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. 2026 Jun 3;65(31):e7088080. doi: 10.1002/anie.7088080

In Situ Amplified Mutational mRNA Imaging Using a Spatially Confined CRISPR Nanoplatform

Wen Zhao 1, Ziyue Zheng 1, Rui Li 1, Haojie Xie 1, Haiyun Yu 1, Yongyuan Zhang 2, Yonghua Wu 1, Yunqing Yang 3, Zhenzhong Zhang 1, Hua Gao 2,✉, Yanan Li 1,✉, Kaixiang Zhang 1,4,✉
PMCID: PMC13411367  PMID: 42233593

ABSTRACT

Highly sensitive spatial analysis of RNA mutations is essential for understanding cellular heterogeneity and disease mechanisms. Herein, we developed an integrated CRISPR/Cas13a‐based nanoprobe system for rapid detection of RNA in tissue sections (Integrated CRISPR/Cas13a‐based RNA Rapid Detection, InCasRD). Unlike conventional “always‐on” probes that rely on accumulated probe hybridization, InCasRD leverages the trans‐cleavage activity of Cas13a to achieve spatially confined signal amplification and a high signal‐to‐background ratio (SBR). Using InCasRD, we achieved imaging of multiple target RNAs in tumor cells within 0.5 h of incubation, including mRNA (survivin), microRNA (miR‐21), and circular RNA (circ1785). Furthermore, the engineered InCasRD system enabled mapping of RNA mutations, such as the EGFR L858R and ovarian tumor domain (OTUD) single‐nucleotide variant (SNV, 23439980 G>T), in tumor tissue sections, thereby facilitating clear tumor boundary delineation. Collectively, InCasRD is a powerful, one‐step tool for in situ RNA analysis with potential for diagnosis and precision medicine.

Keywords: CRISPR/Cas13a, in situ imaging, RNA detection, single nucleotide variants, tissue sections

We developed an integrated CRISPR/Cas13a‐based nanoprobe system for rapid detection of RNA mutants in tissue sections (Integrated CRISPR/Cas13a‐based RNA Rapid Detection, InCasRD). We achieved imaging of multiple target RNAs in tumor cells after 30‐min InCasRD incubation and facilitated clear tumor boundary delineation. This is a powerful, one‐step tool for in situ RNA analysis with potential for diagnosis and precision medicine.

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1. Introduction

Highly sensitive visualization of RNA in cells and tissues is essential for investigating complex biological processes, such as the tumor microenvironment and disease pathogenesis [1, 2, 3]. Fluorescence in situ hybridization (FISH) is the most widely used technique for visualization of the subcellular distribution of RNAs in fixed cells and tissues by complementary probes [4, 5, 6]. By coupling multistage hybridization or branched DNA amplification technologies, several emerging FISH techniques, such as π‐FISH rainbow [7], RNAscope [8], and NanoString [9], have been applied for high‐resolution imaging of individual RNAs. However, these techniques require complex, long experimental procedures (often >10 h) and demand high technical skill, all of which limit their clinical application, especially in primary healthcare settings [10]. Thus, the development of RNA imaging technologies that are simpler and faster remains a critical need in this field.

Recently, the clustered regularly interspaced short palindromic repeats (CRISPR) system has emerged as a powerful tool for RNA imaging, prized for its high specificity and programmability [11, 12, 13]. In these applications, a guide RNA (gRNA) directs a catalytically inactive Cas protein (such as dCas13 or dCas9) to a target RNA sequence. Its specificity not only from base‐pairing but is further reinforced by the Cas protein's stringent requirement for contextual motifs like the protospacer flanking sequence (PFS) or protospacer adjacent motif (PAM), allowing it to effectively distinguish between homologous sequences [14, 15, 16]. Nevertheless, these methods generally rely on pre‐complexed dCas proteins with fluorescent reporters, resulting in an “always‐on” signal state [17, 18], leading to substantial background signal that limits sensitivity. Additionally, the limited signal intensity from a single gRNA‐dCas complex presents great challenge for detecting subtle features in RNA, such as single‐nucleotide variants (SNV).

Beyond the highly specific molecular recognition capabilities, the trans‐cleavage activity of the CRISPR system, discovered in recent years, has provided a “turn‐on” technical paradigm for high‐sensitivity RNA detection with single‐base resolution [19, 20, 21, 22]. Not relying on pre‐labeled protein complexes, such methods activate the trans‐cleavage activity of Cas proteins when binding target RNA, triggering cleavage of fluorescent reporters and resulting in signal amplification [23]. Although such strategies significantly improve detection sensitivity and effectively reduce background signal, the trans‐cleavage activity of Cas proteins is difficult to effectively constrain after activation. The fluorescent molecules generated by the reaction can diffuse freely within the cell, resulting in signals that cannot be confined to the original location of RNA and leading to the loss of spatial localization information. These limitations collectively restrict the ability of this strategy to perform refined RNA localization analysis and designing “turn‐on” detection strategies with spatial localization information remains challenging.

Herein, we developed InCasRD, a nanoprobe system that leverages the spatial confinement effect of nanoflower‐based carriers to significantly enhance CRISPR‐powered RNA detection. This system enables efficient co‐localization of Cas13a/crRNA complexes and fluorescent reporters within a restricted nanoscale space, which greatly improves localized reaction kinetics and minimizes nonspecific signal diffusion. The mechanism of CASFISH (Cas9‐mediated fluorescence in situ hybridization) and InCasRD is shown in Scheme 1. Conventional CASFISH relies on preassembled dCas9/sgRNA complexes with persistent signal output, leading to high background noise and no applicability for SNV analysis. In contrast, InCasRD overcomes these limitations by engineering sgRNAs and a confining nanoflower carrier to establish a target‐responsive “turn‐on” detection system. Upon target RNA binding, the trans‐cleavage activity of Cas13a becomes activated, enabling highly efficient localized signal amplification. This spatial confinement strategy not only significantly improves the signal‐to‐background ratio but also enables effective discrimination of mutational mRNA, establishing a rapid and highly sensitive detection platform for imaging low‐abundance mRNA in clinical tumor specimens.

SCHEME 1.

SCHEME 1

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Schematic diagrams of the conventional CASFISH for RNA in situ imaging and the rapid InCasRD RNA imaging system based on an integrated Cas13a nanoprobe with single‐base resolution. In this work, we constructed a nano‑Cas13a sensor termed InCasRD, which consists of RNA nanoflower (RNF), Cas13a/engineered crRNA complexes, and a circular reporter (CLR). The constructed sensor was hybridized to fixed cells to recognize target RNA mutations. Once target RNA mutations are recognized, the sensor triggers the trans‐cleavage function of Cas13a. This activated Cas13a then cleaves the co‐hybridized CLR, ultimately yielding strong fluorescence signals that enable the reporting of RNA mutations.

2. Results and Discussion

2.1. Construction and Optimization of InCasRD

To establish an in situ detection method for nucleic acid information in tissue sections based on the trans‐cleavage activity of the CRISPR system, the core challenge lies in how to specifically enrich Cas13a/crRNA complexes and a large number of fluorophore‐quencher reporter molecules around target RNA rather than allowing their free diffusion. Spatially confined nanostructures, such as nanoprobe and biomolecular condensates, can enrich specific substrates and enhance local reaction rates, thereby providing a versatile platform for high‐sensitivity bioassays [24]. Based on this, we plan to develop a biomimetic molecular condensate‐integrated nanoprobe to achieve the targeted assembly of fluorescent reporter molecules and the CRISPR system near target RNA, ultimately realizing in situ sensing of RNA information.

Nucleic acid nanocarriers have been widely applied for CRISPR system loading due to their high CRISPR affinity advantages of good biocompatibility and high programmability [25, 26, 27], we synthesized three distinct nanoflower structures: DNA nanoflower by rolling circle amplification (RCA), RNA nanoflower by rolling circle transcription (RCT), and DNA/RNA hybrid nanoflower by RCA and RCT to achieve this goal (Figures 1a–c and S1a–c). The DNA template for nanoflower assembly comprised two functional regions: (i) a binding element for a circular reporter (CLR), which is a fluorophore and quencher labeled circular DNA serving as a fluorescent reporter [21], and (ii) a binding element for the Cas13a/crRNA complex, used for target RNA recognition. To immobilize the CLR onto the nanoflower substrate, we engineered this ligand to harbor a sequence complementary to the first functional domain of the RNF. This sequence design allowed targeted hybridization and stable immobilization of the CLR onto the nanoflower. We then incubated Cas13a with a crRNA featuring a 28‐nt 5’‐terminal extension. During this process, the bulge‐bearing stem‐loop structure in the crRNA anchored itself into the cleft region of the Cas13a recognition (REC) lobe. This binding event promoted the preassembly of the Cas13a/crRNA complex [28, 29]. The 5’‐terminal extension segment of the crRNA was capable of forming complementary base pairs with the RNF. This interaction enhanced the efficient immobilization of the preassembled Cas13a/crRNA complex, ultimately contributing to the successful construction of InCasRD.

FIGURE 1.

FIGURE 1

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Construction of InCasRD. (a) Schematic diagram of the formation of DNF by rolling circle amplification. (b) Schematic diagram of DNA/RNA hybrid nanoflower formation by rolling circle amplification and rolling circle transcription. (c) Schematic diagram of the formation of RNF by rolling circle transcription. (d)–(f) In vitro fluorescence cleavage of InCasRD with different nanoprobes and the representative InCasRD images are also shown, demonstrating the status of MCF‐7 cells following 30‐minute incubation at 50 nM. Scale bar, 10 µm. NC, negative control; NT, non‐target group. (*p < 0.05, ****p < 0.0001; determined by two‐­tailed unpaired Student's t tests). (g) Agarose gel electrophoresis was employed for the analysis of the fabrication steps of InCasRD. (h) Scanning electron microscope (SEM) characterization of InCasRD. Scale bar, 1 µm. Inset shows the corresponding transmission electron microscope (TEM) image. Scale bar, 500 nm. (i) Feasibility validation of the proposed system, which was utilized to image MCF‐7 (high survivin expression). Scale bar: 10 µm. (j) Quantitative assessment of fluorescence intensity was conducted on fixed MCF‐7 cell samples at indicated time points (0, 10, 30, 60, 120 min) following InCasRD survivin treatment. (n = 3, mean ± SD). (*p < 0.05, ****p < 0.0001; determined by two­tailed unpaired Student's t tests). (k) Low‐abundance targets detection of the target survivin mRNA in vitro using the InCasRD survivin . (l) The result shows the linear relationship between the fluorescence spots and the survivin mRNA concentration (n = 3, mean ± SD).

Subsequently, we used survivin mRNA as the model RNA to evaluate the effects of different nanoflower structures on the activation of LwaCas13a‐mediated RNA cleavage activity. In vitro fluorescence cleavage assay results demonstrated that LwaCas13a exhibited excellent RNA‐activated trans‐cleavage activity in the RNF group, whereas the DNA nanoflower/RNA nanoflower (DNF/RNF) group showed moderate trans‐cleavage activity and the DNF group remained inactive (Figure 1d–f). Imaging results in fixed cells further confirmed that strong signals were generated only when RNF were employed as the carrier (Figure 1d–f). These findings highlight that matching the scaffold composition to the enzyme's substrate preference is essential. Specifically, the U‐rich RNA backbone of RNF enables rapid enzymatic cleavage [29, 31, 32], which is absent in DNA‐based frameworks. Therefore, RNFs were selected to construct final InCasRD platform. This constructed system exhibited a diameter of approximately 992.08 nm and a zeta potential of about −21.5 mV (Figures 1g,h and S2).

We further evaluated the imaging capability of InCasRD, with survivin mRNA serving as the target RNA. For control purposes, we employed a non‐target crRNA that fails to recognize any sequence of human mRNA, and the corresponding system was designated as InCasRDNT. As exhibited in Figure 1j, intense red fluorescent signals were detected in the cytoplasm of fixed cells treated with InCasRD. By contrast, no analogous fluorescent signals could be observed in the cells of the non‐targeting control group (Figure 1i). Notably, the intracellular fluorescent signal intensity gradually increased over time, and stable and clear fluorescence imaging results were achieved after 30 min of probe incubation (Figure S3). Compared with conventional imaging techniques, the detection efficiency of this integrated Cas13a nanoprobe imaging system was significantly enhanced. This improvement is mainly attributed to two factors: the efficient Cas13a‐driven enzymatic reactions responsible for both target detection and signal output, and the spatial confinement effect of the integrated nanoprobe. Specifically, each RNF accommodates ∼880 CLR molecules on average, achieving the high local reporter concentration required for sensitive detection (Figure S4). Additionally, the glass slide imaging experiments using survivin RNAs with different concentrationswere used to test the detection sensitivity of InCasRD. In the concentration range of 0–104 aM, the number of fluorescent spots exhibited a linear correlation with the concentration of survivin mRNA, with the correlation equation Y = 4.5667 × X−0.1667 (R 2 = 0.9956) (Figure 1k,l). By virtue of the spatial confinement effect mediated by the CRISPR nanoprobe, the limit of detection (LOD) of InCasRD was as low as ∼1 aM, laying a foundation for the sensitive detection of intracellular low‐abundance RNA targets. Collectively, these results demonstrated that the spatial confinement property of InCasRD renders it a rapid and sensitive RNA imaging probe.

Notably, we observed that the fluorescence from Cas13a trans‐cleavage was diffusely distributed throughout the cytoplasm, leading to loss of spatial information regarding the target RNA. We hypothesized that this was because Cas13a cleaves single‐stranded RNA and scattered RNF carrying cleaved fluorescent reporters were diffusely distributed in the surrounding environment. To address this, we strategically reduced and optimized the Cas13a‐to‐RNF molar ratio, confining Cas13a's trans‐cleavage to release RNF‐borne reporters while preserving RNF integrity. Specifically, a streptavidin‐modified biochip was used to analyze non‐specific cleavage of InCasRD survivin prepared at different ratios [30]. Two InCasRD constructs targeting distinct RNAs were immobilized on the chip by biotin‐modified short DNA strands. Subsequent addition of target mRNA engaged InCasRD, activating specific Cas13a probes to cleave corresponding CLRs and generate bright fluorescent spots countable by confocal microscopy (Figure 2a). As shown in Figure 2b, at an optimized Cas13a‐to‐RNF molar ratio of 2: 5, target mRNA‐dependent Cas13a selectively cleaved cognate CLRs but not non‐cognate CLRs, producing spatially discrete fluorescent foci for high‐specificity visualization. At other ratios (4: 5 or 3: 5); however, Cas13a mediated undesired cleavage of InCasRD targeting other mRNAs, causing cross‐reactivity (Figure S5). Parallel cell‐based experiments conducted at corresponding molar ratios revealed punctate Cy3 fluorescence signals via surface plot analysis, demonstrating spatially confined foci rather than diffuse signal distribution (Figure 2c,d).

FIGURE 2.

FIGURE 2

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Optimization of InCasRD for enhancing spatial confinement. (a) Schematic diagram of the InCasRD imaging range detection using the biochip. (b) InCasRD system for the in vitro imaging shows InCasRD enables the CLR to generate in situ signals. Scale bar: 10 µm. (c) and (d) Confocal Laser Scanning Microscope (CLSM) and corresponding surface plot analyses of MCF‐7 cells treated by InCasRD survivin . Scale bar: 10 µm. (e) Schematic of cells respectively treated with CasRD and InCasRD. (f) Confocal microscopic imaging was performed on MCF‐7 cells. The cells were respectively exposed to CasRD or InCasRD for subsequent observation. Scale bar: 10 µm. (g) and (h) Cellular fluorescence and SBR values measured in the samples in panel b and c (n = 20, mean ± SD). (i) CLSM images of cells treated with InCasRDNT, InCasRD survivin , and InCasRD survivin following treatment with Docetaxel or YM155 to monitor survivin mRNA fluctuations. (j) Quantitative analysis of the mean fluorescence intensity (left) (n = 20) and relative expression levels (right) of survivin mRNA under different pharmacological modulations (n = 3, mean ± SD).

Using a free formulation (CasRD survivin , without RNF‐integrated) as control, we observed strong red fluorescence in MCF‐7 cells treated with 50 nM InCasRD survivin for 0.5 h, whereas no obvious signals appeared in the CasRD survivin group (Figure 2e,f). Quantitative analysis revealed a significantly amplified fluorescence intensity and an improved signal‐to‐background ratio (SBR) in the RNF‐integrated system, these values were 4.3‐fold and 3.6‐fold higher, respectively, than those for the control formulation (Figure 2g,h). Furthermore, the fluorescence signal intensity of the InCasRD probe targeting survivin mRNA varied significantly with changes in the intracellular expression level of survivin mRNA, demonstrating its imaging capability for RNA at different abundance levels (Figure 2i,j). Additionally, we demonstrated that all individual components of InCasRD can be stored at −20°C for at least 7 days without loss of function (Figure S6a–d), and that the post‐reaction fluorescence signal remains reliable for imaging within a 3‐day window (Figure S6e,f). Overall, InCasRD survivin represents a rapid and sensitive nucleic acid nanoprobe for detecting low‐abundance targets.

2.2. InCasRD for Imaging Different RNA Species in Various Cell Lines

In addition to mRNA, other intracellular RNA species such as circular RNAs (circRNAs) and microRNAs (miRNAs) play interconnected yet distinct critical roles in regulating gene expression, maintaining cellular functions, and driving disease pathogenesis [33, 34]. Visualizing their intracellular abundance and spatial information holds significant importance in multiple fields including basic biological research, disease diagnosis, therapeutic monitoring, and drug development [35]. While the FISH assay has shown great utility in mRNA imaging, its mechanism that relies on oligonucleotide probe accumulation for signal reporting limits its application in imaging special RNAs such as circRNAs and miRNAs [36]. In contrast, Cas13a is an RNA‐activated gene editor that has been utilized for the accurate and sensitive detection of various types of RNA biomarkers [37, 38]. Therefore, InCasRD has the potential to accurately image different RNA species in cells via simple crRNA design.

To validate this potential, we selected survivin mRNA, circ1785, and miR‐21 as distinct RNA target types for proof‐of‐concept experiments, all of them are highly expressed in breast cancer cells [38] (Figure 3a). We first validated the feasibility of InCasRD in detecting diverse RNA species using an in vitro cleavage assay (Figure S7). Using MCF‐7 and MCF‐10A cells as model cell lines, we treated them with InCasRD survivin , InCasRDmiR‐21, and InCasRDcirc1785, respectively. As shown in Figure 3b, the number of fluorescent signals exhibited by InCasRD survivin in MCF‐7 cells was significantly higher than that in MCF‐10A cells, indicating a difference in the expression level of survivin mRNA between the two cell lines, which was consistent with the detection result of quantitative real‐time PCR (qRT‐PCR) (Figure 3e). Additionally, both InCasRDmiR‐21 and InCasRDcirc1785 yielded more bright fluorescent signal spots in MCF‐7 cells (Figure 3c,d). The difference in the number of these signal spots between MCF‐7 and MCF‐10A cells was consistent with the target RNA expression levels detected by qRT‐PCR (Figure 3f,g).

FIGURE 3.

FIGURE 3

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InCasRD for imaging different RNA species in various cell lines. (a) Schematic of multiple RNA types present in eukaryotic cells. (b)–(d) Discrete imaging detection of survivin mRNA (b), miR‐21 (c), and circ1785 (d) in distinct cell lines using InCasRD. Scale bar: 10 µm. e)–g) Correlation between the fluorescence signal of InCasRD method and qRT‐PCR for the detection of survivin mRNA (e), miR‐21 (f), circ1785 (g) in cells. (n = 3, mean ± SD). h) Schematic of fluorescence imaging showing differential expression of survivin mRNA in MCF‐7 and MCF‐10A cells at a 5:1 co‐culture ratio. i) Imaging analysis of the survivin mRNA in co‐cultured cells using InCasRD survivin . j) Single‐cell scatter plot demonstrating the selectivity of InCasRD survivin . The distinct clustering of MCF‐7 cells clearly distinguishes them from normal MCF‐10A cells, confirming the highly specific detection of survivin mRNA (n = 25).

To further investigate the specificity of InCasRD, we performed survivin mRNA imaging in co‐cultured tumor cells (MCF‐7) and normal cells (MCF‐10A, pre‐labeled with MitoTracker Green) (Figure 3h). As the CLSM result shows, a distinct separation between the red and green fluorescent signals was observed. The results indicated that InCasRD survivin generates strong fluorescence signals in MCF‐7 cells, whereas it is only weakly activated in MCF‐10A cells (Figures 3i and S8). Results of semi‐quantitative analysis of single‐cell fluorescence intensity further demonstrated that InCasRD survivin can clearly distinguish between tumor cells and normal cells in the co‐cultured cell model (Figure 3j). In addition, imaging of both survivin mRNA (red) and miR‐21 (green) in MCF‐7 cells suggested that InCasRD allowed orthogonal imaging of different RNA targets with no obvious cross‐reactivity (Figure S9), demonstrating its potential for multiplexed detection. Overall, leveraging Cas13a's excellent target recognition capability, InCasRD enables accurate imaging of different types of RNAs of interest in diverse cells.

2.3. Engineered InCasRD for in Situ Visualization of RNA SNV

Since SNVs hold vital clinical and biological information, their analysis has demonstrated broad applications in assessing disease susceptibility [39] and predicting individual treatment responses [40]. For example, the common point mutation at codon 858 in exon 21 (L858R) of the EGFR gene, predominantly occurring in non‐small cell lung cancer patients, is of significant biological importance for the diagnosis, treatment selection, and prognosis assessment of lung cancer patients [41, 42, 43]. Notably, visualizing the spatial distribution patterns of such RNA single‐nucleotide variants facilitates a deeper comprehension of the aforementioned processes [44, 45]. Thus, we further examined the capability of InCasRD. Specifically, this assessment focused on its capacity to identify single‐base mutations in RNA molecules. Given that the position of nucleotide mismatches in the crRNA spacer sequence can influence the cleavage activity of Cas13a [46], we positioned the mutation sites to be identified within distinct regions of the crRNA spacer for screening the optimal crRNA design (Figure 4a). The specificity of Cas13a probes was assessed using a fluorophore‐quencher‐modified RNA reporter. Discrimination factor (DF) was determined as the ratio of fluorescence intensity between mutant‐type (MT) and wild‐type (WT) RNA sequences to indicate the discrimination capacity of Cas13a probe for SNV. As shown in Figure 4b, the fluorescence results showed that the highest DF (∼3.86) was achieved when the mutation was located at position 3 of the spacer region, which was selected to construct InCasRDL858R. This observation is also in line with previous studies, which emphasized that position 3 in the crRNA spacer sequence plays a critical role in SNV identification [47, 48, 49].

FIGURE 4.

FIGURE 4

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Construction of engineered crRNA to accurately visualize L858R in fixed cells. (a) Schematic illustration of positioning mutation sites at various locations within the crRNA spacer region. (b) Fluorescence analysis of InCasRD system using different engineered crRNAs for detecting wild‐type and mutant variants in panel a. For the mutation site, crRNAs that yielded the highest DF are highlighted in red, which was specifically calculated as (FMT) / (FWT) (n = 3). (c) Sequencing identification of an SNV (L858R T>G) in H1975 cells (MT). MCF‐7 cells were used as the wild‐type control (WT). (d) Engineered crRNAs with different designs were used to image the EGFR L858R in co‐cultured MCF‐7 (WT) and H1975 (MT) cells. Top: the raw images of EGFR L858R detection by each engineered crRNA. Bottom: the render of spot localizations. Scale bar: 10 µm. (e) Biplots showing the SNV selectivity profile of all engineered crRNAs targeting intracellular EGFR L858R in this study. Each point represents the detection efficiency of a single cell against L858R (y‐axis) versus WT (x‐axis) targets. Cells falling within the upper‐left quadrant (light red; indicating no MitoTracker Green labeling yet activating Cas13a and emitting fluorescent signals) were identified as selectively detecting L858R using InCasRDL858R (n = 25).

Based on RNA sequencing results (Figure 4c), the non‐small cell lung cancer (NSCLC) cell line H1975 served as the mutant type [50], while the breast cancer cell line MCF‐7 was selected as the wild‐type cell line for single‐cell imaging validation [51, 52]. As shown in Figure S10, InCasRDL858R‐3 generated abundant red fluorescent signal spots exclusively in the mutant cell line, whereas no such signal was observed in the wild‐type cells. A cell co‐culture model was employed to further investigate the imaging specificity of InCasRD for RNA SNV. As shown in Figure 4d, only the red fluorescent signals generated by InCasRDL858R‐3 were completely segregated from the green signals of wild‐type cells, whereas in other groups, the red signals were either absent or exhibited a certain degree of colocalization with the green signals. Single‐cell semi‐quantitative fluorescence intensity analysis further confirmed the accurate discrimination capability of InCasRDL858R‐3 for cells harboring EGFR single‐base mutations (Figure 4e).

Additionally, the low‐abundance KRAS G12D mutation (G>A), which is a common mutation in malignant tumors, was used to test the recognition universality of InCasRD for different types of RNA mutations [53]. Based on the systematic crRNA screening and optimization for KRAS G12D reported [54], we adopted a double‐mismatch design strategy to enhance discrimination specificity. The InCasRDG12D19 probe with a perfect‐match crRNA for KRAS G12D, was activated with comparable potency by both WT and SNV RNAs. In contrast, the crRNA guided by InCasRDG12D19‐14 ,which contains one mismatch between the SNV site exhibited significant discrimination between WT and G12D. Single‐cell imaging results demonstrated that InCasRDG12D19‐14 successfully detected KRAS G12D in PANC‐1 cells, generating distinct signals, whereas HepG2 cells (KRAS WT) exhibited only extremely low background signals due to the absence of detectable targets (Figure S11). Collectively, these results demonstrate that InCasRD can accurately visualize various RNA single‐base mutations in fixed cells.

2.4. Imaging of RNA SNV in Clinical Tissue Samples

Inspired by the remarkable capability of InCasRD to image diverse intracellular RNA species with single‐nucleotide resolution, we sought to explore the potential of target‐activated InCasRD for visualizing oncogenic RNA in tissue specimens (Figure 5a). Specifically, we established a tumor‐bearing mouse model using MCF‐7 cells to obtain pathological sections, and proceeded with the InCasRD assay for detecting different types of RNA species. As shown in Figure 5b, the highly expressed survivin mRNA in tumor cells at the tissue center significantly activated the InCasRD survivin probe, generating strong fluorescent signals; whereas no detectable signals were observed in the surrounding normal cells not infiltrated by tumors. This evidence demonstrates that InCasRD can map its characteristic tumor profile with only 30 min of probe incubation, and the tumor boundary delineated by it is highly consistent with that identified in hematoxylin and eosin (H&E) stained sections (Figure S12a). We further confirmed the superior capability of InCasRDmiR‐21 to map the spatial distribution patterns of miR‐21 in KYSE30 tumor tissue sections (Figures 5c and S12b). The versatility of InCasRD was further demonstrated by the detection of ACTB mRNA across multiple mouse organs (Figure S13).

FIGURE 5.

FIGURE 5

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RNA imaging of tissue samples using InCasRD with Single‐molecule sensitivity. (a) Procedures for InCasRD detection of target RNA in tissue samples. (b) survivin mRNA imaging analysis of MCF‐7 derived tumors samples by InCasRD. Scale bar: 1 mm. White dashed lines in the imaging results demarcate tumor tissues. (c) miR‐21 imaging analysis of KYSE30 derived tumors samples by InCasRD. Scale bar: 750 µm. White dashed lines in the imaging results demarcate tumor tissues. (d) L858R MT imaging analysis of H1975 derived tumors samples by InCasRD. Left images scale bar: 750 µm, right images scale bar: 50 µm. White dashed lines in the imaging results demarcate tumor tissues. (e) The H&E‐stained sections corresponding to the confocal scanning images are shown, scale bar: 1 mm. (f) InCasRD labeled the subsets of cells in human breast cancer. Scale bar: 1 mm. The representative view of a breast cancer specimen is shown (left). Cells with OTUD mutations were enriched in the tumor tissue region (as indicated by the yellow inset). Additionally, few OTUD mutated cells were observed in the normal tissue at the tumor margin (as shown in the green inset). Scale bar: 1 mm, left images scale bar: 50 µm.

Subsequently, we introduced InCasRDL858R‐3 to pathological sections of a H1975 (EGFR L858R) tumor‐bearing mouse model for 30 min to investigate whether InCasRDL858R‐3 retains its ability to distinguish SNV at the pathological tissue level. As shown in Figure 5d, the results demonstrated a significantly higher fluorescence intensity in the dense tumor regions compared to the surrounding looser normal tissue. Notably, even normal tissue immediately adjacent to the tumor showed no detectable signal interference, revealing a clear tumor boundary consistent with the H&E staining results (Figure S12c). These results suggest that InCasRD is a powerful tool for visualizing SNV in RNA in primary tissue sections for genetic diagnosis applications.

To further evaluate the potential of InCasRD for detection and analysis in clinical pathological sections, we selected a clinical breast cancer sample for SNV analysis at a specific locus. Exome sequencing was first performed on the tumor tissue with matched normal tissue as a control, which identified a G>T SNV in the OTUD gene with a 100% minor allele frequency (Figure S14a). A series of crRNA modifications were designed against this mutation to enhance specificity, and the OTUD‐9 crRNA was screened via in vitro cleavage assays for the construction of InCasRD OTUD ‐9 (Figure S14b,c). The optimized system was applied to tissue section imaging. As shown in Figure 5e,f, fluorescence imaging of the sections revealed that InCasRD OTUD ‐9 successfully illuminated tumor regions, demonstrating its ability to achieve single‐nucleotide resolution even in the highly complex environment of human tumor tissue. In conclusion, InCasRD is expected to enable rapid detection with high spatial resolution and single‐nucleotide precision in clinical pathological section detection, facilitating early diagnosis and prognostic evaluation, thus contributing to personalized treatment.

3. Conclusion

Mapping the spatial distribution patterns of RNA in cells and tissues is of significant importance for deciphering mechanisms of gene expression regulation, uncovering pathological processes of diseases, and guiding clinical diagnosis. Traditional RNA imaging techniques, whether relying on oligonucleotide probes or CRISPR systems, primarily depend on an “always‐on” detection mode based on probe accumulation, and thus struggle to achieve high specificity and low background signals while maintaining detection accuracy. Herein, we developed InCasRD, a nanoprobe system that leverages the spatial confinement effect of nanoflower‐based carriers to substantially enhance CRISPR‐powered RNA detection. As a CRISPR‐based “turn‐on” assay, InCasRD enabled mapping of spatial distribution patterns of different types of RNA with single‐base resolution in tumor tissue sections.

A key advancement of InCasRD lies in constructing a spatially confined reaction environment using nucleic acid nanocarriers, facilitating rapid in situ signal readout based on CRISPR trans‐cleavage activity. By colocalizing the Cas13a/crRNA complex with fluorescent reporters within a nanoscale space, InCasRD achieves rapid and sensitive RNA detection, with a detection sensitivity of 1 aM. Conventional methods like FISH and in situ RCA typically take several hours to days to complete. This extended duration stems from their dependence on multiple rounds of probe hybridization, enzymatic reactions, or complex signal amplification procedures. Furthermore, these methods require repeated washing steps to minimize background noise resulting from nonspecific binding. Although the trans‐cleavage activity of Cas12a has been utilized for developing rapid RNA imaging strategies [55], unrestrained trans‐cleavage activity results in the loss of RNA spatial information. InCasRD addresses this critical limitation through nano‐confinement‐enhanced Cas13a trans‐cleavage, ultimately achieving superior performance over FISH in both detection speed and spatial resolution. Despite this imaging potential, the capability and sensitivity limits of InCasRD for detecting very low abundance RNA remain to be further explored and characterized in future research [56, 57, 58, 59].

Through programmable crRNA design, InCasRD is endowed with broad detection capability for diverse RNA species, including mRNA, microRNA, and circRNA. Benefiting from the high‐specificity recognition capability of the Cas13a system, InCasRD has further broken through the bottleneck of traditional technologies in distinguishing SNV. Conventional FISH based on traditional oligonucleotide probe hybridization is difficult to effectively distinguish SNV. While elaborate design of Toehold probes or utilization of ligases’ high fidelity can enhance, to some extent, the ability to discriminate SNV, their complex design and operational procedures impede widespread adoption. Through engineered crRNAs, InCasRD can visualize the spatial distribution patterns of RNA SNV in clinical samples, such as the EGFR L858R and the OTUD gene variant (23439980 G>T), thus clearly delineating tumor margins.

In summary, InCasRD represents a high‐performance in situ imaging platform that integrates rapid detection, single‐nucleotide resolution, and broad‐spectrum RNA recognition into a single system. By employing a spatially confined architecture, the system achieves high SBR detection without requiring washing steps, while enabling single‐base resolution analysis of diverse RNA species including mRNA, miRNA, and circRNA. This advanced platform provides a powerful tool for basic and clinical research related to the spatial distribution patterns of RNA.

Ethics Statement

Clinical specimens analyzed in this study were recruited by the First Affiliated Hospital of Zhengzhou University, with approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University (Approval No. 2024‐KY‐1071). The Ethics Committee determined that the specimens included in this study represented the secondary use of post‐test clinical specimens, involved no patient privacy concerns or additional interventions; therefore, the requirement for informed consent was not required.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supporting File: anie72900‐sup‐0001‐SuppMat.docx

ANIE-65-e7088080-s001.docx (58MB, docx)

Acknowledgments

This work was supported by funds to KX.Z., ZZ.Z., and H.G. from the National Natural Science Foundation of China (No.22377110, No.82402749, No.U23A20531, No.22122409); the Natural Science Foundation of Henan Province (No. 252300421073); Key scientific research projects, Science and Technology Department of Henan Province (No.252102311025); State Key Laboratory of Metabolic Dysregulation & Prevention and Treatment of Esophageal Cancer (No. 2025SGAQZ‐MS‐03), Henan Provincial Key Research and Development Program (No.261111313300). All the animal experiments were performed in accord with the guide lines of the Regional Ethics Committee for Animal Experiments and Zhengzhou University Institutional Animal Care and Use Committee. The authors thank Modern Analysis and Computing Center of Zhengzhou University for technical assistance.

Contributor Information

Hua Gao, Email: gaohua212@zzu.edu.cn.

Yanan Li, Email: Lyn0122@zzu.edu.cn.

Kaixiang Zhang, Email: zhangkx@zzu.edu.cn.

Data Availability Statement

The data that supports the findings of this study are available in the Supporting Information of this article.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting File: anie72900‐sup‐0001‐SuppMat.docx

ANIE-65-e7088080-s001.docx (58MB, docx)

Data Availability Statement

The data that supports the findings of this study are available in the Supporting Information of this article.

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