A method and system for molecular dissimilation coding microscopy imaging for spatially dense biomolecule digital quantification

Abstract

The application discloses a molecular differentiation coding microscopic imaging method and system for spatially dense biomolecule digital quantification, and belongs to the technical field of fluorescence microscopy and cell imaging analysis. By converting spectral overlap and adopting a molecular differentiation coding technology, the overlapping problem in imaging is solved, and the accuracy and resolution are improved. A specific bifunctional chimeric recognition probe and a ring template probe are designed to realize efficient recognition and coding of biomolecules. Each biomolecule is given a unique identifier, signals are captured through N rounds of fluorescence microscopic imaging, and advanced image processing technology is used to distinguish overlapping signals to obtain accurate position information. The method provides a powerful tool for digital quantification and visualization of spatially dense biomolecules under the spatial resolution of a microscope, and can be used for exploring the spatial distribution and digital quantification of three kinds of spatially dense biomolecules, such as DNA epigenetic modification in a cell nucleus, N-glycan on a cell surface and glycosylated RNA under the spatial resolution of a microscope.

Description

Technical Field

[0001] This invention belongs to the field of fluorescence microscopy and cell imaging analysis technology, and specifically relates to a molecular dissimilatory coding microscopy imaging method and system for digital quantification of spatially dense biomolecules. Background Technology

[0002] Biomolecules are always densely distributed within the crowded cellular environment. In the cell nucleus, chromatin DNA is highly folded and its basic structural subunits, known as nucleosomes, are compressed (approximately 10 nm). Epigenetic modifications of DNA can occur at multiple sites within the same genomic region or between two adjacent regions. On the cell surface, glycans extensively modify lipids and proteins to regulate countless fundamental cellular functions. In recent years, numerous articles have reported on glycosylated RNAs on the cell surface, revealing a direct link between RNA biology and glycobiology, but the functional roles of glycosylated RNAs remain unclear. Understanding the spatial density and multivalent organization of these biomolecules in the cellular nanoenvironment is crucial for elucidating complex biological processes.

[0003] Fluorescence microscopy has become a powerful tool for mapping the spatial distribution of biomolecules in single cells. However, due to the optical diffraction limit (approximately 200-400 nm), conventional fluorescence microscopy struggles to distinguish these densely packed biomolecules at the nanoscale in nanoscale environments (e.g., ranges smaller than 200 nm). To overcome this challenge, researchers have developed super-resolution fluorescence techniques, which can bypass the diffraction limit and achieve imaging resolutions as low as 10-80 nm. Most of these super-resolution techniques provide single-molecule sensitivity for digital quantification, enabling researchers to observe and analyze the spatial distribution of biomolecules more accurately. However, these techniques require highly specialized setups and procedures and have limited resolution along the optical axis. Furthermore, minute sample drifts in multichannel imaging often lead to registration errors and incorrect co-localization analyses. Single-molecule fluorescence in situ hybridization (smFISH) is a well-established technique for visualizing single-cell RNA expression, allowing the counting of individual RNA molecules using conventional fluorescence microscopy. However, because single-molecule fluorescence in situ hybridization requires dozens of hybridization regions in the target sequence for signal amplification, smFISH cannot detect short sequences and other non-nucleic acid biomolecules. To address this issue, the applicant has previously developed various signal amplification fluorescence in situ hybridization strategies, such as DNA rolling circle amplification or hybridization chain reaction, for in situ signal amplification in single-molecule fluorescence in situ hybridization. These methods can convert different non-nucleic acid biomolecule characteristics into DNA barcodes, thereby enabling in situ analysis and visualization of these molecules.

[0004] In summary, despite significant progress, resolving spatially dense biomolecules at the spatial resolution of a microscope remains a challenge. The optical diffraction limit makes nanoscale imaging difficult, limiting in-depth understanding. While super-resolution techniques can overcome this limit, they are complex to implement, have limited resolution along the optical axis, and face difficulties in multi-channel imaging registration. Single-molecule fluorescence in situ hybridization cannot detect short sequences and non-nucleic acid molecules, and signal amplification is complex. Summary of the Invention

[0005] Addressing the challenges of heterogeneity and digital quantification of spatially dense biomolecules under current microscope spatial resolution, particularly the limitations of existing imaging technologies in terms of resolution and accuracy, and ensuring the precise identification and counting of biomolecules in complex environments, this invention aims to provide a molecular heterogeneity-encoded microscopic imaging method and system for the digital quantification of spatially dense biomolecules.

[0006] This invention is specifically achieved through the following technical solutions:

[0007] This invention provides a molecular dissimilarity-encoded microscopic imaging method for the digital quantification of spatially dense biomolecules, comprising the following steps:

[0008] S1. Based on the target biomolecule sequence, design multiple bifunctional chimeric recognition probes and bifunctional chimeric circular template probes. One end of the bifunctional chimeric recognition probe can recognize the biomolecule, and the other end is a complementary sequence that can hybridize with the bifunctional chimeric circular template probe. The bifunctional chimeric circular template probe can hybridize with the bifunctional chimeric recognition probe.

[0009] S2, each copy of the same biomolecule is labeled with a different type of bifunctional chimeric recognition probe to obtain biomolecules labeled with bifunctional chimeric recognition probes;

[0010] S3, based on biomolecules labeled with bifunctional chimeric recognition probes, uses bifunctional chimeric circular template probes as templates to encode molecular identifiers and generate different amplicones;

[0011] S4, based on amplicon, obtains imaging data maps through N rounds of fluorescence microscopy imaging;

[0012] S5, based on the imaging data map, identifies and distinguishes dense and overlapping fluorescent signal points in the imaging data map, extracts the position information of each biomolecule, and determines the position of dense biomolecules in space.

[0013] The biomolecules are any one of the following: nucleic acids and their epigenetic modifications in the cell nucleus and cell membrane, proteins, and carbohydrates.

[0014] Furthermore, the biomolecule is any one of the following: epigenetic modifications of DNA in the cell nucleus, N-glycans on the cell surface, and glycosylated RNA.

[0015] In S1, the bifunctional chimeric ring template probe includes a hybridization recognition sequence and a signal amplification response sequence.

[0016] In S2, the method of using bioorthogonal reaction labeling to label each copy of the same biomolecule with different types of bifunctional chimeric recognition probes.

[0017] S3 specifically includes hybridizing a biomolecule labeled with a recognition probe with a bifunctional chimeric circular template probe, using the hybridized circular template probe as a template to perform a signal amplification reaction, generating an amplicon containing multiple repetitive sequences, and separating and purifying the amplicon.

[0018] Furthermore, the signal amplification reaction is rolling circle amplification or hybridization chain reaction.

[0019] Furthermore, the signal amplification reaction is rolling circle amplification.

[0020] S4 includes: designing and synthesizing fluorescent probes that can bind to amplicones based on their sequence characteristics; binding the fluorescent probes to amplicones to form detectable fluorescent signals; and capturing and recording the fluorescent signals of each copy of a biomolecule through N rounds of fluorescence microscopy to obtain imaging data maps.

[0021] S5 includes using ImageJ software and the rolling ball algorithm to identify and distinguish dense and overlapping fluorescence signal points in the imaging data image; using Otsu's method to determine the threshold, converting the image into a binary image based on the threshold, identifying and segmenting dense and overlapping signal points in the binary image; extracting the position information of the segmented signal points; and correcting and analyzing to obtain the spatial position of dense biomolecules.

[0022] Furthermore, the step of using ImageJ software and the rolling ball algorithm to identify and distinguish dense, overlapping fluorescence signal points in the imaging data image includes: for each pixel (i,j) in the image, the background intensity B(i,j) is calculated in the following way:

[0023]

[0024] Where I(x,y) is the intensity value of the image at point (x,y), and C(i,j) is a circular region centered at (i,j) with radius r.

[0025] The foreground image is obtained by subtracting the background intensity from the original image;

[0026] F(i,j)=I(i,j)-B(i,j)

[0027] Where F(i,j) is the intensity value of the foreground image at point (i,j).

[0028] Furthermore, the threshold is determined using Otsu's method, employing the principle of maximizing between-class variance. The threshold that maximizes the between-class variance is selected as the optimal threshold. The formula for calculating the between-class variance is:

[0029] σ 2 =ω0×(μ0-μ) 2 +ω1×(μ1-μ) 2

[0030] Where, σ 2 ω0 is the inter-class variance, μ0 is the proportion of foreground pixels to the image, μ1 is the average gray value of the foreground, μ1 is the proportion of background pixels to the image, μ is the average gray value of the background, and μ is the total average gray value of the image. μ = ω0 × μ0 + ω1 × μ1.

[0031] This invention provides a system for implementing the above-described molecular dissimilarity-encoded microscopic imaging method for the digital quantification of spatially dense biomolecules, the system comprising:

[0032] The probe design module, based on the sequence of the target biomolecule, is used to design multiple bifunctional chimeric recognition probes and bifunctional chimeric circular template probes.

[0033] The labeling module is used to label each copy of the same biomolecule with different types of bifunctional chimeric recognition probes;

[0034] The molecular identifier encoding module is based on biomolecules labeled with bifunctional chimeric recognition probes. It uses bifunctional chimeric circular template probes as templates to generate amplicon through signal amplification reaction. Each amplicon contains a unique molecular identifier encoding.

[0035] The fluorescence microscopy imaging module is used to obtain imaging data maps through N rounds of fluorescence microscopy imaging;

[0036] The data analysis and localization module is used to identify and distinguish dense and overlapping fluorescence signal points in the imaging data map, and uses image processing algorithms and machine learning to extract the position information of each biomolecule and determine the location of dense biomolecules in space.

[0037] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described molecular dissimilarity coding microscopic imaging method for digital quantification of spatially dense biomolecules.

[0038] The present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described molecular dissimilarity coding microscopic imaging method for digital quantification of spatially dense biomolecules.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] This invention provides a novel molecular dissimilarity coding microscopy imaging method for the digital quantification of spatially dense biomolecules. This method combines molecular identifier encoding and fluorescence microscopy to distinguish dense biomolecules at the nanoscale, overcoming the optical diffraction limit of traditional fluorescence microscopy and improving imaging resolution. By designing bifunctional chimeric probes, it enables the detection and imaging of various biomolecules, including short sequences and non-nucleic acid biomolecules, overcoming the limitations of single-molecule fluorescence in situ hybridization. Simultaneously, molecular identifier encoding technology enables the digital quantification of biomolecules. Compared to traditional super-resolution techniques, this invention does not require highly specialized equipment or complex setup procedures; through ingenious molecular design, it achieves efficient labeling and imaging of biomolecules. Furthermore, by transforming the overlap of similar spectra into the overlap of different spectra, this invention effectively solves the overlap problem in the imaging of spatially dense biomolecules, improving imaging accuracy and resolution.

[0041] Furthermore, through molecular heterogeneous coding technology, each biomolecule is assigned a unique molecular identifier, enabling accurate identification and counting of each biomolecule during imaging, thus achieving digital quantification of spatially dense biomolecules. Using probes, each copy of the same biomolecule is labeled with different types of bifunctional chimeric recognition probes. This labeling method ensures that even if biomolecules overlap in space, they can be distinguished by different labels. This labeling also provides the foundation for subsequent molecular identifier encoding, as each copy of a biomolecule is assigned a unique label that can be identified and counted in subsequent amplification and imaging steps. Through N rounds of fluorescence microscopy, the fluorescence signal of each biomolecule copy was captured and recorded, providing a reliable data source for subsequent data analysis and localization. ImageJ software and a series of rolling ball algorithms were developed to identify and distinguish dense, overlapping fluorescence signal points in the imaging data, solving the overlap problem during imaging. By extracting the positional information of the segmented signal points and performing correction and analysis, accurate spatial location information of dense biomolecules was obtained.

[0042] The system provided by this invention covers the entire process from probe design, biomolecule labeling, amplification, fluorescence microscopy imaging to data analysis and localization. By designing specific bifunctional chimeric recognition probes and bifunctional chimeric circular template probes, it achieves efficient recognition and encoding of biomolecules. This design not only improves recognition accuracy but also assigns a unique molecular identifier to each biomolecule, providing a foundation for subsequent imaging and counting. Furthermore, by employing molecular heterogeneous coding technology and different types of bifunctional chimeric recognition probe labeling, it effectively solves the overlap problem in the imaging of spatially dense biomolecules. Even if biomolecules overlap spatially, they can be distinguished by different labels, thereby improving imaging accuracy and resolution. High resolution; through molecular dissimilarity coding technology and fluorescence microscopy, digital quantification of spatially dense biomolecules is achieved. Each copy of a biomolecule is assigned a unique label and identified and counted during imaging, thus providing accurate biomolecule quantity information. ImageJ software and a series of advanced image processing algorithms, such as the rolling ball algorithm and Otsu's method, are used to identify and distinguish dense, overlapping fluorescence signal points in the imaging data. This not only improves the accuracy of signal recognition but also enables the system to automatically extract and correct the positional information of signal points, thereby obtaining accurate spatial location information of dense biomolecules. The system has a high degree of automation, reducing the tediousness and errors of manual operation. Furthermore, the system's user interface is friendly and easy to use, allowing researchers to conduct experiments and data analysis more conveniently. It also has a certain degree of scalability and flexibility, allowing for customization and optimization according to different experimental needs, providing strong support for research in the life sciences. Attached Figure Description

[0043] Figure 1 This diagram illustrates the molecular dissimilarity coding microscopic imaging method for resolving spatially dense biomolecules; where a is a schematic diagram of existing amplification FISH methods for resolving spatially dense biomolecules, b is a schematic diagram of the method of this invention for resolving spatially dense biomolecules, c is the DNA markers of four major biomolecules, and d is the algorithm for automatically quantifying overlapping points and individual points.

[0044] Figure 2 To visualize the differences in spatially dense molecules on nanoscale DNA origami; where a) is a simplified equation for DNA classification and a probability plot as the number of DNA identifiers increases; b) is a comparison between the amplification FISH method and the method of this invention, with scale bars at 2 μm (left) and 10 μm (right); c) is an intensity curve of different fluorescence signals showing the colocalization of the arrow lines in b; d) is the average ratio of overlapping points or single points to the total number of points detected by the method of this invention; and e) is the digital quantification of all RCP spots by these two methods.

[0045] Figure 3To achieve differential visualization of N-glycans on the cell surface; where a is a schematic diagram of molecular differentiation encoding through sequential multipath imaging using the OTRDI method of this invention; b is CLSM imaging of N-glycan metabolic markers in MCF-7 cells at different concentrations of Ac4ManNAz, with a scale bar of 40 μm; c is the relationship between Ac4ManNAz concentration and single-cell RCP spot count in each fluorescence channel; d is the relationship between Ac4ManNAz concentration and single-cell RCP spot count in all 6 channels;

[0046] Figure 4 To visualize the differential epigenetic modification of DNA 5hmC in the cell nucleus; wherein, a is a schematic diagram of the visualization of the differentiation of dense 5hmC by the OTRDI method of the present invention, b is CLSM imaging of dense 5hmC in A549 cells with a scale bar of 5 μm, c is the ratio of overlapping spots and single spots to total spots in a single cell (n=30), and d is the intensity curve of different fluorescence signals showing the colocalization of the arrow lines in b.

[0047] Figure 5 To achieve differential visualization of glycosylated RNA on the cell surface, a is a schematic diagram of the method of the present invention; b is CLSM imaging of dense glycoRNA in MCF-7 cells with a scale bar of 10 μm; c is the intensity curve of different fluorescence signals showing the colocalization of the arrow lines in b; d is the quantitative analysis of single-cell RCP spots of individual fluorescence channels (R, G, B) and overlapping channels (RG, RB, GB, RGB); e is the statistical analysis of overlapping points and individual points; f is the average ratio of overlapping points or individual points in a single cell to the total number of points. Detailed Implementation

[0048] To enable those skilled in the art to understand the features and effects of the present invention, the following descriptions and definitions are only general descriptions of the terms and expressions mentioned in the specification and claims. Unless otherwise specified, all technical and scientific terms used herein have the ordinary meaning understood by those skilled in the art regarding the present invention, and in the event of any conflict, the definitions in this specification shall prevail.

[0049] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. It should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0050] The present invention will now be described in further detail with reference to the accompanying drawings:

[0051] See appendix Figure 1As shown, this invention provides a molecular dissimilarity-encoded microscopic imaging method for the digital quantification of spatially dense biomolecules, comprising the following steps:

[0052] S1. Based on the target biomolecule sequence, design multiple bifunctional chimeric recognition probes and bifunctional chimeric circular template probes. One end of the bifunctional chimeric recognition probe can recognize the biomolecule, and the other end is a complementary sequence that can hybridize with the bifunctional chimeric circular template probe. The bifunctional chimeric circular template probe can hybridize with the bifunctional chimeric recognition probe.

[0053] S2, each copy of the same biomolecule is labeled with a different type of bifunctional chimeric recognition probe to obtain biomolecules labeled with bifunctional chimeric recognition probes;

[0054] S3, based on biomolecules labeled with bifunctional chimeric recognition probes, uses bifunctional chimeric circular template probes as templates to encode molecular identifiers and generate different amplicones;

[0055] S4, based on amplicon, obtains imaging data maps through N rounds of fluorescence microscopy imaging;

[0056] S5, based on the imaging data map, identifies and distinguishes dense and overlapping fluorescent signal points in the imaging data map, extracts the position information of each biomolecule, and determines the position of dense biomolecules in space.

[0057] The biomolecules are any one of the following: nucleic acids and their epigenetic modifications in the cell nucleus and cell membrane, proteins, and carbohydrates.

[0058] In S1, the bifunctional chimeric ring template probe includes a hybridization recognition sequence and a signal amplification response sequence.

[0059] In S2, bioorthogonal reactions are used to label each copy of the same biomolecule with different types of bifunctional chimeric recognition probes.

[0060] Using multiple orthogonal DNA probes, each copy of a biomolecule is labeled with a different DNA identifier through a random multiplex labeling reaction, ensuring that the probes do not interfere with each other.

[0061] For circular template probes, a universal sequence is used for hybridization with the recognition probe, while a unique sequence is used for multiple rolling circle amplification.

[0062] S3 specifically includes hybridizing a biomolecule labeled with a recognition probe with a bifunctional chimeric circular template probe, using the hybridized circular template probe as a template to perform a signal amplification reaction to generate an amplicon containing multiple repetitive sequences, and separating and purifying the amplicon. The signal amplification reaction is either rolling circle amplification or hybridization chain reaction.

[0063] Each rolling circle amplification reaction generates different long single-stranded amplification products containing hundreds of orthogonal tandem repeat sequences. These rolling circle amplification products can be detected as different fluorescent spots by multiplex fluorescence in situ hybridization imaging, thereby enabling the counting of fluorescence signals in the nanoenvironment, even if they overlap spatially. By encoding the number of molecules as DNA sequence types, the digitization of spatially dense biomolecules at microscope resolution is achieved. According to probability theory, the probability that each copy is tagged with a different type of DNA identifier can be described by the following formula (molecular differential probability, P). md ):

[0064]

[0065] Where A(m, n) represents the number of permutations, and n m Let represent the total number of m copies labeled with any of n identifiers (where n > m > 0 and is a natural number). This formula shows that as n increases, the probability of distinguishing high-density m copies asymptotically approaches 1 (e.g., ...). Figure 1 (As shown in a). Especially when m=1, the probability is always 1. In fact, to achieve unique labeling, n should be much larger than m. To conduct a proof-of-concept experiment, the study decided to use 3 or 6 DNA identifiers (corresponding to one or two multiple imaging operations, respectively), balancing economy and practical feasibility. Samples were imaged using a confocal laser scanning microscope, and with a well-designed DNA differential coding system, it is also theoretically applicable to super-resolution microscopy.

[0066] S4 includes: designing and synthesizing fluorescent probes that can bind to amplicones based on their sequence characteristics; binding the fluorescent probes to amplicones to form detectable fluorescent signals; and capturing and recording the fluorescent signals of each copy of a biomolecule through N rounds of fluorescence microscopy to obtain imaging data maps.

[0067] N-glycans on the cell surface: differential labeling is achieved through metabolic glycan engineering and bioorthogonal reactions with various DNA barcode probes; 5-hydroxymethylcytosine (5hmC) modification of DNA in the cell nucleus: azide labels are introduced into the modification site using phage T4β-glucosyltransferase, and differential labeling is achieved through click chemistry with various DNA barcode probes; GlycoRNAs on the cell surface: dual recognition is achieved through DNA proximity reactions of sialic acid-recognizing glycoaptamer probes and complementary DNA probes of cell surface RNA, triggering in situ ligation reactions to form circular template probes, thereby achieving multiplex rolling circle amplification.

[0068] S5 includes using ImageJ software and the rolling ball algorithm to identify and distinguish dense and overlapping fluorescence signal points in the imaging data image; using Otsu's method to determine the threshold, converting the image into a binary image based on the threshold, identifying and segmenting dense and overlapping signal points in the binary image; extracting the position information of the segmented signal points; and correcting and analyzing to obtain the spatial position of dense biomolecules.

[0069] Background signals are removed using ImageJ and the rolling ball algorithm; an appropriate threshold is determined using Otsu's method; signal points in the image are located using binarization; and the pixel coordinates of multiple images are compared to automatically quantify and distinguish between overlapping points and individual points.

[0070] The specific steps are as follows:

[0071] (1) Input image set I and region of interest set R.

[0072] For each image img in each image I, perform the following operations: remove background signals using the rolling ball algorithm (img = RollingBallMIJ(img)); align the image according to its magnetic position (img = PosAlign(img,MagneticPos)); for each region of interest roi, read the ROI image (roiImg = ReadImageJROI(roi)) and multiply it with the processed image (imgSeg = img * roiImg) to extract the signal within the region; binarize the segmented image imgSeg and use the bwLabel function to label connected regions ([L,num] = bwLabel(imgSeg)), where L is the label matrix and num is the number of connected regions; traverse each connected region, extract its position information ([c,y] = ind2sub(size(L),spotInd)), and add this information to spotSet.

[0073] (2) Perform the following operations on each signal point in spotSet: use the divideSpot function to separate overlapping signal points; check whether the segmented signal points meet the overlap condition; if the overlap condition is met, increment the number of overlapping signal points numP by 1; add the number of overlapping signal points for each image to the output list P.

[0074] (3) Return the set of overlapping signal spots P.

[0075] The threshold is determined using Otsu's method, employing the principle of maximizing between-class variance. The threshold that maximizes the between-class variance is selected as the optimal threshold. The formula for calculating the between-class variance is as follows:

[0076] σ 2 =ω0×(μ0-μ)2 +ω1×(μ1-μ) 2

[0077] Where, σ 2 ω0 is the inter-class variance, μ0 is the proportion of foreground pixels to the image, μ1 is the average gray value of the foreground, μ1 is the proportion of background pixels to the image, μ is the average gray value of the background, and μ is the total average gray value of the image. μ = ω0 × μ0 + ω1 × μ1.

[0078] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0079] The following examples use instruments and equipment conventional in the art. Experimental methods in the following examples, unless otherwise specified, are generally performed under conventional conditions or as recommended by the manufacturer. All raw materials used in the following examples are conventional commercially available products with specifications conventional in the art. In this specification and the following examples, unless otherwise specified, "%" represents a percentage by mass, and "ratio" represents a mass ratio.

[0080] Example 1

[0081] The artificial DNA origami target was imaged using identifiers. The sequences required for imaging the artificial DNA origami target are shown in the table below:

[0082]

[0083] (1) Coverslip treatment

[0084] Treat the coverslip (24×60mm) with 30% H2O2 and concentrated H2SO4 (1:3, v / v) for 1 hour. After treatment, rinse the coverslip three times with deionized water, dry it with nitrogen, and then perform plasma treatment.

[0085] (2) Polydimethylsiloxane (PDMS) adhesive adhesion

[0086] Immediately after plasma treatment of the coverslip, PDMS adhesive is adhered to the coverslip.

[0087] (3) DNA origami fixation

[0088] DNA origami was fixed in the coverslip chamber using the following steps: Prepare buffer A (10 mM Tris-HCl, 100 mM NaCl, 0.1% (v / v) Tween 20, pH 8.0) and buffer B (10 mM Tris-HCl, 10 mM MgCl2, 1 mM EDTA, 0.1% (v / v) Tween 20, pH 8.0); wash the chamber three times with 20 μL of buffer A; inject 20 μL of 1 mg / mL BSA-Biotin solution (prepared in buffer A), incubate for 5 minutes, wash the chamber three times with 20 μL of buffer A; inject 20 μL of... Incubate with 0.5 mg / mL streptavidin solution (prepared in buffer A) for 5 minutes, then wash the chamber three times with 20 μL buffer A; wash the chamber three times with 20 μL buffer B, inject 20 μL of biotin-labeled DNA origami solution (prepared in buffer B), and incubate for 5 minutes; wash the chamber three times with 20 μL buffer B.

[0089] (4) Hybridization and amplification

[0090] Add 20 μL of hybridization buffer (buffer B contains a total concentration of 250 μM closed-loop probes) to the chamber and incubate at 37°C in a humid environment for 2 hours; wash the chamber three times with buffer B, add 20 μL of rolling circle amplification reaction mixture, and incubate at 37°C for 2 hours; the reaction mixture contains 10 U Phi29 DNA polymerase, 3 mM dNTP, REAL, and 1 × Phi29 DNA polymerase reaction buffer; wash the chamber three times again with buffer B.

[0091] (5) Fluorescence imaging

[0092] Add 20 μL of buffer B containing 100 nM fluorescent probe solution for further incubation. The imaging results are as follows: Figure 2 As shown.

[0093] From the appendix Figure 2Data shows that the amplification FISH method detects only one copy by observing a large dot on each DNA origami. This means that if there are multiple copies of the target sequence on the DNA origami, the amplification FISH method cannot distinguish them and can only detect a single overall fluorescence signal. In contrast, the method of this invention detects overlapping points in two different spectral channels. This indicates that when there are multiple copies of the target sequence on the DNA origami, these sequences can hybridize simultaneously with fluorescent probes in different spectral channels, thereby generating overlapping fluorescence signals during imaging. A large portion of the dots corresponding to multiple copies of the DNA origami can be observed from the overlap of different fluorescence signals. This is likely due to the combined effects of the limited efficiency of continuous DNA reactions at the water / glass interface and the formation of incomplete DNA origami substrates. Statistical analysis of overlapping and single spots revealed that overlapping spots accounted for approximately 53.15% of the total number of spots. This indicates that the method of this invention can more accurately detect multiple copies of the target sequence on the DNA origami, whereas the fluorescent spots mentioned above cannot be distinguished by the RCAFISH method. The OTRDI method of this invention increases the detectable copy number of the target molecule by dissecting overlapping spots. This method can distinguish molecules that are tightly packed in a nanoscale environment, achieving differential visualization and digital quantification. Compared with the traditional amplification FISH method, the method of this invention has higher sensitivity and accuracy, and can more accurately reflect the copy number of the target sequence on the DNA origami.

[0094] In summary, the method of this invention demonstrates significant advantages in detecting the copy number of target sequences on DNA origami. By dissecting overlapping points, this method can increase the detectable copy number of target molecules, achieving differential visualization and digital quantification. This is of great significance for molecular detection and quantitative analysis in nanoscale environments.

[0095] Example 2

[0096] Differential visualization and digital quantification of N-glycans were achieved, and bifunctional chimeric probes were designed for N-glycans. The probe sequences are as follows:

[0097]

[0098] like Figure 3As shown, after treating cells with Ac4ManNAz for 24 hours, the cells were washed with DMEM medium and 1×PBS to remove unbound Ac4ManNAz and other impurities. When the cells reached ideal confluence, they were fixed with 4% paraformaldehyde and 0.2% glutaraldehyde at room temperature for 30 minutes, washed with 1×PBS, and then chemically reacted. A 200 nM probe was incubated at 37°C for 2 hours to allow the probe to bind to the target molecule within the cells. After incubation, the cells were washed again. The cells were then treated with a closed-loop hybridization buffer containing 160 nM closed-loop probe, salmon sperm DNA, 2×SSC, and 20% formamide, and incubated at 37°C for 2 hours to further amplify the target molecule signal. For identifiable calyx imaging, the total concentration of the closed-loop probe was consistent with that used in monochrome imaging. Cells were treated with 1×Phi29... After washing with DNA polymerase reaction buffer, a rolling circle amplification reaction was performed at 37°C for 2 hours to further increase the copy number of the target molecule. Then, the cells were washed with 1×PBS. The target molecule was labeled with a fluorescent probe to emit a fluorescent signal, and the cell nucleus was stained with DAPI to distinguish the cell nucleus from the cytoplasm during imaging. The cells were then imaged using a laser scanning confocal microscope to capture the fluorescent signal and observe the distribution of the target molecule.

[0099] From the appendix Figure 3 Data shows that using six identifiers and two rounds of multi-channel imaging to resolve densely distributed N-glycans can improve imaging resolution and sensitivity. Metabolic labeling of MCF-7 cells with different concentrations of Ac4ManNAz (0–100 μM) revealed more overlapping spots with increasing Ac4ManNAz concentration, indicating a high-density distribution of glycosyl groups on the cell surface. Statistical analysis of single-cell spot counts in each fluorescence channel and all six channels showed that the total number of spots across all channels was significantly higher than the total number of spots in a single channel, further confirming the ability to distinguish and visualize dense N-glycans using the OTRDI method of this invention.

[0100] In summary, this experiment successfully achieved high-resolution and high-sensitivity imaging of cell surface N-glycans through a series of chemical reactions, probe incubation, closed-loop hybridization, rolling-loop amplification, and imaging steps. By using six identifiers, two-round multiplexing techniques, and statistical analysis, the experiment demonstrated the effectiveness of the OTRDI method in distinguishing and visualizing dense N-glycans. These results are of great significance for understanding the structure and function of cell surface glycosyl groups.

[0101] Example 3

[0102] Differential visualization and digital quantification of 5hmC were achieved by designing a bifunctional chimeric probe for 5hmC. The probe sequence is as follows:

[0103]

[0104]

[0105] like Figure 4 As shown, cells cultured in PDMS chambers were fixed with 4% formaldehyde at room temperature for 10 minutes; after permeation with 0.5% Triton X-100 in 1×PBS at room temperature for 5 minutes, the cell samples were carefully washed three times with 1×PBS; 5hmC glycosylation was performed by adding 1×NEB Buffer 4, 50μM UDP-N3-Glu, and 5UT4β-GT to 20μL of reaction solution, and incubating at 37°C for 2 hours. Then, 200nM 5hmC barcode probe and 10U T4 DNA ligase were added, and in 1×T4 DNA ligase buffer were used for in situ hybridization and ligation with primer DNA probe, so that the barcode probe binds to the azide group on the 5hmC site, providing a template for subsequent amplification; rolling circle amplification was performed for 2 hours in 1×phi29 DNA polymerase buffer, the reaction solution including phi29 DNA polymerase (10U) and 2mM dNTPs; A 200 nM fluorescent probe was hybridized with the rolling circle amplification product in 2×SSC buffer (containing 20% ​​(v / v) formamide) to bind the fluorescent probe to the amplification product, allowing the location of the 5 hmC site to be observed during imaging. After each reaction step, the sample was washed three times with the buffer of the next step to remove unbound reagents and impurities.

[0106] From the appendix Figure 4 Data shows that the method of this invention uses OTRDI to differentiate and display dense 5hmC sites in the cell nucleus. The detection principle involves using T4β-GT to label 5hmC sites with an azide group, followed by DNA labeling, identifier encoding, and multiplexing. This method enables the differentiation and display of dense 5hmC sites in the cell nucleus. Fluorescent spots are almost clustered within the cell nucleus, indicating that 5hmC sites are mainly distributed in the nucleus. Many spots overlap in different channels, meaning that multiple 5hmC sites exist in the cell nucleus, and the close proximity of these sites leads to overlap during imaging. Overlapping spots account for approximately 20% of the total spots within a single cell, further confirming the dense distribution of 5hmC sites in the cell nucleus. This means that our method can visualize more 5hmC sites in a nanoscale environment. The spatially dense distribution of 5hmC sites may indicate specific gene-rich DNA hydroxymethylation.

[0107] In summary, this experiment successfully visualized the differentiation of densely packed 5hmC sites in the cell nucleus through a series of chemical reactions and imaging steps. The method of this invention enabled the observation of a dense distribution of 5hmC sites in the cell nucleus, and revealed overlap between multiple sites. These results are significant for understanding the role of DNA hydroxymethylation in biological processes such as gene expression regulation and cell differentiation.

[0108] Example 4

[0109] Differential visualization and digital quantification of glycosylated RNA were achieved. Bifunctional chimeric probes were designed for glycosylated RNA, and the probe sequences are as follows:

[0110]

[0111]

[0112] like Figure 5As shown, cells were fixed at room temperature for 30 minutes with 4% paraformaldehyde and 0.2% glutaraldehyde to maintain cell morphology and structure. To block nonspecific binding, cells were blocked at 37°C for 30 minutes with 1× hybridization buffer (50 mM Tris-HCl buffer and 10 mM MgCl2, pH 7.4) containing 100 nM poly(T) oligonucleotides and 0.25 μg / μL BSA, without permeabilization. Cells were then treated with 1.5 μM RNA binding probe, 0.25 μg / μL BSA, and 250 mM... Cells were incubated with a solution of NaCl and 1× hybridization buffer at 37°C for 30 minutes. Cells were then washed three times for 10 minutes each time with hybridization wash buffer (2×SSC and 10% formamide), followed by three washes with PBS to remove residual formamide. Next, sialic acid aptamer recognition and proximity-assisted in situ ligation were performed. Cells were treated with 100 μL of aptamer and ligation probe solution containing 100 nM sialic acid aptamer, 0.25 μg / μL LBSA, 100 nM poly(T) oligonucleotide, 125 nM ligation probe, 125 nM ligation barcode probe, and 1× aptamer binding buffer (50 mM Tris-HCl, 5 mM KCl, 100 mM NaCl, 1 mM MgCl2, pH 7.4). Incubation at 37°C for 30 minutes ensured aptamer binding to sialic acid on the glycoRNA and facilitated hybridization of the ligation and barcode probes. Subsequently, the ligation reaction mixture was added to the aptamer and ligation probe solutions and thoroughly mixed by pipetting. The final solution contained 1 U / μL T4 DNA ligase, 1 mM ATP, and 1×T4 DNA ligase reaction buffer (provided by NEB). Next, in situ rolling circle amplification was performed at 37°C for 90 minutes using the rolling circle amplification working solution. The rolling circle amplification working solution contained 2.5 U / μL phi29 DNA polymerase, 0.25 mM dNTPs, 0.2 μg / μL BSA, and 1×phi29 DNA polymerase reaction buffer (provided by NEB). The amplified single-stranded rolling circle amplification products were detected by in situ hybridization. The hybridization solution contained 100 nM fluorescent probe, 2×SSC buffer, 0.25 μg / μL BSA, and 100 nM poly(T) oligonucleotides, and was incubated at 37°C for 30 minutes. From this step onwards, the slides must be handled in the dark. Finally, the slides were mounted with mounting medium, and images were acquired using a confocal microscope.

[0113] From the appendix Figure 5 Data shows that the combination of Optical Resolution Differential Display (OTRDI) and DNA Proximity Reaction in this invention enables differential visualization of dense glycoRNAs in a nanoenvironment. Figure 5a). In short, we designed a sugar aptamer probe to label Neu5Ac and hybridized cell surface RNA using a complementary DNA probe. These two recognition responses enable the following differentiation: labeling, proximity linkage, and identifier encoding. After multipath imaging, bright spots in different fluorescent channels were observed in the cell images ( Figure 5 b). Through colocalization analysis, overlapping points were clearly distinguished. Figure 5 c). Furthermore, we performed statistical analysis on RCP spots in single cells within individual fluorescence channels and overlapping channels ( Figure 5 (d and 5e). We found that the average ratio of overlapping spots to total spots within a single cell was approximately 17%. This dense distribution information may suggest an unexpected cellular function of glycoRNAs.

[0114] In summary, this experiment successfully achieved differential visualization of dense glycoRNAs by combining OTRDI and DNA proximity reaction. GlycoRNAs were found to be highly concentrated within cells, and signals from different fluorescence channels clearly distinguished overlapping areas. This dense distribution suggests unexpected functions of glycoRNAs in cells, providing important clues for further research into their biological roles. This experimental method also offers new ideas and techniques for high-resolution imaging and functional studies of other biomolecules.

[0115] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A molecular dissimilation coding microscopy method for spatially dense biomolecule digital quantification, characterized in that, Includes the following steps: S1. Based on the target biomolecule sequence, design multiple bifunctional chimeric recognition probes and bifunctional chimeric circular template probes. One end of the bifunctional chimeric recognition probe is a recognition end that can specifically recognize the target biomolecule, and the other end is a complementary sequence end that can hybridize with the bifunctional chimeric circular template probe. The bifunctional chimeric circular template probe includes a hybridization recognition sequence and a signal amplification response sequence. S2, using bioorthogonal reactions, labels each copy of the same biomolecule with different types of bifunctional chimeric recognition probes to obtain biomolecules labeled with bifunctional chimeric recognition probes; S3, based on biomolecules labeled with bifunctional chimeric recognition probes, uses bifunctional chimeric circular template probes as templates to perform rolling circle amplification reaction to generate different amplicones; S4, based on amplicon, obtains imaging data maps through N rounds of fluorescence microscopy imaging; S5, based on the imaging data map, uses ImageJ software and rolling ball algorithm to identify and distinguish dense and overlapping fluorescence signal points in the imaging data map; The threshold is determined using the Otsu method, and the image is converted into a binary image based on the threshold. Dense and overlapping signal points in the binary image are identified and segmented. The positional information of the segmented signal points is extracted. After correction and analysis, the spatial position of dense biomolecules is obtained.

2. The method of claim 1, wherein the method is used for spatially dense biomolecule digital quantification. The biomolecules are any one of the following: nucleic acids and their epigenetic modifications in the cell nucleus and cell membrane, proteins, and carbohydrates.

3. The method of claim 1, wherein the method is used for spatially dense biomolecule digital quantification. S3 specifically includes hybridizing a biomolecule labeled with a recognition probe with a bifunctional chimeric circular template probe, using the hybridized circular template probe as a template to perform a rolling circle amplification reaction to generate an amplicon containing multiple repeating sequences, and then separating and purifying the amplicon.

4. The method of claim 1, wherein the molecularly resolved encoded microscopy imaging is used for spatially dense biomolecule digital quantification. S4 includes: designing and synthesizing fluorescent probes that can bind to amplicones based on their sequence characteristics; binding the fluorescent probes to amplicones to form detectable fluorescent signals; and capturing and recording the fluorescent signals of each copy of a biomolecule through N rounds of fluorescence microscopy to obtain imaging data maps.

5. A system for implementing the molecular dissimilarity coding microscopic imaging method for digital quantification of spatially dense biomolecules as described in any one of claims 1 to 4, characterized in that, The system includes: The probe design module, based on the sequence of the target biomolecule, is used to design multiple bifunctional chimeric recognition probes and bifunctional chimeric circular template probes. The labeling module is used to label each copy of the same biomolecule with different types of bifunctional chimeric recognition probes via a bioorthogonal reaction. The molecular identifier encoding module is based on biomolecules labeled with bifunctional chimeric recognition probes. It uses bifunctional chimeric circular template probes as templates to generate amplicon through rolling circle amplification reaction. Each amplicon contains a unique molecular identifier encoding. The fluorescence microscopy imaging module is used to obtain imaging data maps through N rounds of fluorescence microscopy imaging; The data analysis and localization module uses a rolling ball algorithm to remove background signals to identify dense and overlapping fluorescent signal points; it uses the Otsu method to determine the threshold and convert the image into a binary image to segment dense and overlapping signal points; it extracts the position information of the segmented signal points and performs analysis and correction to determine the spatial position of dense biomolecules. 6.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-5 when the computer program is executed by the processor. When the processor executes the computer program, it implements the steps of the molecular dissimilatory coding microscopic imaging method for digital quantification of spatially dense biomolecules as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, wherein the computer program comprises the following steps of: When the computer program is executed by the processor, it implements the steps of the molecular dissimilatory coding microscopic imaging method for digital quantification of spatially dense biomolecules as described in any one of claims 1 to 4.

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