 which combines simultaneous amplification and testing (SAT) reactions with Cas13a-mediated cleavage in a single-pot system (Table 1). The process begins with reverse transcription of the RNA target into complementary DNA (cDNA), followed by hybridization and extension using specific primers. A T7 promoter is introduced during this stage, enabling the newly synthesized double-stranded DNA to undergo transcription by T7 RNA polymerase, resulting in abundant RNA products. Each template can yield hundreds to thousands of RNA copies, which are then recognized by Cas13a. Cas13a's trans-cleavage activity is triggered, and it is able to cut RNA reporter molecule, releasing a fluorescence signal. This integrated and efficient system achieved a limit of detection (LOD) as low as 0.1 aM in 40 minutes, showing its potential for rapid and sensitive viral diagnostics.
Table 1 CRISPR–Cas13-based RNA detection methods integrated with various preamplification strategies
Type of enzyme Preamplification Target LOD Read out Rxn time Ref.
LbuCas13a RT-PCR SARS-CoV-2 10 nM Lateral flow and flour-plate reader ∼90 min for fluorescence 35 min for lateral flow 41
Cas13a RT-PCR SARS-CoV-2 RNA, HBV RNA, LMP1 gene of EBV, EBNA gene of EBV 0.1 aM Fluorescence (naked eye under blue light) 40 min 31
LbuCas13a RT-PCR SARS-CoV-2 2 aM Fluorescence, colorimetric, or electrochemical methods 90 min 42
Cas13a RT-PCR, RT-RAA Hepatitis delta virus (HDV) 10 copies per μL Fluorescence and lateral flow strip 60 min for fluorescence; 33 min for lateral flow 43
LwaCas13a LAMP miRNA-7 (ciRS-7) 1 fM Fluorescence 30 min 44
LwCas13a RT-LAMP SARS-CoV-2 42 copies per reaction Fluorescence, lateral flow 70 min 45
Cas13 RT-RPA Mitochondrial DNA or RNA, reverse transcription of environmental RNA (eRNA) 22.6 ng μL−1 Lateral flow/flour-plate reader 60 min 46
LwCas13a RPA Y. ruckeri DNA and transcripts sRNAs 2 fM Fluorescence or lateral flow 70 min 47
LwaCas13a RPA V. alginolyticus 10 copies per μL Fluorescence or lateral flow 50 min 48
LwaCas13a RT-RPA Fusarium graminearum and Fusarium verticillioides Few copies of DNA/RNA targets Fluorescence, lateral flow 26 min 49
LwaCas13a CHA SARS-CoV-2 84 aM Fluorescence 45 min 50
Cas13a CHA, HCR SARS-CoV-2 285 fM Fluorescence 70 min 51
Cas13a HCR Biomarker such as brain natriuretic peptide (BNP) 3.2 fg mL−1 Electrochemiluminescence (ECL) 80 min 52
LbuCas13a bHCR miRNA-106a 8.55 aM Polyacrylamide gel electrophoresis (PAGE), fluorescence and SERS 60 min 36
LwaCas13a CHDC miRNA-17, miRNA-155, TTF-1 mRNA, miRNA-19b, miRNA-210 and EGFR mRNA 50 aM Electrochemical biosensor 36 min 33
Cas13a Endonuclease-mediated cyclic fluorescent amplification SARS-CoV-2 74.13 aM Fluorescence 60 min 53
LwaCas13a Endonuclease cycle amplification sja-miR-2c5p 83.2 fM Magnetic upconversion nanoparticles (MUCNPs) as a biosensor 60 min 54
Cas13a DNAzyme-mediated signal amplification miRNA-21 27 fM Colorimetry 30 min 55
Cas13a Entropy-driven cyclic amplification strategy SARS-CoV-2 7.39 aM Electrochemiluminescence (ECL) 80 min 56
Cas13a T7 circRNA, miRNA, piRNA, and 16S rRNA 1.65 aM QD-based FRET nano sensor 60 min 34
Cas13a Rolling circle transcription (RCT) piR-hsa-14 3.32 fM Flour-plate reader 70 min 57
LwaCas13a Transcription mediated amplification (TMA) P. jirovecii-mitochondrial large subunit ribosomal RNA 2 copies per μL Fluorescence 60 min 58
Cas13a Mn/NiCo2O4 nanozyme as a signal amplifier miRNA-143 Tens of aM Colorimetric and fluorometric 60 min 59
Cas13a Exo-III activity on the Ag+-aptamer miRNA-155 5.12 fM Colorimetry 75 min 37
Cas13a No miRNA-21 10 aM Fluorescence 60 min 40
Cas13a No E. coli, bacterial RNA 0.65 fM Electrochemiluminescence, bipolar electrode-ECL lateral flow chip 20 min 60
LwaCas13a No SARS-CoV-2 100 aM Capillary sensor (RNA-cross-linking DNA hydrogel film) 30 min 61
Cas13a No SARS-CoV-2 10 fM Fluorescence 60 min 62
LwaCas13a No Synthetic RNA, pseudovirus 100 fM On-chip total internal reflection fluorescence microscopy ∼45 min 63
Cas13a No miRNA-21 9 fM Photoelectrochemical (PEC) biosensing platform 60 min 64
Cas13a No Enterovirus B, Lassa, dengue, influenza A 1010 copies per μL Fluorescence 65
Cas13a No Human circular RNA 0.089 fM Electrochemical biosensor ∼10 min 66
LbCas13a No SARS-CoV-2 2 aM Fluorescence 15 min 39
LwCas13a No Y. pestis, F. tularensis, Chlamydia psittaci, B. mallei, B. pseudomallei, and Brucella melitensis 1 pM FAM-RNA-MB electrochemical signal probe 25 min 67
LwCas13a No SARS-CoV-2 26.2 and 53.5 copies per μL Electrochemical biosensor 200 min 68
LwaCas13a No Ebola RNA 291 aM Fluorescence 40 min 69
LbuCas13a No SARS-CoV-2 10 aM Fluorescence, digital droplets ∼10–20 min 70
LbuCas13a No miRNA-21 75 aM Fluorescent 30 min 71
LwCas13a No SARS-CoV-2 ∼200 copies per sample Lateral-flow assay (LFA) 2 min lateral flow plus SHERLOCK 72
Cas13a No lncRNA H19 Fluorescence 73
6× His-twinstrep-SUMO-huLwCas13a Light-triggered exponential amplification, RCA miRNA-21 1 fM ssDNA reporter with photocleavable linker 80 min 38
Cas13a–Cas12 Cas13a–12a amplification miRNA-155 0.35 fM Fluorescence 75 min 35
Hybrid Cas12a and Cas13a with SpyTag–SpyCatcher RT-RPA SARS-CoV-2 10 copies per reaction tube Fluorescence   74


Building on the same principle of integrating isothermal amplification with Cas13 detection, Xiao Wang et al. in 202432 reported a system for detecting synthetic monkeypox virus (MPXV) using recombinase-aided amplification (RAA) followed by T7 transcription and Cas13a–crRNA targeting (Fig. 3d). The output relies on a fluorogenic RNA aptamer (mango III), which emits fluorescence upon binding to the TO3 fluorophore. When Cas13a remains inactive, the aptamer stays intact, and fluorescence is observed. However, if the target is present, Cas13a becomes activated and cleaves the RNA aptamer, disrupting its structure and suppressing fluorescence. This fluorescent change, detectable under UV light, offers a simple and equipment-free readout. Impressively, the system achieved detection of as few as 4 copies of the target RNA within 40 minutes.


image file: d5cc03257a-f3.tif
Fig. 3 (a) Schematic illustrating detection of RNA target using PddCas13a.40 (b) Schematic of Cas13-triggered PC-ssDNA release from the MB@PC-NAC, padlock generation and under UV light and RCA amplification.38 (c) Cas-CHDC-powered electrochemical RNA-sensing technology (COMET) chip: off-chip signal amplification (i), on-chip RNA measurements (ii), and square wave voltammetry (SWV) readout (iii).33 (d) Schematic illustrating detection with RAA-Cas13a-Apt system.32 (e) Schematic illustrating RNA detection using CRISPR/Cas13a-triggered exonuclease-iii-assisted colorimetric assay.37 (f) Schematic illustration of Cas13a–12a amplification fluorescence biosensing platform.35 Pdd: polydisperse droplet digital; PC: photocleavable linker; MB: methylene blue; PC-NAC: photocleavable linker-nucleic acid probe; RCA: rolling circle amplification; CHDC: catalytic hairpin DNA circuit; RAA: recombinase-aided amplification; Apt: aptamer.

Taking a different approach to amplify, Yan Sheng and colleagues (2021)33 developed a reusable electrochemical biosensor that combines Cas13a with a catalytic hairpin DNA circuit (CHDC) (Fig. 3c). After Cas13a is activated by a target RNA, it initiates the CHDC, an enzyme-free signal amplification mechanism made up of two DNA hairpins. The first hairpin undergoes structural changes upon hybridizing with a nucleic acid trigger, while the second hairpin displaces the initial trigger in a strand-exchange reaction. Multiple cycles of hybridization process amplify the detection signal, which is then observed by an integrated screen-printed electrode chip. The detection, performed using square-wave voltammetry, achieves a LOD of 50 aM with a total process time of 36 minutes, highlighting the potential for low-cost, reusable, and highly sensitive RNA diagnostics.

Researchers have also identified additional preamplification strategies that offer unique advantages to enhance signal amplification further and broaden diagnostic applicability. One such method further refines sensitivity through a transcription-driven amplification mechanism. In a 2024 study, Wen-jing Liu and colleagues introduced a nanosensor system that couples Cas13a with quantum dot (QD)-based fluorescence readout (Table 1). Upon target RNA recognition, Cas13a cleaves a substrate probe that subsequently serves as a promoter for T7 RNA polymerase. The resulting RNA transcripts are amplified and hybridized with both biotinylated capture probes and Cy5-labeled reporter probes anchored to a single QD.34 This proximity facilitates fluorescence resonance energy transfer (FRET) between the QD and Cy5, generating a detectable signal with remarkable sensitivity—achieving a LOD of 1.65 aM in 60 minutes. The strategy elegantly combines enzymatic signal amplification with nanotechnology to offer precise detection in complex clinical samples, such as breast cancer tissue.

In a complementary direction, Dan Zhao et al. (2023)35 introduced a dual-enzyme platform that integrates Cas13a with Cas12a in a sequential activation cascade (Fig. 3f). In this system, the target RNA activates Cas13a, which then cleaves a bulge structure within a blocker strand immobilized on magnetic beads. This cleavage event releases a primer strand that subsequently activates Cas12a. Once triggered, Cas12a initiates its well-known trans-cleavage activity, cutting a fluorescent ssDNA reporter and producing a measurable signal. This elegant two-stage mechanism enhances the detection sensitivity by combing two CRISPR activities, enabling signal amplification while maintaining sequence specificity. It exemplifies how multiple Cas systems can be strategically combined to maximize diagnostic performance.

Another creative use of amplification comes from the work of Jingjing Zhang et al. (2023),36 who merged Cas13a activity with a branched hybridization chain reaction (bHCR) (Table 1). In this system, Cas13a's trans-cleavage is used to initiate the bHCR by cleaving an RNA substrate that releases an initiator. This initiator then hybridizes to a DNA hairpin, triggering a chain reaction where subsequent hairpins unfold and extend the signal cascade. The resulting branched DNA structures are then detected using a surface-enhanced Raman scattering (SERS) sensor composed of silver nanorods. This method achieved a detection limit of 8.55 aM within 60 minutes, offering not only high sensitivity but also compatibility with SERS-based biosensing for label-free, multiplex-capable detection.

Building on these innovative signal amplification designs, Yunxiao Li et al. (2024)37 introduced an exonuclease-III (Exo-III)-assisted Cas13a detection platform, integrating a clever aptamer-based reporting mechanism (Fig. 3e). Here, target activation triggers Cas13a to cleave a structured probe, releasing an intermediate strand that unlocks a silver ion (Ag+)-binding aptamer. This structural change forms a protruding 3′-terminus, which is specifically recognized and cleaved by Exo-III, releasing Ag+ ions. The liberated silver ions are then chelated again by excess aptamer, generating a visible color change. This multi-step cascade achieved a detection limit of 5.12 fM in around 110 minutes, demonstrating the effectiveness of integrating enzymatic cleavage and metal-ion signaling into CRISPR diagnostics.

Taking signal amplification a step further, Tao Hu et al. (2023)38 presented a light-triggered system combining Cas13a and rolling circle amplification (RCA) (Fig. 3b). Upon target binding, Cas13a cleaves RNA substrates embedded in a photocleavable complex, enabling the light-controlled release of short DNA oligos with 5′ phosphate ends. These oligos serve as triggers for RCA, generating long ssDNA products that emit strong fluorescence signals. This system achieves an excellent detection limit of 1 fM for RNA targets in 80 minutes.

1.2. Preamplification-free RNA detection. Cas13a has also been effectively used in systems that do not require preamplification and instead rely only on its inherent enzymatic activity. Many of these approaches capitalize on fluorescence-based readouts, where signal generation is directly linked to Cas13a's collateral cleavage once the target RNA is recognized.

One such example is described by Dou Wang (2023),39 who developed a digital detection platform using magnetic beads to enrich and capture RNA targets (Table 1). Upon recognition, the Cas13a–crRNA complex initiates cleavage of a reporter molecule. The cleavage products are then compartmentalized into a femtoliter-scale microwell array, allowing individual fluorescent events to be resolved and quantified digitally. This digital assay approach minimizes background noise and supports highly sensitive detection.

A different but equally elegant strategy was proposed by Ke Wang et al. (2025)40 through the development of polydisperse droplet digital (Pdd) Cas13a, a system utilizing polydisperse water-in-oil droplets as independent reaction chambers (Fig. 3a). In this setup, RNA molecules—including noncoding RNAs relevant to cancer—are randomly partitioned into thousands of droplets. Following incubation, only those droplets that contain target RNA activate Cas13a and emit a fluorescent signal. After 60 minutes, the droplets can be visualized under a fluorescence microscope, achieving a remarkable LOD as low as 10 aM. By combining microfluidics with Cas13-based collateral cleavage, this droplet system provides a scalable and precise method for digital RNA quantification without amplification.

2. RNA detection using CRISPR–Cas12

CRISPR–Cas12 enzymes, part of the type V CRISPR–Cas systems, have attracted increasing attention in the field of molecular diagnostics due to their robust and programmable collateral cleavage activity. Among the 11 known subtypes of CRISPR–Cas12 (Cas12a–u), only a subset—Cas12a (V-A), Cas12b (V-B), Cas12g (V-G), and more recently Cas12j (also known as CasΦ), has been utilized in RNA detection platforms (Table 2).26,75 However, these enzymes differ in their mechanisms of RNA sensing. Cas12a, Cas12b, and Cas12j are inherently DNA-targeting nucleases, meaning they require the target RNA to be first reverse transcribed into complementary DNA (cDNA) before detection. In these systems, the detection is typically coupled with preamplification methods such as RT-RPA or RT-LAMP to generate DNA substrates that activate Cas12-mediated collateral cleavage. In contrast, Cas12g stands out as a unique subtype with the intrinsic ability to directly bind and cleave single-stranded RNA (ssRNA) targets, eliminating the need for reverse transcription or DNA intermediates.76 This feature makes Cas12g a particularly attractive tool for streamlined and rapid RNA diagnostics. The following sections detail the use of each Cas12 subtype in RNA detection, with a focus on the preamplification techniques applied to identify various RNA targets.
Table 2 CRISPR–Cas12-based RNA detection methods integrated with various preamplification strategies
Type of enzyme Preamplification Target RNA LOD Read out Rxn time Ref.
Cas12a PCR Infected cell culture aM Fluorescence ∼1 h 77
Cas12a RT-PCR SARS-CoV-2 0.5 copies per mL Fluorescence 110 minutes 78
Cas12a RT-LAMP SARS-CoV-2 5 copies Fluorescent detection by naked eye under blue light   81
LbCas12a LAMP RNAseP POP7 mRNA 16 copies per μL 110 minutes 109
Cas12a RPA/RT-RPA Ebola virus 11 aM μPAD readout 1–4 h 110
Cas12a RPA HRSV 100 copies per mL Fluorescence 60 minutes 111
Cas12a RPA HIV 200 copies per test Glucose meter ∼70 minutes 112
AsCas12a, LbCas12a RPA/RT-RPA Rice black-streaked dwarf virus (RBSDV) 1 aM Lateral flow and fluorescence 30–60 minutes 49
Cas12a RT-RPA Potato virus X (PVX), and potato virus Y (PVY) fM levels Fluorescence ∼20 minutes 113
Cas12a RT-RPA Conserved fragments within the VP2 gene of the norovirus GII.2 subtype 10 copies per μL Lateral flow 25–35 minutes 114
LbCas12a RPA/RT-RPA Apple necrotic mosaic virus (ApNMV), apple stem pitting virus (ASPV), apple stem grooving virus (ASGV), apple chlorotic leaf spot virus (ACLSV) and apple scar skin viroid (ASSVd) 25 viral copies per reaction Oligonucleotide-conjugated gold nanoparticles ∼50 minutes 115
LbCas12a RPA/RT-RPA microRNAs ∼aM G-quadruplex (G4) containing a hairpin structure as the reporter 1 hour preamplification + 40 minutes CRISPR rxn 116
Cas12a RPA miRNA-21 3.43 aM Fluorescence 40 min preamplification + 30 min cleavage reaction 117
Cas12a RPA/RT-RPA SARS-CoV-2 0.38 copies per μL Fluorescence detection using a smartphone-based device 15 minutes 79
SuCas12a2 RPA Transcripts of the EhPrx and p1 genes 102 copies per reaction Fluorescence 40 min preamplification + 45 min cleavage reaction 118
MeCas12a (manganese-enhanced Cas12a) RT-RAA MERS-CoV 5 copies Fluorescence ∼45 min 82
Cas12a RT-RAA SARS-CoV-2 1 copy per μL Fluorescence 15 min preamplification + 10 min cleavage reaction 119
Cas12a RCA SARS-CoV-2 604 fM Portable glucose meter (PGM) 3 h 120
Cas12a RCA miRNAs 0.52 aM Electrochemical ∼4 h 121
Cas12a RCA miRNA-21 16 aM Chemiluminescence (CL) ∼4 h 122
Cas12a Hyperbranched rolling circle amplification (HRCA) miRNA-21 10.02 fM Fluorescence 223 min preamplification + 50 min cleavage reaction 123
LbCas12a HRCA miRNA-143 1 fM Gold nanoparticle (AuNP)-based visual assay 45–50 minutes 124
Cas12a Branched rolling circle amplification (BRCA) Primers incorporating ncRNA sequences of circulating CRC-associated RNAs (piRNA) 12 pM Fluorescence One hour 125
FnCas12a Multiply-primed rolling circle amplification (MRCA) SARS-CoV-2 1.625 copies per reaction Lateral flow and fluorescence 20 to 60 minutes 83
Cas12a 3D DNA walker cascade amplification miRNA-214 20.42 fM Fluorescence   126
LbCas12a 3D DNA walker cascade amplification miRNA-141 0.331 fM Electrochemiluminescence (ECL) 1 day and a few hours 127
Cas12a Reverse transcription-free exponential amplification reaction (RTF-EXPAR) SARS-CoV-2 3.77 aM Fluorescence 40 minutes 128
Cas12a EXPAR miRNA-21 3.2 fM Electrochemical 80 minutes preamplification + 40 minutes cleavage reaction 85
Cas12a EXPAR miRNA-155 85 aM Dual-channel fluorescence and colorimetric signal output 80 minutes preamplification + 20 minutes cleavage reaction 84
LbCas12a CHA miRNAs ∼100 fM ECL 2 hours 129
LbCas12a CHA miRNA-122 2.04 fM Photoelectrochemical (PEC) biosensor 3 hours preamplification + 1 hour cleavage reaction 130
LbCas12a CHA miRNA-21 0.48 fM ECL 131
LbCas12a CHA miR-128-3p 2.5 to 8.98 fM Fluorescence 75 minutes preamplification + 60 minutes cleavage reaction 87
Cas12a HCR miRNA-141 3.3 fM ECL 3 hours preamplification + 1 hour cleavage reaction 132
LbCas12a HCR miRNA-21 75.4 aM Fluorescence 2 hours preamplification + 8 minutes cleavage reaction 86
Cas12a T7 RNA polymerase miRNAs 1 pM Fluorescence 160 minutes preamplification + 150 minutes cleavage reaction 89
FnCas12a RT-HDA Deformed wing virus (DWV) RNA 500 fM Fluorescence 90 minutes preamplification + 45 minutes cleavage reaction 133
AsCas12a HDA SARS-CoV-2, SCGB2A2 (mammaglobin A) RNA 0.6 copies per μL Lateral flow and fluorescence 90 minutes 92
Cas12a Target-induced transcription process via split-T7 promoter miRNA-21 isothermal amplification 43.9 fM Fluorescence ∼1 h 134
Cas12a RCA + CHE Rabies viral RNA 2.8 pM ECL ∼11 h 135
Cas12a RCA + CHA microRNA-320d 0.342 fM ECL 3 hours RCA + 3 hours CHA + 2 hours Cas12a rxn 136
Cas12a Dual-signal amplification (Exo III amplification + RCA) miRNA-21 6.01 fM Fluorescence 5.5 hours 137
LbCas12a Strand displacement reaction (SDR) miRNA-21 2.42 fM Fluorescence ∼4 h 88
Cas12a Toehold-mediated strand displacement reaction (TSDR) SARS-CoV-2 40 aM Electrochemical 90 minutes preamplification + 40 minutes cleavage reaction 138
Cas12a Entropy-driven catalysis (EDC) cycle amplification miRNA-21 1.5 fM PEC ∼4 h 90
Cas12a Strand displacement amplification (SDA) miRNA-let-7a 6.28 pM Distance-based biosensor 1 hours preamplification + 30 minutes cleavage reaction 139
Cas12a SDA miRNA-21 0.5 fM Single-particle inductively coupled plasma-mass spectrometry 2 hours and 15 minutes 91
Cas12a Self-primer-initiated (SPI)-CRISPR–Cas12a-assisted amplification miRNAs 254 aM Functionalized gold nanoparticle (AuNP)-based color generation 60 minutes preamplification + 30 minutes cleavage reaction 140
Cas12a T7 exonuclease-assisted target recycling bacterial 16S rRNA (rRNA) 3.6 pM Split G-quadruplex (G4) catalytic signal output 141
Cas12a DENV 100 fM Electrochemical ∼30 min 142
Cas12a HCV RNA, miRNA-155 767 pM Fluorescence 1 h 98
Cas12a SARS-CoV-2 50 copies per μL Fluorescence ∼71 min 93
Cas12a SARS-CoV-2 50 copies per μL PGM 94
Cas12a SARS-CoV-2 10 fM Fluorescence 30 minutes 101
Cas12a miRNA-21 10 fM Fluorescence 5 minutes 96
Cas12a SARS-CoV-2 2.2 pM Fluorescence 45 minutes 95
Cas12a miRNA-21 301 fM Lateral flow assay 60 minutes 143
Cas12a miRNA-19a 856 aM Fluorescence 20 minutes 99
Cas12a miRNA-10b 5.53 fM Reverse fluorescence-enhanced lateral flow test strip (rFLTS) 40 minutes 144
Cas12a miRNA-21 10 pM Lateral flow and fluorescence 30 minutes 102
Cas12b RT-LAMP SARS-CoV-2, human adenovirus, herpes simplex virus 1 copy per μl Fluorescence 105
eBrCas12b RT-LAMP HCV Fluorescence ∼1 h 103
Cas12b LAMP mRNAs 10−8 nM Fluorescence 145
AapCas12b LAMP SARS-CoV-2 10 copies per reaction Lateral flow and fluorescence 146
AapCas12b RT-RPA SARS-CoV-2 8 copies per μL Fluorescence 30 to 60 minutes 106
BhCas12b No CJ8421_04975 mRNA from Campylobacter jejuni 1 μM Fluorescence 45 min 147
Cas12j EXPAR miRNA-92a, miRNA-122, and miRNA-155 3.2 fM Fluorescence 30 minutes 107
Cas12g PCR Gel electrophoresis 30 minutes 76
Cas12g Running the reaction samples on 20% PAGE TBE-urea denaturing gels stained with GelRed nucleic acid gel stain, and the results were visualized using alpha innotech (fluouchem TM) 108
Cas12g1 100–125 pM Gel electrophoresis 148


2.1. RNA detection using CRISPR–Cas12a.
2.1.1. Preamplification-based RNA detection. CRISPR–Cas12a has been widely employed for nucleic acid diagnostics across a variety of amplification strategies. These strategies can be broadly categorized into non-isothermal and isothermal methods, each with their own advantages and use cases.
2.1.1.1. Non-isothermal (PCR-based) methods. One of the earliest and most influential PCR–Cas12a platforms is the HOLMES system (one-hour low-cost multipurpose highly efficient system), developed by Li et al. (2018). This platform demonstrated Cas12a's utility in nucleic acid detection by leveraging its robust collateral cleavage activity. Although HOLMES is primarily a DNA detection system, it can also detect RNA targets by incorporating an initial reverse transcription (RT) step to convert RNA into cDNA. HOLMES also showcased the ability to distinguish single-nucleotide polymorphisms (SNPs) by modifying the protospacer adjacent motif (PAM) region or adjusting the crRNA guide sequence, with shorter guide RNAs enhancing specificity.77

Building on this, Ning et al. (2022) applied a Cas12a-based platform to detect RNA variants, again using reverse transcription by either RT-PCR or RT-RPA to generate DNA amplicons as targets for Cas12a detection. Notably, their system could discriminate between different viral variants by targeting mutations within the PAM or seed region and showed potential for smartphone-based POC diagnostics.78


2.1.1.2. Isothermal methods. While PCR-based amplification has long been the gold standard for nucleic acid detection, its reliance on thermal cycling limits its utility in low-resource and POC settings. By combining the specific collateral cleavage activity of Cas12a with various isothermal preamplification strategies, such as RPA, LAMP, RAA, RCA, and others, new diagnostic platforms have emerged that achieve high sensitivity and specificity without the need for complex thermal cycling equipment. Among these strategies, reverse transcription-recombinase polymerase amplification (RT-RPA) has proven particularly effective, enabling rapid RNA-to-DNA conversion and amplification under simple conditions. By integrating RT-RPA with Cas12a, the Crispr diagnostic platform enhanced fluorescence signal strength dramatically, making results detectable even with a cell phone (Fig. 4a). This not only boosted sensitivity but also pushed CRISPR diagnostics closer to true portability.79
image file: d5cc03257a-f4.tif
Fig. 4 (a) CRISPR–FDS assay optimization using spiked saliva samples. Steps 1–3 tested different lysis buffer dilutions to enhance RNA release and fluorescence detection.79 (b) Schematic showing CRISPR/LbCas12a trans-cleavage activity on gold nanoparticles (AuNPs) for direct RNA detection.96 (c) Schematic illustrating the core principle and overall workflow of the RT-LAMP and Cas12a-based RNA detection assay.80 (d) Schematic of iSCAN-V2: a one-tube assay combining reverse transcription, amplification, and Cas12b-mediated collateral cleavage for fluorescent RNA detection from swab samples.106 (e) The process of viral RNA detection using reverse transcription recombinase-aided amplification (RT-RAA) combined with MeCas12a-based detection.82 (f) Workflow of the EXP-J reaction for lung cancer diagnosis.107 (g) Demonstration of the enzyme-catalyzed rolling circle amplification-assisted CRISPR/FnCas12a assay through stepwise reactions, with a conceptual diagram illustrating the padlock probe and PAM-free design.83 RT-LAMP: reverse transcription loop-mediated isothermal amplification; iSCAN: in vitro specific CRISPR-based assay for nucleic acids; RT-RAA: reverse transcription recombinase-aided amplification; EXP-J: exponential junction (EXP-junction) amplification.

Building on the momentum of RPA, researchers also explored loop-mediated isothermal amplification (RT-LAMP) as a complementary strategy. Unlike multi-step processes, LAMP allowed amplification and Cas12a-mediated detection to occur in a single vessel, reducing contamination risks (Fig. 4c).80 Systems like opvCRISPR exemplified this streamlined design, achieving near single-molecule detection sensitivity and offering simple visual readouts, making sophisticated molecular diagnostics accessible beyond laboratory environments.81 As efforts continued to refine speed and field applicability, recombinase-aided amplification (RT-RAA) emerged as another promising technique (Fig. 4e).82 Its low-temperature operation and rapid amplification kinetics made it ideal for time-critical diagnostics. When integrated into a centrifugal microfluidic platform, RT-RAA combined with Cas12a achieved detection of viral RNA at single-copy sensitivity within 30 minutes, with diagnostic accuracy matching that of RT-PCR. This demonstrated that high performance could be maintained even in settings where complex equipment is impractical.

While many platforms focused on cDNA intermediates, others sought to simplify the workflow further by bypassing reverse transcription altogether. Rolling circle amplification (RCA) provided such an opportunity. RCA-based systems like OPERATOR83 used RNA-templated DNA ligation to initiate multiply-primed amplification, producing products that directly activated Cas12a without prior cDNA synthesis (Fig. 4g). Ingenious variations like CRISPR–PGM even converted nucleic acid detection into glucose readouts measurable by handheld meters, illustrating how CRISPR and RCA could be combined for affordable, decentralized diagnostics.83

In pursuit of even faster and more sensitive assays, researchers turned to an exponential amplification reaction (EXPAR). EXPAR has emerged as a powerful partner to Cas12a in RNA diagnostics, offering rapid, highly sensitive, and versatile detection. By leveraging EXPAR's strong amplification capability alongside Cas12a's collateral cleavage activity, several studies have demonstrated detection of miRNAs and viral RNAs at femtomolar to attomolar concentrations.84 One key advantage of this approach is the high specificity achieved through Cas12a's sequence-specific recognition, which significantly reduces false positives often associated with isothermal amplification alone. Additionally, multiplex detection has been demonstrated using EXPAR-generated dual ssDNA triggers to activate Cas12a in an “AND” logic circuit, enabling the simultaneous detection of multiple miRNA targets with a single crRNA complex. EXPAR–Cas12a platforms have also introduced dual-mode signal outputs (e.g., fluorescence and colorimetric readouts), increasing result reliability and flexibility. Smartphone integration for POC testing (POCT) has further enhanced the portability and accessibility of these systems.85

Alongside these enzyme-driven amplifications, catalytic hairpin assembly (CHA) offered a simpler, enzyme-free alternative. Integrating CHA with Cas12a enabled one-step, highly sensitive RNA detection, sidestepping multiple handling steps and reducing contamination risk. Creative adaptations pushed detection sensitivity into the femtomolar range, while combination strategies involving photoelectrochemical and electrochemiluminescence methods enhanced signal clarity and minimized background noise. Notably, platforms like MCM-CRISPR/Cas12a, combining CHA with hybridization chain reaction (HCR),86 demonstrated detection of low-abundance non-coding RNAs with remarkable precision.87

Beyond the major amplification strategies discussed, other methods such as hybridization chain reaction (HCR),86 strand displacement reaction (SDR),88 binding-induced primer-triggered cascade (BPTC),89 enzyme-driven cascade amplification (EDC),90 and strand displacement amplification (SDA)91 have also been employed in Cas12a-based RNA detection. Though less commonly used, these approaches provide valuable enhancements in sensitivity, specificity, or workflow flexibility. Among these, one particularly notable system combined SDA with Cas12a and utilized single-particle inductively coupled plasma mass spectrometry (spICP-MS) for detection. This platform integrated SDA with Cas12a trans-cleavage and employed gold nanoparticle labels to achieve a quantification limit of 0.5 aM. With a 45-minute reaction time, this system offers an optimal balance of sensitivity and detection kinetics for clinical applications.91 Another innovative approach is the flap endonuclease, Taq ligase and CRISPR–Cas for diagnostics (X) (FELICX) platform, which uniquely uses flap endonuclease (FEN) for target recognition, enabling PAM-independent detection. This is followed by adaptor ligation and Cas12a activation, and when combined with RTx-HDA, FELICX achieved RNA detection at 1 aM sensitivity within 90 minutes. Its independence from guide RNA optimization and compatibility with both DNA and RNA targets make FELICX an exceptionally versatile and practical platform for molecular diagnostics.92


2.1.2. Preamplification-free methods.
2.1.2.1. Use of complete crRNA and complete activator. While many CRISPR/Cas12a-based platforms rely on nucleic acid preamplification to enhance sensitivity, several studies have developed strategies that enable RNA detection without preamplification. These amplification-free systems still harness the collateral cleavage activity of Cas12a, but differ in how they handle the RNA target, particularly in their requirements for reverse transcription and activation mechanisms. One major class within these approaches involves the use of a complete crRNA and a complete activator, preserving the canonical structure of the Cas12a system without splitting either component.

In some of these systems, although the detection phase is preamplification-free, the RNA target is first reverse transcribed into cDNA before Cas12a engagement which is due to the intrinsic nature of Cas12a that will be activated by DNA targets. In one such example, viral RNA was reverse transcribed using a gene-specific primer and a commercial reverse transcription kit. The resulting cDNA acted as a trigger for Cas12a, allowing the detection step to proceed without preamplification. This system maintained high sensitivity, reaching detection limits in the femtomolar range, despite skipping the preamplification phase.93 Another approach under the same category departs from direct activation of Cas12a by instead employing the RNA as a competitive inhibitor. In this method, Cas12a is initially activated by a target DNA sequence (tcDNA), which triggers its nonspecific ssDNA cleavage activity. When the target RNA is present, it competes with the DNA activator for binding to the crRNA–Cas12a complex, effectively suppressing Cas12a's trans-cleavage function. This change in enzymatic behaviour is then monitored electrochemically. Unlike the previous example, this system does not require reverse transcription, relying instead on RNA's ability to modulate Cas12a activity through competitive inhibition. This approach maintains an amplification-free and label-free detection process and reveals a different dimension of Cas12a's programmability, where signal generation can occur through inhibition rather than activation.94

Progressing further within the same category, recent studies have shown that Cas12a can, under certain optimized conditions, be directly activated by RNA targets, challenging the previous assumption that Cas12a only responds to DNA triggers. In one such example, functionalized magnetic beads were employed to capture the target RNA alongside signal probes containing activation sequences for Cas12a. Once bound, magnetic separation isolated the RNA-probe complex, and the addition of Cas12a and crRNA allowed the probe itself to activate Cas12a, leading to collateral cleavage of fluorescent reporters. Notably, the use of manganese ions in place of magnesium was found to enhance Cas12a's cleavage efficiency, enabling RNA detection with high precision in clinical settings, all without preamplification or reverse transcription. This technique delivered results within 45 minutes with a detection limit of 2.2 pM.95

In a parallel strategy, another group introduced a nanomaterial-enhanced system, where gold nanoparticle-based nanobeacons (Au-nanobeacons) were used as reporters. In this case, Cas12a's collateral activity was directly triggered by RNA, specifically miRNA, without any DNA intermediates or pre-treatment. The Au-nanobeacons offered both faster kinetics and enhanced sensitivity compared to traditional fluorescent reporters. With a detection limit reaching as low as 10 fM in just five minutes of reaction, and validated performance in serum, this method pushes the boundaries of what Cas12a can achieve in its unamplified, native state (Fig. 4b).96 In addition, a recent crRNA spacer regulation-based SCas12aV2 assay demonstrated robust and programmable Cas12a activation by leveraging stem–loop-structured crRNAs, enabling sensitive and modular detection of structured RNA targets, including SARS-CoV-2, miRNAs, and circRNAs, with high specificity and one-pot isothermal readout capability.97


2.1.2.2. Use of complete crRNA and split activator. Expanding on amplification-free strategies, a distinct class of RNA detection methods uses complete crRNA while employing split activators to initiate Cas12a trans-cleavage activity. A key advancement in this category was demonstrated by the SAHARA method, which revealed that while Cas12a requires DNA at the PAM-proximal seed region for activation, the PAM-distal region of the crRNA can interact with RNA. By supplying a short DNA oligonucleotide to occupy the seed region and using the RNA target to complement the distal part of crRNA, this method enables direct RNA detection without reverse transcription or preamplification.98

A separate yet related method employed a competitive displacement mechanism for signal enhancement. This technique utilized both a full-length crRNA and a split crRNA that compete for Cas12a binding. Initially, the full crRNA activates Cas12a upon binding to the RNA, triggering trans-cleavage. Subsequently, the split crRNA replaces the full one, initiating a secondary round of activation—thus creating a cascade signal amplification. While still based on complete crRNA recognition of the RNA target, this approach enhances sensitivity by exploiting the dynamic binding interactions between full and split crRNAs.99 More recently, a flexible “splice-at-will” crRNA engineering strategy was introduced, enabling precise and sensitive Cas12a-mediated detection of ultrashort RNAs (as short as 6–8 nt) by reconstituting functional crRNAs through modular hybridization at nearly any site within the spacer region using a DNA splint and target RNA.100

Together, these studies demonstrate how split activators, whether composed of synthetic DNA, endogenous RNA, or a combination can be paired with complete crRNA to create preamplification-free Cas12a systems. These systems elegantly bypass the need for reverse transcription by selectively engaging different regions of the crRNA with engineered or native sequences, broadening the toolkit for rapid, sensitive, and versatile RNA diagnostics.


2.1.2.3. Use of split crRNA and complete activator. The third and most recent strategy in the realm of amplification-free Cas12a-based RNA detection introduces a fundamentally different design: split crRNA systems paired with a complete activator. In these approaches, the standard crRNA is intentionally divided into two or more RNA fragments, each alone insufficient to activate Cas12a. However, when brought into close proximity by an activator or target molecule, these fragments reassemble into a functional guide, enabling controlled and highly specific RNA sensing.

A notable example of this concept is the PARC–Cas12a platform (proximity-activated RNA-guided Cas12a), where the crRNA is split within its 5′ handle domain. In this system, activation of Cas12a is contingent on the reconstitution of the guide RNA, which occurs only when the split fragments are spatially brought together. This is achieved through the hybridization of the fragments to a shared trigger molecule, such as an RNA sequence, aptamer, or small molecule. This proximity-dependent assembly transforms Cas12a into a programmable biosensor, enabling logic-gated detection schemes and multiplexing. The system has also been successfully integrated into platforms like arrayPARC–Cas12a and ICP-MS-based assays, enhancing its diagnostic versatility.101

Another important contribution in this space is the development of SCas12a, a system that fully separates the scaffold RNA from the spacer RNA. Here, the target RNA, such as a mature miRNA, directly serves as the spacer component. In the presence of a complementary ssDNA activator and the scaffold RNA, the miRNA completes the crRNA assembly and triggers Cas12a's trans-cleavage activity. This method achieves direct RNA detection without preamplification or reverse transcription and does so with high sensitivity in the femtomolar range. Moreover, SCas12a exhibits strong discrimination power, capable of distinguishing closely related sequences, as well as single-nucleotide variants.102

2.2. RNA detection using Cas12b. Recent years have seen considerable progress in adapting Cas12b enzymes for RNA detection, especially by leveraging their compatibility with one-pot isothermal amplification strategies. Among these, RT-LAMP has been the most widely utilized due to its high efficiency and optimal temperature range aligning with engineered Cas12b activity.

A major advancement in this area came with the engineering of a thermostable Cas12b variant, BrCas12b, derived from Brevibacillus sp. and further optimized through hydrophobic core mutations to enhance its trans-cleavage activity at elevated temperatures. This engineered enzyme, eBrCas12b, retains robust performance up to 67 °C, allowing seamless integration with RT-LAMP reactions.103 Utilizing this capability, the SPLENDID platform was developed, a single-pot LAMP-mediated detection assay clinically validated for RNA detection in human serum and saliva. The system demonstrated high specificity and accuracy, completing detection within one hour and outperforming standard RT-LAMP by significantly reducing false positives.103 Similarly, the STOPCovid assay used AapCas12b from Alicyclobacillus acidiphilus in a one-pot RT-LAMP setup, optimized through guide RNA engineering and the addition of taurine to accelerate reaction kinetics.104

Moving beyond conventional detection, the WS-RADICA platform introduced a digital format for Cas12b-based diagnostics. Combining warm-start RT-LAMP with Cas12b in a droplet-based digital system, this method achieved a detection limit as low as 1 copy per μL, offering both qualitative and quantitative output. Notably, WS-RADICA performed well under challenging conditions, showing tolerance to common inhibitors like EDTA and SDS, and providing quantification accuracy comparable to RT-qPCR and RT-dPCR, while offering faster results and broader application to both RNA and DNA viruses.105

In addition to RT-LAMP, RT-RPA has also been explored as a lower-temperature alternative. While less widely reported, some systems have demonstrated effective coupling of Cas12b with RT-RPA for rapid RNA detection, particularly when rapid deployment or reduced thermal requirements are needed. However, further engineering is typically required to adapt Cas12b's activity to RPA's temperature window. The iSCAN-V2 platform is another example of using RT-RPA, offering a one-pot, preamplification-based assay where Cas12b outperformed Cas12a in both signal strength and speed (Fig. 4d). The assay achieved a LOD of 8 copies per μL, and in clinical validation, it showed 93.75% sensitivity and 100% specificity, reliably detecting RNA in samples with cycle threshold (Ct) ≤ 30 in under an hour.106

Overall, Cas12b-based systems have rapidly evolved into highly sensitive, amplification-compatible diagnostic tools. Their thermal stability, compatibility with one-pot formats, and adaptability to digital platforms make them particularly promising for clinical, field-based, and POC RNA diagnostics, especially in settings requiring speed, specificity, and minimal equipment.

2.3. RNA detection using Cas12j. Cas12j, also known as CasΦ, represents a relatively new addition to the CRISPR–Cas family, and recent studies have begun to uncover its diagnostic potential. One significant advancement came from the work of Ju-Eun Kang, who demonstrated that Cas12j enzymes possess robust trans-cleavage activity suitable for nucleic acid detection. In particular, this study focused on detecting miRNA targets, which are often challenging due to their short length and low abundance. The researchers developed a method called EXP-J (exponential amplification reaction with Cas12j), which harnesses Cas12j's trans-cleavage activity to generate a fluorescent signal in response to target miRNAs (Fig. 4f). The system uses a two-part design: a converter and a repeater that together amplify a trigger DNA sequence (X) in the presence of the miRNA. This amplified trigger then activates Cas12j to cleave a fluorescent reporter, enabling sensitive detection. Three Cas12j variants, Cas12j1, Cas12j2, and Cas12j3, were tested, with Cas12j3 emerging as the most effective, showing the fastest signal response and the highest sensitivity. The method also proved to be highly specific, with signal generation observed only in the presence of the target miRNA. Furthermore, receiver operating characteristic (ROC) curve analysis revealed high diagnostic accuracy, confirming the assay's potential in clinical applications. Together, these findings highlight Cas12j3 as a promising enzyme for rapid, sensitive, and specific RNA detection, especially in the context of small RNA biomarkers like miRNAs.107
2.4. RNA detection using Cas12g. Among the Cas12 family, Cas12g stands out for its unique ability to function as an RNA-guided ribonuclease that directly targets single-stranded RNA (ssRNA). Unlike other Cas12 subtypes such as Cas12a, Cas12b, or Cas12e—which are primarily DNA-targeting enzymes, Cas12g operates more like a type VI effector, offering a natural platform for RNA-focused applications.76

Structural and biochemical analyses have confirmed Cas12g's capacity for RNA detection. It employs a dual-guide RNA system, a crRNA and a tracrRNA, or a fused single-guide RNA (sgRNA), to recognize RNA sequences with no requirement for a PAM or protospacer flanking sequence (PFS). This feature removes a major limitation in CRISPR targeting and simplifies assay design for RNA diagnostics. Importantly, studies have shown that Cas12g demonstrates comparable RNA detection sensitivity to top-performing Cas13 effectors, while maintaining thermostability, broadening its potential for both laboratory and POC applications.108

While no fully realized Cas12g-based diagnostic assay has yet been reported, recent cryo-EM and crystal structure analyses have provided critical insight into its molecular mechanism. These studies revealed Cas12g's bilobed architecture and highlighted key structural components, including the REC and NUC lobes, zinc finger motifs, and the lid motif within the RuvC domain, offering a blueprint for engineering future RNA detection platforms.76,108

3. RNA detection using CRISPR–Cas9

While Cas9 is widely known for its DNA-targeting capabilities, recent innovations have expanded its role into RNA detection, particularly by integrating it with preamplification strategies to enhance sensitivity (Table 3). One early approach, the Cas9 nickase-assisted isothermal amplification reaction (Cas9 nAR and its optimized version Cas9 nAR-v2), cleverly combines reverse transcription with strand displacement activity. Here, RNA is first transcribed into cDNA, and a rationally engineered Cas9 nickase introduces specific nicks that enable DNA polymerase to extend and amplify the target sequence under isothermal conditions, allowing rapid detection through fluorescence or lateral flow assays in a single reaction tube.149 To build on this specificity without relying on fluorescent labels, researchers adapted catalytically inactive Cas9 (dCas9) into a biosensing platform coupled with surface plasmon resonance (SPR). In this system, amplified targets hybridize to primers immobilized on a sensor surface, and dCas9 binding creates significant refractive index changes, boosting the detection signal through a simple, label-free readout.150
Table 3 RNA detection strategies employing CRISPR enzymes beyond Cas13 and Cas12 in combination with various preamplification methods
Type of enzyme Preamplification Target LOD Read out Rxn time Ref.
Cas10 subunit from the type III-A CRISPR complex RT-LAMP SARS-CoV-2 108 copies per reaction Fluorometric + colorimetric detection ∼1 h 165
Cas10 subunit of the type III-B Cmr complex RT-LAMP SARS-CoV-2 800 aM Fluorescence 180 minutes 166
Cas10 subunit within the type III-A CRISPR complex SARS-CoV-2 106 copies per μL Fluorescence, gel electrophoresis, thin-layer chromatography (TLC) 10 minutes 175
Cas10 subunit within the type III-B CRISPR complex SARS-CoV-2 8 fM Fluorescence 30–50 minutes 167
Cas10 subunit within the type III-A CRISPR complex Isothermal amplification SARS-CoV-2 aM Fluorescence 176
Cas10 No T. congolense 7SL-sRNA 10–100 fM Lateral flow and fluorescence ∼2 h 168
Cas10 subunit of the Staphylococcus epidermidis Cas10–Csm complex No Target RNA Denaturing PAGE and autoradiography 30–60 minutes 177
Cas10 subunit within various Csm complexes No Target RNA Denaturing gel electrophoresis and autoradiography Up to 120 minutes 170
Cas10 subunit of the Lactobacillus delbrueckii subsp. No miRNA-155 500 pM–2 nM Fluorescence 30–60 minutes 169
Cas10 subunit of the Sulfolobus tokodaii Csm (StCsm) complex No SARS-CoV-2 Agarose gel electrophoresis with EB staining 60 minutes 178
Cas7–11 Rt-RPA SARS-CoV-2 2 fM Fluorescence ∼2 h 171
Type III-E Cas7–11 Various target RNAs Fluorescence 172
Cas14a EDC miRNA-10b 2.1 pM Fluorescence spectrophotometry 2 hours 159
Cas14a1 Asymmetric PCR Bacterial RNAs (Streptococcus pyogenes and Eberthella typhi) 105 CFU per mL Fluorescence 2 hours 162
Cas14a RCA miRNA156a from banana 1.839 pM Fluorescence 2 hours 160
Cas14a SDA miRNA-21 680 fM Fluorescence 1 hours 164
Cas14 RT-LAMP RNA2 gene of the red-spotted grouper nervous necrosis virus (RGNNV) 63.4 aM Fluorescence 2 hours 161
Cas14a Toehold-containing three-way junction (TWJ) RNA viruses Magnetic separation enhanced colorimetry 179
Cas14 (AsCas12f1) No CJ8421_04975 mRNA from Campylobacter jejuni Fluorescence 147
Cas14a1 Transcription by T7 RNA polymerase 16S rRNA of bacteria (specifically E. typhi) 0.6 aM Fluorescence 1 hours 163
Pyrococcus furiosus (Pfu) Cas3 No ssRNA target 0.1–1 nM Fluorescence 15 minutes 180
E. coli Cas3 RT-LAMP SARS-CoV-2 <102 copies Lateral flow and fluorescence 32–42 minutes 173
fastCas9n (derived from Streptococcus pyogenes Cas9 (SpCas9)) Salmonella typhimurium 16S rRNA, Escherichia coli O157:H7 16S rRNA, synthetic SARS-CoV-2 genes (Orf1ab-a, Orf1ab-b, S gene, E gene, N gene), and HIV virus RNA ∼10 copies per rxn (20 μL volume) Lateral flow and fluorescence 50 minutes preamplification + 5–10 minutes cleavage reaction 149
dCas9 RT-RPA Bunyavirus RNA, the causative agent of severe fever with thrombocytopenia syndrome (SFTS) 0.63 aM Single microring resonator (SMR) biosensor 30 minutes 150
dCas9 LAMP HIV-1 0.96 copies per mL Bright field microscopy 70 minutes 152
dCas9 No Epstein-Barr virus encoded RNA (EBER) Fluorescence in situ hybridization (FISH) 20 minutes or less 157
dCas9 RCA miRNAs (miRNA-195) fM level Colorimetric ∼4 hours 153
Cas9 No SARS-CoV-2 3 × 108 copies of RNA Gel electrophoresis, fluorescence 156
Cas9 No Target RNA Picomolar level Denaturing polyacrylamide gel electrophoresis (PAGE) 158
Cas9 RCA miRNA-21, miRNA-221 90 fM Fluorescence 2 hours preamplification + 30–60 minutes cleavage reaction 154
Cas9 PCR RFP transgene Targeted sequencing 181
Cas9 Exponential amplification reaction (EXPAR) L. monocytogenes hemolysin (hly) mRNA 0.82 aM Fluorescence 151
Cas9 NASBA Zika virus Colorimetric 182
SpyCas9 RAA Respiratory syncytial virus A 98 copies per μL Fluorescence 2 hours 25 minutes 183
dCas9 RT-RPA SARS-CoV-2 2.5 copies per μL Lateral flow assay (LFA) 55 minutes 155


Recognizing the need for even faster and more sensitive detection, other strategies combined Cas9 cleavage with exponential amplification reactions (CAS-EXPAR). After site-specific cleavage of DNA or cDNA by Cas9/sgRNA, short fragments are generated that trigger an EXPAR cascade, exponentially amplifying the signal for highly sensitive, real-time RNA detection.151 The evolution toward digital detection led to the development of dCRISTOR, which uses dCas9-engineered magnetic micromotors. Following LAMP152 amplification of RNA, the micromotors bind specific amplicons and change their motion when exposed to a magnetic field, allowing deep learning algorithms to digitally classify positive or negative detection events. Further innovations explored rolling circle amplification (RCA) paired with Cas9 systems. In the RACE method, a padlock probe is ligated to the target miRNA, generating a circular template that undergoes RCA to produce long ssDNA, which is then recognized and cleaved by Cas9.153 Cleavage of a TaqMan probe releases fluorescence, directly indicating the presence of the target. Similarly, the RCH system harnesses RCA to bring together split horseradish peroxidase (HRP) fragments mediated by dCas9 binding, resulting in a visible color change upon substrate reaction (Fig. 5c).153,154


image file: d5cc03257a-f5.tif
Fig. 5 (a) Scheme of the EDC–Cas14a system for miRNA detection, where the target generates multiple activators that trigger Cas14a/sgRNA collateral cleavage for signal amplification.159 (b) Workflow of the CONAN RNA detection assay, including RNA extraction, RT-LAMP at 62 °C for 20–30 minutes, the CONAN reaction at 37 °C for 10 minutes, and lateral flow detection at room temperature for 2 minutes.173 (c) Schematic of the RCH workflow for miRNA detection using RCA to amplify target signals into large DNA structures. CRISPR–dCas9 with split-HRP fusion enables secondary amplification by binding to RCA products.153 (d) Outline of the type III CRISPR–Cas (Cmr)/NucC assay for RNA detection, where target RNA binding activates Cas10 to produce cA3 molecules. cA3 activates NucC, leading to degradation of a dsDNA reporter labeled with a fluorophore–quencher pair.168 EDC: entropy-driven circuit; CONAN: CRISPR–Cas3-operated nucleic acid detection; RCH: rolling circle hybridization; RCA: rolling circle amplification; dCas9: catalytically dead CRISPR-associated protein 9; HRP: horseradish peroxidase.

Finally, aiming for the simplest and most portable format, the Vigilant platform was introduced. This system fuses dCas9 with the VirD2 relaxase to enable a lateral flow assay, where amplified biotinylated targets are captured by VirD2–dCas9 complexes tagged with FAM-labeled oligonucleotides. A standard lateral flow strip detects these complexes visually through streptavidin and anti-FAM antibodies, allowing rapid and accessible RNA diagnostics.155

Beyond preamplification-assisted methods, research has revealed that Cas9 can directly enable amplification-free RNA detection, offering simpler and faster diagnostic possibilities. A major breakthrough was LEOPARD, where reprogrammed tracrRNA allowed Cas9 to bind RNA targets and cleave fluorescent DNA reporters, enabling multiplexed RNA detection without amplification.156 Expanding into imaging, Chen et al. introduced RCasFISH, using dCas9 combined with sgRNAs bearing MS2 aptamers. Upon binding to RNA inside fixed cells, fluorescent MS2 proteins visualize RNA targets without amplification, preserving spatial information with high specificity.157

Even more striking, Strutt et al. discovered that certain Cas9 homologs, such as Staphylococcus aureus Cas9 (SauCas9) and Campylobacter jejuni Cas9 (CjeCas9), can inherently recognize and cleave RNA without the need for a PAM or PAMmer, instead solely guided by sgRNA. This native RNA-targeting ability expands Cas9's potential for amplification-free RNA diagnostics and direct RNA manipulation in cells.158

4. RNA detection using CRISPR–Cas14 (Cas12f)

Cas14, also known as Cas12f, is the smallest effector in the type V CRISPR–Cas family and has recently gained attention for its strong trans-cleavage activity on single-stranded DNA (ssDNA). Although initially applied in DNA detection, Cas14a and Cas14a1 have been increasingly adapted for RNA diagnostics using strategies that convert RNA into DNA intermediates or activate the enzyme directly (Table 3).75

Several preamplification strategies have been employed to harness Cas14's potential in RNA detection. Among these, a dual amplification system developed by Shu et al. that integrates an entropy-driven circuit (EDC) with Cas14a showed significant performance enhancement. This one-pot setup continuously produces ssDNA activators that stimulate Cas14a's trans-cleavage, improving sensitivity by 100-fold over Cas14a alone (Fig. 5a).159 In another approach, a rolling circle amplification (RCA)–Cas14 system enabled the detection of plant miRNAs without requiring reverse transcription. The ligation-triggered amplification and Cas14a activation provided single-nucleotide resolution and a detection limit as low as 1 pM, demonstrating applicability in plant molecular diagnostics.160 In viral detection, Cas14a was paired with a RT-LAMP assay, using primers engineered with PAM sequences to enable Cas14a activation. This system achieved an impressive LOD of aM level, demonstrating exceptional sensitivity and compatibility with simplified magnetic bead-based RNA extraction.161

Additionally, Cas14a1 has been shown to function in asymmetric PCR-based assays for DNA diagnostics, with reported LODs down to the attomolar range. While primarily applied to DNA targets such as the SMN1 exon 7 deletion, these results support Cas14a1's potential for high-sensitivity nucleic acid detection. Most notably, Cas14a1 has also been shown to support amplification-free RNA detection.162 A platform known as ATCas-RNA demonstrated that RNA can directly trigger Cas14a1's trans-cleavage activity without undergoing degradation. This system exhibited excellent specificity, including the ability to distinguish single-base mismatches, and achieved a remarkable LOD of 1 aM. By removing the need for reverse transcription or amplification, this method represents a major step forward in streamlining RNA diagnostics.163

In summary, Cas14a and Cas14a1 have proven to be flexible, powerful tools for RNA detection, operating effectively across diverse preamplification strategies, including EDC,159 RCA,160 SDA,164 and RT-LAMP,161 and excelling even in amplification-free147 formats. Their compact size, high sensitivity, and programmability make them strong contenders for portable, rapid, and precise RNA diagnostics.

5. RNA detection using other CRISPR–Cas platforms

5.1. CRISPR–Cas10. While most CRISPR-based RNA diagnostic platforms have centered around type V (e.g., Cas12) and type VI (e.g., Cas13) systems, a growing number of studies have demonstrated the diagnostic potential of less conventional CRISPR–Cas effectors such as Cas10, Cas7–11, and Cas3 (Table 3). These systems offer unique mechanisms and modular components that expand the versatility of RNA detection approaches.

A notable example is Cas10, the signature effector of type III CRISPR–Cas systems, which has been successfully adapted for RNA detection using RT-LAMP-based preamplification.165 In one study, the type III-A TtCsm complex from Thermus thermophilus was repurposed for RNA detection. Upon RNA target binding, the Cas10 subunit exhibited polymerase activity, producing cyclic oligoadenylates (cOAs), pyrophosphates, and protons. These by-products were detected using a combination of colorimetric dyes (e.g., phenol red, malachite green), fluorometric reporters (calcein), and TtCsm6-activated fluorescent cleavage. Sensitivity was significantly enhanced by combining RT-LAMP with T7 transcription, reducing the detection limit from ∼108 copies per reaction to 106 copies per mL in clinical samples, with results available in under an hour.165

A second study introduced SCOPE, a diagnostic platform based on the type III-B Cmr complex, which similarly used RNA-activated Cas10 to generate cOAs. These activated a downstream CARF-domain RNase (TTHB144), which cleaved quenched RNA reporters to produce a fluorescent signal. In a two-step RT-LAMP–CRISPR workflow, SCOPE achieved a detection limit of 40 aM (∼25 copies per μL) and demonstrated robust sensitivity even in a one-pot format (LOD: 800 aM). Reaction times varied between 35 minutes (two-step) and up to 180 minutes (one-pot), depending on the configuration.166

Beyond preamplification-based detection, recent studies have also explored amplification-free RNA diagnostics using Cas10. One approach utilized the VmeCmr complex from Vibrio metoecus, where RNA binding triggers Cas10 to generate cOAs, activating the nuclease NucC, which cleaves a DNA reporter (Fig. 5d).167,168 Other amplification-free approaches have leveraged the direct RNA cleavage activity of Cas10-containing complexes. The Staphylococcus epidermidis Cas10–Csm complex demonstrated crRNA-guided ssRNA cleavage without amplification, while the LdCsm system from Lactobacillus delbrueckii achieved miRNA detection via Cas10-mediated collateral DNase activity, with a detection limit of 500 pM to 1 nM in buffer and ∼2 nM in serum.169 Additionally, the StCsm complex from Sulfolobus tokodaii was used in RNA editing applications, further supporting Cas10's intrinsic RNA-targeting capabilities.170

5.2. CRISPR–Cas7–11. Continuing with emerging systems, the type III-E CRISPR–Cas system, also known as craspase, introduces a distinct detection mechanism through Cas7–11, which functions not as a nuclease but as an RNA-guided protease. In this system, target ssRNA binding activates the Csx29 protease, which in turn cleaves a protein substrate, Csx30, providing a novel basis for RNA detection. Unlike traditional CRISPR-based detection systems that rely on nucleic acid cleavage, craspase utilizes engineered protein probes tagged with a fluorescent marker and His-tag. Upon cleavage, fluorescence is released into the supernatant and quantified, with signal increase indicating target RNA presence. This strategy allows for amplification-free detection down to 25 pM, and sensitivity improves to ∼2 fM when combined with RT-RPA.171

While sensitivity remains slightly lower than platforms like DETECTR or SHERLOCK, the stability of protein probes and programmability of the protease system offer promising avenues for future clinical applications and assay expansion. In a separate approach, researchers developed a programmable RNA sensor named CASP, utilizing Cas7–11 (DiCas7–11) from Desulfonema ishimotonii as the RNA-binding component. Upon target RNA binding, Cas7–11 activates an associated protease (Csx29), triggering downstream gene expression via release of transcriptional effectors. A mutant version of Cas7–11 with deactivated ribonuclease activity showed 20-fold improved sensitivity. While highly sensitive to mid-to-high expression RNAs, the system faces challenges with low-abundance targets, suggesting further enhancement through signal amplification strategies.172

5.3. CRISPR–Cas3. Beyond class 2 systems, class 1 CRISPR–Cas3 effectors have also been repurposed for RNA detection, offering unique capabilities through their collateral ssDNA cleavage activity. Although primarily DNA-targeting, systems like the CRISPR–Cas3-operated nucleic acid detection (CONAN) assay adapted Cas3 for rapid and sensitive detection of RNA viruses. In CONAN, RNA is reverse-transcribed into cDNA, activating Cas3's trans-cleavage of nearby ssDNA reporters, enabling detection with single-copy sensitivity when paired with isothermal preamplification methods like RPA or LAMP173 (Fig. 5b). Building on this, the discovery of a novel type I-A Cas3 variant from Thermococcus siculi (TsiCas3) introduced the hyper-active-verification establishment (HAVE) assay, achieving rapid (within 35 minutes) and accurate nucleic acid detection with strong resilience to harsh conditions.174 Notably, TsiCas3 exhibited dual activation by both DNA and ssRNA, although direct ssRNA cleavage remains less efficient. Despite current sensitivity limitations compared to attomolar-level Cas12 or Cas13 systems, Cas3-based platforms like CONAN173 and HAVE174 highlighted the versatility and ruggedness of type I CRISPR enzymes for point-of-care RNA diagnostics, especially in resource-limited environments.

To better understand the characteristics and advantages of different CRISPR–Cas platforms for RNA diagnostics, we systematically compared the detection performance of major Cas enzyme families, such as Cas13, Cas12, Cas9, Cas14, Cas10, Cas7–11 (craspase), and Cas3, based on their molecular targets, collateral activities, preamplification requirements, sensitivity, specificity, assay time, thermal stability, and POC compatibility (Table 4). Each system exhibits distinct biochemical properties that influence its suitability for diagnostic applications. For instance, Cas13 systems are favored for direct RNA detection with high specificity and no need for reverse transcription,68,72 while Cas12 enzymes, especially Cas12g, offer high thermal stability, better accessibility, and compatibility with visual or smartphone-based detection.108 Emerging systems like Cas10 and Cas7–11 expand detection mechanisms to include signal relay and protease-based readouts, respectively.168,171 Table 4 summarizes the key features of these systems, highlighting the trade-offs between sensitivity, speed, and ease of deployment in resource-limited settings.

Table 4 Comparative features of CRISPR–Cas platforms for RNA detection
Feature Cas13 Cas12 Cas9 Cas14 Cas10 Cas7–11 (craspase) Cas3
Target RNA Primarily DNA (via RT); Cas12g directly targets RNA Primarily DNA; engineered for RNA Primarily ssDNA; Cas14a1 adapted for RNA RNA RNA Primarily DNA; adapted to RNA via RT
Collateral activity Trans-cleaves RNA reporters trans-Cleaves ssDNA reporters Recently discovered: trans-cleaves both RNA and ssDNA reporters trans-Cleaves ssDNA Indirect trans-cleavage by triggering cyclic oligoadenylates (cOAs) synthesis and activation of downstream RNases trans-Cleaves RNA reporters trans-Cleaves ssDNA reporters
Preamplification requirement Both preamplification-based and preamplification-free detection Typically required; Cas12g is an exception Often requires preamplification, but preamplification-free possible Both preamplification and preamplification-free Both preamplification and preamplification-free Both preamplification and preamplification-free Mostly preamplification
Sensitivity (w/o preamplification) As low as 10 aM As low as 10 fM Picomolar range As low as 1 aM As low as 8 fM 25 pM 0.1–1 nM
Sensitivity (w/preamplification) ∼0.1 aM ∼0.5 aM or single-copy detection As low as 0.63 aM As low as 0.6 aM As low as 800 aM ∼2 fM Single-copy sensitivity
Turnover rate 1–740 s−1 0.01–20 s−1 <0.02 s−1 0.01–0.5 s−1 for Csm6 0.01–10 s−1
Assay speed 15–40 min 5–30 min 5–70 min 1–2 hours 10–180 min ∼2 hours 15–42 min
Specificity (e.g., SNP detection) High Very high High Very high High
Thermal stability Variable; generally, requires cold chain High for engineered variants (e.g., eBrCas12b at 67 °C) Thermostable (TtCsm, TthB144) Thermostable (TsiCas3)
POC compatibility Excellent: portable, adaptable to fluorescent, lateral flow, electrochemical Excellent: smartphone-based, paper-based, visual readouts Smartphone, lateral flow, digital detection (e.g., dCRISTOR) Portable, magnetic bead-based, visual detection Colorimetric/fluorescent, rugged for low-resource use Fluorescent protein cleavage, protease-based sensing Lateral flow, robust for field use
Limitations Cold chain, reagent cost Needs reverse transcription (except Cas12g), format complexity Needs reverse transcription step, complex multiplexing, primarily DNA-targeting Originally DNA-based, limited RNA usage without engineering Lower sensitivity vs. Cas12/13, limited SNP selectivity Lower sensitivity Weak ssRNA cleavage, lower sensitivity


6. Clinical applications

CRISPR–Cas systems have been extensively applied in the detection of disease-associated RNA targets, spanning both infectious diseases and cancer diagnostics. Various Cas enzymes have been harnessed to develop innovative RNA detection platforms. Among these, Cas13 stands out as the most commonly used due to its intrinsic RNA-targeting mechanism and potent collateral cleavage activity.43,45 These technologies have enabled the detection of a wide range of clinically and biologically significant RNA molecules, from viral175 genomes, bacterial transcripts,179 to microRNAs140 and messenger RNAs implicated in cancer. In the following sections, we explore the application of CRISPR-based diagnostics across different disease contexts, beginning with RNA-based pathogens, and then moving to cancer-related RNA biomarkers.
6.1. Infectious disease diagnostics (viral and bacterial RNA detection). RNA virus detection has been one of the most prominent applications of CRISPR diagnostics, with Cas13 used extensively due to its inherent RNA-targeting capability. Among the RNA viruses, SARS-CoV-2 has been the most studied, prompting the development of numerous Cas13-based assays.51 Innovative amplification-free approaches such as hydrogel-based capillary flow sensors have enabled visual and quantitative detection of viral RNA directly from clinical samples, with detection limits reaching the attomolar range.61 To further enhance sensitivity, various signal amplification strategies have been introduced, including catalytic hairpin assembly (CHA)50,51 and hybridization chain reaction (HCR), allowing for enzyme-free yet ultrasensitive detection. The flexibility of crRNA design has also enabled discrimination between viral strains by exploiting single-nucleotide differences. These strategies are now being adapted to other RNA viruses such as Influenza, Dengue, Ebola, Zika, and HCV, demonstrating the broad potential of CRISPR-based diagnostics for viral detection.41
6.1.1. SARS-CoV-2. CRISPR-based detection systems have been widely applied for the detection of SARS-CoV-2 RNA, offering advantages such as rapid detection, high sensitivity, and compatibility with point-of-care testing. Multiple Cas enzymes, including Cas12aCCC,128 Cas12b,106 Cas13a,39 type III Cas systems (Cas10,166 Cas7–11171), Cas3, have been explored across different diagnostic formats.

In the realm of Cas12 enzymes, researchers leveraged both Cas12a and Cas12b to build diverse diagnostic platforms for SARS-CoV-2 (Fig. 6b).184 One amplification-free method used Cas12a with magnetic bead-assisted separation and manganese-enhanced fluorescence signaling, enabling detection within 45 minutes with a LOD of 2.2 pM.95 Another Cas12a-based system integrated toehold-mediated strand displacement reactions (TSDR), converting RNA into DNA activators that triggered Cas12a's transcleavage activity, achieving ultrasensitive electrochemical detection down to 40 aM.138 In a parallel effort, Ning et al. demonstrated an RT-RPA-Cas12a-based assay capable of ultrasensitive and quantitative detection of SARS-CoV-2 directly from saliva samples, bypassing the need for conventional nasal swabs. Remarkably, this work was also among the first to integrate CRISPR diagnostics with smartphone readout, enabling portable and accessible testing. The combination of high sensitivity, non-invasive sampling, and smartphone compatibility positioned this platform as a promising tool for point-of-care COVID-19 diagnostics.79 Separately, the iSCAN-V2 platform integrated RT-RPA with Cas12b, showing enhanced performance over Cas12a and achieving reliable detection at 40 copies per μL using a low-cost fluorescence readout device.106


image file: d5cc03257a-f6.tif
Fig. 6 (a) The control flow of HIV-1 detection using the dCRISTOR (dCas9) assay with CNN-MOT, combining inactivation, extraction-free RT-LAMP, micromotor binding to LAMP amplicons, and motion-based detection.152 (b) Working mechanism of a CRISPR–Cas12a-based biosensor that allows quantitative RNA detection through a portable personal glucose meter readout.184 (c) Workflow of HCR combined with Cas12a for miR-21 detection.86 (d) Overview of the digital dual CRISPR–Cas-powered single EV evaluation (ddSEE) system design.190 (e) miRNA detection by miRoll-Cas for prostate cancer diagnosis.191 dCRISPR: digital clustered regularly interspaced short palindromic repeats; CNN-MOT: convolutional neural network classification-based multiobject tracking algorithm; HCR: hybridization chain reaction; miRoll-Cas: microRNA rolling circle amplification-Cas system.

Cas13a added another dimension with the SATCAS platform, which integrated reverse transcription, amplification, and detection in a one-pot setup. It achieved single-copy detection (∼aM range) within 40 minutes, proving effective in clinical validation.31 Meanwhile, type III CRISPR enzymes introduced alternative mechanisms, Cas10 in the SCOPE platform produced cyclic oligoadenylates (cOAs) upon RNA recognition, activating CARF-domain nucleases for signal generation.166 A type III-B complex (VmeCmr) utilized cA3 signaling to trigger NucC nuclease activity, allowing direct SARS-CoV-2 RNA detection at 2 fM without preamplification.171

Additionally, Cas3 has been adapted into the CONAN assay, which utilizes EcoCas3's collateral ssDNA cleavage activity for SARS-CoV-2 RNA detection, offering a rapid and low-cost diagnostic alternative.173 In addition to Cas3-based systems, Cas9 has also been adapted for diagnostic use through the Vigilant platform. The vigilant platform repurposes dCas9 fused with VirD2 to detect SARS-CoV-2 RNA after RT-RPA amplification. By binding the target sequence and linking to a FAM-labelled probe, it enables visual detection on a lateral flow strip. Vigilant offers a low detection limit (2.5 copies per μL), high sensitivity (96.4%), and perfect specificity (100%), making it a simple and cost-effective tool for point-of-care diagnostics.155


6.1.2. Hepatitis-associated RNA viruses (HCV and HDV). CRISPR technology has shown remarkable adaptability in detecting hepatitis C virus (HCV) RNA, leveraging various Cas enzymes to enhance sensitivity and clinical utility. One of the standout methods is the SPLENDID platform, which integrates RT-LAMP with a thermostable engineered Cas12b (eBrCas12b) in a single-pot format. This setup simplifies the detection process while maintaining high accuracy, achieving 97.5% specificity and 90% overall accuracy in clinical serum samples.103 In contrast, the SAHARA system employed Cas12a to detect HCV RNA without requiring reverse transcription. By exploiting Cas12a's unique ability to recognize RNA at the PAM-distal end of the crRNA, when anchored by a short PAM-containing DNA fragment, this method enabled direct detection. Pooling multiple crRNAs targeting different regions of the HCV genome improved performance, reaching a detection limit of 132 pM, although RNA secondary structure still posed a challenge.98

Cas13a has also been explored for HCV diagnostics, especially for amplification-free strategies. Screening 13 distinct crRNAs revealed that pooling them significantly enhanced Cas13a's trans-cleavage efficiency, improving assay sensitivity for RNA detection. The same approach was extended to hepatitis D virus (HDV), where CRISPR–Cas13a was combined with RT-PCR or RT-RAA for detection. These methods offered strong performance and were visualized using lateral flow strips, demonstrating the system's potential in both laboratory and POC settings.185


6.1.3. Human immunodeficiency virus (HIV). CRISPR-based technologies have been effectively adapted for the detection of HIV RNA, demonstrating strong potential for sensitive, specific, and POC diagnostics.186 One notable approach employs a CRISPR-mediated cascade reaction (CRISPR-MCR) biosensor integrating RT-RPA amplification with Cas12a detection. In this system, Cas12a is activated upon recognition of amplified HIV RNA, leading to cleavage of ssDNA-invertase conjugates immobilized on magnetic beads. This platform achieved a detection sensitivity of 200 copies of HIV RNA per test and was successfully validated using clinical plasma samples.112

Another approach combines RT-RAA amplification with Cas13a-mediated detection for HIV-1 RNA. This system uses a degenerate base-binding Cas13a–crRNA complex. Results are visualized using a lateral flow strip, offering a portable, rapid, and user-friendly format. The assay demonstrated a limit of detection of 1 copy per μL, with 91.81% sensitivity and 100% specificity in clinical evaluations, detecting viral loads as low as 112 copies per mL.187

Additionally, the dCRISTOR (Fig. 6a) assay uses dCas9-functionalized magnetic micromotors to bind amplified HIV-1 RNA and convert target presence into a binary digital signal. The system combines extraction-free LAMP, magnetic motion tracking, and deep learning for simple, label-free detection. It achieved 100% sensitivity and specificity in plasma samples, with a detection limit of 0.96 copies per μL, making it a low-cost and effective tool for point-of-care HIV diagnostics.152


6.1.4. Ebola virus. CRISPR-based detection systems have shown considerable promise for the detection of Ebola virus RNA, offering speed, specificity, and potential for point-of-care diagnostics. Two major approaches using different Cas enzymes have been explored:

A key innovation is the Cas-Roller assay, which employs Cas13a from Leptotrichia wadei (LwaCas13a) for direct RNA detection without amplification. Upon recognition of Ebola RNA, the Cas13a–crRNA complex is activated and cleaves a specially designed RNA-modified DNA hairpin probe attached to gold nanoparticles. The cleavage releases a single-stranded DNA “leg” that initiates a DNA nanomachine via catalytic hairpin assembly (CHA), leading to an amplified fluorescent signal. This system achieved a limit of detection as low as 291 aM (∼175 copies per μL) and completed the detection process in about 40 minutes at 37 °C.69

In a separate approach, Cas12a was incorporated into a microfluidic paper-based analytical device (mPAD), where it was combined with reverse transcription-recombinase polymerase amplification (RT-RPA) to detect synthetic Ebola genomic RNA. This method achieved a limit of detection of 11 aM, underscoring the ultra-sensitive capabilities of CRISPR-based detection when paired with isothermal amplification techniques.110


6.1.5. RNA respiratory viruses. CRISPR-based diagnostics have demonstrated strong potential for the sensitive and specific detection of RNA respiratory viruses. Both Cas12a and Cas13a enzymes have been applied in various formats to target these pathogens.

A notable development is the LOC-CRISPR microfluidic system, which employs Cas12a to detect multiple respiratory viruses, including SARS-CoV-2 variants (BA.1, BA.2, BA.5), H1N1, H3N2, influenza B virus (IVB), and human respiratory syncytial virus (HRSV). This chip-based platform integrates nucleic acid extraction, isothermal amplification (RPA), and Cas12a-mediated cleavage in a sealed, contamination-free format. Clinical validation showed 97.8% sensitivity and 100% specificity, with detection possible for viral RNA concentrations as low as 100 copies per mL within 60 minutes.111 Another Cas12a-based method, designed for rabies virus RNA, combines target binding-induced isothermal amplification with Cas12a's trans-cleavage activity. This electrochemiluminescence biosensor achieved a detection limit of 2.8 pM, offering high sensitivity and specificity without relying on complex instrumentation.135


6.1.6. Flaviviruses and similar arboviruses. CRISPR diagnostics has shown great promise in addressing arboviruses such as Dengue virus (DENV) and Zika virus (ZIKV), both members of the Flavivirus genus. Two distinct approaches underscore the versatility of CRISPR technology for these viruses, one focused on ultrasensitive detection, the other on therapeutic inhibition.

For DENV detection, an electrochemical biosensor was developed using the CRISPR–Cas12a system. This method exploits the trans-cleavage activity of Cas12a, activated upon recognition of a DNA analog of DENV-4 RNA, to cleave a methylene blue (MB)-linked ssDNA probe immobilized on gold nanoparticles. Cleavage of the probe results in a measurable decrease in electrochemical signal, enabling sensitive detection. This system achieved a detection limit of 100 fM for DENV-4 RNA without any RNA amplification and demonstrated high specificity by discriminating against other DENV serotypes and unrelated viral RNA sequences, including ZIKV.142

In contrast, the ZIKV study employed a CRISPR–Cas13b system not for detection but for targeted inhibition of viral replication in mammalian cells. crRNAs designed against conserved regions of the ZIKV genome successfully guided Cas13b to cleave viral RNA, reducing infection levels. A fluorescent reporter system (mCherry fused to the ZIKV capsid) was used to quantify viral load, offering a potential foundation for diagnostic development.188 To improve strain-level detection of Zika virus, researchers developed NASBACC, a low-cost CRISPR/Cas9-based diagnostic module integrated with NASBA amplification. By exploiting Cas9's sequence-specific cleavage, NASBACC can distinguish between closely related Zika strains based on single-nucleotide differences. This adds a crucial layer of specificity to paper-based diagnostics, enabling accurate strain identification while remaining portable and suitable for low-resource settings.182


6.1.7. Bacterial RNA detection. CRISPR-based diagnostic systems have also been effectively adapted for the detection of bacterial RNA. Various Cas enzymes, especially Cas13a,60 Cas12a,141 and more recently Cas14a1,162 have been utilized in these platforms.

One notable strategy uses Cas13a in an amplification-free electrochemiluminescence (ECL) biosensor for detecting Escherichia coli O157:H7 RNA. This system exploits Cas13a's transcleavage activity to cleave self-enhanced ECL probes upon target recognition. The result is signal amplification without the need for nucleic acid amplification or co-reactants. This method demonstrated rapid detection (∼20 minutes), a wide linear detection range, and strong specificity, including successful application to clinical urine and blood samples.60

Another approach incorporates Cas12a in a method called T7/G4-CRISPR, which enables sensitive detection of bacterial RNA such as 16S rRNA. This assay converts a single RNA target into many DNA activators via a toehold-mediated strand displacement and T7 exonuclease-assisted recycling circuit. The DNA activators trigger Cas12a's trans-cleavage, preventing assembly of a split G-quadruplex (G4) reporter, leading to a measurable fluorescence decrease. The platform showed improved sensitivity over direct detection, was able to distinguish single-nucleotide variants, and was validated against clinical urine samples, avoiding complex thermal cycling.141

More recently, Cas14a1 has been shown to directly respond to RNA, leading to the development of the ATCas-RNA platform. This system uses RNA to activate Cas14a1's trans ssDNA cleavage activity, achieving high specificity and ultralow detection limits (attomolar range). It was successfully applied to detect bacterial RNA in contaminated milk samples, extending Cas14a1's utility beyond its original DNA-targeting scope.162

6.2. Cancer biomarker detection (miRNA-based CRISPR diagnostics). MicroRNAs (miRNAs) have emerged as crucial biomarkers in cancer diagnosis and prognosis due to their differential expression in various types of tumors. These small, non-coding RNAs play vital roles in regulating gene expression and are often found to be upregulated or downregulated in cancerous tissues compared to healthy ones. Detecting specific miRNAs associated with cancer has thus become a valuable strategy for early diagnosis, monitoring progression, and evaluating treatment response.89,129,140 To enable effective analysis from clinical biofluids, microfluidic systems offer high-throughput, label-free separation of low-abundance miRNAs and CTCs, supporting upstream preparation for molecular diagnostics.189

CRISPR-based systems, particularly those involving Cas12a and Cas13a, have been extensively adapted to detect cancer-related miRNAs with high sensitivity and specificity. By integrating these enzymes with amplification strategies or signal transduction mechanisms, researchers have developed robust platforms capable of detecting miRNAs in clinical samples such as blood, serum, urine, and tissue lysates.44,145

In the following subsections, we explore how different CRISPR–Cas-based systems have been applied to detect miRNA biomarkers associated with specific types of cancer, including lung, breast, colorectal, prostate, and glioblastoma. Each subsection highlights the key miRNAs linked to these cancers and summarizes CRISPR strategies developed for their detection.


6.2.1. Lung cancer biomarker. The application of CRISPR-based detection systems for detecting lung cancer biomarkers has opened exciting new possibilities for early, non-invasive cancer diagnosis. In particular, miRNAs, small RNA molecules known for their regulatory roles, have gained attention as powerful biomarkers for non-small-cell lung carcinoma (NSCLC). Their aberrant expression in cancerous tissues compared to healthy controls makes them attractive and accessible targets for molecular diagnostics. Among the various Cas enzymes, Cas12a,85 Cas13a,33 Cas12j,107 cas9,154 and LdCsm (a Cas10-related system)169 have been effectively adapted for lung cancer miRNA analysis, offering rapid, sensitive, and highly specific detection strategies.

Among these, miR-21 stands out as the most commonly targeted marker, consistently overexpressed in lung cancer samples. It has been detected using a range of CRISPR–Cas systems from serum, plasma, and cell lysates with attomolar sensitivity.86 Similarly, miR-155, another key biomarker, has been identified using Cas12a-driven electrochemical sensors84 and the LdCsm system,169 showing excellent specificity suitable for point-of-care applications.

Broader panels including miR-17, miR-92a, and EGFR mRNA33 have also been investigated using Cas13a-based electrochemical biosensors. These platforms can measure multiple targets on a single chip, distinguishing early-stage NSCLC from healthy samples. Cas12j, a more recently explored enzyme, was used to sensitively detect miR-21 and miR-92a in plasma, delivering results comparable to RT-qPCR standards.107

Further innovations extended to diagnostic validation. For example, miR-195 was used in an RCA-CRISPR-split-HRP (RCH) assay to confirm the specificity of NSCLC-targeted miRNA detection. The platform correctly showed no differential miR-195 expression between NSCLC patients and healthy individuals, reinforcing its accuracy.33

In another study, the Cas9-based RACE system was used to detect miR-221, miR-21, and miR-222 simultaneously. By coupling RCA with Cas9-mediated cleavage, the system generated fluorescent signals correlating to miRNA abundance in extracellular vesicles from lung cancer samples. Its results matched RT-qPCR, supporting its potential for clinical use.154


6.2.2. Breast cancer biomarker. CRISPR-based technologies, particularly those employing Cas12a and Cas14a, have been extensively adapted for the sensitive and specific detection of breast cancer-associated miRNAs.85 Key targets include miR-10b and miR-21, both known for their significant roles in breast cancer progression and metastasis (Fig. 6d).86,96,159,190

The detection of miR-21, one of the most studied biomarkers, has led to the development of several CRISPR/Cas12a-based approaches. These included systems combining split T7 polymerase transcription with Cas12a for fluorescence-based detection,134 electrochemical sensing platforms that simultaneously detected miR-21 and miR-155,85 and Au-nanobeacon biosensors achieving direct, attomolar-level sensitivity without the need for RNA amplification.96 Additional strategies, such as entropy-driven catalysis cycles, hybridization chain reaction (HCR) controllers, and hyperbranched rolling circle amplification (HRCA) coupled with Cas12a, further boosted detection sensitivity, reaching femtomolar limits (Fig. 6c).86 In another approach, platforms like SCas12a, employing split crRNA designs, enabled amplification-free and multiplexed detection of mature miRNAs, maintaining strong agreement with traditional RT-qPCR results.102

On the other hand, for miR-10b, another microRNA associated with breast cancer, an entropy-driven circuit (EDC) integrated with Cas14a enabled dual amplification, dramatically enhancing sensitivity and allowing detection even in complex samples like serum and cell lysates. This method capitalized on continuous ssDNA activator production to robustly trigger Cas14a's trans-cleavage activity.159


6.2.3. Colorectal cancer biomarker. In addition to miRNA detection in lung and breast cancers, the techniques and strategies developed with CRISPR-based detection systems also offer strong potential for adaptation to colorectal cancer diagnostics. The powerful RNA-targeting capabilities of Cas enzymes like Cas13a,33 Cas12a,99 and Cas12j107 have led to the creation of highly sensitive, specific, and innovative miRNA detection platforms.

Among these technologies, the COMET system stands out, using Cas13a in combination with a catalytic hairpin DNA circuit (CHDC) to achieve two-stage signal amplification. This system successfully detected a panel of RNAs associated with non-small-cell lung carcinoma (NSCLC), including miR-17, miR-155, miR-19b, and miR-210, demonstrating its capacity to distinguish cancer patients from healthy individuals through analysis of serum samples. The high sensitivity of the COMET platform, capable of attomolar detection, highlights its adaptability for other cancers like colorectal cancer.33

In another approach, researchers utilized an asymmetric CRISPR assay based on Cas12a, leveraging competitive crRNA binding to enhance cascade signal amplification without the need for reverse transcription. This system demonstrated successful quantitative detection of miR-19a in plasma samples from bladder cancer patients, achieving excellent correlation with RT-qPCR results.99

Additionally, the EXP-J assay, employing Cas12j, further advanced miRNA detection by coupling exponential amplification reaction (EXPAR) with Cas12j's trans-cleavage activity. Applied to lung cancer biomarkers, the method effectively detected miR-21 and miR-92a in plasma samples, yielding results that closely matched traditional RT-qPCR data. The success of EXP-J in clinical samples suggests strong potential for its application in detecting colorectal cancer-associated miRNAs.107


6.2.4. Prostate cancer biomarker. In recent years, CRISPR/Cas12a systems have been at the forefront of developing highly sensitive tools for detecting miRNAs associated with prostate cancer. Although the reviewed studies were not solely focused on prostate cancer, they reveal powerful strategies for miRNA analysis that directly target miR-141 (Fig. 6e),127,191 and miR-21,86 two miRNAs widely recognized as important biomarkers in prostate cancer progression and diagnosis.

The detection strategies center around the trans-cleavage activity of Cas12a. Upon recognizing a target-specific crRNA and a corresponding activator DNA sequence, Cas12a is activated and begins cleaving surrounding single-stranded DNA (ssDNA) reporters like fluorescence-based systems, and electrochemiluminescence (ECL)-based biosensors, generating strong and measurable signals. Signal amplification played a crucial role in boosting sensitivity.132 Strategies like the 3D DNA walker helped convert a single miRNA-141 molecule into multiple DNA activators, while hybridization chain reaction (HCR) circuits amplified the pre-crRNA needed for Cas12a activation. These smart designs allowed researchers to achieve extremely low detection limits, reaching the femtomolar and even attomolar range.127

While the primary applications were broader than just prostate cancer, the strong focus on miR-141 and miR-21 detection clearly signals the high potential of CRISPR-based diagnostics for prostate cancer screening. With their rapid detection times, high sensitivity, and ability to adapt to portable formats, these systems pave the way for future POC applications and early cancer diagnostics.


6.2.5. Glioblastoma biomarker. CRISPR technologies have opened new horizons in the sensitive detection of miRNAs related to aggressive cancers like glioblastoma.137 Initially, CRISPR/Cas12a systems faced limitations in direct RNA detection, as they required a complete crRNA structure to activate their powerful trans-cleavage activity. To overcome this, scientists engineered the mini crRNA-mediated CRISPR/Cas12a system (MCM-CRISPR/Cas12a). This innovation enabled partial crRNA structures to function effectively without compromising their trans-cleavage potential, allowing direct detection of non-coding RNAs like miRNAs. Using the MCM-CRISPR/Cas12a platform, researchers achieved impressive sensitivity levels, down to 10 pM for miRNA-21, a miRNA strongly linked to glioblastoma. To push the detection limits even further, they combined the MCM system with amplification strategies such as hybridization chain reaction (HCR) and catalytic hairpin assembly (CHA). This powerful combination drove detection sensitivity into the femtomolar range, achieving 2.5 fM for miRNA-21, 8.98 fM for miRNA-128-3p, and 81.6 fM for lncRNA PACER.87

In a complementary study, researchers designed a novel sensor that integrated upconverted nanoparticles (UCNPs) with the CRISPR/Cas12a system, leveraging a dual enzymatic amplification strategy using exonuclease III (Exo III) and phi29 DNA polymerase. Upon near-infrared light activation, a hairpin probe on the UCNPs bound to miRNA-21, triggering Exo III-mediated recycling and subsequent rolling circle amplification (RCA) via phi29 polymerase. The resulting amplified RNA sequences activated Cas12a, which cleaved a fluorescent reporter to produce a strong, detectable signal. This dual amplification platform achieved a remarkable limit of detection of 6.01 fM for miRNA-21 and demonstrated excellent performance in real biological samples, including serum and cell lysates from cancer cell lines like MCF-7 and HeLa.137 Moreover, both studies confirmed that these CRISPR-based approaches offer excellent specificity, showing minimal cross-reactivity with non-target miRNAs, a critical feature for clinical application.

In essence, the combination of innovative crRNA designs, advanced signal amplification strategies, and the precise activity of Cas12a is pushing CRISPR technology toward becoming a transformative tool for cancer biomarker detection, offering the promise of earlier and more accurate cancer diagnostics.

7. Agricultural applications

Recently, a variety of biosensors employing diverse sensing mechanisms have been developed for plant health and gas monitoring, aiming to advance sustainable agriculture and environmental management.192–195 CRISPR-based biosensors have recently emerged as powerful tools for detecting RNA biomarkers associated with plant diseases, offering new capabilities for monitoring plant health and improving crop yield outcomes.196 Isothermal reaction conditions, rapid response times, high sensitivity and specificity, and simplified workflows make CRISPR diagnostic platforms one of the most appealing diagnostic methods for agricultural applications, especially well-suited for in-field tracking of plant pathogens and insect infestations, enhancing disease surveillance and precision agriculture applications.197 In order to detect RNA-based analytes for tracking plant and insect infestations in agricultural applications, CRISPR–Cas13 and CRISPR–Cas12 assays have been mostly applied to directly and indirectly detect RNAs, respectively.

Recent studies have demonstrated the utility of CRISPR-based platforms for detecting various plant-related RNA targets with high sensitivity and future prospects for crop field compatibility. For instance, tomato spotted wilt virus (TSWV) in tomato and thrips was detected using an indirect Cas13a-based assay following RPA amplification, achieving a detection limit of 2.26 × 102 copies per μL with a sensor response in 20 minutes.198 The glyphosate resistance gene transcript in soybean was detected indirectly using LwaCas13a via the SHERLOCK platform, providing a LOD of 2 aM.199 Banana ripeness profiling was demonstrated through direct detection of 1.839 pM.160 Tomato brown rugose fruit virus (ToBRFV), a major threat to solanaceous crops, was detected indirectly via LbCas12a in conjunction with RT-LAMP, offering detection sensitivity comparable to RT-qPCR and enabling visual detection in field settings.200

Mixed infections involving tobacco mosaic virus (TMV), tobacco etch virus (TEV), and potato virus X (PVX) were diagnosed using both Cas12a (indirect, via RT) and Cas13a/d (direct), with Cas13 also enabling viral load quantification and compatibility with lateral flow strips.201 Rice black-streaked dwarf virus (RBSDV) was detected indirectly using RfxCas13d, integrated with isothermal amplification for field deployment, achieving detection of only a few RNA copies.49 Maize chlorotic mottle virus (MCMV), a serious quarantine pathogen in maize production, was detected using Cas12a paired with RT-RAA, allowing visual fluorescence-based detection at dilutions as low as 10−5 from 2000 ng of total RNA.202 These recent findings highlight the significant potential of CRISPR diagnostic platforms for detecting RNA targets in agricultural applications.196,197

8. Environmental applications

RNA detection using CRISPR/Cas platforms could also be useful for various environmental applications, such as tracking waterborne pathogens, including viral and bacterial RNAs (e.g., E. coli, Listeria, SARS-CoV-2), that may exist in drinking and surface water. It would also be valuable for monitoring pathogens or antibiotic resistance genes in wastewater systems. In addition, CRISPR-based RNA detection could help identify RNA signatures from pathogens or transgenic organisms to mitigate their presence in farms, which could impact both agricultural productivity and environmental health. This approach could also serve as a rapid detector of zoonotic viruses or parasites in natural ecosystems, while aiding in the detection of invasive or endangered species. Importantly, it could be used to detect RNA-based contaminants in food and soil, supporting improved biosafety and ecological monitoring.203

A recent study demonstrated the applicability of the Cas13a platform to detect RNA targets from the 16S rRNA gene of Cyprinus carpio and Oryzias latipes. C. carpio is an invasive species that disrupts aquatic ecosystems, while O. latipes is a native model species widely used in ecological research. In addition, the study showed that Cas13a could detect environmental RNA (eRNA) from filtered water samples, highlighting its potential for monitoring invasive species to mitigate biodiversity threats, tracking endangered species in aquatic systems, and supporting habitat conservation. This platform enables on-site detection of live organisms in ecosystems and facilitates real-time biodiversity surveillance in dynamic environmental conditions. The Cas13a assay achieved a LOD as low as 1 copy per μL for C. carpio RNA and 1000 copy per μL for O. latipes RNA.46

In addition, future perspectives have proposed the integration of CRISPR platforms for RNA detection into marine biomonitoring systems, aiming to enable rapid, sensitive, and on-site detection of environmental RNA targets such as those from harmful algal blooms (HABs), marine pathogens, and invasive species, with the support of AI-driven CRISPR RNA design for enhanced precision and field deployment.204

9. Challenges and future directions

While CRISPR/Cas-based technologies offer great versatility for RNA detection and have rapidly advanced over the past few years, several challenges still hinder their optimal efficiency in targeting RNA molecules.16,19,205 We categorize the current challenges into four key aspects: (1) biological, (2) readout performance and sample preparation, (3) scalability and cost-effectiveness, and (4) real-world clinical considerations. We also propose future prospects to advance CRISPR-based biosensors for RNA detection by addressing each of these identified challenges.

From the biological aspect, factors such as variability in efficacy among different Cas enzymes, off-target (non-target) detection issues, indirect or direct RNA detection mechanism, the frequent need for preamplification steps to achieve ultra-high sensitivity, the use of low-yield or non-specific guide RNAs for various diseases, and the biological stability of reagents all critically influence the efficiency of CRISPR/Cas platforms.16,19,205 Minimizing off-target effects and designing unique, high-yield, and efficient guide RNAs (gRNAs) through the use of machine learning and bioinformatics platforms could significantly reduce non-specific detection and enhance robust performance across diverse sequence contexts—both of which remain key areas of ongoing research.206,207 Also, synergistic innovation in both crRNA and DNA activator design is recommended to enhance the biological aspects of CRISPR/Cas platforms for RNA detection, considering the following key aspects. For crRNA design, future advancements are expected to focus on structurally dynamic architectures such as split,102 caged,208 or switchable209 crRNAs, which allow conditional activation of the CRISPR–Cas system in response to specific RNA targets. Additionally, for in vivo diagnostic platforms, incorporating chemically modified nucleotides is expected to improve crRNA stability against nuclease degradation.210 On the DNA activator side, to reduce background signals, utilizing refined toehold-mediated strand displacement systems is recommended.211,212 Also, integrating aptamers213 into activators can expand detection capabilities beyond nucleic acids. Finally, to facilitate the construction of more intelligent diagnostic platforms capable of multi-input processing and decision-making, the development of nucleic acid-based logic circuits (e.g., AND, OR gates) is recommended.214 Together, these innovative strategies provide promising opportunities to significantly increase the sensitivity, specificity, and flexibility of next-generation CRISPR diagnostics.

From the result readout aspect, the lack of digitized and portable platforms restricts the broader deployment of CRISPR diagnostics. Many existing systems still rely on conventional laboratory instruments such as plate readers or fluorescence microscopes, limiting their use in POC or field settings.215 The development of integrated, portable, and user-friendly readout systems, such as smartphone-based readers, is critical to unlocking the full potential of CRISPR technologies for decentralized testing.216–218 From the sample preparation aspect, the complexity of upstream sample handling remains a major bottleneck. Many biological samples require nucleic acid extraction and purification to remove inhibitors that can interfere with amplification or Cas activity.12,219 Therefore, innovations such as rapid lysis methods compatible with CRISPR reactions, microfluidic sample prep systems,220 and integrated “sample-to-answer”221 platforms are paramount to truly enable field-deployable CRISPR diagnostics.

From the scalability and cost-effectiveness aspect, several factors inhibit the mass adoption of CRISPR-based diagnostics. These include the relatively high costs and availability of reagents (Cas enzymes, guide RNAs, reporters), the complexity of device fabrication for POC use, and the need for regulatory approval processes such as those required by the FDA.12 Also the need for cold chain storage of Cas proteins and RNA components significantly restricts the deployment of CRISPR-based diagnostics in remote or resource-limited settings. Addressing this issue requires the development of thermostable Cas variants222 that can remain functional at ambient temperatures.

Finally, from the real clinical application aspect,223 challenges such as multiplexed detection where multiple targets need to be detected simultaneously remain.224 While the programmability of CRISPR offers exciting potential for multiplexing, practical issues such as guide RNA cross-reactivity, optimization of multiple Cas–gRNA reactions in a single assay, and clear signal differentiation still need to be systematically addressed. Additionally, while delivery of CRISPR-based detection systems into cells is less critical for ex vivo diagnostics, it becomes a major challenge for any potential in vivo applications, demanding further advancements in safe and efficient delivery methods.225

Conclusion

CRISPR–Cas systems have rapidly emerged as a transformative force in RNA diagnostics, offering unmatched programmability, specificity, and adaptability for a diverse array of biomedical applications. This review has outlined both the fundamental mechanisms and technological innovations underlying RNA detection using a broad spectrum of Cas effectors, including Cas12, Cas13, Cas14, Cas9, Cas10, Cas3, and the recently characterized Cas7–11. Through preamplification-based and amplification-free approaches, these systems now support rapid, sensitive, and highly specific detection across multiple contexts such as infectious diseases (e.g., SARS-CoV-2, HIV, HCV, ZIKV) and cancer-related biomarkers (e.g., miRNAs in NSCLC).

Particularly, Cas13's innate RNA-targeting capability has driven major advances in amplification-free detection, while Cas12a and Cas12b have demonstrated powerful one-pot isothermal amplification strategies suited for POC deployment. Cas9, traditionally a DNA-targeting enzyme, has been ingeniously re-engineered for RNA diagnostics, expanding its utility across imaging, amplification, and digital quantification platforms. Meanwhile, class 1 systems like Cas3 and Cas10 contribute unique signal amplification routes and ruggedness for low-resource applications. Emerging platforms, including Cas7–11-based protease sensors and Cas14-driven ultrasensitive assays, further push the boundaries of detection sensitivity and modular design.

Despite tremendous progress, ongoing challenges such as improving sensitivity in amplification-free systems, multiplexing capacity, and field-deployable integration remain focal points for future development. Nonetheless, the convergence of CRISPR technology with advanced biosensing, microfluidics, and AI-driven signal analysis signals a promising future for decentralized, rapid, and precise RNA diagnostics. As these tools continue to mature, their integration into clinical workflows has the potential to revolutionize early disease detection, therapeutic monitoring, and pandemic preparedness on a global scale.

Author contributions

MB conducted the literature review, prepared the figures, and drafted the manuscript. SJ assisted in summarizing the findings, contributed to drafting the manuscript, and prepared schematic figures. AS assisted in interpreting the findings and contributed to drafting the manuscript. Q. W. supervised the project, provided critical revisions, and contributed to the final manuscript. All authors read and approved the final version.

Conflicts of interest

The authors declare that they have no conflict of interest.

Data availability

No primary research results, software or code have been included and no new data were generated or analysed as part of this review.

Acknowledgements

The authors sincerely thank funding support from the National Science Foundation (Award # 1944167) for this work.

Notes and references

  1. F. Crick, Nature, 1970, 227, 561–563 CrossRef CAS PubMed.
  2. Imaging of endogenous RNA in live cells using sequence-activated fluorescent RNA probes|Nucleic Acids Research|Oxford Academic, https://academic.oup.com/nar/article/53/2/gkae1209/7919505?login=false, (accessed April 30, 2025).
  3. Sensors for surveillance of RNA viruses: a One Health perspective – The Lancet Microbe, https://www.thelancet.com/journals/lanmic/article/PIIS2666-5247(24)00297-0/fulltext, (accessed April 30, 2025).
  4. X. Xi, T. Li, Y. Huang, J. Sun, Y. Zhu, Y. Yang and Z. J. Lu, Noncoding RNAs, 2017, 3, 9 Search PubMed.
  5. Y. Li and S. Sun, EMBO J., 2025, 44, 613–638 CrossRef PubMed.
  6. T. A. Omran, I. L. Madsø, P. C. Sæther, V. Bemanian and H. S. Tunsjø, Sci. Rep., 2024, 14, 27468 CrossRef CAS PubMed.
  7. Comparison of RT-qPCR and RT-dPCR Platforms for the Trace Detection of SARS-CoV-2 RNA in Wastewater|ACS ES&T Water, https://pubs.acs.org/doi/10.1021/acsestwater.1c00387, (accessed April 30, 2025).
  8. Impact of RNA Isolation Protocols on RNA Detection by Northern Blotting|SpringerLink, https://link.springer.com/protocol/10.1007/978-1-4939-2547-6_4, (accessed April 30, 2025).
  9. A. Yaqoob, N. K. Verma, R. M. Aziz and M. A. Shah, J. Cancer Res. Clin. Oncol., 2024, 150, 455 CrossRef CAS PubMed.
  10. J. Lai and J. Jedrych, J. Cutaneous Pathol., 2025, 52, 266–268 CrossRef PubMed.
  11. MicroRNA detection by microarray|Analytical and Bioanalytical Chemistry, https://link.springer.com/article/10.1007/s00216-008-2570-2, (accessed April 30, 2025).
  12. A. Ghouneimy, A. Mahas, T. Marsic, R. Aman and M. Mahfouz, ACS Synth. Biol., 2022, 12, 1–16 Search PubMed.
  13. History of CRISPR–Cas from Encounter with a Mysterious Repeated Sequence to Genome Editing Technology|Journal of Bacteriology, https://journals.asm.org/doi/full/10.1128/jb.00580-17, (accessed April 30, 2025).
  14. P. Horvath and R. Barrangou, Science, 2010, 327, 167–170 CrossRef CAS PubMed.
  15. RNA targeting with CRISPR–Cas13|Nature, https://www.nature.com/articles/nature24049, (accessed April 30, 2025).
  16. M. Burmistrz, K. Krakowski and A. Krawczyk-Balska, Int. J. Mol. Sci., 2020, 21, 1122 CrossRef CAS PubMed.
  17. Z. Weng, Z. You, J. Yang, N. Mohammad, M. Lin, Q. Wei, X. Gao and Y. Zhang, Angew. Chem., Int. Ed., 2023, 62, e202214987 CrossRef CAS PubMed.
  18. N. Mohammad, S. S. Katkam and Q. Wei, CRISPR J., 2022, 5, 500–516 CrossRef CAS PubMed.
  19. RNA-targeting CRISPR–Cas systems|Nature Reviews Microbiology, https://www.nature.com/articles/s41579-022-00793-y, (accessed April 30, 2025).
  20. T. Zhu, W. Jiang, Y. Wu, R. Fang, F. Deng and D. Yang, Talanta, 2025, 128223 CrossRef CAS PubMed.
  21. C. Zhu, C. Liu, X. Qiu, S. Xie, W. Li, L. Zhu and L. Zhu, Biotechnol. Bioeng., 2020, 117, 2279–2294 CrossRef CAS PubMed.
  22. F. X. Liu, J. Q. Cui, Z. Wu and S. Yao, Lab Chip, 2023, 23, 1467–1492 RSC.
  23. M. M. Kaminski, O. O. Abudayyeh, J. S. Gootenberg, F. Zhang and J. J. Collins, Nat. Biomed. Eng., 2021, 5, 643–656 CrossRef CAS PubMed.
  24. R. Aman, A. Mahas and M. Mahfouz, ACS Synth. Biol., 2020, 9, 1226–1233 CrossRef CAS PubMed.
  25. H. Li, Y. Xie, F. Chen, H. Bai, L. Xiu, X. Zhou, X. Guo, Q. Hu and K. Yin, Chem. Soc. Rev., 2023, 52, 361–382 RSC.
  26. A. Bonini, N. Poma, F. Vivaldi, A. Kirchhain, P. Salvo, D. Bottai, A. Tavanti and F. Di Francesco, J. Pharm. Biomed. Anal., 2021, 192, 113645 CrossRef CAS PubMed.
  27. S. Chen, R. Wang, C. Lei and Z. Nie, Chem. Res. Chin. Univ., 2020, 36, 157–163 CrossRef CAS.
  28. W. Feng, H. Zhang and X. C. Le, Anal. Chem., 2023, 95, 206–217 CrossRef CAS PubMed.
  29. L. B. Harrington, D. Burstein, J. S. Chen, D. Paez-Espino, E. Ma, I. P. Witte, J. C. Cofsky, N. C. Kyrpides, J. F. Banfield and J. A. Doudna, Science, 2018, 362, 839–842 CrossRef CAS PubMed.
  30. J. S. Gootenberg, O. O. Abudayyeh, J. W. Lee, P. Essletzbichler, A. J. Dy, J. Joung, V. Verdine, N. Donghia, N. M. Daringer and C. A. Freije, Science, 2017, 356, 438–442 CrossRef CAS PubMed.
  31. T. Wang, L. Bai, G. Wang, J. Han, L. Wu, X. Chen, H. Zhang, J. Feng, Y. Wang and R. Wang, Biosens. Bioelectron., 2024, 263, 116636 CrossRef CAS PubMed.
  32. X. Wang, X. Deng, Y. Zhang, W. Dong, Q. Rao, Q. Huang, F. Tang, R. Shen, H. Xu and Z. Jin, Biosens. Bioelectron., 2024, 257, 116268 CrossRef CAS PubMed.
  33. Y. Sheng, T. Zhang, S. Zhang, M. Johnston, X. Zheng, Y. Shan, T. Liu, Z. Huang, F. Qian and Z. Xie, Biosens. Bioelectron., 2021, 178, 113027 CrossRef CAS PubMed.
  34. W. Liu, H. Li, J. Tao, L. Wang, J. Hu and C. Zhang, Nano Today, 2024, 59, 102529 CrossRef CAS.
  35. D. Zhao, J. Tang, Q. Tan, X. Xie, X. Zhao and D. Xing, Talanta, 2023, 260, 124582 CrossRef CAS PubMed.
  36. J. Zhang, C. Song, Y. Zhu, H. Gan, X. Fang, Q. Peng, J. Xiong, C. Dong, C. Han and L. Wang, Biosens. Bioelectron., 2023, 219, 114836 CrossRef CAS PubMed.
  37. Y. Li, Q. Wang and Y. Wang, J. Anal. Sci. Technol., 2024, 15, 21 CrossRef CAS.
  38. T. Hu, Y. Yu, Y. Lin and C. Chen, Anal. Chem., 2023, 95, 18587–18594 CrossRef CAS PubMed.
  39. D. Wang, X. Wang, F. Ye, J. Zou, J. Qu and X. Jiang, ACS Nano, 2023, 17, 7250–7256 CrossRef CAS PubMed.
  40. K. Wang, H. Yin, S. Li, Y. Wan, M. Xiao, X. Yuan, Z. Huang, Y. Gao, J. Zhou and K. Guo, Biosens. Bioelectron., 2025, 267, 116825 CrossRef CAS PubMed.
  41. A. M. Molina Vargas, S. Sinha, R. Osborn, P. R. Arantes, A. Patel, S. Dewhurst, D. J. Hardy, A. Cameron, G. Palermo and M. R. O’Connell, Nucleic Acids Res., 2024, 52, 921–939 CrossRef CAS PubMed.
  42. C. Zhang, P. Zhang, H. Ren, P. Jia, J. Ji, L. Cao, P. Yang, Y. Li, J. Liu, Z. Li, M. You, X. Duan, J. Hu and F. Xu, Chem. Eng. J., 2022, 446, 136864 CrossRef CAS.
  43. Y. Tian, Z. Fan, X. Zhang, L. Xu, Y. Cao, Z. Pan, Y. Mo, Y. Gao, S. Zheng, J. Huang, H. Zou, Z. Duan, H. Li and F. Ren, Emerging Microbes Infect., 2023, 12, 2276337 CrossRef PubMed.
  44. P. Song, P. Zhang, K. Qin, F. Su, K. Gao, X. Liu and Z. Li, Talanta, 2022, 246, 123521 CrossRef CAS PubMed.
  45. M. Patchsung, K. Jantarug, A. Pattama, K. Aphicho, S. Suraritdechachai, P. Meesawat, K. Sappakhaw, N. Leelahakorn, T. Ruenkam, T. Wongsatit, N. Athipanyasilp, B. Eiamthong, B. Lakkanasirorat, T. Phoodokmai, N. Niljianskul, D. Pakotiprapha, S. Chanarat, A. Homchan, R. Tinikul, P. Kamutira, K. Phiwkaow, S. Soithongcharoen, C. Kantiwiriyawanitch, V. Pongsupasa, D. Trisrivirat, J. Jaroensuk, T. Wongnate, S. Maenpuen, P. Chaiyen, S. Kamnerdnakta, J. Swangsri, S. Chuthapisith, Y. Sirivatanauksorn, C. Chaimayo, R. Sutthent, W. Kantakamalakul, J. Joung, A. Ladha, X. Jin, J. S. Gootenberg, O. O. Abudayyeh, F. Zhang, N. Horthongkham and C. Uttamapinant, Nat. Biomed. Eng., 2020, 4, 1140–1149 CrossRef CAS PubMed.
  46. J. Yang, S. Matsushita, F. Xia, S. Yoshizawa and W. Iwasaki, Methods Ecol. Evol., 2024, 15, 1408–1421 CrossRef.
  47. I. L. Calderón, M. J. Barros, N. Fernández-Navarro and L. G. Acuña, Microorganisms, 2024, 12, 283 CrossRef PubMed.
  48. Y. Wang, Y. Hou, X. Liu, N. Lin, Y. Dong, F. Liu, W. Xia, Y. Zhao, W. Xing and J. Chen, World J. Microbiol. Biotechnol., 2024, 40, 51 CrossRef CAS PubMed.
  49. L. Li, C. Duan, J. Weng, X. Qi, C. Liu, X. Li, J. Zhu and C. Xie, Sci. China: Life Sci., 2022, 65, 1456–1465 CrossRef CAS PubMed.
  50. Y. Yang, W. Yi, F. Gong, Z. Tan, Y. Yang, X. Shan, C. Xie, X. Ji, Z. Zheng and Z. He, Anal. Chem., 2023, 95, 1343–1349 CAS.
  51. X. Gao, Y. Yin, J. Xie, S. Gong and X. Li, Sens. Actuators, B, 2025, 422, 136706 CrossRef CAS.
  52. Z. Zhang, J. Li, C. Chen, Y. Tong, D. Liu, C. Li, H. Lu, L. Huang, W. Feng and X. Sun, Anal. Chim. Acta, 2024, 1300, 342409 CrossRef CAS PubMed.
  53. J. Zhong, Z. Xu, J. Peng, L. Guan, J. Li, Z. Zhou, Y. Zhang, J. Zhang, S. Liu and Y. Yang, Talanta, 2025, 291, 127852 CrossRef CAS PubMed.
  54. L. Guan, J. Peng, T. Liu, S. Huang, Y. Yang, X. Wang and X. Hao, Anal. Chem., 2023, 95, 17708–17715 CrossRef CAS PubMed.
  55. N. Yan, Z. Hu and L. Zhang, Appl. Biochem. Biotechnol., 2024, 196, 7896–7907 CrossRef CAS PubMed.
  56. J. Wei, Z. Song, J. Cui, Y. Gong, Q. Tang, K. Zhang, X. Song and X. Liao, J. Hazard. Mater., 2023, 452, 131268 CrossRef CAS PubMed.
  57. Y. Dou, Y. He, H. Zhang, M. Yang, Q. Liu, W. Ma, X. Fu and Y. Chen, Anal. Methods, 2024, 16, 6810–6818 RSC.
  58. Y. Zhan, X. Gao, S. Li, Y. Si, Y. Li, X. Han, W. Sun, Z. Li and F. Ye, Front. Cell. Infect. Microbiol., 2022, 12, 904485 CrossRef CAS PubMed.
  59. S. Li, F. Wang, L. Hao, P. Zhang, G. Song, Y. Zhang, C. Wang, Z. Wang and Q. Wu, Int. J. Biol. Macromol., 2024, 283, 137594 CrossRef PubMed.
  60. X. Liu, S. Zhou, R. Sun, K. Ye, Y. Lu, A. He, Y. Yang, J. Lin, J. Hu and C. Zhang, Sens. Actuators, B, 2025, 426, 137048 CrossRef CAS.
  61. H. Wang, H. Wang, H. Pian, F. Su, F. Tang, D. Chen, J. Chen, Y. Wen, X. C. Le and Z. Li, Anal. Chem., 2024, 96, 12022–12029 CrossRef CAS PubMed.
  62. T. Li, D. Chen, X. He, Z. Li, Z. Xu, R. Li, B. Zheng, R. Hu, J. Zhu and Y. Li, Chem. Commun., 2024, 60, 3166–3169 RSC.
  63. Q. He, Q. Chen, L. Lian, J. Qu, X. Yuan, C. Wang, L. Xu, J. Wei, S. Zeng and D. Yu, Microchim. Acta, 2024, 191, 466 CrossRef CAS PubMed.
  64. Y. Zhang, P. Miao, J. Wang, Y. Sun, J. Zhang, B. Wang and M. Yan, Sensors, 2024, 24, 6138 CrossRef CAS PubMed.
  65. S. Mantena, P. P. Pillai, B. A. Petros, N. L. Welch, C. Myhrvold, P. C. Sabeti and H. C. Metsky, Nat. Biotechnol., 2024, 1–8 Search PubMed.
  66. L. Cheng, F. Yang, Y. Zhao, Z. Liu, X. Yao and J. Zhang, Biosens. Bioelectron., 2023, 222, 114982 CrossRef CAS PubMed.
  67. J. Dong, X. Wu, Q. Hu, C. Sun, J. Li, P. Song, Y. Su and L. Zhou, Biosens. Bioelectron., 2023, 241, 115673 CrossRef CAS PubMed.
  68. L. Kashefi-Kheyrabadi, H. V. Nguyen, A. Go and M.-H. Lee, Bioelectrochemistry, 2023, 150, 108364 CrossRef CAS PubMed.
  69. X.-M. Hang, P.-F. Liu, S. Tian, H.-Y. Wang, K.-R. Zhao and L. Wang, Biosens. Bioelectron., 2022, 211, 114393 CrossRef CAS PubMed.
  70. Y. Xue, X. Luo, W. Xu, K. Wang, M. Wu, L. Chen, G. Yang, K. Ma, M. Yao, Q. Zhou, Q. Lv, X. Li, J. Zhou and J. Wang, Anal. Chem., 2023, 95, 966–975 CAS.
  71. H. Zeng, P. Zhang, X. Jiang, C. Duan, Y. Yu, Q. Wu and X. Yang, Anal. Chim. Acta, 2022, 1217, 340009 CrossRef CAS PubMed.
  72. I. Azmi, M. I. Faizan, R. Kumar, S. Raj Yadav, N. Chaudhary, D. Kumar Singh, R. Butola, A. Ganotra, G. Datt Joshi and G. Deep Jhingan, Front. Cell. Infect. Microbiol., 2021, 11, 632646 CrossRef CAS PubMed.
  73. L. Liu, Z. Wang, Y. Wang, J. Luan, J. J. Morrissey, R. R. Naik and S. Singamaneni, Adv. Healthcare Mater., 2021, 10, 2100956 CrossRef CAS PubMed.
  74. K. Guk, S. Yi, H. Kim, Y. Bae, D. Yong, S. Kim, K.-S. Lee, E.-K. Lim, T. Kang and J. Jung, Biosens. Bioelectron., 2023, 219, 114819 CrossRef CAS PubMed.
  75. J. E. van Dongen, J. T. W. Berendsen, R. D. M. Steenbergen, R. M. F. Wolthuis, J. C. T. Eijkel and L. I. Segerink, Biosens. Bioelectron., 2020, 166, 112445 CrossRef CAS PubMed.
  76. Z. Li, H. Zhang, R. Xiao, R. Han and L. Chang, Nat. Chem. Biol., 2021, 17, 387–393 CrossRef CAS PubMed.
  77. S.-Y. Li, Q.-X. Cheng, J.-M. Wang, X.-Y. Li, Z.-L. Zhang, S. Gao, R.-B. Cao, G.-P. Zhao and J. Wang, Cell Discovery, 2018, 4, 20 CrossRef PubMed.
  78. B. Ning, B. M. Youngquist, D. D. Li, C. J. Lyon, A. Zelazny, N. J. Maness, D. Tian and T. Y. Hu, Cells Rep. Methods, 2022, 2(2), 100173 CrossRef CAS PubMed.
  79. B. Ning, T. Yu, S. Zhang, Z. Huang, D. Tian, Z. Lin, A. Niu, N. Golden, K. Hensley and B. Threeton, Sci. Adv., 2021, 7, eabe3703 CrossRef CAS PubMed.
  80. B. Pang, J. Xu, Y. Liu, H. Peng, W. Feng, Y. Cao, J. Wu, H. Xiao, K. Pabbaraju and G. Tipples, Anal. Chem., 2020, 92, 16204–16212 CrossRef CAS PubMed.
  81. R. Wang, C. Qian, Y. Pang, M. Li, Y. Yang, H. Ma, M. Zhao, F. Qian, H. Yu, Z. Liu, T. Ni, Y. Zheng and Y. Wang, Biosens. Bioelectron., 2021, 172, 112766 CrossRef CAS PubMed.
  82. P. Ma, Q. Meng, B. Sun, B. Zhao, L. Dang, M. Zhong, S. Liu, H. Xu, H. Mei, J. Liu, T. Chi, G. Yang, M. Liu, X. Huang and X. Wang, Adv. Sci., 2020, 7, 2001300 CrossRef CAS PubMed.
  83. Z. Zhu, Y. Guo, C. Wang, Z. Yang, R. Li, Z. Zeng, H. Li, D. Zhang and L. Yang, Biosens. Bioelectron., 2023, 228, 115179 CrossRef CAS PubMed.
  84. X. Li, J. Dong, L. Deng, D. Huo, M. Yang and C. Hou, Talanta, 2025, 286, 127413 CrossRef CAS PubMed.
  85. J. Dong, C. Hou, L. Deng, T. Gu, S. Zhu, J. Hou and D. Huo, Anal. Chem., 2025, 97, 1028–1036 CrossRef CAS PubMed.
  86. K. Long, G. Cao, Y. Qiu, N. Yang, J. Chen, M. Yang, C. Hou and D. Huo, Talanta, 2024, 266, 125130 CrossRef CAS PubMed.
  87. X. Chen, C. Huang, J. Zhang, Q. Hu, D. Wang, Q. You, Y. Guo, H. Chen, J. Xu and M. Hu, Talanta, 2024, 268, 125350 CrossRef CAS PubMed.
  88. X. Zhang, H. Li, W. Zhao, J. Xu, S. Wang and R. Yu, Microchim. Acta, 2023, 190, 458 CrossRef CAS PubMed.
  89. Z. Guo, X. Tan, H. Yuan, L. Zhang, J. Wu, Z. Yang, K. Qu and Y. Wan, Talanta, 2023, 252, 123837 CrossRef CAS PubMed.
  90. L. Zhang, Z. Zhang, R. Liu, S. Wang, L. Li, P. Zhao, Y. Wang, S. Ge and J. Yu, Sens. Actuators, B, 2023, 394, 134334 CrossRef CAS.
  91. Y. Xu, B. Chen, M. He, G. Yuan and B. Hu, Anal. Chem., 2025, 97, 811–817 CrossRef CAS PubMed.
  92. N. Aggarwal, Y. Liang, J. L. Foo, H. Ling, I. Y. Hwang and M. W. Chang, Biosens. Bioelectron., 2023, 222, 115002 CrossRef CAS PubMed.
  93. F. S. Silva, E. Erdogmus, A. Shokr, H. Kandula, P. Thirumalaraju, M. K. Kanakasabapathy, J. M. Hardie, L. G. Pacheco, J. Z. Li and D. R. Kuritzkes, Adv. Mater. Technol., 2021, 6, 2100602 CrossRef CAS PubMed.
  94. B. Fang, Z. Jia, C. Liu, K. Tu, M. Zhang and L. Zhang, Talanta, 2022, 249, 123657 CrossRef CAS PubMed.
  95. D. Li, Y. Yao, W. Cheng, F. Yin, M. He and Y. Xiang, Sens. Actuators, B, 2025, 426, 137154 CrossRef CAS.
  96. T. Luo, J. Li, Y. He, H. Liu, Z. Deng, X. Long, Q. Wan, J. Ding, Z. Gong, Y. Yang and S. Zhong, Anal. Chem., 2022, 94, 6566–6573 CrossRef CAS PubMed.
  97. J. Zhang, W. Yin, Q. Jiang, W. Mao, W. Deng, S. Jin, X. Wang, R. He, J. Qiao and Y. Liu, Commun. Biol., 2025, 8, 366 CrossRef CAS PubMed.
  98. S. R. Rananaware, E. K. Vesco, G. M. Shoemaker, S. S. Anekar, L. S. W. Sandoval, K. S. Meister, N. C. Macaluso, L. T. Nguyen and P. K. Jain, Nat. Commun., 2023, 14, 5409 CrossRef CAS PubMed.
  99. J. Moon and C. Liu, Nat. Commun., 2023, 14, 7504 CrossRef CAS PubMed.
  100. X. Fei, C. Lei, W. Ren and C. Liu, Nucleic Acids Res., 2025, 53, gkaf002 CrossRef CAS PubMed.
  101. Z. Hu, S. Ling, J. Duan, Z. Yu, Y. Che, S. Wang, S. Zhang, X. Zhang and Z. Li, Nucleic Acids Res., 2025, 53, gkaf017 CrossRef PubMed.
  102. Y. Chen, X. Wang, J. Zhang, Q. Jiang, B. Qiao, B. He, W. Yin, J. Qiao and Y. Liu, Nat. Commun., 2024, 15, 8342 CrossRef PubMed.
  103. L. T. Nguyen, S. R. Rananaware, L. G. Yang, N. C. Macaluso, J. E. Ocana-Ortiz, K. S. Meister, B. L. M. Pizzano, L. S. W. Sandoval, R. C. Hautamaki, Z. R. Fang, S. M. Joseph, G. M. Shoemaker, D. R. Carman, L. Chang, N. R. Rakestraw, J. F. Zachary, S. Guerra, A. Perez and P. K. Jain, Cell Rep. Med., 2023, 4(5), 101037 CrossRef CAS PubMed.
  104. J. Joung, A. Ladha, M. Saito, M. Segel, R. Bruneau, M.-L. W. Huang, N.-G. Kim, X. Yu, J. Li, B. D. Walker, A. L. Greninger, K. R. Jerome, J. S. Gootenberg, O. O. Abudayyeh and F. Zhang, medRxiv, 2020, preprint DOI:10.1101/2020.05.04.20091231.
  105. X. Wu, C. Chan, S. L. Springs, Y. H. Lee, T. K. Lu and H. Yu, Anal. Chim. Acta, 2022, 1196, 339494 CrossRef CAS PubMed.
  106. R. Aman, T. Marsic, G. Sivakrishna Rao, A. Mahas, Z. Ali, M. Alsanea, A. Al-Qahtani, F. Alhamlan and M. Mahfouz, Front. Bioeng. Biotechnol., 2022, 9, 800104 CrossRef PubMed.
  107. J. Kang, H. Kim, Y. Lee, H. Lee, Y. Park, H. Jang, J. Kim, M. Lee, B. Jeong and J. Byun, Adv. Sci., 2024, 11, 2402580 CrossRef CAS PubMed.
  108. M. Liu, Z. Li, J. Chen, J. Lin, Q. Lu, Y. Ye, H. Zhang, B. Zhang and S. Ouyang, PLoS Genet., 2023, 19, e1010930 CrossRef CAS PubMed.
  109. A. Garcia-Venzor, B. Rueda-Zarazua, E. Marquez-Garcia, V. Maldonado, A. Moncada-Morales, H. Olivera, I. Lopez, J. Zuñiga and J. Melendez-Zajgla, Front. Med., 2021, 8, 627679 CrossRef PubMed.
  110. M. A. English, L. R. Soenksen, R. V. Gayet, H. de Puig, N. M. Angenent-Mari, A. S. Mao, P. Q. Nguyen and J. J. Collins, Science, 2019, 365, 780–785 CrossRef CAS PubMed.
  111. J. Shen, Z. Chen, R. Xie, J. Li, C. Liu, Y. He, X. Ma, H. Yang and Z. Xie, Biosens. Bioelectron., 2023, 237, 115523 CrossRef CAS PubMed.
  112. Z. Li, N. Uno, X. Ding, L. Avery, D. Banach and C. Liu, ACS Nano, 2023, 17, 3966–3975 CrossRef CAS PubMed.
  113. R. Aman, A. Mahas, T. Marsic, N. Hassan and M. M. Mahfouz, Front. Microbiol., 2020, 11, 610872 CrossRef PubMed.
  114. T. Wang, H. Zeng, J. Kang, L. Lei, J. Liu, Y. Zheng, W. Qian and C. Fan, Pol. J. Microbiol., 2024, 73, 253 CrossRef PubMed.
  115. J. Jiao, K. Kong, J. Han, S. Song, T. Bai, C. Song, M. Wang, Z. Yan, H. Zhang and R. Zhang, Plant Biotechnol. J., 2021, 19, 394–405 CrossRef CAS PubMed.
  116. R. Zhao, Y. Xiao, Y. Tang, B. Lu and B. Li, ACS Sens., 2024, 9, 4803–4810 CrossRef CAS PubMed.
  117. H. Liu, M.-M. Lv, X. Li, M. Su, Y.-G. Nie and Z.-M. Ying, Biosens. Bioelectron., 2025, 272, 117106 CrossRef PubMed.
  118. H. Yu, M. Feng, C. Liu, F. Wang, S. Pan, G. Sui, W. Jing and X. Cheng, Int. J. Biol. Macromol., 2025, 290, 138996 CrossRef CAS PubMed.
  119. Y. Chen, N. Zong, F. Ye, Y. Mei, J. Qu and X. Jiang, Anal. Chem., 2022, 94, 9603–9609 CrossRef CAS PubMed.
  120. R. Liu, Y. Hu, Y. He, T. Lan and J. Zhang, Chem. Sci., 2021, 12, 9022–9030 RSC.
  121. M. Qing, S. L. Chen, Z. Sun, Y. Fan, H. Q. Luo and N. B. Li, Anal. Chem., 2021, 93, 7499–7507 CrossRef CAS PubMed.
  122. Y. Zhou, S. Xie, B. Liu, C. Wang, Y. Huang, X. Zhang and S. Zhang, Anal. Chem., 2023, 95, 3332–3339 CrossRef CAS PubMed.
  123. S. Zhou, M. Liu, L. Deng, Y. Qiu, T. Gu, J. Chen, M. Yang, D. Huo and C. Hou, Sens. Actuators, B, 2024, 408, 135490 CrossRef CAS.
  124. W. Jiang, Z. Chen, J. Lu, X. Ren and Y. Ma, Talanta, 2023, 251, 123784 CrossRef CAS PubMed.
  125. H. Chen, Z. Zhuang, Y. Chen, C. Qiu, Y. Qin, C. Tan, Y. Tan and Y. Jiang, Anal. Chim. Acta, 2023, 1246, 340896 CrossRef CAS PubMed.
  126. L. Luo, F. Dong, D. Li, X. Li, X. Li, Y. Fan, C. Qi, J. Luo, L. Li and B. Shen, ACS Sens., 2024, 9, 1438–1446 CrossRef CAS PubMed.
  127. Q. Wang, Y. Liu, J. Yan, Y. Liu, C. Gao, S. Ge and J. Yu, Anal. Chem., 2021, 93, 13373–13381 CrossRef CAS PubMed.
  128. X.-M. Hang, H.-Y. Wang, P.-F. Liu, K.-R. Zhao and L. Wang, Biosens. Bioelectron., 2022, 216, 114683 CrossRef CAS PubMed.
  129. P. Chen, L. Wang, P. Qin, B.-C. Yin and B.-C. Ye, Biosens. Bioelectron., 2022, 207, 114152 CrossRef CAS PubMed.
  130. X. Wang, F. Wang, J. Wang, Y. Liu, C. Gao, S. Ge and J. Yu, Sens. Actuators, B, 2022, 370, 132480 CrossRef CAS.
  131. X. Wang, H. Chen, M. Sun, B. Chen, H. Xu, Y. Fan, H. Zhou and J. Liu, Sens. Actuators, B, 2025, 425, 137016 CrossRef CAS.
  132. Q. Wang, Z. Zhang, L. Zhang, Y. Liu, L. Xie, S. Ge and J. Yu, ACS Appl. Mater. Interfaces, 2022, 14, 32960–32969 CrossRef CAS PubMed.
  133. M. Karlikow, E. Amalfitano, X. Yang, J. Doucet, A. Chapman, P. S. Mousavi, P. Homme, P. Sutyrina, W. Chan and S. Lemak, Nat. Commun., 2023, 14, 1505 CrossRef CAS PubMed.
  134. D. Li, W. Cheng, F. Yin, Y. Yao, Z. Wang and Y. Xiang, Chem. Commun., 2025, 61, 4555–4558 RSC.
  135. S. Liu, C. Wang, Z. Wang, K. Xiang, Y. Zhang, G.-C. Fan, L. Zhao, H. Han and W. Wang, Biosens. Bioelectron., 2022, 204, 114078 CrossRef CAS PubMed.
  136. L. Zhang, Z. Zhang, Y. Zheng, S. Wang, L. Ruifang, L. Zhu, C. Li, L. Xie, S. Ge and J. Wu, Sens. Actuators, B, 2025, 426, 137140 CrossRef CAS.
  137. W. Zhao, X. Zhang, R. Tian, H. Li, S. Zhong and R. Yu, Sens. Actuators, B, 2023, 393, 134238 CrossRef CAS.
  138. K. Shi, Z. Yi, Y. Han, J. Chen, Y. Hu, Y. Cheng, S. Liu, W. Wang and J. Song, Anal. Biochem., 2023, 664, 115046 CrossRef CAS PubMed.
  139. S. Feng, H. Chen, Z. Hu, T. Wu and Z. Liu, ACS Appl. Mater. Interfaces, 2023, 15, 28933–28940 CrossRef CAS PubMed.
  140. Y. Kang, J. Zhang, L. Zhao and H. Yan, Biotechniques, 2023, 74, 172–178 CrossRef CAS PubMed.
  141. D. Zhang, B. Tian, Y. Ling, L. Ye, M. Xiao, K. Yuan, X. Zhang, G. Zheng, X. Li and J. Zheng, Anal. Chem., 2024, 96, 10451–10458 CrossRef CAS PubMed.
  142. Y. Lee, J. Choi, H.-K. Han, S. Park, S. Y. Park, C. Park, C. Baek, T. Lee and J. Min, Sens. Actuators, B, 2021, 326, 128677 CrossRef CAS.
  143. B. Tian, Y. Wang, W. Tang, J. Chen, J. Zhang, S. Xue, S. Zheng, G. Cheng, B. Gu and M. Chen, Talanta, 2024, 266, 124995 CrossRef CAS PubMed.
  144. S. Zhao, Q. Zhang, R. Luo, J. Sun, C. Zhu, D. Zhou and X. Gong, Chem. Sci., 2024, 15, 18347–18354 RSC.
  145. L. Li, S. Li, N. Wu, J. Wu, G. Wang, G. Zhao and J. Wang, ACS Synth. Biol., 2019, 8, 2228–2237 CrossRef CAS PubMed.
  146. S. Li, J. Huang, L. Ren, W. Jiang, M. Wang, L. Zhuang, Q. Zheng, R. Yang, Y. Zeng and L. D. W. Luu, Talanta, 2021, 233, 122591 CrossRef CAS PubMed.
  147. C. Jiao, N. L. Peeck, J. Yu, M. Ghaem Maghami, S. Kono, D. Collias, S. L. Martinez Diaz, R. Larose and C. L. Beisel, Nat. Commun., 2024, 15, 5909 CrossRef CAS PubMed.
  148. W. X. Yan, P. Hunnewell, L. E. Alfonse, J. M. Carte, E. Keston-Smith, S. Sothiselvam, A. J. Garrity, S. Chong, K. S. Makarova and E. V. Koonin, Science, 2019, 363, 88–91 CrossRef CAS PubMed.
  149. T. Wang, Y. Wang, P. Chen, B.-C. Yin and B.-C. Ye, Anal. Chem., 2022, 94, 12461–12471 CrossRef CAS PubMed.
  150. B. Koo, D. Kim, J. Kweon, C. E. Jin, S.-H. Kim, Y. Kim and Y. Shin, Sens. Actuators, B, 2018, 273, 316–321 CrossRef CAS PubMed.
  151. M. Huang, X. Zhou, H. Wang and D. Xing, Anal. Chem., 2018, 90, 2193–2200 CrossRef CAS PubMed.
  152. Y. Song, H. Park, P. Thirumalaraju, N. Kovilakath, J. M. Hardie, A. Bigdeli, Y. Bai, S. Chang, J. Yoo and M. K. Kanakasabapathy, ACS Nano, 2025, 19, 8646–8660 CrossRef CAS PubMed.
  153. X.-Y. Qiu, L.-Y. Zhu, C.-S. Zhu, J.-X. Ma, T. Hou, X.-M. Wu, S.-S. Xie, L. Min, D.-A. Tan and D.-Y. Zhang, ACS Synth. Biol., 2018, 7, 807–813 CrossRef CAS PubMed.
  154. R. Wang, X. Zhao, X. Chen, X. Qiu, G. Qing, H. Zhang, L. Zhang, X. Hu, Z. He and D. Zhong, Anal. Chem., 2019, 92, 2176–2185 CrossRef PubMed.
  155. T. Marsic, Z. Ali, M. Tehseen, A. Mahas, S. Hamdan and M. Mahfouz, Nano Lett., 2021, 21, 3596–3603 CrossRef CAS PubMed.
  156. C. Jiao, S. Sharma, G. Dugar, N. L. Peeck, T. Bischler, F. Wimmer, Y. Yu, L. Barquist, C. Schoen and O. Kurzai, Science, 2021, 372, 941–948 CrossRef CAS PubMed.
  157. K. Chen, M. Wang, R. Zhang and J. Li, Anal. Biochem., 2021, 625, 114211 CrossRef CAS PubMed.
  158. S. C. Strutt, R. M. Torrez, E. Kaya, O. A. Negrete and J. A. Doudna, eLife, 2018, 7, e32724 CrossRef PubMed.
  159. X. Wang, P. Lv, C. Zhao, N. Yin, T. Fei, Y. Shu and J. Wang, Sens. Actuators, B, 2023, 397, 134666 CrossRef CAS.
  160. H. Yang, J. Chen, S. Yang, T. Zhang, X. Xia, K. Zhang, S. Deng, G. He, H. Gao and Q. He, Anal. Chem., 2021, 93, 12602–12608 CrossRef CAS PubMed.
  161. X. Yang, L. Lu, Y. Luo, Q. Wang, J. Wang, Y. Ren, Y. Wu, M. Negahdary, K. E. Yunusov and S. A. Yuldoshov, Aquaculture, 2025, 595, 741661 CrossRef CAS.
  162. Z. Hu, M. Chen, C. Zhang, Z. Li, M. Feng, L. Wu, M. Zhou and D. Liang, Biosensors, 2022, 12, 268 CrossRef CAS PubMed.
  163. Y. Wei, Z. Yang, C. Zong, B. Wang, X. Ge, X. Tan, X. Liu, Z. Tao, P. Wang and C. Ma, Angew. Chem., 2021, 133, 24443–24449 CrossRef.
  164. Z. Chi, Y. Wu, L. Chen, H. Yang, M. R. Khan, R. Busquets, N. Huang, X. Lin, R. Deng and W. Yang, Anal. Chim. Acta, 2022, 1205, 339763 CrossRef CAS PubMed.
  165. A. Santiago-Frangos, L. N. Hall, A. Nemudraia, A. Nemudryi, P. Krishna, T. Wiegand, R. A. Wilkinson, D. T. Snyder, J. F. Hedges and C. Cicha, Cell Rep. Med., 2021, 2(6), 100319 CrossRef CAS PubMed.
  166. J. A. Steens, Y. Zhu, D. W. Taylor, J. P. Bravo, S. H. Prinsen, C. D. Schoen, B. J. Keijser, M. Ossendrijver, L. M. Hofstra and S. J. Brouns, Nat. Commun., 2021, 12, 5033 CrossRef CAS PubMed.
  167. S. Grüschow, C. S. Adamson and M. F. White, Nucleic Acids Res., 2021, 49, 13122–13134 CrossRef PubMed.
  168. S. Grüschow, P. C. Steketee, E. Paxton, K. R. Matthews, L. J. Morrison, M. F. White and F. Grey, PLoS Neglected Trop. Dis., 2025, 19, e0012937 CrossRef PubMed.
  169. Z. Yu, J. Xu and Q. She, Int. J. Mol. Sci., 2023, 24, 2857 CrossRef CAS PubMed.
  170. C. Rouillon, J. S. Athukoralage, S. Graham, S. Grüschow and M. F. White, eLife, 2018, 7, e36734 CrossRef PubMed.
  171. Q. He, X. Lei, Y. Liu, X. Wang, N. Ji, H. Yin, H. Wang, H. Zhang and G. Yu, ChemBioChem, 2023, 24, e202300401 CrossRef CAS PubMed.
  172. M. Zhang, X. Zhang, Y. Xu, Y. Xiang, B. Zhang, Z. Xie, Q. Wu and C. Lou, Nat. Commun., 2024, 15, 8768 CrossRef CAS PubMed.
  173. K. Asano, K. Yoshimi, K. Takeshita, S. Mitsuhashi, Y. Kochi, R. Hirano, Z. Tingyu, S. Ishida and T. Mashimo, ACS Synth. Biol., 2024, 13(12), 3926–3935 CrossRef CAS PubMed.
  174. T. Hu, Q. Ji, X. Ke, H. Zhou, S. Zhang, S. Ma, C. Yu, W. Ju, M. Lu and Y. Lin, Commun. Biol., 2024, 7, 858 CrossRef CAS PubMed.
  175. A. Nemudraia, A. Nemudryi, M. Buyukyoruk, A. M. Scherffius, T. Zahl, T. Wiegand, S. Pandey, J. E. Nichols, L. N. Hall and A. McVey, Nat. Commun., 2022, 13, 7762 CrossRef CAS PubMed.
  176. M. Paraan, M. Nasef, L. Chou-Zheng, S. A. Khweis, A. J. Schoeffler, A. Hatoum-Aslan, S. M. Stagg and J. A. Dunkle, PLoS One, 2023, 18, e0287461 CrossRef CAS PubMed.
  177. P. Samai, N. Pyenson, W. Jiang, G. W. Goldberg, A. Hatoum-Aslan and L. A. Marraffini, Cell, 2015, 161, 1164–1174 CrossRef CAS PubMed.
  178. P. Lin, G. Shen, K. Guo, S. Qin, Q. Pu, Z. Wang, P. Gao, Z. Xia, N. Khan and J. Jiang, Nucleic Acids Res., 2022, 50, e47 CrossRef CAS PubMed.
  179. M. Chen, X. Jiang, Q. Hu, J. Long, J. He, Y. Wu, Z. Wu, Y. Niu, C. Jing and X. Yang, ACS Sens., 2023, 9, 62–72 CrossRef PubMed.
  180. C. Hu, D. Ni, K. H. Nam, S. Majumdar, J. McLean, H. Stahlberg, M. P. Terns and A. Ke, Mol. Cell, 2022, 82, 2754–2768 CrossRef CAS PubMed.
  181. B. Spanjaard, B. Hu, N. Mitic, P. Olivares-Chauvet, S. Janjuha, N. Ninov and J. P. Junker, Nat. Biotechnol., 2018, 36, 469–473 CrossRef CAS PubMed.
  182. K. Pardee, A. A. Green, M. K. Takahashi, D. Braff, G. Lambert, J. W. Lee, T. Ferrante, D. Ma, N. Donghia and M. Fan, Cell, 2016, 165, 1255–1266 CrossRef CAS PubMed.
  183. J. Chen, Y. Chen, L. Huang, X. Lin, H. Chen, W. Xiang and L. Liu, Nat. Biotechnol., 2024, 1–11 Search PubMed.
  184. D. Huang, Z. Shi, J. Qian, K. Bi, M. Fang and Z. Xu, Biotechnol. Bioeng., 2021, 118, 1568–1577 CrossRef PubMed.
  185. A. Kumaran, N. J. Serpes, T. Gupta, A. James, A. Sharma, D. Kumar, R. Nagraik, V. Kumar and S. Pandey, Biosensors, 2023, 13(2), 202 CrossRef CAS PubMed.
  186. N. Mohammad, A. Steksova, Y. Tang, L. Huang, A. Velayati, S. Zhang, A. Dey Poonam, S. Jamalzadegan, M. Breen and G. Jiang, bioRxiv, 2025, preprint DOI:10.1101/2025.06.15.659809.
  187. X. Li, B. Su, L. Yang, Z. Kou, H. Wu, T. Zhang, L. Liu, Y. Han, M. Niu and Y. Sun, BMC Infect. Dis., 2023, 23, 627 CrossRef CAS PubMed.
  188. P. Chen, M. Chen, Y. Chen, X. Jing, N. Zhang, X. Zhou, X. Li, G. Long and P. Hao, Virus Res., 2022, 312, 198707 CrossRef CAS PubMed.
  189. M. Bagi, F. Amjad, S. M. Ghoreishian, S. Sohrabi Shahsavari, Y. S. Huh, M. K. Moraveji and S. Shimpalee, BioChip J., 2024, 1–23 Search PubMed.
  190. X. Xu, Y. Zhang, J. Liu, S. Wei, N. Li, X. Yao, M. Wang, X. Su, G. Jing and J. Xu, ACS Nano, 2024, 19, 1271–1285 CrossRef PubMed.
  191. X. Ma, F. Zhou, D. Yang, Y. Chen, M. Li and P. Wang, Anal. Chem., 2023, 95, 13220–13226 CrossRef CAS PubMed.
  192. G. Lee, O. Hossain, S. Jamalzadegan, Y. Liu, H. Wang, A. C. Saville, T. Shymanovich, R. Paul, D. Rotenberg and A. E. Whitfield, Sci. Adv., 2023, 9, eade2232 CrossRef CAS PubMed.
  193. O. Hossain, Y. Wang, M. Li, B. Mativenga, S. Jamalzadegan, N. Mohammad, A. Velayati, A. D. Poonam and Q. Wei, Biosens. Bioelectron., 2025, 278, 117341 CrossRef CAS PubMed.
  194. S. Jamalzadegan, J. Xu, Y. Shen, B. Mativenga, M. Li, M. Zare, A. Penumudy, Z. Hetzler, Y. Zhu and Q. Wei, Chem Bio Eng., 2025, 5(3), e00027 Search PubMed.
  195. Z. Hetzler, Y. Wang, D. Krafft, S. Jamalzadegan, L. Overton, M. W. Kudenov, F. S. Ligler and Q. Wei, Front. Chem., 2022, 10, 983523 CrossRef CAS PubMed.
  196. T. Tanny, M. Sallam, N. Soda, N.-T. Nguyen, M. Alam and M. J. Shiddiky, J. Agric. Food Chem., 2023, 71, 11765–11788 CrossRef CAS PubMed.
  197. S. G. Jaybhaye, R. L. Chavhan, V. R. Hinge, A. S. Deshmukh and U. S. Kadam, Virology, 2024, 110160 CrossRef CAS PubMed.
  198. W. Zhang, Y. Jiao, C. Ding, L. Shen, Y. Li, Y. Yu, K. Huang, B. Li, F. Wang and J. Yang, Front. Microbiol., 2021, 12, 745173 CrossRef PubMed.
  199. O. O. Abudayyeh, J. S. Gootenberg, M. J. Kellner and F. Zhang, CRISPR J., 2019, 2, 165–171 CrossRef CAS PubMed.
  200. J. M. Bernabé-Orts, Y. Hernando and M. A. Aranda, PhytoFrontiers™, 2022, 2, 92–100 CrossRef.
  201. M.-C. Marqués, J. Sánchez-Vicente, R. Ruiz, R. Montagud-Martínez, R. Márquez-Costa, G. Gómez, A. Carbonell, J.-A. Daròs and G. Rodrigo, ACS Synth. Biol., 2022, 11, 2384–2393 CrossRef PubMed.
  202. X. Duan, W. Ma, Z. Jiao, Y. Tian, R. G. Ismail, T. Zhou and Z. Fan, Phytopathol. Res., 2022, 4, 23 CrossRef PubMed.
  203. A. K. Wani, N. Akhtar, C. Chopra, R. Singh, J. C. Hong and U. S. Kadam, Environ. Technol. Innovation, 2024, 34, 103625 CrossRef CAS.
  204. B. Durán-Vinet, K. Araya-Castro, A. Zaiko, X. Pochon, S. A. Wood, J.-A. L. Stanton, G.-J. Jeunen, M. Scriver, A. Kardailsky and T.-C. Chao, CRISPR J., 2023, 6, 316–324 CrossRef PubMed.
  205. Targeting miRNA by CRISPR/Cas in cancer: advantages and challenges|Military Medical Research, https://link.springer.com/article/10.1186/s40779-023-00468-6, (accessed April 30, 2025).
  206. Y. Zhou and S.-J. Chen, Artif. Intell. Chem., 2024, 2, 100053 CrossRef PubMed.
  207. H. Xiao, X. Yang, Y. Zhang, Z. Zhang, G. Zhang and B.-T. Zhang, RNA Biol., 2023, 20, 384–397 CrossRef CAS PubMed.
  208. M. Hu, R. Liu, Z. Qiu, F. Cao, T. Tian, Y. Lu, Y. Jiang and X. Zhou, Angew. Chem., 2023, 135, e202300663 CrossRef.
  209. X. Zhang, F. Li, X. Li, G. Tian and Q. Xia, Sens. Actuators, B, 2025, 138074 CrossRef CAS.
  210. Y. Wan, S. Li, W. Xu, K. Wang, W. Guo, C. Yang, X. Li, J. Zhou and J. Wang, Anal. Chem., 2024, 96, 16346–16354 CrossRef CAS PubMed.
  211. A. M. Ekdahl, A. M. Rojano-Nisimura and L. M. Contreras, J. Mol. Biol., 2022, 434, 167689 CrossRef CAS PubMed.
  212. Y. Wu, W. Luo, Z. Weng, Y. Guo, H. Yu, R. Zhao, L. Zhang, J. Zhao, D. Bai and X. Zhou, Nucleic Acids Res., 2022, 50, 11727–11737 CrossRef CAS PubMed.
  213. U. S. Kadam, Y. Cho, T. Y. Park and J. C. Hong, Appl. Biol. Chem., 2023, 66, 13 CrossRef PubMed.
  214. S. Gong, X. Wang, P. Zhou, W. Pan, N. Li and B. Tang, Anal. Chem., 2022, 94, 15839–15846 CrossRef CAS PubMed.
  215. S. Del Giovane, N. Bagheri, A. C. Di Pede, A. Chamorro, S. Ranallo, D. Migliorelli, L. Burr, S. Paoletti, H. Altug and A. Porchetta, TrAC, Trends Anal. Chem., 2024, 172, 117594 CrossRef CAS.
  216. Portable CRISPR-based diagnostics|Nature Biotechnology, https://www.nature.com/articles/s41587-019-0220-1, (accessed April 30, 2025).
  217. I. Hernández-Neuta, F. Neumann, J. Brightmeyer, T. Ba Tis, N. Madaboosi, Q. Wei, A. Ozcan and M. Nilsson, J. Intern. Med., 2019, 285, 19–39 CrossRef PubMed.
  218. J. C. Contreras-Naranjo, Q. Wei and A. Ozcan, IEEE J. Sel. Top. Quantum Electron., 2015, 22, 1–14 Search PubMed.
  219. R. Paul, E. Ostermann and Q. Wei, Biosens. Bioelectron., 2020, 169, 112592 CrossRef CAS PubMed.
  220. Scalable and automated CRISPR-based strain engineering using droplet microfluidics|Microsystems & Nanoengineering, https://www.nature.com/articles/s41378-022-00357-3, (accessed April 30, 2025).
  221. M. A. Ahamed, A. J. Politza, T. Liu, M. A. U. Khalid, H. Zhang and W. Guan, Nanotechnology, 2025, 36, 042001 CrossRef CAS PubMed.
  222. A thermostable type I-B CRISPR–Cas system for orthogonal and multiplexed genetic engineering|Nature Communications, https://www.nature.com/articles/s41467-023-41973-5, (accessed April 30, 2025).
  223. K. H. Morelli, Q. Wu, M. L. Gosztyla, H. Liu, M. Yao, C. Zhang, J. Chen, R. J. Marina, K. Lee, K. L. Jones, M. Y. Huang, A. Li, C. Smith-Geater, L. M. Thompson, W. Duan and G. W. Yeo, Nat. Neurosci., 2023, 26, 27–38 CrossRef CAS PubMed.
  224. Y. Li, L. Zhao, W. Kang, L. Ma, Y. Bai and F. Feng, TrAC, Trends Anal. Chem., 2025, 191, 118329 CrossRef CAS.
  225. H.-C. Kuo, J. Prupes, C.-W. Chou and I. J. Finkelstein, Nat. Commun., 2024, 15, 498 CrossRef CAS PubMed.

This journal is © The Royal Society of Chemistry 2025
Click here to see how this site uses Cookies. View our privacy policy here.