A histological separation detection system based on a biomimetic near-infrared fluorescent protein

Abstract

The present application relates to pathological diagnosis technical field, especially to a kind of histological separation detection system based on biomimetic near-infrared fluorescent protein (NIR-FPs), comprising: in situ labeling module: using the tumor selective dye including halogen substituent and / or alkynyl structure and the characteristic protein with sulfydryl in tissue section carry out in situ reaction, generate biomimetic near-infrared fluorescent protein;Histological separation module: the protein labeled with above-mentioned biomimetic near-infrared fluorescent protein is carried out molecular weight stratified separation;Processing and analysis module: read and analyze the fluorescence signal of each stratification;Tumor recognition module: according to the signal of molecular weight range and spatial distribution predicts tumor positive edge contour.The method does not need antibody, can realize multiple target point fast labeling and visualization in situ in tissue, and accurately delineate tumor positive edge in combination with stratified separation.

Description

Technical Field

[0001] This invention relates to the field of pathological diagnostic technology, and in particular to a histological separation and detection system based on biomimetic near-infrared fluorescent proteins (NIR-FPs). Background Technology

[0002] Breast cancer remains one of the leading causes of cancer-related deaths among women worldwide. Timely and accurate intraoperative diagnosis of biopsy tissue is crucial for developing treatment plans and improving patient outcomes. Currently, intraoperative frozen section analysis (IFSA) and rapid immunohistochemistry (IHC) are two main real-time pathological diagnostic methods widely used for determining surgical margins and identifying tissue properties in breast cancer.

[0003] However, existing technologies have significant shortcomings. IFSA is highly dependent on the quality of tissue sections and the experience of the pathologist, and it is prone to problems such as unclear interpretation and false negatives when the tumor is small or the tissue is stiff. In addition, this method requires specialized low-temperature sectioning equipment and a pathology reading system, which is not easy to deploy in all intraoperative environments.

[0004] While rapid immunohistochemistry (IHC) can identify target antigens in tissues, its complex procedure involves multiple staining steps, antibody incubation, and colorimetric reactions. This not only prolongs intraoperative diagnostic time but also increases costs and the risk of error. Furthermore, IHC requires high-quality antibodies, which are expensive, require strict storage conditions, and exhibit significant differences in tumor marker expression among patients, potentially leading to insufficient specificity and stability of the assessment results. These factors, to some extent, limit its practicality and widespread application in rapid intraoperative diagnosis.

[0005] In summary, existing intraoperative diagnostic methods generally suffer from problems such as reliance on antibodies, cumbersome operation, long processing time, and high requirements for personnel and equipment. Therefore, there is an urgent need to develop a molecular marker and imaging method that does not rely on immunostaining and can simultaneously detect multiple tumor characteristic proteins. This method should be rapid, sensitive, and specific, and support real-time intraoperative assessment and potential target discovery. Summary of the Invention

[0006] In view of this, the technical problem to be solved by the present invention is to provide a histological separation and detection system based on biomimetic near-infrared fluorescent protein, which does not require antibodies and can achieve rapid labeling and visualization of multiple targets in situ in tissues.

[0007] This invention provides a histological separation and detection system based on biomimetic near-infrared fluorescent proteins, comprising:

[0008] In situ labeling module: Tumor-selective dyes including halogen substituents and / or alkynyl structures react in situ with characteristic proteins with thiol groups in tissue sections to generate biomimetic near-infrared fluorescent proteins;

[0009] Histological separation module: performs molecular weight separation of the above-mentioned biomimetic near-infrared fluorescent protein labeled proteins;

[0010] Processing and Analysis Module: Reads and analyzes fluorescence signals from each layer;

[0011] Tumor identification module: Predicts positive tumor margin contours based on signals with molecular weight range and spatial distribution.

[0012] The tumor-selective dye structure includes halogen substituents and / or alkynyl structures, such as C-Cl bonds, which can be inserted into the hydrophobic pocket of tumor characteristic proteins and undergo nucleophilic substitution reactions with their thiol groups (-SH) to generate stable NIR-FPs.

[0013] In some specific embodiments, the tumor-selective dye is IR-780-alkyne, a derivative of IR-780, with an alkyl side chain containing an alkynyl group (e.g., a terminal alkynyl side chain containing 6 carbon atoms) introduced into the IR-780 structure to impart click chemistry reactivity. The structural formula of the tumor-selective dye is as follows:

[0014]

[0015] In some specific implementations, the tissue slices are slices of cancerous tissue, including but not limited to one or more of breast cancer, cervical cancer, lung cancer, bile duct cancer, and thyroid cancer.

[0016] The tumor-selective dye covalently binds to characteristic proteins with thiol groups in tissue sections in situ through a nucleophilic substitution reaction via halogen substituents and / or alkynyl groups in its structure, forming stable biomimetic near-infrared fluorescent proteins, thereby achieving highly selective fluorescent labeling of tumor characteristic proteins.

[0017] Preferably, the in-situ marking module includes the following steps:

[0018] S1) Frozen embedding of cancerous tissue;

[0019] S2) Cut the frozen-embedded tissue into tissue sections;

[0020] S3) The tissue slices are attached to the surface of the microporous array sample plate;

[0021] S4) Protein lysis and labeling were performed by co-incubating cell tissue lysate with dye.

[0022] First, the cancerous tissue removed during surgery is rapidly frozen and embedded.

[0023] Step S2) can specifically involve preparing tissue sections using a cryostat, wherein the thickness of the tissue sections is preferably 5 to 60 micrometers.

[0024] The micropore array sample plate can be a micropore array sample plate well known to those skilled in the art, preferably made of transparent material, containing 10 to 100 micropores per square centimeter.

[0025] The cell tissue lysis buffer can be any cell tissue lysis buffer known to those skilled in the art, including but not limited to RIPA lysis buffer.

[0026] The histological separation module is used to spatially separate NIR-FPs-labeled proteins while preserving tissue morphology and spatial structure information, so that proteins of different molecular weight ranges can be analyzed in layers while retaining their histological location information.

[0027] Preferably, the histological separation module includes: a cathode plate, a microporous array template, a separation gel medium, an anode plate, an electrophoresis tank, and a constant voltage power supply.

[0028] After separation, the separation gel medium is cut into layers perpendicular to the electric field direction, and imaging equipment is used to read the fluorescence intensity, molecular weight range, and xy position information of the proteins in each layer.

[0029] In some specific implementations, the histological separation module includes the following steps:

[0030] Step 1: Insert the tissue sections after sample loading into the electrophoresis module. From top to bottom, the modules are cathode plate, microporous array template, separation gel medium, and anode plate. Place the modules in the electrophoresis tank and connect a constant voltage power supply.

[0031] Step 2: Under the action of an external electric field, the target protein is spatially separated in the separation gel medium along the vertical direction (z-axis) according to its molecular weight, while retaining its tissue position information in the xy direction;

[0032] Step 3: After separation, the separation medium is cut into layers perpendicular to the electric field direction to form a gel layer of uniform thickness.

[0033] Step 4: Perform near-infrared fluorescence imaging on each layer, record and export the fluorescence intensity, molecular weight hierarchy correspondence, and xy position information corresponding one-to-one with the micropore array coordinates of each layer.

[0034] The processing and analysis module is used to collect near-infrared fluorescence signals from each separation layer and analyze the fluorescence intensity, molecular weight range, and two-dimensional spatial location information.

[0035] In some specific implementations, the labeled tissue sections, along with the sample plate, are placed in an uncrosslinked stacking gel and fused with the underlying 10% crosslinked separating gel (polyacrylamide) to form a complete three-dimensional gel structure. This structure is then placed in an NIR-FPs histological separation and detection system, where electrophoresis is performed at constant voltage, allowing proteins to migrate along the z-axis molecular weight direction while retaining their original xy positions.

[0036] Preferably, the processing and analysis module includes the following steps:

[0037] A) Near-infrared fluorescence imaging was performed on different layers obtained in the histological separation module to record the fluorescence signal distribution of proteins in each layer in the xy direction;

[0038] B) Compare the changes and distribution differences in fluorescence signal intensity in different layers to identify the enrichment layer of biomimetic near-infrared fluorescent proteins;

[0039] C) Based on the spatial location of high-intensity fluorescent signal regions in the enriched layer on the array template, the distribution area of ​​tumor tissue can be determined and positive edges of tumors can be identified.

[0040] The tumor identification module is used to automatically predict and draw the positive edge contour of the tumor based on the signal characteristics of a specific molecular weight range and its spatial distribution, so as to achieve rapid and objective edge determination.

[0041] In some specific implementations, the tumor recognition module includes the following steps:

[0042] Step 1: In the multidimensional data output by the processing and analysis platform, filter the layered signals that meet the preset molecular weight range, and construct a spatial distribution map by combining the xy position information;

[0043] Step 2: Use threshold segmentation and morphological processing algorithms to identify regions in the spatial distribution map where the fluorescence signal is significantly higher than the background.

[0044] Step 3: Automatically fit the boundary of the high-intensity signal region into a closed contour to obtain the tumor positive edge prediction result;

[0045] Step 4: Overlay the predicted contour onto the visualization image of the tissue section for quick intraoperative interpretation and edge localization. Optionally, it can be compared and verified with the pathological section results.

[0046] In some specific implementations, the histological separation and detection system also includes a protein enrichment and analysis module for postoperative omics identification and validation of labeled NIR-FPs.

[0047] Specifically, biotin is coupled to dye-labeled proteins via a copper-catalyzed azido-alkyne cycloaddition reaction (click reaction) using the alkyne structure of the dye molecule. After binding with streptavidin resin, the NIR-FPs complex is enriched and then analyzed by mass spectrometry to achieve omics identification and verification of the target protein, thus meeting the integrated needs of rapid intraoperative imaging and postoperative molecular mechanism research.

[0048] Specifically, biotin is coupled to dye-labeled proteins via a copper-catalyzed azido-alkyne cycloaddition reaction (click reaction) using the alkyne structure on the dye molecule. The biotinylated protein complex is then bound to streptavidin resin to achieve efficient enrichment of NIR-FPs. After elution, the enriched samples are analyzed by mass spectrometry to obtain information on the molecular weight, sequence, and modification sites of the target proteins. This enables postoperative omics identification and molecular mechanism studies of tumor-related proteins based on rapid intraoperative imaging.

[0049] This invention also provides the application of the above-mentioned biomimetic near-infrared fluorescent protein-based histological separation and detection system in rapid intraoperative diagnosis and biopsy detection of solid tumors.

[0050] Specifically, this includes, but is not limited to, rapid diagnosis of tumor tissue during surgery, screening of potential tumor targets, diagnosis of positive tumor margins, and detection of biopsy tissue samples.

[0051] The solid tumors mentioned include, but are not limited to, breast cancer, cervical cancer, lung cancer, bile duct cancer, and thyroid cancer.

[0052] The present invention also provides a method for rapid diagnosis and / or biopsy detection of solid tumors during surgery, comprising: analyzing and detecting tissue samples using the above-mentioned histological separation and detection system based on biomimetic near-infrared fluorescent protein.

[0053] This method does not require antibodies and can achieve rapid labeling and visualization of multiple targets in situ in tissues. Combined with layered separation, it can accurately depict the positive edges of tumors. It solves the problems of existing intraoperative diagnostic methods that rely on antibodies, have low labeling efficiency, limited throughput, and cumbersome operation, as well as the technical difficulties of not being able to integrate imaging and molecular analysis, which affect the accuracy and efficiency of rapid intraoperative assessment of breast cancer.

[0054] Compared with existing technologies, this invention provides a histological separation and detection system based on biomimetic near-infrared fluorescent proteins, comprising: an in-situ labeling module: using a tumor-selective dye including halogen substituents and / or alkyne structures to react in situ with characteristic proteins with thiol groups in tissue sections to generate biomimetic near-infrared fluorescent proteins; a histological separation module: separating the proteins labeled with the above-mentioned biomimetic near-infrared fluorescent proteins by molecular weight stratification; a processing and analysis module: reading and analyzing the fluorescence signals of each stratum; and a tumor identification module: predicting the positive edge contour of the tumor based on the signal of molecular weight range and spatial distribution.

[0055] The present invention achieves the following beneficial effects:

[0056] 1. This invention employs tumor-selective dyes containing halogen substituents (such as C-Cl) and alkynyl structures to undergo specific nucleophilic substitution reactions with characteristic proteins containing thiol groups (-SH) in tissues, generating stable biomimetic near-infrared fluorescent proteins (NIR-FPs) in situ. The labeling process does not rely on antibody labeling, overcoming the limitations of traditional methods that are restricted to a single or few targets. It can simultaneously achieve parallel labeling and signal superposition of multiple tumor characteristic proteins, thereby improving the sensitivity and accuracy of detection.

[0057] 2. This invention, while separating proteins by molecular weight, preserves the two-dimensional positional information of tissue sections, enabling a one-to-one correspondence between protein molecular characteristics and tissue structure. Near-infrared fluorescence imaging can visually display the spatial enrichment pattern of tumor-related proteins in tissues, facilitating accurate identification and delineation of positive tumor margins.

[0058] 3. The system of the present invention completes the closed-loop operation of "lysis-labeling-separation-imaging-recognition" sequentially in a single tissue section, eliminating the complex steps of traditional multi-step staining, washing and antibody incubation, significantly shortening the detection time and meeting the needs of rapid auxiliary decision-making during surgery for solid tumors such as breast cancer;

[0059] 4. This invention utilizes the alkyne structure in the dye molecule to couple biotin to the dye-labeled protein via a copper-catalyzed azide-alkyne cycloaddition reaction (click reaction), and binds to streptavidin resin to achieve efficient enrichment of the target protein complex. Combined with mass spectrometry analysis, the labeled protein can be identified and its mechanism verified through omics, thereby enabling in-depth postoperative molecular-level research based on intraoperative imaging, supporting the discovery of new targets and personalized diagnosis and treatment.

[0060] 5. The microwell array sample plate of the present invention supports parallel processing of multiple samples and position encoding, and can flexibly adjust parameters such as slice thickness, layer range, and imaging threshold according to clinical needs; in addition to breast cancer, the system is also suitable for pathological detection and boundary assessment of various solid tumors, and has good scalability and clinical translation potential. Attached Figure Description

[0061] Figure 1 This is a schematic diagram of the workflow of the histological separation and detection system based on biomimetic near-infrared fluorescent proteins (NIR-FPs) in this invention;

[0062] Figure 2 This is a schematic diagram illustrating the separation and analysis of proteins within a tissue section using a histological separation unit.

[0063] Figure 3 To illustrate the results of identifying the edge of human breast cancer tumors using this invention, a is an H&E staining image of breast cancer tumor in patient R0176, and b is an H&E staining image of breast cancer tumor in patient R0235.

[0064] Figure 4 This is a comparative analysis of the tumor contours predicted by this invention and the contours of pathological sections.

[0065] Figure 5 The images shown are the results of determining the contour of human breast cancer tumors using this invention. a is a tumor contour image generated by the NIR-FPs-based detection system, and b is a staining image of breast cancer tumors (R0252 and R0227) after H&E staining.

[0066] Figure 6 To compare the three-dimensional resolution of different microwell array modules in the histological separation and detection system, a is a microwell array mold used for 3D histological electrophoresis, and b is a comparison and analysis of breast cancer tumor tissue using 20×20 and 40×40 microwell array modules respectively.

[0067] Figure 7 To verify the accuracy of 20×20 and 40×40 microwell arrays in predicting tumor margins in human breast cancer biopsy samples and to validate the results against pathological comparisons, a) shows the system-predicted tumor profiles of clinical biopsy samples and pathologically confirmed tumor profiles based on NIR-FPs; b) shows the calculated similarity between the two results under 20×20 and 40×40 microwell arrays; c) shows the comparison between the pathologically confirmed tumor area and the tumor area predicted by the NIR-FPs-based detection system after H&E staining of breast biopsy samples.

[0068] Figure 8 The 1H NMR spectrum of intermediate compound 1 in the preparation process of the tumor-selective dye IR-780-alkyne in this invention;

[0069] Figure 9 This is the 1H NMR spectrum of the tumor-selective dye IR-780-alkyne used in this invention. Detailed Implementation

[0070] To further illustrate the present invention, a detailed description is provided below with reference to embodiments. However, it should be understood that these descriptions are merely for further illustrating the features and advantages of the present invention, and not for limiting the scope of the claims.

[0071] There are no particular restrictions on the source of any raw materials used in this invention; they can be purchased from the market or prepared using conventional methods known to those skilled in the art.

[0072] Example 1:

[0073] Preparation of tumor-selective dye IR-780-alkyne

[0074] The reaction equation for the synthesis of IR-780-alkyne is shown below:

[0075] b

[0076] The specific steps for synthesizing compound 1 in this embodiment are as follows: 2,3,3-trimethyl-3H-indole (1.50 mL, 9.34 mmol) and anhydrous acetonitrile (10 mL) were added to a sealed reaction vessel, followed by 6-iodo-1-hexyne (1.00 mL, 7.70 mmol). The reaction was carried out under heating for a period of time. After the reaction was completed, the mixture was cooled, and ethyl acetate was added to precipitate the product. The product was then filtered to obtain a light gray solid with a yield of 40%. The characterization data of compound 1 are as follows: 1 H NMR (400 MHz, CDCl3) δ 7.77 – 7.75(m, 1H), 7.61 – 7.58 (m, 3H), 4.77 (t, J = 8.0 Hz, 2H), 3.18 (s, 3H), 2.38 –2.34 (m, 2H), 2.16-2.09 (m, 2H), 2.00 (q, J = 2.4 Hz, 1H), 1.81 – 1.76 (m,2H), 1.67 (s, 6H).

[0077] The specific steps for synthesizing compound 2 in this embodiment are as follows: Compound 1 (400 mg, 1.10 mmol) and 2-chloro-3-(hydroxymethylene)-1-cyclohexene-1-carboxaldehyde (100 mg, 0.54 mmol) were added to a reaction flask, followed by sodium acetate (160 mg, 1.80 mmol) and acetic anhydride (20 mL). The mixture was stirred at approximately 35 °C until the reaction was complete (approximately 3-5 h). After the reaction, the solvent was removed by vacuum distillation. The residue was purified by silica gel column chromatography (using dichloromethane / methanol as the eluent, for example, a volume ratio of approximately 100:1) to obtain a green solid with a yield of 70%. The characterization data of compound 2 are as follows:1 H NMR(400 MHz, CDCl3) δ 8.34 (d, J = 14.0 Hz, 2H), 7.42 – 7.37 (m, 4H), 7.23 –7.20 (m, 4H), 6.30 (d, J = 14.4 Hz, 2H), 4.29 (t, J = 7.6 Hz, 4H), 2.78 (t, J= 6.0 Hz, 4H), 2.36 – 2.32 (m, 4H), 2.05 – 1.97 (m, 8H), 1.79 – 1.73 (m,16H). HR-MS Calcd for [M + 615.3501, Found 615.3492.

[0078] Example 2:

[0079] This embodiment demonstrates the application of the method of the present invention in rapid intraoperative tissue diagnosis of breast cancer. Specifically, the method utilizes the tumor-selective dye IR-780-alkyne, containing halogen substituents (such as C-Cl) and alkynyl structures, to specifically nucleophilically substitute the thiol groups of tumor-characteristic proteins in tissue sections, generating stable biomimetic near-infrared fluorescent proteins (NIR-FPs) in situ. This is combined with an NIR-FPs histological separation and detection system to achieve spatial separation and rapid imaging of the proteins, thereby assisting in the identification of tumor regions and positive margins. The steps for applying this method intraoperatively are attached. Figure 1 As shown, the details are as follows:

[0080] 1) Tissue preparation and in-situ marking:

[0081] Fresh breast cancer tissue removed during surgery was stored at -80°C. o Rapidly freeze at C for approximately 5 minutes and then freeze-embed. Prepare frozen sections with a thickness of 60 micrometers using a cryostat (applicable range: 5–100 micrometers). Transfer the tissue sections to a custom-made 20×20 microwell array mounting plate (prepared with transparent photocrosslinked resin) and attach them smoothly.

[0082] Subsequently, the IR-780-alkyne dye solution was prepared and mixed with RIPA lysis buffer (5 mL per section), and incubated in culture dishes at room temperature to 60°C. o Incubation at C conditions simultaneously completes protein cleavage and dye labeling. The halogen substituent (-Cl) in the dye covalently binds to the protein thiol group through a nucleophilic substitution reaction, forming stable NIR-FPs.

[0083] 2) Histological separation and molecular weight stratification:

[0084] After labeling, the tissue sections, along with the sample plate, were placed in an uncrosslinked stacking gel and fused with the crosslinked 10% separation gel (polyacrylamide) below to form a complete three-dimensional gel structure. This was then placed in an NIR-FPs histological separation and detection system. Electrophoresis was performed at constant voltage, allowing proteins to migrate along the z-axis molecular weight direction while retaining their original x and y positions. The process took approximately 15 minutes.

[0085] 3) Fluorescence scanning and three-dimensional map reconstruction:

[0086] After separation, the entire gel was removed and rapidly frozen (-80°C). After freezing, it was sliced ​​along the electrophoresis direction to a thickness of 600 micrometers, yielding 14 gel fragments. Each gel layer was imaged (including but not limited to an 800 nm channel scanner) to obtain protein signal intensity maps, xy coordinates, and gel layer numbers (i.e., molecular weight ranges), constructing a three-dimensional distribution map of NIR-FPs.

[0087] 4) Signal collection, analysis, and positive region identification:

[0088] In the fluorescence signal map, the distribution of protein signals in the gel layer corresponding to the molecular weight position of breast cancer tumor-specific proteins can help determine the tumor region and its boundary. The signal intensity and distribution characteristics of this region can help identify the positive boundary of the tumor.

[0089] This embodiment establishes a NIR-FPs histological separation imaging data model based on the in-situ reaction of IR-780-alkyne dye with tumor characteristic proteins in frozen sections of tissues removed during human breast cancer surgery. This model, constructed based on real samples, clarifies the characteristic distribution of covalent binding between the dye and proteins in tumor lysate, providing standard signal thresholds and spatial localization criteria for rapid intraoperative interpretation in multiple subsequent breast cancer cases.

[0090] Example 3:

[0091] This embodiment demonstrates the application process of the present invention in breast cancer tissue samples, whereby the method uses the tumor-selective dye IR-780-alkyne for in-situ covalent labeling of proteins, combined with click chemistry for selective enrichment of specific target proteins, and finally mass spectrometry analysis to screen potential tumor targets. The details are as follows:

[0092] 1) Protein extraction and dye incubation from human breast cancer tissue:

[0093] The breast cancer tissue obtained during the operation was cut into sufficiently small pieces, and an appropriate amount of RIPA buffer was added to a centrifuge tube containing the tissue fragments. The mixture was then incubated at low temperature (e.g., 4°C). oUnder condition C), the tissue was intermittently lysed using a non-contact ultrasound device. The lysate was then centrifuged, the precipitate was discarded, and the supernatant was collected to obtain a protein sample. The obtained protein sample was mixed with IR-780-alkyne dye and incubated, allowing the active intermediate chlorine in the dye molecule to undergo a nucleophilic substitution reaction with the protein thiol group, completing covalent labeling.

[0094] 2) Click on the chemical reaction:

[0095] To selectively capture dye-labeled tumor-specific proteins, biotin is coupled to the dye-labeled protein via a copper-catalyzed azide-alkyne cycloaddition reaction (click reaction) after covalent labeling.

[0096] 3) Target protein enrichment and purification:

[0097] After the click reaction is complete, non-specifically bound proteins and impurities are removed through multiple rounds of organic solvent precipitation and washing. Then, streptavidin agarose resin is introduced, utilizing its high affinity for biotin to selectively bind the target protein complex. After washing to remove impurities, a high-purity dye-labeled protein fraction is obtained through gentle elution in a buffer system.

[0098] 4) Mass spectrometry analysis and target protein screening:

[0099] After trypsin digestion of the enriched samples, the proteins were identified using a high-resolution liquid chromatography-tandem mass spectrometry (LC-MS / MS) platform. The obtained data were qualitatively and quantitatively processed using standard proteomics analysis software, and functional annotation and enrichment analysis were performed using a tumor database to screen for potential target proteins highly associated with the occurrence and development of breast cancer.

[0100] Example 4:

[0101] This embodiment describes the application of the present invention for the diagnosis of positive margins in human breast cancer tumors. Breast cancer tumors (including corresponding adjacent normal tissue) were collected from 17 patients. The tumor tissue, along with its corresponding adjacent and normal tissues, were then embedded in frozen tissue embedding medium (OCT) and processed into frozen sections with a thickness of 60 micrometers, following the same steps as described in Example 1.

[0102] Example 5:

[0103] This embodiment demonstrates the application of the system of the present invention in the detection of breast cancer biopsy tissue samples, and specifically illustrates the improvement in the recognition accuracy and spatial resolution of small-volume samples after adopting the upgraded 40×40 microwell array module.

[0104] Preoperative tissue samples were obtained from breast cancer patients via biopsy, and the samples were then subjected to OCT embedding and -80...o After cryopreservation at C, frozen sections with a thickness of 60 micrometers were prepared using a cryostat. The sections were transferred to a high-throughput 40×40 microwell array (total of 1600 microwells), following the same steps as described in Example 1. To verify the system's diagnostic effectiveness, adjacent tissue sections from the biopsy samples were stained with hematoxylin and eosin, and the tumor region was manually delineated by a professional pathologist as a control. The tumor regions predicted by the system were compared with the gold standard pathological contour for similarity fitting analysis. The contour comparison algorithm script was written using MATLAB. The results showed that compared to the traditional 20×20 microwell array, the 40×40 microwell array provided more detailed predicted contours in the biopsy samples, significantly improving the tumor edge fitting accuracy, with an average quantitative similarity greater than 90%. In multiple biopsy samples, the system's predicted region consistently and completely covered the tumor region determined by the pathologist, without misclassifying non-tumor areas, indicating that the system of this invention has high sensitivity and accuracy in biopsy tissue detection.

[0105] This embodiment verifies the advantages of the upgraded 40×40 module in detecting small-volume breast cancer biopsy samples, improving the clinical adaptability of the system in preoperative early diagnosis and micro-tissue analysis. Furthermore, this embodiment demonstrates that compared to clinical pathological examination, this platform can be operated by researchers without pathological diagnostic experience in a shorter time, thus contributing to faster and more accurate intraoperative decision-making.

[0106] The above description of the embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. It should be noted that those skilled in the art can make several improvements and modifications to the present invention without departing from the principles of the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

Claims

1. A histological separation and detection system based on biomimetic near-infrared fluorescent proteins, characterized in that, include: In situ labeling module: Tumor-selective dyes including halogen substituents and / or alkynyl structures react in situ with characteristic proteins with thiol groups in tissue sections to generate biomimetic near-infrared fluorescent proteins; Histological separation module: performs molecular weight separation of the above-mentioned biomimetic near-infrared fluorescent protein labeled proteins; Processing and Analysis Module: Reads and analyzes fluorescence signals from each layer; Tumor identification module: Predicts positive tumor margin contours based on signals with molecular weight range and spatial distribution.

2. The histological separation and detection system based on biomimetic near-infrared fluorescent protein according to claim 1, characterized in that, The tumor-selective dye is IR-780-alkyne, having the structure shown in Formula I: 。 3. The histological separation and detection system based on biomimetic near-infrared fluorescent protein according to claim 1, characterized in that, The tissue section was a section of cancerous tissue; The cancer mentioned is one or more of the following: breast cancer, cervical cancer, lung cancer, bile duct cancer, and thyroid cancer.

4. The histological separation and detection system based on biomimetic near-infrared fluorescent protein according to claim 1, characterized in that, The in-situ marking module includes the following steps: S1) Frozen embedding of cancerous tissue; S2) Cut the frozen-embedded tissue into tissue sections; S3) The tissue slices are attached to the surface of the microporous array sample plate; S4) Protein lysis and labeling were performed by co-incubating cell tissue lysate with dye.

5. The histological separation and detection system based on biomimetic near-infrared fluorescent protein according to claim 4, characterized in that, The micropore array sample plate is made of transparent material and contains 10 to 100 micropores per square centimeter.

6. The histological separation and detection system based on biomimetic near-infrared fluorescent protein according to claim 4, characterized in that, The cell tissue lysis buffer was RIPA lysis buffer.

7. The histological separation and detection system based on biomimetic near-infrared fluorescent protein according to claim 1, characterized in that, The histological separation module includes: a cathode plate, a microporous array template, a separation gel medium, an anode plate, an electrophoresis tank, and a constant voltage power supply.

8. The histological separation and detection system based on biomimetic near-infrared fluorescent protein according to claim 7, characterized in that, After separation, the separation gel medium is cut into layers perpendicular to the electric field direction, and imaging equipment is used to read the fluorescence intensity, molecular weight range, and xy position information of the proteins in each layer.

9. The histological separation and detection system based on biomimetic near-infrared fluorescent protein according to claim 1, characterized in that, The processing and analysis module includes the following steps: A) Near-infrared fluorescence imaging was performed on different layers obtained in the histological separation module to record the fluorescence signal distribution of proteins in each layer in the xy direction; B) Compare the changes and distribution differences in fluorescence signal intensity in different layers to identify the enrichment layer of biomimetic near-infrared fluorescent proteins; C) Based on the spatial location of high-intensity fluorescent signal regions in the enriched layer on the array template, the distribution area of ​​tumor tissue can be determined and positive edges of tumors can be identified.

10. The histological separation and detection system based on biomimetic near-infrared fluorescent protein according to claim 1, characterized in that, It also includes a protein enrichment and analysis module.

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