Methods and Systems for Analyzing Complex Cytokine Exosomes in Bone Marrow and Blood from Elderly Patients with Hip Fractures

Abstract

This invention relates to a method and system for analyzing complex cytokine exosomes in bone marrow and blood from elderly patients with hip fractures. The method includes bone marrow sample collection (predicting the degree of fibrosis and adjusting the collection volume), pretreatment (optimizing sample composition), dynamic gradient bone marrow digestion (adjusting enzyme ratios according to the fibrosis index and monitoring in multiple dimensions), exosome extraction (ultracentrifugation and nanofiltration), and complex cytokine exosome analysis (constructing a seven-dimensional matrix, etc.). The system consists of units for bone marrow sample collection, pretreatment, dynamic gradient bone marrow digestion, exosome extraction, and complex cytokine exosome analysis. This invention solves the problems of exosome damage and low separation efficiency in traditional methods, and has advantages such as increased exosome extraction volume, preservation of integrity, enhanced reliability of analytical results, and improved standardization and reproducibility.

Description

Technical Field

[0001] This invention belongs to the field of biomedicine and relates to a method for the isolation and analysis of exosomes from biological samples (bone marrow and blood). Background Technology

[0002] Exosomes are nanoscale membrane vesicles secreted by cells, widely distributed in various biological fluids, containing bioactive molecules such as proteins and nucleic acids, and playing an important role in intercellular communication. In elderly patients with femoral neck fractures, complex cytokine exosomes in bone marrow blood are closely related to fracture healing. Traditional methods for analyzing complex cytokine exosomes in bone marrow blood from elderly patients with femoral neck fractures mainly include bone marrow sample collection, pretreatment, digestion, exosome extraction, and analysis. However, existing exosome analysis methods have a series of problems that urgently need to be addressed.

[0003] First, traditional exosome isolation processes employ fixed parameters for enzymatic digestion. However, the degree of bone marrow fibrosis varies, resulting in different structures and compositions. This fixed-parameter enzymatic digestion method is unsuitable for various samples, leading to poor adaptability, insufficient exosome release, and consequently, low separation efficiency. Second, during digestion, traditional methods rely heavily on manual judgment of the digestion endpoint, lacking a multi-dimensional real-time monitoring system. Manual judgment carries significant errors, potentially causing over-digestion of exosomes, damaging their structure and function, and severely impacting the accurate analysis of the biological information they carry. Third, traditional bone marrow sample pretreatment is inadequate, with a limited source of exosomes, often focusing solely on plasma while neglecting the abundance of exosome-derived cells in basal hematopoietic cells. This is particularly problematic for elderly samples, which have relatively fewer cellular components, significantly limiting exosome extraction yield due to the singular source. Finally, traditional exosome purification techniques have limitations, failing to effectively remove impurities and apoptotic body contamination. Low exosome purity can interfere with subsequent proteomics and gene sequencing analyses, reducing the accuracy of the results. Furthermore, traditional analytical methods lack comprehensive and precise multi-dimensional data analysis tools and rigorous data quality control, resulting in insufficient reliability of the analytical results. Moreover, due to the lack of automated control systems, the analytical process is highly susceptible to human factors, making standardized operation difficult. Different operators may obtain different results, limiting the widespread application of this analytical method in various laboratory environments. Summary of the Invention

[0004] The present invention aims to solve the following technical problems:

[0005] 1. Enzymatic digestion adaptability issues: Traditional exosome analysis methods use fixed parameters during enzymatic digestion, failing to consider the differences in the degree of bone marrow fibrosis. Since bone marrow with different degrees of fibrosis has different structures and compositions, fixed-parameter enzymatic digestion methods cannot meet the needs of various samples, resulting in poor enzymatic digestion adaptability, insufficient exosome release, and thus affecting separation efficiency.

[0006] 2. Problems with determining the digestion endpoint: During digestion, traditional methods often rely on human experience to determine the digestion endpoint, lacking a multi-dimensional real-time monitoring system. Human judgment is prone to error, easily leading to over-digestion of exosomes, which damages the structure and function of exosomes and affects the accurate analysis of the biological information carried by exosomes.

[0007] 3. Issues with limited exosome sources: Traditional bone marrow sample pretreatment processes are not optimized enough, resulting in a limited source of exosomes. They focus only on the plasma portion, neglecting the presence of exosome-rich cells in the basal blood cells. This is especially problematic for elderly samples, where cellular components are relatively scarce, and the limited source restricts the amount of exosomes that can be extracted.

[0008] 4. Exosome purity issues: Traditional exosome purification techniques have limitations, making it difficult to effectively remove impurities and apoptotic body contamination. Low exosome purity can interfere with subsequent proteomics and gene sequencing analyses, reducing the accuracy of the results.

[0009] 5. Issues with the Reliability and Standardization of Analytical Results: Traditional analytical methods lack comprehensive and accurate multi-dimensional data analysis tools and rigorous data quality control, resulting in insufficient reliability of analytical results. Furthermore, due to the lack of automated control systems, the analytical process is significantly affected by human factors, making standardized operation difficult. Different operators may obtain different results, limiting the widespread application of this analytical method in different laboratory and clinical environments.

[0010] This invention provides the following technical solution:

[0011] A method for analyzing complex cytokine exosomes in bone marrow blood of elderly patients with hip fractures, comprising the following steps:

[0012] Step 1: Collect bone marrow blood samples to predict the degree of bone marrow stromal fibrosis. Fibrosis grade is assessed by detecting serum markers of bone marrow fibrosis and combining them with bone mineral density values. The sample collection volume is adjusted according to the grade. Step 2: After centrifuging to separate plasma, the basal blood cells are pretreated, resuspended in PBS buffer, and lysed. After centrifugation, lysed red blood cells are removed, and the nucleated cell layer is retained and combined with the plasma. Step 3: The fibrosis index of the pretreated samples is measured and graded. A complex digestive enzyme is dynamically prepared based on the grade, and multi-dimensional real-time monitoring is performed during digestion. Primary digestion is dynamically terminated based on the monitoring results. After primary centrifugation, the enzyme ratio is adjusted according to the residual cell type for secondary digestion. Exosomes are then enriched by density gradient centrifugation and magnetic bead sorting. Step 4: The samples treated in Step 3 are centrifuged to precipitate exosomes, retaining the bottom precipitate. The precipitate is then purified by nanofiltration to remove impurities, yielding a high-purity exosome sample. Step 5: The high-purity exosome sample is subjected to proteomics analysis and gene sequencing analysis.

[0013] In a preferred embodiment of the present invention, step 1 includes: step 1-1: inputting the patient's basic examination information, serum sample and iliac bone X-ray examination data, and detecting serum markers of myelofibrosis; step 1-2: adjusting the amount of bone marrow blood sample collected according to the degree of myelofibrosis; for mild fibrosis, collect 2-3 mL; for moderate fibrosis, collect 3-4 mL; for severe fibrosis, collect 4-5 mL.

[0014] In a preferred embodiment of the present invention, step 2 is specifically performed as follows: the plasma is separated from the underlying blood cells by centrifugation, and after the plasma is separated, the underlying blood cells are resuspended in PBS buffer; then red blood cell lysis buffer is added, and the cells are incubated at room temperature to allow the red blood cells to lyse. After centrifugation, the lysis products are removed, and the nucleated cell layer is retained. The retained nucleated cell layer is then combined with the upper plasma layer.

[0015] In a preferred embodiment of the present invention, step 3 further includes: Step 3-1: Taking a certain amount from the pretreated mixed sample, determining the collagen content using the hydroxyproline colorimetric method, and calculating the fibrosis index FI based on the serum markers PⅠNP and PⅢNP detected in step 1-1. According to the calculation results, the degree of fibrosis is reclassified as: mild fibrosis, FI < 50; moderate fibrosis, 50 ≤ FI < 100; severe fibrosis, FI ≥ 100; Step 3-2: Determining the basic formula of the compound digestive enzyme as a combination of collagenase, hyaluronidase, and trypsin in a mass ratio, and calculating the fibrosis index based on the fibrosis index. FI dynamically adjusts the ratio of the three enzymes; Step 3-3: Digestion is performed under isothermal oscillation conditions, with multi-dimensional monitoring at regular intervals, including morphological monitoring, nanoparticle tracking of NTA, rapid ELISA detection, and determination of digestion endpoint; Step 3-4: After centrifugation at a certain speed for a period of time, the fluorescence intensity of the exosome marker CD9-FITC in each layer of the sample is detected by flow cytometry, and the middle layer is defined as the fluorescence intensity peak layer, and the liquid in this layer is aspirated; Step 3-5: The enzyme ratio is adjusted according to the type of cell residue; Step 3-6: The sample after secondary digestion is subjected to density gradient centrifugation and magnetic bead sorting.

[0016] In a preferred embodiment of the present invention, step 4 further includes: step 4-1: centrifugation precipitation, after centrifugation, discarding the supernatant and retaining the bottom precipitate; step 4-2: using a positive pressure filtration device to intercept impurities >150nm and retain intact exosomes.

[0017] In a preferred embodiment of the present invention, in step 5, the analysis of compound cytokines and exosomes, a seven-dimensional data matrix containing spatiotemporal coordinates and single-cell sequencing indicators is first constructed. The weights of the indicators are determined by the entropy weight-Gini weighting algorithm. Then, the characteristics of the data at different time scales and frequencies are mined through joint analysis in the time and frequency domains. Further analysis is performed from both proteomics and gene sequencing perspectives.

[0018] In a preferred embodiment of the present invention, step 5 includes: Step 5-1: Indicator detection of the exosome samples obtained in step 4: For single-cell sequencing indicators, gene expression levels of specific cell types are measured, protein expression levels of specific cell types are measured, and spatial location information of fracture sites is determined; the detected single-cell sequencing indicator data is integrated with spatiotemporal coordinate information to construct a seven-dimensional data matrix; the first dimension of the matrix is ​​time, the second dimension is the X-axis information of spatial coordinates, the third dimension is the Y-axis information of spatial coordinates, the fourth dimension is the Z-axis information of spatial coordinates, and the fifth to seventh dimensions correspond to different categories of single-cell sequencing indicators; Step 5-2: For the constructed seven-dimensional data matrix, the initial weight of each indicator is calculated using the entropy weight method, and the initial weight is adjusted using the Gini coefficient; Step 5-3: The initial weight in the seven-dimensional data matrix is ​​used as the input signal for time-frequency domain joint analysis, and the data patterns are mined in both time and frequency dimensions.

[0019] In a preferred embodiment of the present invention, step 5 further includes: Step 5-4: performing two-dimensional electrophoresis on the exosome sample obtained in step 4, separating proteins in the first direction of electrophoresis, and forming a two-dimensional distribution pattern of proteins on the gel in the second direction of electrophoresis, thus initially separating the proteins in the exosomes; enriching phosphorylated proteins after two-dimensional electrophoresis, removing unbound proteins by centrifugation and washing, and enriching phosphorylated proteins; detecting the enriched phosphorylated proteins; Step 5-5: performing nucleic acid extraction and RNA purity detection on the exosome sample, as well as cDNA synthesis, PCR amplification, gene sequencing, and data analysis.

[0020] This invention also provides a bone marrow and blood complex cytokine exosome analysis system for elderly patients with hip fractures, the system comprising:

[0021] The bone marrow sample collection unit collects bone marrow blood samples to predict the degree of bone marrow matrix fibrosis. It assesses the fibrosis grade by detecting serum markers of bone marrow fibrosis and combining them with bone mineral density values, and adjusts the collection volume according to the grade.

[0022] The bone marrow sample pretreatment unit receives bone marrow blood samples from the bone marrow sample collection unit. After centrifugation to separate plasma, the basal blood cells are pretreated, resuspended in PBS buffer, and the lysed red blood cells are centrifuged to remove the lysis products, retaining the nuclear cell layer and combining it with the plasma.

[0023] The dynamic gradient bone marrow digestion unit receives the combined plasma and nucleated cell sample output from the bone marrow sample pretreatment unit, measures the fibrosis index of the pretreated sample and grades it, dynamically prepares compound digestive enzymes according to the grade, monitors the digestion process in real time from multiple dimensions, and dynamically terminates the primary digestion based on the monitoring results; after primary centrifugation, the enzyme ratio is adjusted according to the residual cell type for secondary digestion, and then exosomes are enriched by density gradient centrifugation and magnetic bead sorting.

[0024] The exosome extraction unit receives the exosome enrichment sample output from the dynamic gradient bone marrow digestion unit, first centrifuges the sample to precipitate the exosomes, retains the bottom precipitate, and then purifies it through nanofiltration to remove impurities, thus obtaining a high-purity exosome sample.

[0025] The composite cytokine exosome analysis unit performs proteomics analysis and gene sequencing analysis on high-purity exosome samples.

[0026] The technical effects of this invention are as follows:

[0027] 1. Increased Exosome Extraction: This invention innovatively adds a step for predicting the degree of bone marrow stromal fibrosis and adjusts the sample collection volume and enzymatic digestion parameters according to the fibrosis grade. By detecting serum markers of bone marrow fibrosis and assessing bone mineral density, the degree of fibrosis is accurately graded, allowing for dynamic adjustment of the ratio and dosage of compound digestive enzymes, making the enzymatic digestion process more closely match the actual sample conditions. This fibrosis-graded enzymatic digestion method uses appropriate enzymatic digestion schemes for samples with different degrees of fibrosis, resulting in a 40% increase in exosome extraction from severely fibrotic samples compared to a fixed enzyme ratio. Overall, this increases the amount of exosomes obtained, providing more sufficient samples for subsequent analysis.

[0028] 2. Exosome Integrity Protection: A multi-dimensional real-time monitoring system was introduced during primary digestion, including phase-contrast microscopy for morphological observation of cell disruption rate, nanoparticle tracking (NTA) using NanoSight LM10 to detect exosome particle size distribution, and rapid ELISA detection of exosome membrane protein CD63 concentration. Real-time feedback of this data automatically triggers a digestion termination signal, avoiding errors from manual judgment of the digestion endpoint. This measure effectively reduced the exosome membrane damage rate from 30% in traditional methods to below 12%, ensuring the structural and functional integrity of exosomes, improving exosome quality, and facilitating accurate subsequent analysis of the biological information they carry.

[0029] 3. Enhanced Reliability of Analytical Results: This invention employs a multi-technology combined purification approach. In the exosome extraction stage, a coupled process of ultracentrifugation, density gradient centrifugation, and low-pressure filtration, combined with magnetic bead sorting technology, effectively removes impurities and apoptotic body contamination through a dual mechanism of physical density screening and biomarker capture, significantly improving exosome purity from 75% in the original protocol to over 90% of the theoretical value. In the analytical phase, a phosphorylated protein enrichment step is added to proteomics analysis, and label-free quantification technology is used to improve the sensitivity of cytokine detection; in gene sequencing analysis, the nucleic acid extraction process is optimized, a DNaseI digestion step is added to remove genomic DNA contamination, and RNA purity is rigorously tested. These measures ensure the reliability and accuracy of proteomics and gene sequencing analysis data.

[0030] 4. Improved Standardization and Repeatability: This invention achieves standardized operation of the analysis process through the integration of an automated control system, reducing the impact of human factors on the results. Different operators following the standardized process significantly improve the repeatability of the analysis results. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0032] Figure 1 A flowchart of a method for analyzing complex cytokine exosomes in bone marrow blood of elderly patients with hip fractures, provided for the invention.

[0033] Figure 2 A logic block diagram of a bone marrow and blood complex cytokine exosome analysis system for elderly patients with hip fractures, provided for the invention. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0035] It should be noted that when a component is referred to as being "fixed to" or "set on" another component, it can be directly on or indirectly on that other component. When a component is referred to as being "connected to" another component, it can be directly connected to or indirectly connected to that other component.

[0036] In the description of this invention, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0037] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0038] To better understand the above technical solutions, the technical solutions of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0039] like Figure 1 As shown, this invention provides a method for analyzing complex cytokine exosomes in bone marrow blood from elderly patients with hip fractures. This method effectively solves the problems of exosome damage and low separation efficiency in traditional methods through multi-level gradient digestion and an intelligent quality control system. Specifically, it includes the following steps:

[0040] Step 1: Bone marrow sample collection: Bone marrow blood samples are collected under strict aseptic conditions to predict the degree of bone marrow stromal fibrosis. The fibrosis grade is assessed by detecting serum markers of bone marrow fibrosis and combining them with the bone mineral density value of the iliac bone X-ray. The amount of samples collected is adjusted according to the grade.

[0041] Step 2: Bone marrow sample pretreatment: After separating plasma by low-speed centrifugation, the basal blood cells are pretreated by resuspending them in PBS buffer. After lysing the red blood cells, the lysate is removed by centrifugation, and the nucleated cell layer is retained and combined with the plasma.

[0042] Step 3: Dynamic gradient bone marrow digestion: First, the fibrosis index of the pretreated samples is measured and graded. Based on the grading, a complex digestive enzyme is dynamically prepared. Multidimensional real-time monitoring is conducted during digestion, and primary digestion is dynamically terminated based on the monitoring results. After primary centrifugation, the enzyme ratio is adjusted according to the residual cell type for secondary digestion. Exosomes are then enriched by density gradient centrifugation and magnetic bead sorting.

[0043] Step 4: Exosome extraction: The sample processed in Step 3 is first subjected to ultracentrifugation to precipitate the exosomes, and the bottom precipitate is retained. Then, it is purified by nanofiltration. The pressure and flow rate are controlled, and impurities are removed by a combination of filter membranes with specific pore sizes to obtain a high-purity exosome sample.

[0044] Step 5: Analysis of complex cytokine exosomes: Proteomics analysis and gene sequencing analysis were performed on high-purity exosome samples.

[0045] In this embodiment, step 1, by predicting the degree of bone marrow stromal fibrosis and adjusting the collection volume, provides a foundation for subsequent enzymatic digestion that aligns with the sample characteristics. Different degrees of fibrosis result in variations in the structure and composition of the bone marrow, affecting the difficulty of subsequent enzymatic digestion and exosome release. An appropriate collection volume also ensures sufficient sample for subsequent analysis. In step 2, the bone marrow sample is pretreated to fully utilize basal blood cells, increasing the number of exosome-derived cells and optimizing the sample composition for step 3. This makes the sample more suitable for exosome extraction, complementing the sample collected in step 1 and further preparing for exosome acquisition. Based on the fibrosis information obtained in step 1, the enzymatic digestion process is dynamically adjusted in step 3, which is crucial for obtaining high-quality exosomes. Real-time monitoring and dynamic adjustment ensure the integrity and full release of exosomes during digestion. The results of digestion and separation directly affect the purity and quality of subsequent exosome extraction, serving as a vital link between sample processing and exosome acquisition. Step 4 follows the exosome enrichment process from Step 3, further purifying the exosomes using techniques such as ultracentrifugation and nanofiltration. This provides a high-purity sample for subsequent accurate analysis and serves as a transitional step between sample processing and analysis, ensuring that the exosome quality meets analytical requirements. Step 5 utilizes the high-purity exosome sample obtained in Step 4 for analysis at both the proteomics and gene sequencing levels to obtain exosome information related to femoral neck fractures in the elderly.

[0046] These five steps are closely linked, with the results of each step forming the basis for the next, together constituting a complete and logically rigorous analysis process for complex cytokine exosomes in bone marrow and blood from elderly patients with femoral neck fractures.

[0047] In a preferred embodiment of the present invention, step 1 is further subdivided into:

[0048] Step 1-1: Input the basic examination information of the patient, serum samples, and iliac bone X-ray examination data. After being processed by specific detection methods and evaluation criteria, detect the serum markers of myelofibrosis, such as procollagen type I N-terminal propeptide (PⅠNP) and procollagen type III N-terminal propeptide (PⅢNP). Combine the iliac bone X-ray bone density value (T value), refer to the World Health Organization's diagnostic criteria for myelofibrosis, and evaluate the grade of bone marrow stromal fibrosis; output the evaluation result of the bone marrow stromal fibrosis grade, which will be used for the adjustment of the collection volume in Step 1-2 and the calculation of the fibrosis index in Step 3-1. Obtain the T value by detecting the iliac bone X-ray bone density, and combine the serum markers of myelofibrosis (such as procollagen type I N-terminal propeptide (PⅠNP) and procollagen type III N-terminal propeptide (PⅢNP)) to evaluate the grade of bone marrow stromal fibrosis. This grade result is used to guide the adjustment of the bone marrow blood sample collection volume and the setting of relevant parameters in the subsequent dynamic gradient bone marrow digestion process.

[0049] More preferably, the specific detection method is: Use dual-energy X-ray absorptiometry (DXA) to measure the bone density of the iliac bone. DXA uses two different energy X-rays to penetrate the bone and surrounding soft tissues. Since different tissues have different absorption degrees for different energy X-rays, detect the change in X-ray energy after passing through the human body by a detector, and obtain the bone density value after computer processing. The measured bone density value will be compared with the bone density peak value of healthy young people of the same gender, and the T value is calculated. In the standard formulated by the World Health Organization (WHO), T value ≥ -1.0 is normal; -2.5 < T value < -1.0 is osteopenia; T value ≤ -2.5 is osteoporosis. In this solution, the T value is used to assist in evaluating the grade of bone marrow stromal fibrosis. The principle is that myelofibrosis may be accompanied by changes in bone metabolism, which in turn affect bone density, and the T value can be used as an indicator to reflect this change.

[0050] Step 1-2: Adjust the collection volume of the bone marrow blood sample according to the grade of bone marrow stromal fibrosis. For mild fibrosis (T ≥ -2.5), collect 2-3 mL; for moderate fibrosis (-3.5 < T < -2.5), collect 3-4 mL; for severe fibrosis (T ≤ -3.5), collect 4-5 mL. In this way, ensure sufficient digestion substrates for the follow-up. The input of this step is the evaluation result of the bone marrow stromal fibrosis grade. According to the preset collection volume standard, output the bone marrow blood sample of the corresponding volume, and this sample will enter Step 2 for pretreatment.

[0051] In a schematic embodiment, assume that a patient's detected PⅠNP concentration is 50 ng / mL, PⅢNP concentration is 30 ng / mL, and the iliac bone X-ray bone density T value is -2.8. According to the formula and standard, the grade of its bone marrow stromal fibrosis can be further evaluated.

[0052] In a more preferred embodiment, step 2 involves: first, low-speed centrifugation to separate plasma from the basal blood cells. After separating the plasma, the basal blood cells are resuspended in PBS buffer. Then, red blood cell lysis buffer is added, and the mixture is incubated at room temperature for 5 minutes to induce erythrocyte lysis. Next, the mixture is centrifuged again to remove the lysis products, retaining the nuclear cell layer, which contains exosome-derived cells such as mesenchymal stem cells. Finally, the retained nuclear cell layer is combined with the upper plasma layer and proceeded to step 3 for digestion. The input for this step is the low-speed centrifuged plasma and basal blood cells; after a series of processing steps, the combined plasma and nuclear cell sample is output for bone marrow digestion in step 3.

[0053] In one illustrative embodiment, plasma and blood cells were separated by centrifugation at 2000 rpm for 10 minutes. Red blood cell lysis was performed by incubation at room temperature for 5 minutes using 1× red blood cell lysis buffer.

[0054] Furthermore, step 3 further includes:

[0055] Step 3-1: Take 0.1 mL from the pretreated mixed sample (plasma + nucleated cells) and determine the collagen content using the hydroxyproline colorimetric method. Combined with the serum markers (PⅠNP, PⅢNP) detected in Step 1-1, calculate the fibrosis index (FI = 0.6 × PⅠNP + 0.4 × hydroxyproline concentration). Based on the fibrosis index, the degree of fibrosis is reclassified into mild (FI < 50), moderate (50 ≤ FI < 100), and severe (FI ≥ 100).

[0056] The input for step 3-1 is the pre-processed mixed sample and the serum biomarker data from step 1-1. After detection and calculation, the fibrosis index and the corresponding fibrosis degree classification are output. This result will be used for the preparation of the compound digestive enzyme in step 3-2.

[0057] Serum biomarkers PⅠNP (type I procollagen N-terminal peptide) and PⅢNP (type III procollagen N-terminal peptide) provide standardized quantitative data in clinical testing. By comparing with known concentrations of PⅠNP or PⅢNP standards, the concentration of PⅠNP or PⅢNP in the sample can be accurately calculated using a standard curve. The concentrations of these standards are rigorously calibrated to ensure the accuracy and repeatability of the test results. Therefore, from the perspective of detection methods and quantification approaches, PⅠNP and PⅢNP represent standard quantitative data.

[0058] In this step, classifying the degree of fibrosis based on the FI (fibrosis index) can be seen as a more precise reclassification. The calculation of FI integrates serum markers of myelofibrosis (such as type I procollagen N-terminal peptide PⅠNP and type III procollagen N-terminal peptide PⅢNP) and the concentration of hydroxyproline in the sample. These markers and indicators reflect the biological changes in the process of myelofibrosis from different perspectives. PⅠNP and PⅢNP are markers related to collagen synthesis, while hydroxyproline is a characteristic amino acid of collagen, and its content can reflect the overall collagen level by measuring its content colorimetrically. In contrast, relying solely on the iliac bone X-ray bone mineral density T-score, while reflecting the correlation between myelofibrosis and bone mineral density to some extent, lacks a direct representation of the biochemical changes in the extracellular matrix within the bone marrow.

[0059] The fibrosis index (FI) integrates biochemical indicators within bone marrow samples, specifically collagen content measured using the hydroxyproline colorimetric method and serum markers of bone marrow fibrosis (PINP, PIIINP). It reflects changes in the extracellular matrix composition within the bone marrow, precisely quantifying the degree of fibrosis at a biochemical level. For example, even if two patients have similar bone mineral density (T) values, their FI values ​​may differ significantly due to variations in intramedullary cellular activity and matrix metabolism, thus reflecting differences in the actual degree of fibrosis.

[0060] The T-value is primarily used to preliminarily determine the amount of bone marrow blood collected, ensuring sufficient sample for subsequent analysis. The FI (fibrillation enzyme) value, on the other hand, provides more precise guidance for the preparation of complex digestive enzymes and the regulation of the digestion process during bone marrow digestion. As mentioned earlier, different FI values ​​correspond to different enzyme ratios and volume ratios to meet the digestive needs of bone marrow with varying degrees of fibrosis.

[0061] Step 3-2: Intelligent Complex Digestive Enzyme Formulation. The basic formulation of the complex digestive enzyme is determined as follows: collagenase (Type I, 200 U / mL), hyaluronidase (100 U / mL), and trypsin (0.25%) in a specific mass ratio. Then, the proportions of the three enzymes are dynamically adjusted based on the fibrosis index (FI).

[0062] For mild fibrosis (FI<50), to avoid over-digestion, the ratio of collagenase:hyaluronidase:trypsin was adjusted to 2:2:1.

[0063] For moderate fibrosis (50≤FI<100), the default ratio of 3:2:1 is used, which is the original ratio.

[0064] For severe fibrosis (FI≥100), the ratio is adjusted to 5:2:1 to break down the dense matrix, and the proportion of collagenase is increased.

[0065] Meanwhile, an adaptive algorithm is used to adjust the enzyme solution to sample volume ratio: volume ratio = 1:(5+0.1×FI), ensuring that the enzyme concentration matches the matrix stiffness. The input for this step is the fibrosis index. After ratio adjustment and algorithm calculation, the output is a compound digestive enzyme solution prepared in a specific ratio, which will be mixed with the sample for digestion.

[0066] In an illustrative embodiment, the hydroxyproline concentration was determined to be 40 μg / mL by a hydroxyproline colorimetric method. Combined with the serum markers from step 1-1, the fibrosis index FI was calculated as 0.6 × 50 + 0.4 × 40 = 46, indicating mild fibrosis. For the mild fibrosis sample, collagenase (200 U / mL), hyaluronidase (100 U / mL), and trypsin (0.25%) were prepared in a 2:2:1 ratio. Assuming the previously calculated fibrosis index FI = 46, the corresponding volume of enzyme solution was prepared according to the volume ratio formula 1:(5 + 0.1 × 46) = 1:9.6.

[0067] Step 3-3: Digestion is carried out under constant temperature oscillation conditions of 37℃ (oscillation speed of 80 rpm), with multi-dimensional monitoring every 15 minutes. The multi-dimensional monitoring includes, but is not limited to:

[0068] Morphological monitoring: Take a 10μL sample drop and observe the cell disruption rate using a phase contrast microscope. The goal is to control the cell disruption rate at 60%-70% to avoid excessive disruption.

[0069] Nanoparticle tracking (NTA): Exosome particle size distribution was detected using NanoSight LM10, with the main peak required to be maintained between 80-120 nm. If the proportion of particles with a diameter <60 nm >15%, it indicates that the exosome membrane may be damaged.

[0070] Rapid ELISA test: Simultaneously detects the concentration of exosomal membrane protein CD63. If the CD63 concentration decreases by more than 20%, it indicates over-digestion.

[0071] Digestion endpoint determination: Primary digestion is terminated when the cell disruption rate reaches 65% and the CD63 concentration stabilizes. The expected digestion time is 30-90 minutes, replacing the original fixed time range. The input for this step is a sample containing complex digestive enzymes. After digestion and multi-dimensional monitoring, the output includes various monitoring data during the digestion process and a decision signal indicating whether digestion has been terminated. If digestion is terminated, the digested sample is sent to step 3-3 for primary centrifugation.

[0072] In one illustrative embodiment, after 30 minutes of digestion, the cell disruption rate was observed to be 50% using phase-contrast microscopy, the main peak of exosome size was found to be 90 nm using NTA, and the CD63 concentration was 10 μg / mL using ELISA. Digestion was continued for 60 minutes, at which point the cell disruption rate reached 65% and the CD63 concentration stabilized at 9 μg / mL, at which point digestion was terminated.

[0073] Steps 3-4: Primary centrifugation. Centrifuge at a specific speed (preferably automatically adjusted according to sample density) for a period of time. After centrifugation, take 10 μL from each layer of sample and detect the fluorescence intensity of the exosome marker CD9-FITC by flow cytometry. Define the middle layer as the fluorescence intensity peak layer and accurately aspirate this layer using a pipette. The input for this step is the sample after primary digestion. After centrifugation and detection, the accurately aspirated middle layer is output, which will proceed to step 3-5 for secondary digestion.

[0074] In one illustrative embodiment, after centrifugation at 1800 rpm for 12 minutes, the CD9-FITC fluorescence intensity of each layer of the sample was detected, the middle layer was identified as the fluorescence intensity peak layer, and the liquid in this layer was accurately aspirated.

[0075] Steps 3-5: Secondary digestion. Adjust the enzyme ratio according to the type of cell residue after primary digestion: if microscopic observation reveals that the residual cells are mainly fibroblasts (spindle cells > 50%), increase the proportion of trypsin to enhance the breakdown of cell membranes.

[0076] If the residue is mainly matrix fragments, increase the hyaluronidase ratio to 1:3:2 to enhance the degradation of the matrix.

[0077] Digestion time was monitored in real time using NTA. Digestion continued when the exosome particle concentration increased by more than 10 particles / μL / min; digestion was terminated when the increase was less than 5 particles / μL / min. The expected digestion time was 15-45 minutes. The input for this step was the middle layer liquid after primary centrifugation. After cell residue type assessment and enzyme ratio adjustment, the output was the sample after secondary digestion, which would then proceed to steps 3-5 for secondary centrifugation.

[0078] In one illustrative embodiment, if microscopic observation reveals that the remaining cells are predominantly fibroblasts, the enzyme ratio is adjusted to 1:1:3. During digestion, the NTA monitors the rate of increase in exosome particle concentration; digestion is terminated when the rate of increase decreases from 12 particles / μL / min to 4 particles / μL / min, at which point the digestion time is 30 minutes.

[0079] Steps 3-6: Secondary centrifugation. Before centrifugation, a sucrose density gradient solution was added to the sample, with a bottom layer of 1.13 g / mL and a top layer of 1.21 g / mL. The sample was then centrifuged at 3000 rpm for 20 minutes. Exosomes were enriched in the 1.13-1.21 g / mL interfacial layer, and the sample was collected from the interfacial layer using a pipette. CD81 magnetic bead sorting was then used to further remove apoptotic body contamination. This step yielded a secondary digested sample, which, after density gradient centrifugation and magnetic bead sorting, was an exosome-enriched sample free of apoptotic body contamination. This sample would then proceed to step 4 for exosome extraction.

[0080] More preferably, step 4 further includes:

[0081] Step 4-1: Ultracentrifugation precipitation. Centrifuge at 110,000 rpm for 80 minutes using a oscillating rotor (this is the central parameter). After centrifugation, discard the supernatant and retain the bottom 100 μL of precipitate. The input for this step is the exosome-enriched sample after secondary centrifugation. After ultracentrifugation, the output is a precipitate containing exosomes at the bottom, which will proceed to the next step.

[0082] Step 4-2: Nanofiltration Purification. A positive pressure filtration device is used. Before filtration, a pre-filtration membrane is used to remove large particles. Then, a combination of filter pore sizes is used for coarse filtration followed by fine filtration to ensure that impurities >150nm are retained while preserving intact exosomes. The input for this step is the precipitate after ultracentrifugation, which is purified by nanofiltration to output a high-purity exosome sample. This sample will proceed to step 5 for complex cytokine exosome analysis.

[0083] In one illustrative embodiment, a positive pressure filtration device is used, with the pressure controlled at 4 kPa and the flow rate at 0.3 mL / min. Large particles are first pre-filtered through a 0.22 μm membrane to remove them, followed by sequential filtration through filters with 150 nm and 30 nm pore sizes. This ensures that impurities larger than 150 nm are retained while preserving intact exosomes, resulting in a high-purity exosome sample.

[0084] In an important embodiment of the present invention, in step 5, the analysis of compound cytokines and exosomes, a seven-dimensional data matrix containing spatiotemporal coordinates and single-cell sequencing indicators is first constructed to provide a multi-dimensional data foundation for comprehensive analysis. Then, the entropy-weighted Gini weighting algorithm is used to determine the weights of each indicator, improving the accuracy of the analysis. Next, time-frequency domain joint analysis (such as wavelet transform) is used to mine the characteristics of the data at different time scales and frequencies. Simultaneously, further analysis is conducted from both proteomics and gene sequencing perspectives. Proteomics analysis involves two-dimensional electrophoresis, phosphorylated protein enrichment, and label-free quantification to detect related signaling pathway proteins. Gene sequencing analysis uses column extraction of nucleic acids, DNase I digestion, and RNA purity detection to ensure the reliability of gene-level data. Preferably, this step further includes:

[0085] Step 5-1: After obtaining high-purity exosome samples, the following index detections are performed on the samples. Preferably, for single-cell sequencing indicators, single-cell sequencing technology is used to accurately determine the gene expression levels of specific cell types. For example, quantitative polymerase chain reaction (qPCR) technology is used to detect the expression levels of genes closely related to fracture healing; simultaneously, Western blot or immunofluorescence technology is used to determine the protein expression levels of specific cell types. For spatiotemporal coordinates, precise imaging examinations (such as high-precision X-ray, CT, or MRI) are used to determine the spatial location information of the fracture site and record its specific coordinates in the bone structure. The time information is set as seven key time points: postoperative day 1, day 3, day 7, day 14, day 21, day 3, and day 60. The obtained single-cell sequencing indicator data and spatiotemporal coordinate information are integrated to construct a seven-dimensional data matrix. Each dimension of the matrix corresponds to a different indicator or coordinate information. For example, the first dimension is time, the second dimension is the X-axis information of spatial coordinates, the third dimension is the Y-axis information of spatial coordinates, the fourth dimension is the Z-axis information of spatial coordinates, and the fifth to seventh dimensions correspond to different categories of single-cell sequencing indicators (such as the expression level of specific genes, the expression level of specific proteins, etc.). In this way, the multi-dimensional information related to exosomes is systematically organized, providing a comprehensive data foundation for subsequent analysis.

[0086] Step 5-2: For the constructed seven-dimensional data matrix, firstly, the initial weights of each indicator are calculated using the entropy weight method. The core principle of the entropy weight method is based on the concept of information entropy, which measures the degree of disorder or variation in data. For each indicator dimension in the seven-dimensional data matrix, its information entropy is calculated. For example, for a gene expression level indicator dimension, its information entropy value is calculated through statistical analysis of the data in that dimension. The smaller the information entropy value, the greater the degree of variation in the indicator data, and the higher its importance in the comprehensive evaluation may be. The initial weights of each indicator are calculated based on the information entropy, reflecting the relative importance of each indicator based on the degree of data variation. The initial weights are then adjusted using the Gini coefficient. The Gini coefficient is mainly used to measure the unevenness of data distribution. For each indicator dimension in the seven-dimensional data matrix, its Gini coefficient is calculated. For example, for a protein expression level indicator dimension, its Gini coefficient is calculated by analyzing the distribution of the data in that dimension. If the Gini coefficient of an indicator is large, it indicates that the data distribution of that indicator is uneven, and it may have special importance in the fracture healing process. Based on the Gini coefficients of each indicator, the initial weights obtained by the entropy weight method are adjusted to make the weights more accurately reflect the actual importance of each indicator in the fracture healing process.

[0087] Step 5-3: Wavelet transform is selected as the primary method for joint time-frequency domain analysis. The index data in the seven-dimensional data matrix are used as the input signal for the wavelet transform. For example, for a gene expression level index that varies over time, a suitable wavelet basis function (such as the Daubechies wavelet, Haar wavelet, etc.) is selected to perform a wavelet transform. The wavelet transform decomposes the index data into coefficients of different frequency components at different time points. These coefficients reflect the variation characteristics of the index at different time scales and frequencies. By analyzing these coefficients, patterns in the data can be discovered simultaneously in both time and frequency dimensions. For example, it may be found that high-frequency changes in certain indicators at specific time points are related to critical stages of fracture healing.

[0088] Step 5-4: Perform two-dimensional electrophoresis on high-purity exosome samples. First, in the first dimension of electrophoresis, proteins are separated using isoelectric focusing based on their isoelectric point differences. For example, isoelectric focusing is performed on a linear gradient gel strip with a pH of 3-10, causing proteins with different isoelectric points to focus at different positions on the strip. Then, in the second dimension, based on the molecular weight differences of proteins, electrophoresis is performed on an SDS-PAGE gel, creating a two-dimensional distribution pattern of proteins on the gel, thus initially separating the proteins in the exosomes. On the gel after two-dimensional electrophoresis, phosphorylated proteins are enriched using TiO2 magnetic beads. The proteins on the gel are transferred to a solution containing TiO2 magnetic beads and incubated under suitable conditions to allow the phosphorylated proteins to bind to the TiO2 beads. Unbound proteins are removed by centrifugation and washing, thus enriching the phosphorylated proteins. Using label-free quantification technology, the enriched phosphorylated proteins are detected, with a focus on detecting signaling pathway proteins related to fracture healing, such as phosphorylated proteins in the Wnt / β-catenin pathway. This method can accurately detect changes in the expression of phosphorylated proteins in signaling pathways related to fracture healing, with a detection limit ≤10 pg / mL, providing key information for revealing the protein regulatory mechanism of exosomes in fracture healing.

[0089] Step 5-5: Nucleic acid extraction from exosome samples was performed using a column extraction nucleic acid extraction kit. During extraction, an appropriate amount of DNase I was added to the extracted nucleic acid solution, and the sample was incubated at 37°C for 15 minutes to remove genomic DNA contamination. This step effectively avoids interference from genomic DNA in subsequent gene sequencing analysis. Before RT-PCR, RNA purity was detected using Qubit 4.0. The A260 / 280 ratio was calculated by measuring the absorbance of the RNA solution at wavelengths of 260 nm and 280 nm. This ratio should be between 1.8 and 2.0 to ensure high RNA purity, meeting the requirements for gene sequencing analysis. Only when RNA purity meets the standard can the reliability of subsequent gene sequencing data be guaranteed, providing accurate data support for in-depth research on fracture healing mechanisms at the gene level.

[0090] To ensure the completeness of the technical solution, after detecting RNA purity in step 5-5, the following steps are also included, but these steps are not the focus of this invention. For example:

[0091] Steps 5-6: After confirming that the RNA purity meets the requirements (A260 / 280 ratio between 1.8 and 2.0), the extracted RNA is used as a template to synthesize cDNA through reverse transcription using reverse transcriptase. This process is usually carried out in a specific reaction buffer, with random primers or oligo-dT primers added to guide the reverse transcriptase to synthesize complementary DNA strands using the RNA as a template. For example, appropriate amounts of RNA template, primers, reverse transcriptase, dNTPs, and buffer are added to the reaction system, and the reaction is carried out according to a specific temperature program (e.g., first incubating at 65°C for 5 minutes to denature the RNA, and then incubating at 42°C for 60 minutes for reverse transcription) to obtain the cDNA product.

[0092] Steps 5-7: If a PCR-based sequencing method is used (such as traditional Sanger sequencing or preprocessing for certain next-generation sequencing technologies), the synthesized cDNA needs to be amplified by PCR. Design specific primers based on the target gene sequence, and add the cDNA template, primers, Taq DNA polymerase, dNTPs, and buffer to the PCR reaction system. Through multiple cycles (e.g., 95℃ denaturation for 30 seconds, 55-65℃ annealing for 30 seconds, 72℃ extension for 30-60 seconds, for a total of 30-40 cycles), the target gene fragment will be amplified in large quantities.

[0093] Steps 5-8: Select the appropriate sequencing technology based on the experimental objectives and requirements, such as next-generation sequencing (Illumina technology) or third-generation sequencing (PacBio technology). Taking Illumina sequencing as an example, a sequencing library is constructed from the processed sample (such as amplified cDNA fragments). This is then amplified using bridge PCR on a FlowCell to form DNA clusters. Fluorescently labeled dNTPs and DNA polymerase are then added. In the sequencer, when dNTPs are incorporated into the synthesizing DNA strand, they emit fluorescence of a specific color. The base sequence is determined by detecting the fluorescence signal.

[0094] Steps 5-9: Processing and analyzing the large amount of data obtained from sequencing. First, quality control is performed to remove low-quality sequencing reads. Then, high-quality reads are compared with a reference genome to determine gene expression levels, mutation sites, and other information. Bioinformatics software (such as BWA for sequence alignment, SAMtools for format conversion and variant detection, and DESeq2 for differential expression analysis) is used to systematically analyze the data and identify gene features associated with femoral neck fractures in the elderly.

[0095] In the method provided by this invention:

[0096] This innovative approach incorporates a step to predict the degree of bone marrow fibrosis and adjusts sample collection volume and enzymatic digestion parameters based on the fibrosis grade. By detecting serum markers of bone marrow fibrosis and assessing bone mineral density, it achieves precise grading of the fibrosis degree, thereby dynamically adjusting the proportion and dosage of compound digestive enzymes. This makes the enzymatic digestion process more closely match the actual sample conditions, solving the problem of poor adaptability in traditional fixed-parameter enzymatic digestion.

[0097] A multi-dimensional real-time monitoring system was introduced during the primary digestion process, including morphological observation of cell disruption rate, NTA detection of exosome particle size distribution, and ELISA detection of exosome membrane protein CD63 concentration. Real-time data feedback automatically triggers a digestion termination signal, avoiding errors from manual judgment of the digestion endpoint, effectively protecting the integrity of exosomes, and improving the controllability of the digestion process and the quality of exosomes.

[0098] The pretreatment process of bone marrow samples was optimized by resuspending the basal blood cells and lysing the red blood cells after separating the plasma. The nuclear cell layer rich in exosome-derived cells was digested together with the plasma, expanding the source of exosomes from plasma alone to nuclear cells, thus increasing the sources of exosome extraction. This is especially suitable for elderly samples with low cellular components.

[0099] In the exosome extraction stage, a coupled process of ultracentrifugation, density gradient centrifugation, and low-pressure filtration was employed, combined with magnetic bead sorting technology. Through a dual mechanism of physical density screening and biomarker capture, impurities and apoptotic body contamination were effectively removed, significantly improving exosome purity from 75% in the original protocol to over 90% of the theoretical value, providing high-quality samples for subsequent accurate exosome analysis.

[0100] In proteomics analysis, a phosphorylated protein enrichment step is added, and label-free quantification technology is used to improve the sensitivity of cytokine detection. In gene sequencing analysis, the nucleic acid extraction process is optimized, a DNaseI digestion step is added to remove genomic DNA contamination, and RNA purity is strictly tested to ensure the reliability and accuracy of proteomics and gene sequencing analysis data.

[0101] like Figure 2 As shown, in another embodiment of the present invention, a bone marrow blood complex cytokine exosome analysis system for elderly patients with hip fractures is provided, the system comprising:

[0102] Bone marrow sample collection unit: Used to collect bone marrow blood samples from elderly patients with femoral neck fractures under strictly aseptic conditions. The fibrosis degree prediction module assesses the degree of bone marrow matrix fibrosis. Specifically, it evaluates the fibrosis grade by detecting serum markers of bone marrow fibrosis (such as type I procollagen N-terminal peptide (PⅠNP) and type III procollagen N-terminal peptide (PⅢNP)) and combining this with iliac bone X-ray bone mineral density values ​​(T-score). Based on this grade, the volume of bone marrow blood sample collected is adjusted, and the collected bone marrow blood samples that meet the standard collection volume for the corresponding fibrosis grade are output. This provides a suitable sample basis for subsequent analysis.

[0103] The patient-related information collected by the bone marrow sample collection unit includes basic examination information, serum samples, and iliac bone X-ray examination data.

[0104] Bone Marrow Sample Preprocessing Unit: This unit receives bone marrow blood samples from the bone marrow sample collection unit and preprocesses the collected samples. First, plasma is separated by low-speed centrifugation. Then, specific treatment is applied to the basal blood cells, including resuspending them in PBS buffer, adding erythrocyte lysis buffer for lysis, centrifuging to remove lysis products, and retaining the nucleated cell layer. This nucleated cell layer is then combined with the upper plasma layer to optimize the source of exosomes and avoid loss of exosome-derived cells. The resulting sample, with plasma and nucleated cells combined, provides a richer sample composition for subsequent digestion steps.

[0105] The dynamic gradient bone marrow digestion unit receives a combined plasma and nucleated cell sample from the bone marrow sample pretreatment unit. This sample is used to determine the fibrosis index and classify the sample. Based on this result, a complex digestive enzyme (collagenase, hyaluronidase, and trypsin in different proportions) is dynamically formulated, and the volume ratio of the enzyme solution to the sample is determined. During digestion, multi-dimensional real-time monitoring (such as morphological monitoring of cell disruption rate, nanoparticle tracking (NTA) detection of exosome particle size distribution, and rapid ELISA detection of exosome membrane protein CD63 concentration) is used to monitor the digestion process. The primary digestion is dynamically terminated based on the monitoring results. After primary centrifugation, the enzyme ratio is adjusted according to the residual cell type for secondary digestion. Following density gradient centrifugation and magnetic bead sorting, a sample enriched and relatively purified for exosomes is output after digestion, centrifugation, and magnetic bead sorting. This achieves the enrichment and preliminary purification of exosomes, providing a suitable sample for exosome extraction.

[0106] Exosome Extraction Unit: Inputs the exosome-enriched sample output from the Dynamic Gradient Bone Marrow Digestion Unit. Used for further extraction and purification of the exosome-enriched sample from the Dynamic Gradient Bone Marrow Digestion Unit. First, exosomes are precipitated by ultracentrifugation, retaining the bottom precipitate. Then, nanofiltration purification technology is used, controlling pressure, flow rate, and a specific combination of filter membrane pore sizes (first 150nm coarse filtration, then 30nm fine filtration) to remove impurities and obtain a high-purity exosome sample that meets the requirements for subsequent precise analysis.

[0107] The composite cytokine exosome analysis unit first constructs a seven-dimensional data matrix containing spatiotemporal coordinates and single-cell sequencing indicators, providing a multi-dimensional data foundation for comprehensive analysis. Then, the entropy-weighted Gini weighting algorithm is used to determine the weights of each indicator, improving analytical accuracy. Next, joint time-frequency domain analysis (such as wavelet transform) is employed to mine the characteristics of the data at different time scales and frequencies. Simultaneously, further analysis is conducted from both proteomics and gene sequencing perspectives. Proteomics analysis utilizes two-dimensional electrophoresis, phosphorylated protein enrichment, and label-free quantification techniques to detect related signaling pathway proteins. Gene sequencing analysis employs column extraction of nucleic acids, DNase I digestion, and RNA purity testing to ensure the reliability of gene-level data.

[0108] Therefore, the technical effects of the present invention are as follows:

[0109] Increased exosome extraction: By graded enzymatic digestion of fibrosis samples and using appropriate digestion schemes for samples with different degrees of fibrosis, the exosome extraction rate of severely fibrotic samples was increased by 40% compared to the fixed enzyme ratio, thus improving the overall exosome acquisition and providing more sufficient samples for subsequent analysis.

[0110] Exosome integrity protection: By using multi-dimensional monitoring methods such as real-time NTA monitoring, the digestion process can be adjusted in a timely manner, effectively reducing the exosome membrane damage rate from 30% in traditional methods to below 12%. This ensures the structural and functional integrity of exosomes, improves exosome quality, and facilitates accurate subsequent analysis of the biological information they carry.

[0111] Enhanced reliability of analytical results: The combined purification of multiple technologies improved the purity of exosomes, while enhanced detection and rigorous data quality control in the analytical process ensured the reliability of proteomics and gene sequencing analysis data.

[0112] Improved standardization and repeatability: The integration of the automated control system enables standardized operation of the analytical process, reducing the impact of human factors on the results. With different operators following the standardized procedures, the repeatability of analytical results is significantly improved, with the CV value optimized to within 15%. This facilitates the widespread application of this analytical method in different laboratory and clinical environments, ensuring the consistency and comparability of the results.

[0113] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An analysis system for bone marrow blood composite cytokine exosomes in elderly hip fractures, the system comprising: A bone marrow sample collection unit that collects bone marrow blood samples and pre-judges the degree of bone marrow stromal fibrosis. The pre-judgment method is to detect the bone marrow fibrosis serum markers PⅠNP and PⅢNP and combine the iliac bone X-ray bone density T value to evaluate the fibrosis grade, and adjust the collection volume according to the grade. The specific adjustment method is as follows: if it is mild fibrosis, that is, T≥-2.5, collect 2-3 mL; if it is moderate fibrosis, that is, -3.5<T<-2.5, collect 3-4 mL; if it is severe fibrosis, that is, T≤-3.5, collect 4-5 mL; A bone marrow sample pretreatment unit that receives the bone marrow blood sample from the bone marrow sample collection unit. After centrifuging to separate the plasma, it pretreats the bottom layer of blood cells, resuspends them with PBS buffer, lyses the red blood cells and then centrifuges to remove the lysis products, and retains the nuclear cell layer and combines it with the plasma; A dynamic gradient bone marrow digestion unit that receives the plasma and nuclear cell combined sample output by the bone marrow sample pretreatment unit, measures and grades the fibrosis index of the pretreated sample, dynamically prepares a composite digestive enzyme according to the grade, and monitors in multiple dimensions in real time during the digestion process, and dynamically terminates the primary digestion according to the monitoring results; After primary centrifugation, adjust the enzyme ratio according to the remaining cell types for secondary digestion, and then enrich the exosomes by density gradient centrifugation and magnetic bead sorting; the fibrosis index is FI = 0.6×PⅠNP + 0.4×hydroxyproline concentration, and the grading standard is mild fibrosis with FI<50, moderate fibrosis with 50≤FI<100, and severe fibrosis with FI≥100. Dynamically prepare a composite digestive enzyme according to the grade. The composite digestive enzyme is composed of collagenase, hyaluronidase, and trypsin in a mass ratio combination. For mild fibrosis, adjust the ratio of collagenase: hyaluronidase: trypsin to 2:2:1; for moderate fibrosis, use the default ratio of 3:2:1; for severe fibrosis, adjust the ratio of collagenase: hyaluronidase: trypsin to 5:2:1; Terminate the primary digestion when the cell breakage rate reaches 65% and the concentration of exosome membrane protein CD63 is stable at 9 μg / mL; after primary centrifugation, adjust the enzyme ratio according to the remaining cell types for secondary digestion. If the spindle cells in the remaining cells >50%, increase the proportion of trypsin. If the spindle cells in the remaining cells ≤50%, adjust the enzyme ratio to 1:3:

2. The digestion time is monitored in real time by NTA. When the growth rate of exosome particle concentration >10 particles / μL / minute, continue digestion; when the growth rate <5 particles / μL / minute, terminate digestion; An exosome extraction unit that receives the exosome-enriched sample output by the dynamic gradient bone marrow digestion unit, first centrifuges and precipitates the exosomes from the sample, retains the bottom precipitate, and then purifies it by nano-filtration to remove impurities to obtain a high-purity exosome sample; A composite cytokine exosome analysis unit that performs proteomics analysis and gene sequencing analysis on the high-purity exosome sample respectively.

2. The analysis system as described in claim 1, characterized in that: The dynamic gradient bone marrow digestion unit extracts a certain amount from the pretreated mixed sample and determines the collagen content using the hydroxyproline colorimetric method. Combined with the detected serum markers PⅠNP and PⅢNP, the fibrosis index FI is calculated. Based on the calculation results, the degree of fibrosis is reclassified as: mild fibrosis (FI < 50); moderate fibrosis (50 ≤ FI < 100); and severe fibrosis (FI ≥ 100). The basic formulation of the compound digestive enzyme is determined to be a combination of collagenase, hyaluronidase, and trypsin in a specific mass ratio. The ratio of the three enzymes is dynamically adjusted according to the fibrosis index FI. Digestion is performed under isothermal oscillation conditions, with multi-dimensional monitoring conducted at regular intervals. This multi-dimensional monitoring includes morphological monitoring, nanoparticle tracking of NTA, rapid ELISA detection, and determination of the digestion endpoint. After centrifugation at a specific speed for a period of time, the fluorescence intensity of the exosome marker CD9-FITC in each layer of the sample is detected by flow cytometry. The middle layer is defined as the fluorescence intensity peak layer, and the liquid in this layer is aspirated. The enzyme ratio is adjusted according to the type of cell residue. The secondary digested sample is then subjected to density gradient centrifugation and magnetic bead sorting.

3. The analysis system as described in claim 2, characterized in that: The composite cytokine exosome analysis unit measures gene expression levels and protein expression levels for specific cell types based on single-cell sequencing indicators, and determines the spatial location information of fracture sites. The detected single-cell sequencing indicator data is integrated with spatiotemporal coordinate information to construct a seven-dimensional data matrix. The first dimension of the matrix is ​​time, the second dimension is the X-axis information of spatial coordinates, the third dimension is the Y-axis information of spatial coordinates, the fourth dimension is the Z-axis information of spatial coordinates, and the fifth to seventh dimensions correspond to different categories of single-cell sequencing indicators. For the constructed seven-dimensional data matrix, the initial weights of each indicator are calculated using the entropy weight method, and the initial weights are adjusted using the Gini coefficient. The initial weights in the seven-dimensional data matrix are used as the input signal for joint time-frequency domain analysis, while simultaneously mining patterns in the data in both time and frequency dimensions. Two-dimensional electrophoresis is performed on exosome samples obtained from the exosome extraction unit. In the first dimension of electrophoresis, proteins are separated, and in the second dimension, proteins form a two-dimensional distribution pattern on the gel, thus initially separating proteins in the exosomes. After two-dimensional electrophoresis, phosphorylated proteins are enriched by centrifugation and washing to remove unbound proteins and obtain phosphorylated proteins. The phosphorylated proteins obtained by enrichment were detected; nucleic acid extraction and RNA purity detection were performed on the exosome samples.

Images