Molecular dissimilatory coding microscopic imaging method and system for digital quantification of spatially dense biomolecules

Abstract

The invention discloses a dissimilatory coding microimaging method and system for digitally quantifying spatially dense biomolecules, and belongs to the technical field of fluorescence microscopy and cell imaging analysis. By converting spectrum overlapping and adopting a molecular dissimilatory coding technology, the overlapping problem in imaging is solved, and the accuracy and the resolution are improved; a specific bifunctional chimeric recognition probe and an annular template probe are designed, and efficient recognition and coding of biomolecules are achieved. According to the method, each biomolecule is endowed with a unique identifier, signals are captured through N rounds of fluorescence microscopic imaging, overlapped signals are distinguished by utilizing an advanced image processing technology, accurate position information is obtained, and the method provides a powerful tool for performing digital quantitative visualization on the spatially dense biomolecules under the spatial resolution of a microscope. The method can be used for exploring the spatial distribution and digital quantification of three spatial dense biomolecules, namely DNA epigenetic modification in a cell nucleus, N-glycan on the surface of a cell and glycosylated RNA, under the resolution ratio of a microscope.

Description

Technical Field

[0001] The invention belongs to the technical field of fluorescence microscopy and cell imaging analysis, and in particular relates to a molecular alienation encoding microscopic imaging method and system for digital quantification of spatially dense biological molecules. Background Art

[0002] Biomolecules are always densely distributed in crowded cellular environments. In the cell nucleus, chromatin DNA is highly folded and compacted (about 10 nm) in basic structural subunits called nucleosomes. DNA epigenetic modifications may occur at multiple sites in the same genomic region or between two adjacent regions. On the cell surface, glycans extensively modify lipids and proteins to regulate countless basic cellular functions. In recent years, multiple articles have reported glycosylated RNA on the cell surface, revealing a direct link between RNA biology and glycobiology, but the functional role of glycosylated RNA is still unclear. Understanding the spatial density and multivalent organization of these biomolecules in the cellular nanoenvironment is crucial to unraveling 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 (about 200-400nm), conventional fluorescence microscopy has difficulty distinguishing these densely packed biomolecules at the nanoscale in nanoenvironments (e.g., ranges less than 200nm). To overcome this challenge, researchers have developed super-resolution fluorescence techniques that can bypass the diffraction limit and achieve imaging resolutions as low as 10-80nm. Most of these super-resolution techniques provide single-molecule sensitivity for digital quantification, allowing researchers to more accurately observe and analyze the spatial distribution of biomolecules. However, these techniques require highly specialized setups and procedures and have limited resolution along the optical axis. In addition, slight drifts of the sample in multichannel imaging often lead to misregistration and incorrect co-localization analysis. Single-molecule fluorescence in situ hybridization (smFISH) is a well-established technique for single-cell RNA expression visualization that can count individual RNA molecules using conventional fluorescence microscopy. However, since 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 solve this problem, 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 of single-molecule fluorescence in situ hybridization. These methods can convert different non-nucleic acid biomolecule features into DNA barcodes, thereby realizing in situ analysis and visualization of these molecules.

[0004] In summary, despite great progress, it is still a challenge to resolve spatially dense biomolecules at the spatial resolution of a microscope. The optical diffraction limit makes nanoscale imaging difficult, limiting in-depth understanding. Although super-resolution technology can break through this limit, the equipment is complex, the resolution along the optical axis is limited, and multi-channel imaging alignment is difficult. 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] In view of the difficulties in the alienation and digital quantification of spatially dense biomolecules under the spatial resolution of existing microscopes, especially how to overcome the limitations of existing imaging technologies in terms of resolution and accuracy to ensure the accurate identification and counting of biomolecules in complex environments, the present invention aims to provide a molecular alienation coded microscopic imaging method and system for digital quantification of spatially dense biomolecules.

[0006] The present invention is specifically implemented through the following technical solutions:

[0007] The present invention provides a molecular anisotropy coded microscopic imaging method for digital quantification of spatially dense biomolecules, comprising the following steps:

[0008] S1, based on the target biomolecule sequence, design a plurality of bifunctional chimeric recognition probes and bifunctional chimeric circular template probes, wherein 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, and the bifunctional chimeric circular template probe can hybridize with the bifunctional chimeric recognition probe;

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

[0010] S3, based on the biomolecules labeled with the bifunctional chimeric recognition probe, the bifunctional chimeric circular template probe is used as a template to perform molecular identifier encoding to generate different amplicons;

[0011] S4, based on the amplicon, through N rounds of fluorescence microscopy imaging, an imaging data graph is obtained;

[0012] S5, based on the imaging data graph, identifies and distinguishes the dense and overlapping fluorescent signal points in the imaging data graph, extracts the position information of each biological molecule, and determines the position of the dense biological molecules in space.

[0013] The biomolecule is any one of nucleic acid and its surface modification, protein and carbohydrate in the cell nucleus and cell membrane.

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

[0015] In S1, the bifunctional chimeric circular template probe comprises a hybridization recognition sequence and a signal amplification reaction sequence.

[0016] In S2, each copy of the same biomolecule is labeled with a different type of bifunctional chimeric recognition probe using bioorthogonal reaction labeling.

[0017] S3 specifically includes hybridizing a biological molecule labeled with a recognition probe with a bifunctional chimeric circular template probe, performing a signal amplification reaction using the hybridized circular template probe as a template, generating an amplicon containing multiple repeating 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 a fluorescent probe that can bind to the amplicon based on the sequence characteristics of the amplicon; combining the fluorescent probe with the amplicon to form a detectable fluorescent signal; and capturing and recording the fluorescent signal of each copy of the biological molecule through N rounds of fluorescence microscopy imaging to obtain an imaging data graph.

[0021] S5 includes using ImageJ software and rolling ball algorithm to identify and distinguish dense and overlapping fluorescent signal points in imaging data graphs; using Otsu's method to determine the threshold, converting the image into a binary image according to the threshold, identifying and segmenting dense and overlapping signal points in the binary image; extracting the position information of the signal points after segmentation; and correcting and analyzing to obtain the position of dense biological molecules in space.

[0022] Further, the method of using ImageJ software and rolling ball algorithm to identify and distinguish dense and overlapping fluorescent signal points in the imaging data graph includes: for each pixel point (i, j) in the image, the background intensity B(i, j) is calculated by the following method:

[0023]

[0024] Among them, I(x,y) is the intensity value of the image at point (x,y), C(i,j) is the circular area with (i,j) as the center and 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] Among them, F(i,j) is the intensity value of the foreground image at point (i,j).

[0028] Furthermore, the threshold is determined by using the Otsu method, and the principle of maximizing the inter-class variance is adopted to select the threshold that maximizes the inter-class variance as the optimal threshold. The calculation formula of the inter-class variance is:

[0029] σ 2 =ω0×(μ0-μ) 2 +ω1×(μ1-μ) 2

[0030] Among them, σ 2 is the between-class variance, ω0 is the proportion of foreground pixels in the image, μ0 is the average grayscale value of the foreground, ω1 is the proportion of background pixels in the image, μ1 is the average grayscale value of the background, μ is the total average grayscale value of the image, μ=ω0×μ0+ω1×μ1.

[0031] The present invention provides a system for implementing the above-mentioned molecular allotropy coded microscopic imaging method for digital quantification of spatially dense biomolecules, the system comprising:

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

[0033] a labeling module for labeling each copy of the same biomolecule with a different type of bifunctional chimeric recognition probe;

[0034] A molecular identifier encoding module, based on a biomolecule labeled with a bifunctional chimeric recognition probe, uses a bifunctional chimeric circular template probe as a template to generate amplicons through a signal amplification reaction, each amplicon containing a unique molecular identifier encoding;

[0035] A fluorescence microscopy imaging module, used to obtain an imaging data graph through N rounds of fluorescence microscopy imaging;

[0036] The data analysis and positioning module is used to identify and distinguish dense and overlapping fluorescent signal points in the imaging data map, use image processing algorithms and machine learning to extract the position information of each biological molecule, and determine the position of dense biological molecules in space.

[0037] The present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned molecular allelic coding microscopic imaging method for digital quantification of spatially dense biological molecules when executing the computer program.

[0038] The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the molecular allelic coding microscopic imaging method for digital quantification of spatially dense biological molecules.

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

[0040] The molecular heterogeneity coding microscopic imaging method for digital quantification of spatially dense biomolecules provided by the present invention is a brand-new molecular heterogeneity coding microscopic imaging method for digital quantification of spatially dense biomolecules. The method can distinguish dense biomolecules at the nanoscale through the combination of molecular identifier coding and fluorescence microscopic imaging, breaking through the optical diffraction limit of traditional fluorescence microscopes and improving the imaging resolution; by designing bifunctional chimeric probes, the detection and imaging of a variety of biomolecules including short sequences and non-nucleic acid biomolecules are realized, overcoming the limitations of single-molecule fluorescence in situ hybridization, and at the same time, through molecular identifier coding technology, digital quantification of biomolecules is also realized; compared with traditional super-resolution technology, the present invention does not require highly specialized equipment and complex setting procedures, and realizes efficient labeling and imaging of biomolecules through ingenious molecular design; the present invention effectively solves the overlap problem in spatially dense biomolecule imaging by converting the overlap of the same type of spectra into the overlap of different types of spectra, and improves the accuracy and resolution of imaging.

[0041] Furthermore, through molecular alienation coding technology, each biomolecule is assigned a unique molecular identifier, so that each biomolecule can be accurately identified and counted during the imaging process, realizing the digital quantification of spatially dense biomolecules; each copy of the same biomolecule is labeled with different types of bifunctional chimeric recognition probes using probes. This labeling method ensures that even if the biomolecules overlap in space, they can be distinguished by different labels. This labeling also provides a basis for subsequent molecular identifier encoding, because each copy of the biomolecule is assigned a unique label, which can be identified and counted in subsequent amplification and imaging steps; through N rounds of fluorescence microscopy imaging, the fluorescence signal of each copy of the biomolecule is captured and recorded, providing a reliable data source for subsequent data analysis and positioning; ImageJ software and the development of a rolling ball series algorithm are used to identify and distinguish dense and overlapping fluorescent signal points in the imaging data map, solving the overlap problem in the imaging process, and by extracting the position information of the signal points after segmentation and performing correction and analysis, the accurate position information of the dense biomolecules in space is obtained.

[0042] The system provided by the present invention covers the entire process from probe design, biomolecule labeling, amplification, fluorescence microscopy imaging to data analysis and positioning. By designing specific bifunctional chimeric recognition probes and bifunctional chimeric circular template probes, efficient recognition and encoding of biomolecules is achieved. This design not only improves the accuracy of recognition, but also enables each biomolecule to be endowed with a unique molecular identifier, providing a basis for subsequent imaging and counting. By adopting molecular alienation encoding technology and different types of bifunctional chimeric recognition probe labels, the overlap problem in spatially dense biomolecule imaging is effectively solved. Even if the biomolecules overlap in space, they can be distinguished by different labels, thereby improving the accuracy of imaging and the accuracy of separation. resolution; through molecular alienation coding technology and fluorescence microscopy imaging, digital quantification of spatially dense biomolecules is achieved. Each copy of a biomolecule is given a unique tag and is identified and counted during the imaging process, thereby providing accurate information on the number of biomolecules; ImageJ software and a series of advanced image processing algorithms such as the rolling ball algorithm and the Otsu method are used to identify and distinguish dense and overlapping fluorescent signal points in the imaging data map, which not only improves the accuracy of signal recognition, but also enables the system to automatically extract and correct the position information of the signal points, thereby obtaining the accurate position information of dense biomolecules in space; the system of the present invention has a high degree of automation, which can reduce the tediousness and errors of manual operation. At the same time, the system's operating interface is friendly and easy to use, allowing scientific researchers to conduct experiments and data analysis more conveniently. It has certain scalability and flexibility, and can be customized and optimized according to different experimental needs, providing strong support for research in the field of life sciences. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Schematic diagram of molecular alienation coded microscopic imaging for parsing spatially dense biomolecules; wherein, a is a schematic diagram of the existing amplification FISH method for parsing spatially dense biomolecules, b is a schematic diagram of the method of the present invention for parsing spatially dense biomolecules, c is the DNA markers of four major biomolecules, and d is an algorithm for automatically quantifying overlapping points and individual points.

[0044] Figure 2 To achieve differential visualization of spatially dense molecules on nanoscale DNA origami; wherein, a is a simplified equation for DNA classification encoding and a probability plot with increasing number of DNA identifiers, b is a comparison between the amplification FISH method and the method of the present invention, with scale bars of 2 μm (left) and 10 μm (right), c is an intensity curve of different fluorescent signals showing the co-localization of the arrow lines in b, d is the average ratio of overlapping spots or single spots to total spots detected by the method of the present 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; wherein, a is a schematic diagram of the OTRDI method of the present invention, which performs molecular differentiation encoding by sequential multiplex imaging, b is CLSM imaging of N-glycan metabolic labeling of MCF-7 cells by Ac4ManNAz at different concentrations, and the scale bar is 40 μm; c is the relationship between the concentration of Ac4ManNAz and the single-cell RCP spot count in each fluorescence channel, and d is the relationship between the concentration of Ac4ManNAz and the single-cell RCP spot count in all 6 channels;

[0046] Figure 4 To achieve differential visualization of DNA 5hmC epigenetic modification in the cell nucleus; wherein, a is a schematic diagram of the OTRDI method of the present invention for differentiation visualization of dense 5hmC, b is the CLSM imaging of dense 5hmC in A549 cells, with a scale 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 co-localization of the arrow lines in b.

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

[0048] In order to enable those skilled in the art to understand the characteristics and effects of the present invention, the following is a general description and definition of the terms and expressions mentioned in the specification and claims. Unless otherwise specified, all technical and scientific terms used in the text are the common meanings understood by those skilled in the art for the present invention. In the event of a conflict, the definition in this specification shall prevail.

[0049] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all of the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0050] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0051] See attached Figure 1As shown, the present invention provides a molecular anisotropy coded microscopic imaging method for digital quantification of spatially dense biomolecules, comprising the following steps:

[0052] S1, based on the target biomolecule sequence, design a plurality of bifunctional chimeric recognition probes and bifunctional chimeric circular template probes, wherein 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, and the bifunctional chimeric circular template probe can hybridize with the bifunctional chimeric recognition probe;

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

[0054] S3, based on the biomolecules labeled with the bifunctional chimeric recognition probe, the bifunctional chimeric circular template probe is used as a template to perform molecular identifier encoding to generate different amplicons;

[0055] S4, based on the amplicon, through N rounds of fluorescence microscopy imaging, an imaging data graph is obtained;

[0056] S5, based on the imaging data graph, identifies and distinguishes the dense and overlapping fluorescent signal points in the imaging data graph, extracts the position information of each biological molecule, and determines the position of the dense biological molecules in space.

[0057] The biomolecule is any one of nucleic acid and its surface modification, protein and carbohydrate in the cell nucleus and cell membrane.

[0058] In S1, the bifunctional chimeric circular template probe comprises a hybridization recognition sequence and a signal amplification reaction sequence.

[0059] In S2, each copy of the same biomolecule is labeled with a different type of bifunctional chimeric recognition probe using a bioorthogonal reaction.

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

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

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

[0063] Each rolling circle amplification reaction generates different long single-stranded amplification products, which contain hundreds of orthogonal tandem repeat sequences. These rolling circle amplification products can be detected as different fluorescent spots through multiple fluorescence in situ hybridization imaging, thereby realizing the counting of fluorescent signals in the nanoenvironment, even if they still overlap in space; by encoding the number of molecules as DNA sequence types, the digitization of spatially dense biological molecules at microscope resolution is achieved; according to probability theory, the probability of each copy being labeled by a different type of DNA identifier can be described by the following formula (molecular differentiation probability, P md ):

[0064]

[0065] Among them, A(m,n) represents the number of permutations, n m represents the total number of m copies marked with any of the 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 will asymptotically approach 1 (e.g. Figure 1 a). In particular, when m=1, the probability is always 1. In practice, to achieve unique labeling, n should be much larger than m. For proof-of-concept experiments, the study decided to use 3 or 6 DNA identifiers (corresponding to one or two multiplex imaging, respectively), taking into account both economy and practical feasibility. The samples were imaged using confocal laser scanning microscopy, and rationally designed DNA differential encoding is in principle also applicable to super-resolution microscopy.

[0066] S4 includes: designing and synthesizing a fluorescent probe that can bind to the amplicon based on the sequence characteristics of the amplicon; combining the fluorescent probe with the amplicon to form a detectable fluorescent signal; and capturing and recording the fluorescent signal of each copy of the biological molecule through N rounds of fluorescence microscopy imaging to obtain an imaging data graph.

[0067] N-glycans on the cell surface: differential labeling through metabolic glycan engineering and bioorthogonal reactions with a variety of DNA barcode probes; 5-hydroxymethylcytosine (5hmC) modification of DNA in the cell nucleus: the azido tag is introduced into the modification site using bacteriophage T4 β-glucosyltransferase, and differential labeling is performed with a variety of DNA barcode probes through click chemistry; sugar RNA on the cell surface: dual recognition is performed through DNA proximity reaction of sugar aptamer probes that recognize sialic acid and complementary DNA probes of cell surface RNA, and in situ ligation reaction is triggered to form circular template probes to achieve multiple rolling circle amplification.

[0068] S5 includes using ImageJ software and rolling ball algorithm to identify and distinguish dense and overlapping fluorescent signal points in imaging data graphs; using Otsu's method to determine the threshold, converting the image into a binary image according to the threshold, identifying and segmenting dense and overlapping signal points in the binary image; extracting the position information of the signal points after segmentation; and correcting and analyzing to obtain the position of dense biological molecules in space.

[0069] ImageJ and the rolling ball algorithm were used to remove background signals. The Otsu method was used to determine the appropriate threshold. Binarization was performed to locate signal points in the image. The pixel coordinates of multiple images were compared to achieve automatic quantitative distinction 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] The following operations are performed on each image img in each image I: use the rolling ball algorithm to remove the background signal (img = RollingBallMIJ(img)); align the images according to the 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 mark the connected regions ([L,num] = bwLabel(imgSeg)), where L is the labeling 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 split the overlapping signal points; Check whether the split signal points meet the overlapping conditions; If the overlapping conditions are met, increase the number of overlapping signal points numP by 1; Add the number of overlapping signal points of each image to the output list P.

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

[0075] The Otsu method is used to determine the threshold, and the principle of maximizing the inter-class variance is adopted to select the threshold that maximizes the inter-class variance as the optimal threshold. The calculation formula of the inter-class variance is:

[0076] σ 2 =ω0×(μ0-μ)2 +ω1×(μ1-μ) 2

[0077] Among them, σ 2 is the between-class variance, ω0 is the proportion of foreground pixels in the image, μ0 is the average grayscale value of the foreground, ω1 is the proportion of background pixels in the image, μ1 is the average grayscale value of the background, μ is the total average grayscale value of the image, μ=ω0×μ0+ω1×μ1.

[0078] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0079] The following examples use conventional instruments and equipment in the art. The experimental methods in the following examples where specific conditions are not specified are usually carried out under conventional conditions or under conditions recommended by the manufacturer. The various raw materials used in the following examples are conventional commercial products unless otherwise specified, and their specifications are conventional specifications in the art. In the specification of the present invention and the following examples, unless otherwise specified, "%" represents mass percentage and ratio represents mass ratio.

[0080] Example 1

[0081] The artificial DNA origami target is imaged through the identifier. The sequence required for imaging the artificial DNA origami target is as follows:

[0082]

[0083] (1) Cover glass processing

[0084] Coverslips (24×60 mm) were treated with 30% H2O2 and concentrated H2SO4 (1:3, v / v) for 1 hour. After treatment, the coverslips were rinsed three times with deionized water, dried with nitrogen gas, and then plasma treated.

[0085] (2) Polydimethylsiloxane (PDMS) adhesive

[0086] After plasma treatment of the coverslip is complete, PDMS glue is immediately attached to the coverslip.

[0087] (3) DNA origami fixation

[0088] The DNA origami was immobilized in the coverslip chamber by 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, and wash the chamber three times with 20 μL of buffer A; inject 20 μL of 0.5 mg / mL streptavidin solution (prepared in buffer A), incubate for 5 minutes, wash the chamber three times with 20 μL buffer A; wash the chamber three times with 20 μL buffer B, inject 20 μL biotin-labeled DNA origami solution (prepared in buffer B), 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 closed-circle probes with a total concentration of 250 μM) to the chamber and incubate at 37°C in a humidified environment for 2 hours; after washing 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 10Uphi29 DNA polymerase, 3mM dNTP, REAL and 1×phi29 DNA polymerase reaction buffer; wash the chamber three times with buffer B again.

[0091] (5) Fluorescence imaging

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

[0093] By the attached Figure 2From the data, it can be seen that the amplified FISH method detected only one copy by observing a large spot on each DNA origami, which means that if there are multiple copies of the target sequence on the DNA origami, the amplified FISH method cannot distinguish them and can only detect an overall fluorescent signal. In contrast, the method of the present invention detected overlapping spots in two different spectral channels, which shows that when there are multiple copies of the target sequence on the DNA origami, these sequences can be hybridized with fluorescent probes in different spectral channels at the same time, thereby generating overlapping fluorescent signals during imaging. A large part of the spots can be observed from the overlap of different fluorescent signals, which correspond to multiple copies on the DNA origami. It may be affected by the limited efficiency of continuous DNA reactions at the water / glass interface and the formation of incomplete DNA origami substrates. Then, the overlapping spots and single spots were statistically analyzed, and the overlapping spots accounted for about 53.15% of the total number of spots, which showed that when there were multiple copies of the target sequence on the DNA origami, the method of the present invention could detect these copies more accurately, and the above fluorescent spots could not be distinguished by the RCAFISH method; the OTRDI method of the present invention increased the detected copy number of the target molecule by dissecting the overlapping spots. This method could distinguish molecules closely arranged in a nanoenvironment, realize differential visualization and digital quantification, and compared with the traditional amplification FISH method, the method of the present invention had higher sensitivity and accuracy, and could more accurately reflect the copy number of the target sequence on the DNA origami.

[0094] In summary, the method of the present invention shows significant advantages in detecting the copy number of the target sequence on the DNA origami. By dissecting the overlapping points, the method can increase the detected copy number of the target molecule and realize differential visualization and digital quantification. This is of great significance for molecular detection and quantitative analysis in nanoenvironments.

[0095] Example 2

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

[0097]

[0098] like Figure 3As shown, after the cells were treated 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 the ideal confluence, they were fixed with 4% paraformaldehyde and 0.2% glutaraldehyde at room temperature for 30 minutes, washed with 1×PBS, and subjected to chemical reaction, incubated with 200nM probe at 37°C for 2 hours to allow the probe to bind to the target molecule in the cells, and after incubation, the cells were washed; treated with closed-loop hybridization buffer, the buffer components included 160nM closed-loop probe, salmon sperm DNA, 2×SSC, 20% formamide, and incubated at 37°C for 2 hours to further amplify the signal of the target molecule; for glycocalyx imaging with identifiers, the total concentration of the closed-loop probe was consistent with that of single-color imaging, and the cells were treated with 1×phi29 After washing with DNA polymerase reaction buffer, rolling circle amplification reaction was performed at 37°C for 2 hours to further increase the copy number of the target molecule, and then washed with 1× PBS; hybridization labeling was performed with fluorescent probes to make the target molecule emit fluorescent signals, and the cell nucleus was stained with DAPI so that the cell nucleus and cytoplasm could be distinguished during imaging. The cells were imaged using a laser scanning confocal microscope to capture the fluorescent signal and observe the distribution of the target molecule.

[0099] By the attached Figure 3 The data show that using 6 identifiers and two rounds of multiplexed imaging to resolve densely distributed N-glycans can improve the resolution and sensitivity of imaging; MCF-7 cells were metabolically labeled with different concentrations of Ac4ManNAz (0-100 μM), and more overlapping points were observed with the increase of Ac4ManNAz. This indicates that sugar groups are densely distributed on the cell surface; statistical analysis of single cell spot counts for each fluorescent channel and all 6 channels shows that the total number of spots in all channels is much higher than the total number of spots in a single channel, further confirming the visualization of the differentiation of dense N-glycans by the OTRDI method of the present 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 incubations, closed-loop hybridization, rolling circle amplification, and imaging steps. By using 6 identifiers and two rounds of multiplexed imaging technology, as well as statistical analysis methods, 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 sugars.

[0101] Example 3

[0102] Differential visualization and digital quantification of 5hmC. A corresponding bifunctional chimeric probe was designed 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; 0.5% Triton X-100 was prepared in 1×PBS, permeabilized for 5 minutes at room temperature, and then the cell samples were carefully washed three times with 1×PBS; 5hmC glycosylation reaction was performed, 1×NEB Buffer 4, 50μM UDP-N3-Glu and 5UT4β-GT were added to 20μL reaction solution, mixed and incubated at 37°C for 2 hours, 200nM 5hmC barcode probe and 10U T4DNA ligase were added, in 1×T4 DNA ligase buffer, in situ hybridization and ligation reaction with primer DNA probe were performed, so that the barcode probe was combined with the azide group on the 5hmC site to provide a template for subsequent amplification; rolling circle amplification reaction was performed in 1×phi29 DNA polymerase buffer for 2 hours, and the reaction solution included phi29 DNA polymerase (10U) and 2mM dNTP; 200nM fluorescent probe was hybridized and labeled with the rolling circle amplification product in 2×SSC buffer (containing 20% ​​(v / v) formamide), so that the fluorescent probe was bound to the amplification product, so that the position of the 5hmC site could 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] By the attached Figure 4 The data show that the method of the present invention uses OTRDI to differentiate and display dense 5hmC sites in the cell nucleus; detection principle: T4β-GT is used to label 5hmC sites with azide groups, followed by DNA labeling, identifier encoding and multiple imaging. This method can achieve differentiated display of dense 5hmC sites in the cell nucleus; the fluorescent spots are almost concentrated in the cell nucleus, which indicates that 5hmC sites are mainly distributed in the cell nucleus; in different channels, many points are overlapping, which means that there are multiple 5hmC sites in the cell nucleus, and the distances between these sites are close, resulting in overlap during imaging; the proportion of overlapping spots in a single cell to the total spots is as high as about 20%, further confirming the dense distribution of 5hmC sites in the cell nucleus. This means that our method can visualize more 5hmC sites in a nanoenvironment. The dense distribution of 5hmC sites in space may indicate specific gene-enriched DNA hydroxymethylation.

[0107] In summary, this experiment successfully achieved the differentiation display of dense 5hmC sites in the cell nucleus through a series of chemical reactions and imaging steps. Through the method of the present invention, the dense distribution of 5hmC sites in the cell nucleus can be observed, and overlap between multiple sites is found. These results are of great significance 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. The corresponding bifunctional chimeric probes were designed for glycosylated RNA. The probe sequences are as follows:

[0110]

[0111]

[0112] like Figure 5As shown, cells were fixed with 4% paraformaldehyde and 0.2% glutaraldehyde at room temperature for 30 minutes to maintain the morphology and structure of the cells; in order to block nonspecific binding, cells were blocked with 1× hybridization buffer (50mM Tris-HCl buffer and 10mM MgCl2, pH 7.4) containing 100nM poly(T) oligonucleotides and 0.25μg / μL BSA at 37°C for 30 minutes without permeabilization; cells were incubated with 1.5μM RNA binding probe, 0.25μg / μL BSA, 250mM NaCl and 1× hybridization buffer were incubated at 37°C for 30 minutes; the cells were washed three times in hybridization wash buffer (2×SSC and 10% formamide) for 10 minutes each time, and then washed three times with PBS to remove residual formamide; then the sialic acid aptamer recognition and proximity-assisted in situ ligation reaction were performed, and the cells were treated with 100 μL of aptamer and ligation probe solution, which contained 100 nM sialic acid aptamer, 0.25 μg / μL BSA, 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). Incubated at 37°C for 30 minutes to ensure that the aptamer binds to the sialic acid on the glycoRNA and assists the hybridization of the ligation probe and the barcode probe. Subsequently, the ligation reaction mixture was added to the aptamer and ligation probe solutions and mixed thoroughly by pipetting. The final solution contained 1U / μL T4 DNA ligase, 1mM ATP, and 1× T4 DNA ligase reaction buffer (provided by NEB). Next, the in situ rolling circle amplification reaction was performed at 37°C for 90 minutes using the rolling circle amplification working solution. The rolling circle amplification working solution contained 2.5U / μL phi29 DNA polymerase, 0.25mM dNTP, 0.2μg / μL BSA, and 1× phi29 DNA polymerase reaction buffer (provided by NEB). The amplified single-stranded rolling circle amplification product was detected by in situ hybridization. The hybridization solution contained 100nM fluorescent probe, 2×SSC buffer, 0.25μg / μL BSA, and 100nM poly(T) oligonucleotide, and was incubated at 37°C for 30 minutes. From this step on, the slides need to be protected from light. Finally, the slides were sealed with sealing solution and images were acquired by confocal microscopy.

[0113] By the attached Figure 5 The data show that the optical resolution differential display (OTRDI) of the present invention is combined with DNA proximity reaction to achieve differential visualization of dense glycoRNA in a nanoenvironment ( Figure 5a). In brief, we designed sugar aptamer probes to label Neu5Ac and used complementary DNA probes to hybridize to cell surface RNA. These two recognition reactions made it possible to distinguish labeling, proximity ligation, and identifier encoding as follows. After multiplex imaging, bright spots in different fluorescence channels were observed in the cell images ( Figure 5 b). By colocalization analysis, overlapping points were clearly distinguished ( Figure 5 c). In addition, we performed statistical analysis of RCP spots in individual cells in both individual fluorescence channels and in overlapping channels ( Figure 5 d and 5e). We found that the average ratio of overlapping spots to total spots in a single cell was about 17%. This dense distribution information may suggest unexpected cellular functions of glycoRNA.

[0114] In summary, this experiment successfully achieved differential visualization of dense glycoRNA by combining OTRDI and DNA proximity reaction. GlycoRNA is very densely distributed in cells, and the signals of different fluorescence channels can clearly distinguish overlapping points. This dense distribution information suggests that glycoRNA has unexpected functions in cells, providing important clues for further research on the biological role of glycoRNA. This experimental method also provides new ideas and technical means for high-resolution imaging and functional research of other biological molecules.

[0115] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A molecular allotropy coded microscopic imaging method for digital quantification of spatially dense biomolecules, characterized in that: The following steps are involved: S1, based on the target biomolecule sequence, design a plurality of bifunctional chimeric recognition probes and bifunctional chimeric circular template probes, wherein 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, and the bifunctional chimeric circular template probe can hybridize with the bifunctional chimeric recognition probe; S2, labeling each copy of the same biomolecule with a different type of bifunctional chimeric recognition probe to obtain a biomolecule labeled with a bifunctional chimeric recognition probe; S3, based on the biomolecules labeled with the bifunctional chimeric recognition probe, the bifunctional chimeric circular template probe is used as a template to perform molecular identifier encoding to generate different amplicons; S4, based on the amplicon, through N rounds of fluorescence microscopy imaging, an imaging data graph is obtained; S5, based on the imaging data graph, identifies and distinguishes the dense and overlapping fluorescent signal points in the imaging data graph, extracts the position information of each biological molecule, and determines the position of the dense biological molecules in space.

2. The molecular allotropy coded microscopic imaging method for digital quantification of spatially dense biomolecules according to claim 1, characterized in that: The biomolecule is any one of nucleic acid and its surface modification, protein and carbohydrate in the cell nucleus and cell membrane.

3. The molecular allotropy coded microscopic imaging method for digital quantification of spatially dense biomolecules according to claim 1, characterized in that: In S1, the bifunctional chimeric circular template probe comprises a hybridization recognition sequence and a signal amplification reaction sequence.

4. The molecular allotropy coded microscopic imaging method for digital quantification of spatially dense biomolecules according to claim 1, characterized in that: In S2, each copy of the same biomolecule is labeled with a different type of bifunctional chimeric recognition probe using a bioorthogonal reaction.

5. The molecular allotropy coded microscopic imaging method for digital quantification of spatially dense biomolecules according to claim 1, characterized in that: S3 specifically includes hybridizing a biological molecule labeled with a recognition probe with a bifunctional chimeric circular template probe, performing a signal amplification reaction using the hybridized circular template probe as a template to generate an amplicon containing multiple repeating sequences, and separating and purifying the amplicon. The signal amplification reaction is a rolling circle amplification or a hybridization chain reaction.

6. The molecular allotropy coded microscopic imaging method for digital quantification of spatially dense biomolecules according to claim 1, characterized in that: S4 includes: designing and synthesizing a fluorescent probe that can bind to the amplicon based on the sequence characteristics of the amplicon; combining the fluorescent probe with the amplicon to form a detectable fluorescent signal; and capturing and recording the fluorescent signal of each copy of the biological molecule through N rounds of fluorescence microscopy imaging to obtain an imaging data graph.

7. The molecular allotropy coded microscopic imaging method for digital quantification of spatially dense biomolecules according to claim 1, characterized in that: S5 includes the use of ImageJ software and rolling ball algorithm to identify and distinguish dense, overlapping fluorescent signal points in imaging data graphs; The threshold is determined using the Otsu method, and the image is converted into a binary image according to the threshold. The dense and overlapping signal points in the binary image are identified and segmented; the position information of the signal points after segmentation is extracted; and the position of the dense biological molecules in space is corrected and analyzed.

8. A system for implementing the molecular allotropy coded microscopic imaging method for digital quantification of spatially dense biomolecules as claimed in any one of claims 1 to 7, characterized in that: The system comprises: A probe design module, which is used to design multiple bifunctional chimeric recognition probes and bifunctional chimeric circular template probes based on the sequence of the target biomolecule; a labeling module for labeling each copy of the same biomolecule with a different type of bifunctional chimeric recognition probe; A molecular identifier encoding module, based on a biomolecule labeled with a bifunctional chimeric recognition probe, uses a bifunctional chimeric circular template probe as a template to generate amplicons through a signal amplification reaction, each amplicon containing a unique molecular identifier encoding; A fluorescence microscopy imaging module, used to obtain an imaging data graph through N rounds of fluorescence microscopy imaging; The data analysis and positioning module is used to identify and distinguish dense and overlapping fluorescent signal points in the imaging data map, use image processing algorithms and machine learning to extract the position information of each biological molecule, and determine the position of dense biological molecules in space.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the molecular heterogeneity encoding microscopic imaging method for digital quantification of spatially dense biomolecules as claimed in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the molecular heterogeneity encoding microscopic imaging method for digital quantification of spatially dense biomolecules as claimed in any one of claims 1 to 7 are implemented.

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