A glycosylated RNA detection kit based on hierarchical coding chain reaction and application thereof

Abstract

The application discloses a glycosylated RNA detection kit based on hierarchical coding chain reaction and application thereof, and relates to the technical field of biological detection. The kit comprises: (1) a metabolic marker module comprising: Ac4ManNAz; (2) a double-recognizing proximity assembly module comprising: 1) a glycosyl recognition probe (T SA ): used for recognizing a sugar chain of glycosylated RNA, and the nucleic acid sequence is shown as SEQ ID NO. 1; 2) an RNA recognition probe (T RNA ): used for recognizing five kinds of glycosylated RNA, namely U1, U3, U35a, Y5 and U8, and the nucleic acid sequences are shown as SEQ ID NO. 2 to SEQ ID NO. 6; (3) a connecting chain: the nucleic acid sequence is shown as SEQ ID NO. 7; and (4) a hierarchical coding chain reaction module. The application can in-situ "code" detection of up to five glycosylated RNA species in the same reaction system, and rich molecular pedigree information can be obtained in one experiment, thereby breaking through the technical bottleneck that most of existing methods can only detect single or limited targets. The whole reaction system is simple to operate and easy to popularize.

Description

Technical Field

[0001] This invention relates to the field of biological detection technology, and in particular to a glycosylated RNA detection kit based on hierarchical coding chain reaction and its application. Background Technology

[0002] Recent studies have revealed that RNA can be directly covalently linked to N-glycans and anchored to the cell surface, forming glycosylated RNA, which plays important roles in immunity, cell communication, and tumor development. Particularly in breast cancer, the expression profiles of glycosylated RNA on the cell surface of different subtypes show significant differences, providing new molecular markers for tumor subtyping.

[0003] The heterogeneity of RNA substrate species for glycosylated RNA remains unclear. Furthermore, the low abundance of glycosylated RNA on membrane surfaces and the difficulty in in-situ detection pose key bottlenecks limiting research and application. Existing methods, such as polysaccharide mass spectrometry or enzymatic hydrolysis-analysis, are mostly conducted in situ settings away from cells, making it difficult to distinguish the glycosylation characteristics of different RNA species at the single-cell level. Metabolic labeling or single-probe hybridization techniques suffer from low sensitivity and the inability to distinguish multiple glycosylated RNA types. While traditional hybridization chain reaction (HCR) offers advantages such as being enzyme-free and having isothermal amplification, there is still room for improvement in terms of multi-target coexistence, low background, and in-situ stability. Therefore, establishing a scheme that can "code" and detect multiple glycosylated RNA species in-situ on the cell surface, and quantify the results into a "glycosylation fingerprint" for further application in distinguishing breast cancer cell subtypes, has significant scientific and applied value. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a glycosylated RNA detection kit based on a hierarchical coding chain reaction and its application. The technical solution is as follows:

[0005] A glycosylated RNA detection kit based on a hierarchical coding chain reaction, the kit comprising:

[0006] (1) A metabolic marker module, which includes: Ac4ManNAz;

[0007] (2) A dual-identification proximity assembly module, comprising:

[0008] 1) Glycosyl recognition probe (T SA ): The glycan chain used to recognize glycosylated RNA, the nucleic acid sequence of which is shown in SEQ ID NO.1;

[0009] 2) RNA recognition probe (T) RNA): Used to recognize the following five glycosylated RNAs: U1, U3, U35a, Y5, and U8, whose nucleic acid sequences are shown in SEQ ID NO.2 to SEQ ID NO.6;

[0010] (3) Linker strand: Its nucleic acid sequence is shown in SEQ ID NO.7;

[0011] (4) A hierarchical coding chain reaction module, which includes:

[0012] 1) First-layer coding chain reaction module: comprising chains H1 to H2, the nucleic acid sequences of which are shown in SEQ ID NO. 8 to SEQ ID NO. 9; and

[0013] 2) Second layer coding chain reaction module: It includes H3 to H6 chains, the nucleic acid sequences of which are shown in SEQ ID NO.10 to SEQ ID NO.13.

[0014] Optionally, the kit further includes:

[0015] (5) Reaction compatibility and blocking module: It includes Tris-HCl buffer, BSA solution and / or SSD solution.

[0016] Optionally, the kit further includes:

[0017] (6) Reference and Analysis Module: This includes: negative / positive controls, and / or instructions for template data used in PCA / clustering.

[0018] Optionally, the ends of the H1 chain, the H4 chain, and / or the H6 chain are respectively labeled with FAM / TAMRA fluorescent groups.

[0019] The kit described herein is used in in situ detection methods for glycosylated RNA based on hierarchical coding chain reaction, in vitro cell subtype differentiation methods, and / or methods for constructing or comparing glycosylated RNA feature profiles on the surface of breast cancer cells.

[0020] A method for in situ detection of glycosylated RNA based on hierarchical coding chain reaction, an in vitro cell subtype differentiation method, and / or a method for constructing or comparing glycosylated RNA feature profiles on the surface of breast cancer cells, the method comprising:

[0021] Using the kit described above, the content of glycosylated RNA in the test sample is detected based on a hierarchical coding chain reaction.

[0022] Optionally, the method simultaneously detects the content of the following glycosylated RNAs: U1, U3, U35a, Y5, and U8.

[0023] The application of the kit in the preparation of a breast cancer diagnostic kit.

[0024] Optionally, the breast cancer diagnostic kit is used to diagnose at least one of Luminal B breast cancer, Luminal A breast cancer, HER2+ breast cancer, and / or triple-negative breast cancer.

[0025] Optionally, the breast cancer diagnostic kit is used to diagnose triple-negative breast cancer.

[0026] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0027] This invention provides a glycosylated RNA detection kit based on hierarchical coding chain reaction. The kit constructs an integrated system for specific and highly sensitive detection and signal amplification of a variety of low-abundance glycosylated RNAs in situ in cells through the synergistic design of metabolic labeling, dual recognition neighbor assembly and hierarchical hybridization chain reaction (HCR).

[0028] This invention also discloses an in situ detection method for various glycosylated RNAs based on hierarchical coding chain reaction. The method utilizes a proximity reaction-mediated dual-module recognition probe to specifically bind to the glycosylated and RNA parts of the glycosylated RNA, thereby triggering hierarchical HCR to form a high molecular weight and high signal gain bilayer DNA polymer.

[0029] This invention designs T-type RNA molecules targeting five different glycosylated RNA sequences: U1, U3, U35a, Y5, and U8. RNA The probes enable this kit to perform in-situ "coding" detection of up to five glycosylated RNA species in the same reaction system, obtaining rich molecular lineage information in a single experiment. This overcomes the technical bottleneck of most existing methods, which can only perform single or limited target detection. To successfully and accurately detect five glycosylated RNAs, it is necessary to avoid homologous regions of the target glycosylated RNA sequence, precisely design binding regions, ensure high affinity and high specificity of the capture probes, and prevent binding to non-targets and interactions with other components of the system. Furthermore, the five sets of capture probes must have similar hybridization thermodynamic stability and be compatible with T... SA First, the connector has no steric hindrance. Second, the detection reaction conditions need to be optimized and balanced to ensure that all probes can achieve highly sensitive and specific binding under uniform conditions. Therefore, achieving the detection of five glycosylated RNAs is not a simple matter of adding up the numbers, but a systematic improvement from probe design and system compatibility to fluorescence image analysis.

[0030] The fluorescence intensity data obtained by this invention can be directly quantified as a "glycosylated RNA characteristic profile" (glycosylated fingerprint), providing a reliable data foundation for cell subtype differentiation based on multi-parameter statistical analysis. The entire reaction system does not require special equipment such as PCR instruments, is carried out under enzyme-free and isothermal conditions, uses low-cost reagents and instruments, requires less expertise, is simple to operate, and is easy to promote. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a schematic diagram illustrating the working principle of the detection reagent of the present invention;

[0033] Figure 2A and Figure 2B This is a diagram illustrating the feasibility and specificity of the detection system in MCF-7 cells, as provided in Embodiment 1 of the present invention.

[0034] Figure 3 This is a graph showing the results of detecting the expression levels of different cell surface glycosylated RNAs (U1, U3, U35a, Y5, U8) in five breast cancer cell lines using the method of the present invention, as provided in Example 2 of the present invention.

[0035] Figure 4 This is a graph of principal component analysis (PCA) for breast cancer subtype classification based on glycosylated RNA expression profiles provided in Example 3 of the present invention;

[0036] Figure 5 This is a heatmap of breast cancer subtypes based on glycosylated RNA expression profiles provided in Example 3 of the present invention. Detailed Implementation

[0037] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0038] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0039] Ac4ManNAz, also known as tetraacetyl azidomannose, is a sialic acid precursor containing an azide group.

[0040] Glycosylated RNA molecules U1, U3, U35a, Y5, and U8 are all nuclear non-coding RNAs that are transported to the cell surface after glycosylation modification. They can participate in intercellular communication and immune recognition through their glycan portions.

[0041] This invention belongs to the interdisciplinary field of molecular diagnostics and glycobiology, specifically relating to an in situ detection kit for glycosylated RNA (glycoRNA) based on a hierarchical coding chain reaction, and its applications in in vitro cell subtype differentiation, construction and comparison of glycosylated RNA feature profiles on the surface of breast cancer cells. Methodologically, this hierarchical coding chain reaction corresponds to a proximity-triggered hybridization chain reaction (HCR), and breast cancer cell subtype differentiation is achieved through the fluorescence feature vectors of glycosylated RNA in each cell subtype and statistical discrimination.

[0042] The purpose of this invention is to provide a glycosylated RNA in situ detection kit based on hierarchical coding chain reaction. This kit can achieve specific in situ detection of various low-abundance glycosylated RNAs at the single-cell level, and provides a method for subtype differentiation of cells / samples in vitro using the detection reagent. It is particularly suitable for the construction and statistical discrimination of glycosylated fingerprints (such as PCA) of breast tumor cell lines, and provides the application of the detection reagent and method in in vitro breast cancer cell typing.

[0043] In a preferred embodiment of the present invention, the detection reagent of the present invention comprises the following core components (any of which may be in repackaged or lyophilized form):

[0044] 1. Metabolic labeling module: Ac4ManNAz, containing azide sugar precursor, is used to introduce N3 into cell surface glycans, providing a target for subsequent click chemistry.

[0045] 2. Dual-identification proximity assembly module:

[0046] (1) Glycosyl recognition (T SA ): The 5′ end has a DBCO (dibenzocyclooctylene) functional group (containing a spacer arm and an initiation domain a′ for subsequent reactions), and the nucleic acid sequence is shown in SEQ ID NO.1. It recognizes the glycan chain of glycosylated RNA by binding to sialic acid labeled with Ac4ManNAz (a sialic acid precursor containing an azide group) through click chemistry.

[0047] (2) RNA recognition (T) RNA): The sequence is complementary to the RNA component (such as U1 / U3 / U35a / Y5 / U8) sequence of the target glycosylated RNA, and the nucleic acid sequence is shown in SEQ ID NO.2~SEQ ID NO.6;

[0048] 3. Connector: The nucleic acid sequence is shown in SEQ ID NO.7, and it connects with the T... SA and T RNA The complementary bridging oligonucleotides at the linking sites enable the two probes to self-assemble into a dual-pivot linkage complex in the presence of the same glycosylated RNA, providing sites for subsequent triggering of hierarchical coding chain reactions.

[0049] 4. Hierarchical coding chain reaction module:

[0050] (1) First layer (H1 / H2 chain): The nucleic acid sequence is shown in SEQ ID NO.8~SEQ ID NO.9, capturing a′ / b′ and initiating self-assembly;

[0051] (2) Second layer (H3-H6 chain): The nucleic acid sequence is shown in SEQ ID NO.10~SEQ ID NO.13. Based on the exposed initiation site at the 3′ end of H2, the signal is further amplified in a cascaded manner to achieve a two-layer signal amplification.

[0052] Preferably, the present invention labels the H1, H4, and H6 segments with FAM / TAMRA fluorescent groups to achieve "signal-on" detection.

[0053] 5. Reaction compatibility and blocking module: Preferably, the present invention includes reagents such as adaptant buffer, BSA (blocking non-specific binding), and salmon sperm DNA (SSD) for cell compatibility and low background reaction.

[0054] 6. Reference and Analysis Module: Includes negative / positive controls, as well as template data and scripts for PCA / clustering.

[0055] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0056] Unless otherwise specified, the experimental methods described in the following embodiments are conventional experimental methods well known to those skilled in the art, and are performed according to the techniques or conditions described in the literature in this field or according to the product instructions. Where specific conditions are not specified in the experimental methods, they are generally operated under conventional conditions.

[0057] Unless otherwise specified, all materials and reagents described in the following examples are commercially available.

[0058] Example 1: Construction and optimization of detection reagents

[0059] In this detection system, a bifunctional chimeric probe was designed, consisting of T SA With T RNA It is covalently connected through a connector. Where T... SA The 5′ end is modified with a DBCO group, which can specifically recognize and capture sialic acid from the glycan portion of glycosylated RNA; T RNA The 5′ end is responsible for specific binding to the target RNA sequence. Once this complex binds to the target, T… SA With T RNA The 5′ end of H1 further binds to the 5′ end of the signal transduction probe H1, thereby initiating a subsequent cascade hybridization reaction. Specifically, the 3′ end sequence of H1 binds to H2 through complementary base pairing. The remaining sequence of H2 then partially hybridizes with H3, and the remaining sequence of H3 binds to H4. Subsequently, H4 pairs with H5. H5 simultaneously partially binds to both H4 and H6, while H6 partially binds to both H5 and H3, ultimately forming a multi-level, autonomous cyclic hybridization network involving H2, H3, H4, H5, and H6. This achieves a hierarchical, chain-like hybridization amplification reaction, thereby enabling highly sensitive signal amplification and detection of glycosylated RNA.

[0060] The specific sequences used are shown in Table 1.

[0061] Table 1 Sequences involved in the invention

[0062]

[0063] The above sequence can be synthesized, for example, by Sangon Biotech (Shanghai) Co., Ltd.

[0064] Using the above sequences, an in situ detection technique for glycosylated RNA based on hierarchical coding chain reaction was developed. The detection principle is as follows: Figure 1 As shown, five glycosylated RNAs on the cell surface have different RNA sequence structures. Based on these differences in RNA sequence, we designed and synthesized five different capture probes (Ti). RNA Different T RNA It specifically binds to the corresponding glycosylated RNA sequence through base pairing; T SA It specifically recognizes and captures sialic acid from the glycan portion of glycosylated RNA. Once both portions bind to the target, the connector strand will transfer the T... SA and T RNA They are pulled closer together to form a complex. SA and T RNAThe 5′ end of the signal transduction probe H1 further binds to the 5′ end of the signal transduction probe H1. Upon the addition of H2, it cyclically hybridizes with H1 to form the first layer of HCR. Subsequently, the addition of H3-H6 triggers the layering of HCR, forming a high-molecular-weight DNA polymer with high signal gain. The difference in expression levels of the five glycosylated RNAs on the cell surface translates into their binding to the reporter probe (T). RNA The dynamic changes in the number of cells can be used to regulate the efficiency of the dual-module recognition complex and HCR polymerization reaction, and finally, by outputting differentiated fluorescence signal intensities, the in situ identification of breast cancer cell subtypes can be achieved.

[0065] The specific steps of the hierarchical coding chain reaction are as follows:

[0066] (1) Prepare T solutions with a concentration of 10 μM. SA T RNA Connector solution, 25 μg / μl BSA solution, 10 mg / ml salmon sperm DNA (purchased from Sigma-Aldrich) (SSD) solution, 10 μM solutions of H1, H2, H3, H4, H5, and H6, wherein H1, H4, and H6 are both labeled with FAM fluorescent groups.

[0067] (2) 10 μL of the solution was seeded in each of the eight confocal dishes. 5 One MCF-7 cell (purchased from China Center for Type Culture Collection (CTCC)) was cultured overnight in a CO2 incubator at 37°C.

[0068] (3) Replace the culture medium of all confocal dishes with culture medium containing 100 μM Ac4 ManNaz (purchased from MedChemExpress) and incubate at 37°C for 48 hours.

[0069] (4) Preparation of the dual recognition module reaction solution: Add 1 μL of T SA 1 μL T RNA Add 1.5 μL connector, 1 μL SSD and 1 μL BSA to a 1.5 mL centrifuge tube, and then bring the volume to 100 μL with Tris-HCl buffer.

[0070] (5) After culturing for 48 hours, the cells were washed three times with PBS buffer and 100 μL of the solution prepared in step (4) was added to each of the eight confocal dishes. The cells were then incubated at 37°C for 1 hour.

[0071] (6) Prepare the layered coding chain reaction solution: add H1-H6 in a ratio of 3:3:2:2:1:1, and make up to 100 μL with Tris-HCl buffer. Prepare 5 portions.

[0072] (7) After incubation, the cells were washed three times with PBS, and the solution prepared in step (6) was added. The cells were incubated at 37°C for 15 min, 30 min, 45 min, 60 min, 75 min, 90 min, 105 min and 120 min respectively.

[0073] (8) Wash three times with PBS, add 100 μl of PBS, use a confocal laser scanning microscope to image, obtain time series images, and quantitatively analyze the change of fluorescence intensity over time.

[0074] Experimental results:

[0075] See the experimental results. Figure 2A and Figure 2B .in, Figure 2A This is a diagram showing the optimized cell incubation time for detecting glycosylated RNA using the method of this invention. Figure 2B This is a quantitative curve of confocal fluorescence intensity based on optimized cell incubation time.

[0076] from Figure 2A As can be seen, the fluorescence intensity on the surface of MCF-7 cells gradually increases with increasing incubation time.

[0077] from Figure 2B It can be seen from this that... Figure 2A Statistical analysis of the fluorescence intensity of confocal images yielded a line graph showing the change in cell surface fluorescence intensity over incubation time. The results indicated that the fluorescence intensity on the MCF-7 cell surface increased with increasing incubation time, reaching a plateau at 120 minutes. Therefore, the optimal reaction time for this method was selected as 120 minutes. This example demonstrates the high efficiency and stability of the hierarchical coding chain HCR signal amplification process. This optimized condition lays the foundation for subsequent high-sensitivity detection.

[0078] Example 2: Validation in different subtypes of breast cancer cells

[0079] (1) Inoculate 10 μL of each of the five confocal dishes. 5 One MCF-10A (normal), BT474 (Luminal B), MCF-7 (Luminal A), SK-BR-3 (HER2+), and MDA-MB-231 (triple negative) cell line (all cells were purchased from the China Center for Type Culture Collection (CTCC)) were cultured overnight in a CO2 incubator at 37°C.

[0080] (2) Prepare T solutions with a concentration of 10 μM. SA T RNAConnector solution, 25 μg / μl BSA solution, 10 mg / ml salmon sperm DNA (SSD) solution, 10 μM H1, H2, H3, H4, H5, and H6 solutions, wherein H1, H4, and H6 are both labeled with FAM and TAMRA groups.

[0081] Based on the unique RNA sequences of the five glycosylated RNAs, corresponding capture probes T were designed. RNA-U1 T RNA-U3 T RNA-U35a T RNA-Y5 and T RNA-U8 The probe binds specifically to a specific sequence segment of the target RNA through the base complementary pairing principle, and the feasibility of its complementary hybridization with glycosylated RNA is verified using NUPACK software.

[0082] (3) Replace the medium in the five confocal dishes with medium containing 100 μM Ac4ManNaz and incubate at 37°C for 48 hours.

[0083] (4) Preparation of the dual recognition module reaction solution: Add 1 μL of T SA 1 μL T RNA Add 1.5 μL connector, 1 μL SSD and 1 μL BSA to a 1.5 mL centrifuge tube, and then add Tris-HCl buffer to bring the total volume to 100 μL.

[0084] (5) After culturing for 48 hours, the cells were washed three times with PBS buffer, and 100 μL of the solution prepared in step (4) was added to each confocal dish. The cells were then incubated at 37°C for 1 hour.

[0085] (6) Prepare the layered coding chain reaction solution: add H1-H6 in a volume ratio of 3:3:2:2:1:1, and make up to 100 μL with Tris-HCl buffer. Prepare 5 portions.

[0086] (7) After incubation, the cells were washed three times with PBS, and the solution prepared in step (6) was added. The cells were incubated at 37°C for 2 hours.

[0087] (8) Wash three times with PBS, add 100 μl of PBS, and use a confocal microscope for imaging.

[0088] The specific steps for detecting the content of the following cell surface glycosylated RNAs—U1, U3, U35a, Y5, and U8—are as follows:

[0089] (a) Multi-field fluorescence imaging was performed on MCF-10A, BT474, MCF-7, SK-BR-3, and MDA-MB-231 cells using confocal microscopy. Images of five independent fields of view were acquired for each cell group. The average fluorescence intensity of cells in each field of view was extracted using ImageJ software as the initial feature parameter.

[0090] (b) Organize the characteristic parameters of each group of cells into a data matrix X, with the rows of the matrix corresponding to samples from different fields of view and the columns corresponding to the extracted characteristic parameters (average fluorescence intensity), and supplement the recording of cell subtype labels.

[0091] (c) Data standardization: Each column of matrix X is Z-score standardized to make the mean of each feature 0 and the standard deviation 1, so as to eliminate the influence of dimensions.

[0092] (d) Using the PCA module of the scikit-learn library (version 1.2.0) in Python, principal component analysis was performed on the standardized data matrix to draw a three-dimensional scatter plot and obtain the cluster map of different subtypes of breast cancer cells.

[0093] Experimental results:

[0094] Experimental results are as follows Figure 3 As shown. Among them,

[0095] Figure 3 Figure A shows the detection of five groups of cell surface glycosylated RNA U1 using the method of this invention; from Figure 3 As can be seen from Figure A, glycosylated RNA U1 is expressed on the surface of all five cell types. From the fluorescence intensity bar chart corresponding to the confocal image, the fluorescence intensity of MCF-10A, BT474, MCF-7, SK-BR-3, and MDA-MB-231 cells decreases in that order.

[0096] Figure 3 Figure B is a graph showing the detection of five groups of cell surface glycosylated RNA U3 using the method of this invention; from Figure 3 As can be seen from Figure B, glycosylated RNA U3 is expressed on the surface of all five cell types. From the fluorescence intensity bar chart corresponding to the confocal image, the fluorescence intensity of MCF-10A, BT474, MCF-7, and SK-BR-3 cells decreases in that order, while the fluorescence intensity of MDA-MB-231 cells is the lowest.

[0097] Figure 3 Figure C is a graph showing the detection of five groups of cell surface glycosylated RNA U35a using the method of this invention; from Figure 3As can be seen from the C-axis, glycosylated RNA U35a is expressed on the surface of all five cell types. From the fluorescence intensity bar chart corresponding to the confocal image, the fluorescence intensity of MCF-10A, BT474, MCF-7, SK-BR-3, and MDA-MB-231 cells decreases in that order.

[0098] Figure 3 Figure D is a graph showing the detection of five groups of cell surface glycosylated RNA Y5 using the method of this invention; from Figure 3 As can be seen from the D-image, glycosylated RNA Y5 is expressed on the surface of all five cell types. From the fluorescence intensity bar chart corresponding to the confocal image, the fluorescence intensity of MCF-10A, BT474, MCF-7, and SK-BR-3 cells decreases in that order, while the fluorescence intensity of MDA-MB-231 cells is the lowest.

[0099] Figure 3 Figure E shows the detection of five groups of cell surface glycosylated RNA U8 using the method of this invention; from Figure 3 As can be seen from the E-image, glycosylated RNA U8 is expressed on the surface of all five cell types. From the fluorescence intensity bar chart corresponding to the confocal image, the fluorescence intensity of MCF-10A, BT474, MCF-7, SK-BR-3, and MDA-MB-231 cells decreases in that order.

[0100] from Figure 3 As can be seen, this kit has good discriminative ability among different breast cancer subtypes. The expression patterns of the five glycosylated RNAs showed significant differences among different cell lines, especially triple-negative breast cancer cells (MDA-MB-231), which showed the lowest expression across all indicators.

[0101] This directly demonstrates the feasibility and specificity of using the "glycan fingerprint" constructed in this invention to differentiate cell subtypes, which is expected to provide a reliable tool for clinical classification of refractory triple-negative breast cancer.

[0102] Example 3: Breast Cancer Subtype Classification

[0103] 1. Cluster analysis was performed on the glycosylated RNA expression data of five glycosylated RNAs using principal component analysis (PCA). The specific steps are as follows:

[0104] (1) Confocal microscopy was used to perform multi-field fluorescence imaging on the five groups of cells (MCF-10A, BT474, MCF-7, SK-BR-3, and MDA-MB-231) after the treatment in step 2 of Example 2. Images of five independent fields of view were acquired for each group of cells. The average fluorescence intensity of cells in each field of view was extracted using ImageJ software as the initial feature parameter.

[0105] (2) Organize the characteristic parameters of each group of cells into a data matrix X, with the rows of the matrix corresponding to samples in different fields of view and the columns corresponding to the extracted characteristic parameters (average fluorescence intensity), and supplement the recording of cell subtype labels.

[0106] (3) Data standardization: Each column of matrix X is Z-score standardized to make the mean of each feature 0 and the standard deviation 1, so as to eliminate the influence of dimensions.

[0107] (4) Using the PCA module of the scikit-learn library (version 1.2.0) in Python, perform principal component analysis on the standardized data matrix. Set the number of principal components to 3, and calculate the variance contribution rate and cumulative contribution rate of each principal component.

[0108] (5) A three-dimensional scatter plot was drawn based on the scores of the first three principal components, with different subtypes of breast cancer cells marked with different colors. At the same time, a feature loading plot was drawn to show the contribution of each original feature to the principal components.

[0109] Experimental results:

[0110] See the experimental results. Figure 4 .from Figure 4 As can be seen, the expression data of the five glycosylated RNAs obtained using this kit can clearly distinguish normal breast cells from different subtypes of breast cancer cells (Luminal A, Luminal B, HER2+, and triple-negative) through principal component analysis (PCA), with significant separation between clusters. This verifies that the glycosylated RNA in situ detection reagent based on hierarchical coding chain reaction can effectively distinguish five breast cancer cell lines by detecting the expression levels of five glycosylated RNAs.

[0111] The results show that each subtype of cells forms an independent cluster, with the first principal component contributing over 90%.

[0112] 2. Heatmap Analysis

[0113] The experimental steps are as follows:

[0114] (1) Five groups of cells (MCF-10A, BT474, MCF-7, SK-BR-3, and MDA-MB-231) treated in step 2 of Example 2 were imaged using confocal microscopy. Five fields of view were randomly selected for each group of cells. The images were opened using ImageJ software, a single cell region was selected, and its average fluorescence intensity was measured and recorded as the fluorescence signal value of the sample.

[0115] (2) Use GraphPad Prism 9.0 software to create a heatmap and analyze the results.

[0116] Experimental results:

[0117] See the experimental results. Figure 5 .from Figure 5 As can be seen from the data, the MDA-MB-231 breast cancer cell line is a triple-negative breast cancer cell line, characterized by high malignancy and strong invasiveness. It exhibits the lowest fluorescence intensity in the thermogram, indicating the lowest expression levels of the five glycosylated RNAs on its cell membrane surface. Following this, the fluorescence intensity of the SK-BR-3, MCF-7, and BT474 cell lines increases sequentially, indicating an increasing expression level of the five glycosylated RNAs on their cell membrane surfaces. Conversely, the normal breast cell line MCF-10A shows the highest fluorescence intensity in the thermogram, indicating the highest expression level of the five glycosylated RNAs on its cell membrane surface. These results further confirm a negative correlation between cell surface glycoRNA expression levels and tumor malignancy and invasiveness.

[0118] This embodiment transforms detection data into reliable classification criteria through heatmaps and PCA analysis. This not only verifies the practicality of this invention in the analysis of complex bioinformatics but also reveals its potential clinical value in assessing tumor biological behavior.

[0119] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A glycosylated RNA detection kit based on a hierarchical coding chain reaction, characterized in that, The kit includes: (1) A metabolic marker module, which includes: Ac4ManNAz; (2) A dual-identification proximity assembly module, comprising: 1) Glycosyl recognition probe (T SA ): The glycan chain used to recognize glycosylated RNA, the nucleic acid sequence of which is shown in SEQ ID NO.1; 2) RNA recognition probe (T) RNA ): Used to recognize the following five glycosylated RNAs: U1, U3, U35a, Y5, and U8, whose nucleic acid sequences are shown in SEQ ID NO.2 to SEQ ID NO.6; (3) Linker strand: Its nucleic acid sequence is shown in SEQ ID NO.7; (4) A hierarchical coding chain reaction module, which includes: 1) First-layer coding chain reaction module: comprising chains H1 to H2, the nucleic acid sequences of which are shown in SEQ ID NO. 8 to SEQ ID NO. 9; and 2) Second layer coding chain reaction module: It includes H3 to H6 chains, the nucleic acid sequences of which are shown in SEQ ID NO.10 to SEQ ID NO.

13.

2. The reagent kit according to claim 1, characterized in that, The kit also includes: (5) Reaction compatibility and blocking module: It includes Tris-HCl buffer, BSA solution and / or SSD solution.

3. The reagent kit according to claim 1, characterized in that, The kit also includes: (6) Reference and Analysis Module: This includes: negative / positive controls, and / or instructions for template data used in PCA / clustering.

4. The reagent kit according to claim 1, characterized in that, The H1 chain, the H4 chain, and / or the H6 chain are each labeled with a FAM / TAMRA fluorescent group at both ends.

5. The application of the kit according to any one of claims 1-4 in in situ detection methods for glycosylated RNA based on hierarchical coding chain reaction, in vitro cell subtype resolution methods, and / or methods for constructing or comparing glycosylated RNA feature profiles on the surface of breast cancer cells.

6. A method for in situ detection of glycosylated RNA based on hierarchical coding chain reaction, an in vitro cell subtype differentiation method, and / or a method for constructing or comparing glycosylated RNA feature profiles on the surface of breast cancer cells, characterized in that, The method includes: Using the kit according to any one of claims 1-4, the content of glycosylated RNA in the test sample is detected based on a hierarchical coding chain reaction.

7. The method according to claim 6, characterized in that, The method simultaneously detects the levels of the following glycosylated RNAs: U1, U3, U35a, Y5, and U8.

8. The use of the kit according to any one of claims 1-4 in the preparation of a breast cancer diagnostic kit.

9. The application according to claim 8, characterized in that, The breast cancer diagnostic kit is used to diagnose at least one of Luminal B breast cancer, Luminal A breast cancer, HER2+ breast cancer, and / or triple-negative breast cancer.

10. The application according to claim 9, characterized in that, The breast cancer diagnostic kit is used to diagnose triple-negative breast cancer.

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