Method and application for measuring the flux rate of water molecules across cell membranes, method and system for measuring magnetic resonance imaging markers of gliomas - Patent Application 20070122999

Abstract

The present invention discloses a method for measuring the flux rate of water molecules across a cell membrane, which includes the steps of setting magnetic resonance imaging parameters and measuring the noise level during scanning, optimizing and resetting the flip angle by Monte Carlo simulation, scanning quantitative T1 magnetic resonance imaging, scanning dynamic contrast-enhanced magnetic resonance imaging, injecting contrast agent, and performing full shutter speed model SSM. full Perform pixel analysis for each pixel point in the tumor region using io and obtaining k io The present invention provides a method for evaluating AQP4 expression levels as a magnetic resonance imaging marker for gliomas for non-disease diagnostic purposes. io The present invention discloses the application of k as a magnetic resonance imaging marker of glioma in the preparation of a product for predicting the sensitivity of glioma to radiotherapy and chemotherapy treatment, as well as a method and system for its measurement. io The application, method or system described above provides non-invasive and quantitative measurement and imaging of AQP4 expression levels in gliomas.

Description

[Technical Field]

[0001] The present invention relates to the technical field of magnetic resonance imaging, and in particular to methods and applications for measuring the flux rate of water molecules across cell membranes, and methods for measuring magnetic resonance imaging markers of glioma. [Background technology]

[0002] Glioma is the most common central nervous system (CNS) disease in adults, accounting for approximately 60%–70% of primary brain tumors. Recently, numerous studies have demonstrated that AQP4 (aquaporin 4) plays an important role in glioma cell migration, proliferation, and peritumoral edema, making it an important biomarker for glioma prognosis. Compared with normal brain astrocytes, AQP4 expression is significantly increased in gliomas, and its expression location is redistributed from the perivascular periphery of astrocytes to the entire cell membrane. Studies have shown that AQP4 expression in various types of glioma cells responds differently to treatment with the anticancer drug temozolomide (TMZ). In summary, changes in AQP4 expression level and location are one of the early indicators of glioma transformation and treatment resistance.

[0003] Currently, traditional biopsy results are the only standard for characterizing AQP4 expression levels in vivo. However, biopsy sampling can only be performed at a single or limited number of points, making it impossible to obtain information on the distribution and dynamics of AQP4 in gliomas. This invasive method carries certain risks. Because gliomas are highly heterogeneous, one-sided information obtained from sparse sampling may lead to misinterpretation. Given the crucial role of AQP4 expression level and location changes in glioma fate, it is necessary to develop noninvasive quantitative imaging techniques with high spatial resolution for in vivo AQP4 expression. For example, Chinese Patent Application No. CN106683081A provides a radiomics-based nondestructive prediction method and prediction system for the glioma molecular marker IDH1. For example, Chinese Patent Application No. CN107169497B provides a method for extracting tumor imaging markers based on genetic imaging. CN107169497B uses genetic imaging to combine the advantages of imaging and molecular techniques to invent a non-invasive, highly interpretable biomarker extraction method. The method extracts high-dimensional quantitative image features from tumor CT scans and associates them with corresponding tumor gene expression patterns. It is hypothesized that specific quantitative image features reflect specific gene expression patterns in tumors and can be used as tumor prognostic markers. The ultimate goal is to extract non-invasive, biologically interpretable imaging markers.

[0004] However, because the expression level of AQP4 in brain tissue is very low (≤[8.65±0.80]ng / ml), existing molecular imaging techniques such as magnetic resonance spectroscopy (MRS) and chemical exchange saturation transfer (CEST) cannot quantify AQP4 expression. Furthermore, exogenous contrast agents that target specific molecules can also be used for MRI molecular imaging. However, currently, no such probes for molecular imaging of AQP4 exist, and even if they were available, the development and approval process for related drugs would be time-consuming and expensive.

[0005] In normal brain astrocytes, AQP4 is the major water channel expressed in the central nervous system and is primarily distributed in the perivascular caudal apex of astrocytes. However, in gliomas, AQP4 is upregulated and redistributed from astrocyte terminals (perivascular) to the entire plasma membrane. Aberrant expression of AQP4 aids glioma invasion into the brain and is one of the earliest indicators of glioma carcinogenesis. More importantly, AQP4 has been shown to be not only a potential therapeutic target but also a highly sensitive prognostic biomarker for migration, progression, edema, and treatment resistance in human gliomas. Summary of the Invention

[0006] The objective of the present invention is to determine the rate of water molecule flux across the cell membrane, k io The goal is to provide a method for measuring k io This invention significantly improves the accuracy of k in magnetic resonance imaging markers for gliomas. io The present invention further provides an application for measuring AQP4 expression levels in gliomas. The present invention further provides a method and system for measuring magnetic resonance imaging markers of gliomas, thereby realizing non-invasive and quantitative measurement and imaging of AQP4 expression levels in gliomas. The present invention also provides a method and system for measuring ... io This provides applications of the present invention that are useful for predicting treatment response and facilitating accurate treatment.

[0007] The present invention uses the following technical solutions:

[0008] The flux rate of water molecules across the cell membrane, k io A method for measuring (1) setting dynamic contrast-enhanced magnetic resonance imaging parameters to measure a noise level during dynamic contrast-enhanced magnetic resonance scanning; (2) optimizing the flip angle in the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquisition parameters by Monte Carlo simulation to reset the flip angle for the dynamic contrast-enhanced magnetic resonance imaging; (3) scanning quantitative T1 magnetic resonance imaging; (4) scanning dynamic contrast-enhanced magnetic resonance imaging and injecting a contrast agent; (5) Full Shutter Speed ​​Model SSM full A pixel analysis was performed for each pixel point in the tumor region using the io (The steady-state flow rate coefficient k io and obtaining the information (also referred to as the information).

[0009] In step (2), the measured vascular contrast agent concentration C p We randomly select one or more datasets from [1], then simulate the actual tissue and scanning parameter conditions to synthesize DCE-MRI data under different flip angles, and then calculate the time series signal of the synthesized DCE-MRI data. JPEG0007761973000001.jpg9146 is the full shutter speed model SSM full Based on the noise level generated by and estimated by the DCE-MRI experiment, Add white noise of the same noise level to JPEG0007761973000002.jpg9146 and use full shutter speed model SSM full Noise was added using A nonlinear least-squares fitting was performed on JPEG0007761973000003.jpg10155. The above steps were repeated multiple times under each flip angle, and the steady-state flow rate coefficient k of water molecules from the cell to the interstitium was calculated. io Finally, k ioThe flip angle for which the fitting result is closest to the predetermined value of the simulation and the variance is the smallest is the optimal flip angle. Preferably, this is repeated 100 times or more. Specifically, the full shutter speed model SSM full Please refer to CN201910621579.6.

[0010] In step (2), the full shutter speed model SSM full has divided water molecules into three compartments: vascular (b), interstitium (o), and intracellular space (i), and divided them into two water exchange processes: water exchange between blood and interstitium and intercellular interstitium-interstitial water exchange, with negligible water exchange between blood and intracellular space. Based on the Gd contrast agent (CA) (Gd-DTPA, gadopentetate dimeglumine), the present invention (MAGNEVIST) is considered an extracellular agent that distributes only in blood vessels and interstitium. The contrast agent concentration [CAo] (T) in the interstitial space is determined by the Kety-Schmidt type rate law.

number

[0011] Preferably, in step (2), under ultra-high magnetic fields (>3 T), additional quantitative measurements of the spatial distribution of the actual flip angles are performed to optimize the flip angles, and in step (3), quantitative T1 magnetic resonance imaging uses short repetitive time series of multiple flip angles.

[0012] Preferably, in step (5), an automatic shutter speed analysis method is used to obtain the vascular leakage rate Ktrans of the contrast agent in the tumor region, and further, SSM full Model fitting with Ktrans > 0.01 min -1 The steady-state water flux coefficient k from the cells to the interstitium at each pixel point was calculated. io Specifically, you can refer to CN201910621579.6.

[0013] The method provided by the present invention comprises: io This is a method for quantitatively measuring

[0014] The present invention provides a novel method for assessing AQP4 expression levels by measuring the efflux rate of water molecules across the cell membrane, k, as a magnetic resonance imaging marker for gliomas. io We further provide an application of io The method of obtaining is as follows: io The present invention is not limited to quantitative measurement of k as a magnetic resonance imaging marker for glioma. io The application of can be used in scientific research.

[0015] Preferably, the flux rate of water molecules across the cell membrane, k io The linear relationship between the AQP4 expression level and the AQP4 cell positivity rate = (k io -A) / B, In the formula, A is 0.1 to 0.2 seconds -1 , B is 13.07~15.04 seconds -1 is.

[0016] The present invention further provides a method for measuring the flux rate of water molecules across a cell membrane for non-disease diagnostic purposes as a magnetic resonance imaging marker for glioma to assess AQP4 expression levels, the method comprising: (1) The steady-state flow rate coefficient k of tissue water molecules from cells to the interstitium io Quantitatively measuring the (2) stereotactically taking biopsy tissue using a biopsy planning system and quantifying the AQP4 expression level in the tissue; (3)k io and establishing a linear relationship between the two based on the expression levels of AQP4. (4) The steady-state flow rate coefficient k of water molecules from cells to the interstitium in the tissue under test. io and obtaining the AQP4 expression level of the tissue to be measured based on the linear relationship of step (3).

[0017] Preferably, the measurement method includes: (1) setting dynamic contrast-enhanced magnetic resonance imaging parameters to measure a noise level during dynamic contrast-enhanced magnetic resonance scanning; (2) optimizing the flip angle in the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquisition parameters by Monte Carlo simulation to reset the flip angle for the dynamic contrast-enhanced magnetic resonance imaging; (3) scanning quantitative T1 magnetic resonance imaging; (4) scanning dynamic contrast-enhanced magnetic resonance imaging and injecting a contrast agent; (5) Full Shutter Speed ​​Model SSM full Pixel analysis was performed for each pixel point within the tumor region using the steady-state flow rate coefficient k of water molecules from the cell to the interstitium at each pixel point. io and obtaining (6) stereotactically collecting biopsy tissue using a biopsy planning system based on dynamic contrast-enhanced magnetic resonance imaging and quantifying the AQP4 expression level in the tissue; (7)k io A linear regression analysis was performed on the AQP4 expression level and k io obtaining a linear equation for (8) Repeat steps (3) to (5) and calculate k according to the linear equation in step (7). io The image is converted into an AQP4 expression level image, achieving intratumoral AQP4 expression imaging.

[0018] Preferably, in step (6), AQP4 immunohistochemistry photographs of the biopsy tissue are obtained to quantify the AQP4 expression level in the tissue.

[0019] Preferably, in step (7), the AQP4 expression level and k io The linear relationship between the AQP4 cell positivity rate and the io -A) / B, In the formula, A is 0.1 to 0.2 seconds -1 , B is 13.07~15.04 seconds -1 is.

[0020] Preferably, in step (8), the spatial distribution map of AQP4 expression levels can be color-coded to improve visibility.

[0021] The present invention further provides the application of the flux rate of water molecules across cell membranes as a magnetic resonance imaging marker of glioma in the preparation of a product for predicting the sensitivity of glioma to radiotherapy and chemotherapy.

[0022] Here, the drug used in the radiotherapy and chemotherapy is temozolomide.

[0023] The present invention further provides a system for measuring a glioma magnetic resonance imaging marker, the system comprising: The steady-state flow rate coefficient k of tissue water molecules from cells to the interstitium io an image extraction and processing module for quantitatively measuring k obtained by the image processing module io a post-processing module for establishing a linear relationship between the AQP4 expression level of the biopsied tissue and the The image extraction module and the image processing module are performed on the tissue to be measured. io and a prediction module for predicting the AQP4 expression level of the tissue to be measured based on the linear relationship of the post-processing module.

[0024] Preferably, the measurement system comprises: a pre-processing module for setting dynamic contrast-enhanced magnetic resonance imaging parameters, measuring a noise level during dynamic contrast-enhanced magnetic resonance scanning, and optimizing a flip angle in the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquisition parameters through Monte Carlo simulation to re-set the flip angle for dynamic contrast-enhanced magnetic resonance imaging; an image extraction module for scanning quantitative T1 magnetic resonance imaging, scanning dynamic contrast-enhanced magnetic resonance imaging, and injecting a contrast agent; Full shutter speed model SSM full Pixel analysis was performed for each pixel point within the tumor region using the steady-state flow rate coefficient k of water molecules from the cell to the interstitium at each pixel point. io an image processing module for obtaining the k obtained by the image processing module io a post-processing module for establishing a linear relationship between the AQP4 expression level of the biopsied tissue and the The image extraction module and the image processing module are performed on the tissue to be measured. io and a prediction module for predicting the AQP4 expression level of the tissue to be measured based on the linear relationship of the post-processing module.

[0025] Here, the repetition time in dynamic contrast-enhanced magnetic resonance imaging parameters remains at or near the minimum.

[0026] Here, the noise level of dynamic contrast-enhanced magnetic resonance imaging can be obtained by scanning a normal healthy subject.

[0027] Here, the present invention optimizes the flip angle in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquisition parameters by Monte Carlo simulation to make it most sensitive to detecting the process of water molecules crossing the cell membrane.

[0028] In this invention, the contrast agent is rapidly injected at the 8th frame after the start of scanning. The resonance contrast agent is a T1 contrast agent, and the recommended dosage is 15-20 ml of saline immediately after injection, with a recommended injection rate of 2 ml.

[0029] Here, based on dynamic contrast-enhanced magnetic resonance imaging and combined with clinical factors, optimal biopsy point coordinates are obtained, and biopsy tissue is stereotactically collected. Here, the tumor area can be manually outlined by a specialized doctor or staff member, or automatically obtained by artificial intelligence techniques. The number of biopsy tissue samples is 45, but if the number of samples is further increased, k io This will improve the accuracy of characterization of AQP4 levels.

[0030] In summary, compared with the prior art, the present invention has the following technical advantages:

[0031] 1. The present invention provides a magnetic resonance imaging marker of AQP4 expression in gliomas, the steady-state flow rate coefficient k of water molecules from cells to the interstitium. io We first proposed a correlation between AQP4 expression level and k io We prove that there is a highly linear relationship between

[0032] 2. This embodiment uses the steady-state flow rate coefficient k of water molecules from the cell to the interstitium. io A dynamic contrast-enhanced magnetic resonance imaging method for quantitatively measuring k is provided, and the dynamic contrast-enhanced magnetic resonance imaging parameters are optimized by Monte Carlo simulation, and the k of the imaging method is calculated. io Greatly improves measurement accuracy.

[0033] 3. This invention enables quantitative imaging of AQP4 expression in glioma tumors and the measurement of heterogeneity in AQP4 expression in glioma tumors. AQP4 expression levels can be used in scientific research, such as investigating the therapeutic effects of certain anticancer drugs and new treatments, investigating the relationship between AQP4 expression levels and pathological processes such as glioma migration and proliferation, and investigating the relationship between AQP4 expression levels and treatment resistance in glioma. [Brief explanation of the drawings]

[0034] [Figure 1] 1 is a schematic diagram of the aquaporin 4 magnetic resonance imaging marker designed in the present invention. The steady-state flow rate coefficient of water molecules from the cell to the interstitium, kio, is a magnetic resonance imaging marker of aquaporin 4 expression in glioma. [Figure 2] 1 is a flow chart of the method for measuring magnetic resonance imaging markers of glioma provided by the present invention. [Figure 3] 1 is a flowchart of a Monte Carlo simulation. [Figure 4] This shows the results of optimizing the flip angle parameters for dynamic contrast-enhanced magnetic resonance imaging using Monte Carlo simulation. [Figure 5] This shows the results of linear regression analysis of AQP4 expression levels and kio mean values ​​from 45 glioma biopsy tissues. [Figure 6] 1 shows the results of quantitative imaging of intratumoral AQP4 expression levels in a patient with low-grade glioma (left) and a patient with high-grade glioma (right) prepared according to this embodiment. [Figure 7]kio can accurately detect the dynamic expression of AQP4 in C6 cell line during TMZ treatment. [Figure 8] The results show the changes in kio, AQP4 and other parameters of C6 cell line after TMZ treatment. [Figure 9] 1 shows confocal results of AQP4 expression and distribution in C6 cell line after TMZ treatment. [Figure 10] The biomarker kio accurately tracks the dynamic regulation of AQP4 in U87MG cell line during the growth cycle. [Figure 11] Figure 10. Kinetograms obtained from water-exchange DCE-MRI accurately revealing AQP4 heterogeneity in vivo. [Figure 12] The kio system can accurately display the intratumoral AQP4 expression level in various rat glioma models. [Figure 13] 1 shows the linear correlation observed between kio and AQP4 expression in a C6 glioma rat orthotopic model. [Figure 14] The effect of specific drug-mediated inhibition of AQP4 on kio in a subcutaneous rat glioma model. [Figure 15] Figure 1 shows kinematic images obtained from water-exchange DCE-MRI revealing intratumoral AQP4 distribution in human glioma. [Figure 16] Average statistical data for the special case and sample of glioma patients. [Figure 17] Low kio (AQP4) is a phenotype of treatment-resistant glioma. DETAILED DESCRIPTION OF THE INVENTION

[0035] In order to make the objectives, technical solutions and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions of the present invention with reference to the drawings.

[0036] Figure 1 shows a schematic diagram of the aquaporin-4 magnetic resonance imaging marker designed in this invention, and shows the steady-state flow rate coefficient k of water molecules from cells to the interstitium.io The rationale for k is that it is a magnetic resonance imaging marker of aquaporin 4 (AQP4) expression in glioma is as follows: one AQP4 molecule transports approximately 0.24 pL of water molecules per second across the cell membrane, whereas the cell volume of a glioma cell is approximately 10 pL. io can characterize and amplify the AQP4 signal in MRI and thus serve as a linear biomarker of cell membrane AQP4 expression levels.

[0037] As shown in FIG. 2, the method for measuring magnetic resonance imaging markers of glioma provided by the present invention includes the following steps:

[0038] Step 1: Set the dynamic contrast-enhanced magnetic resonance imaging parameters to measure the noise level during dynamic contrast-enhanced magnetic resonance scanning. The specific steps are as follows:

[0039] (1) Adjust the DCE-MRI sequence parameters of the scanning device and set the repetition time (TR) to the shortest or near-shortest value (e.g., 3 ms).

[0040] (2) Adjust the repetition number of the DCE-MRI sequence and operate a normal subject for 10 minutes to statistically evaluate the noise level.

[0041] Step 2: As shown in Figure 3, Monte Carlo simulation is used to optimize the flip angle (FA) in the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquisition parameters to make it the most sensitive for detecting the process of water molecules crossing the cell membrane.

[0042] (1) A series of contrast agent concentration-time curves C from previous dynamic contrast-enhanced magnetic resonance imaging data p (t) or artificial simulation C pSelect (t) and specify the initial physiological parameters: contrast leakage rate from blood vessels, K trans = 0.01 (per minute), mole fraction of water in blood vessels p b = 0.05, mole fraction of interstitial water p o = 0.2, the steady-state flow rate coefficient k of water molecules from the cell to the interstitium io = 3 Hz, the steady-state flow rate coefficient k of water molecules from the blood vessels to the interstitium bo =3 Hz.

[0043] (2) Optimize the parameters - set the flip angle adjustment range to 1 to 40 degrees.

[0044] (3) Set the simulated dynamic contrast-enhanced magnetic resonance imaging parameters to be the same as the actual dynamic contrast-enhanced magnetic resonance imaging parameters (except for the flip angle).

[0045] (4) DCE-MRI time series signal JPEG0007761973000005.jpg9140 is generated according to a three-compartment model (this model is the full shutter speed model SSM described in the patent (patent number ZL201910621579.6) full Refer to the model).

[0046] (5) Random white noise is added to the signal, depending on the noise level estimated by the DCE-MRI experiment. JPEG0007761973000006.jpg10142 is added with white noise of the same noise level, and the patented (patent number ZL201910621579.6) full shutter speed model SSM full Noise was added using Perform nonlinear least squares fitting on JPEG0007761973000007.jpg10142.

[0047] (6) Repeat steps (3) to (5) 100 times, and for each iteration, the steady-state flow rate coefficient k ioCalculate the standard deviation and median.

[0048] (7) Repeat steps (3) to (6) until all combinations of scanning parameters are completed. io The flip angle for which the fitting result is closest to the preset value of the simulation and the variance is the smallest is the optimal flip angle.

[0049] Step 3, follow step 2, k io The flip angle that minimizes the standard deviation of and brings the median closest to the set value is selected (see Figure 4), and the flip angle for dynamic contrast-enhanced magnetic resonance imaging is set again.

[0050] Step 4: Perform quantitative T1 magnetic resonance imaging scan; the parameters are set as follows:

[0051] Field of view (FOV): (340 mm)2, layer thickness: 1.5 mm, 80 layers, pixel size 0.8 × 0.8 × 1.5 mm3, echo time (TE) / TR = 2.46 ms / 5.93 ms, flip angle (FA), 2° / 14°, bandwidth, 450 Hz / pixel.

[0052] Step 5: Perform dynamic contrast-enhanced magnetic resonance imaging (DCI). Rapidly inject contrast agent at the 8th frame after scanning begins. The DCE-MRI data acquisition parameters using 3D CAIPIRINHA-Dixon-TWIST were as follows: FOV = 340 × 340 × 120 mm 3 , FA=10°, Bandwidth, 1090Hz / pixel, TR, 6 ms, TE, 1.3 ms. The contrast agent was rapidly injected on the 8th frame after the start of the scan, and immediately after the injection, 15-20 ml of saline was injected at the recommended injection rate of 2 ml / sec.

[0053] Step 6: Full Shutter Speed ​​Model SSM full Pixel analysis was performed for each pixel point within the tumor region using the steady-state flow rate coefficient k of water molecules from the cell to the interstitium at each pixel point.io The detailed steps are as follows:

[0054] (1) Based on the phenomenon that contrast enhancement in tumor regions is higher than that in normal tissues, tumor regions can be manually outlined by specialized doctors or staff, or automatically obtained by artificial intelligence methods.

[0055] (2) Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to obtain the vascular contrast agent concentration time signal (AIF) of a living organism from time-series tumor area data. (3) According to the vascular contrast agent concentration time signal in step (2), a full shutter speed model (SSM) is applied to the DCE-MRI time series signal at each pixel point. full A nonlinear least-squares fitting of the SSM model was performed for each pixel point in the tumor region. full Obtain the DCE-MRI signal fitting results of the model respectively. full After fitting the model, the distribution maps of the five groups of physiological parameters are generated, and the five groups of physiological parameters are calculated as follows: trans , the mole fraction of water in the blood vessels p b , the mole fraction of interstitial water p o , the steady-state flow rate coefficient k of water molecules from the blood vessels to the interstitium bo , and the steady-state flow rate coefficient k of water molecules from the cell to the interstitium io Includes.

[0056] (4) Perform error analysis on kio and kbo in step (3) and [0 seconds -1 20 seconds -1 ] 95% confidence interval within the interval, or 5 seconds -1 Only pixel point results with a lower 95% confidence interval above are kept, and the final k io and k bo Parameter distribution diagram and K trans , p b and p o Generate a distribution diagram of the parameters.

[0057] The above steps 1 to 6 are a dynamic contrast-enhanced magnetic resonance imaging method for quantitatively measuring kio, which is io This makes it possible to achieve the first object of the present invention, which is to significantly improve the accuracy of the measurement.

[0058] Step 7: Use the biopsy planning system to identify the k within the tumor. io Obtain optimal biopsy point coordinates based on the images combined with clinical factors. The detailed steps are as follows:

[0059] (1) One to two days before stereotactic biopsy, all patients will have a stereotactic frame attached to their head and undergo a pre-biopsy structural MRI scan.

[0060] (2) k for the entire tumor area calculated according to the description in step 6 io The map is registered and fused with the structural image and input into a stereotactic biopsy planning system for stereotactic biopsy planning.

[0061] (3) The stereotactic surgical planning system realizes three-dimensional image reconstruction and surgical simulation by calculating the target coordinates and trajectory approach angle, and determines the target coordinate approach point and needle insertion angle in the cerebral cortex region. For each patient, under the most favorable clinical factors, it is necessary to design the biopsy trajectory to avoid access through, for example, sulci, cortical arteries, venous structures, or ventricles. Different k io Select multiple ROIs with values.

[0062] Step 8: Stereotactically collect biopsy tissue, obtain AQP4 immunohistochemistry images of the biopsy tissue, and quantify the AQP4 expression level of the tissue; (1) Tissue samples from all biopsy sites were surgically collected by three neurosurgeons according to the designated trajectory plan of the stereotactic biopsy entrance and target point in step 7. Biopsies were performed under local or general anesthesia. A biopsy needle (inner diameter 2.0 mm, lateral cutting window 10 mm) was carefully and gently inserted into the target site and rotated clockwise (0°, 90°, 180°, 270°) to collect biopsy tissue samples.

[0063] (2) Stereotactic biopsy is performed to obtain specimens 5–10 mm in length. The samples are fixed and embedded for immunohistochemistry to obtain the distribution of AQP4 expression. AQP4 immunohistochemical staining is then performed according to conventional immunohistochemistry procedures.

[0064] (3) Scanning high-resolution images of whole-section AQP4 immunohistochemistry using a microscope.

[0065] (4) The digital processing process of the AQP4 immunohistochemical scanning image is as follows: First, the ratio of the total AQP4 gray value to the number of cell nuclei (AQP4mean) of the entire slice is calculated. Next, in the immunohistochemical slice, the region of interest (ROI) with the strongest AQP4 staining intensity (i.e., the area with the highest gray value in the photograph) is selected as the area of ​​complete AQP4 positive expression. The ratio of the total AQP4 gray value to the number of cell nuclei (AQP4max) of the region is calculated. Finally, the AQP4 expression level (i.e., the positive rate) of the biopsy point is calculated as (AQP4mean / AQP4max) × 100%.

[0066] Step 9: Repeat steps 4 to 8 to obtain dynamic contrast-enhanced magnetic resonance images and stereotactic biopsy tissues of multiple glioma patients.

[0067] Step 10: AQP4 expression levels and k in all biopsies io Linear regression analysis was performed on the mean values ​​to compare AQP4 expression levels and k io The linear equation (i.e., AQP4 cell positivity rate = (k io -0.2 seconds -1 ) / 14.1 seconds -1 The specific steps are as follows:

[0068] 1. First, 19 patients (6 cases were WHO I-II, 13 cases were WHO III-IV, of which 10 cases were recurrent gliomas) and 45 biopsy point samples were analyzed. ioThe magnetic resonance parameters including the values ​​and the corresponding immunohistochemistry results (i.e., AQP4 positivity rate) are statistically analyzed one by one.

[0069] 2. Next, the linear fitting coefficients for each magnetic resonance parameter and AQP4 expression level are evaluated by linear regression analysis. The optimal linear correlation parameter k is io and obtain the linear equation and 95% confidence interval of AQP4 expression level.

[0070] 3. The final results are shown in Figure 5, which shows the relationship between AQP4 expression levels and k io The linear equation, i.e., AQP4 positive rate = (k io -0.2 seconds -1 ) / 14.1 seconds -1 is.

[0071] In addition, this method can also use machine learning algorithms to introduce multiple magnetic resonance parameters to establish an AQP4 positivity rate prediction model instead of linear regression analysis, further improving the prediction accuracy.

[0072] Step 11: According to the above linear equation, k for each pixel point in the tumor io The map is converted into an AQP4 expression level image, realizing noninvasive imaging of intratumoral AQP4 expression.

[0073] Step 12: For new glioma patients, steps 4-6 and 11 are repeated without performing stereotactic biopsy. The spatial distribution map of AQP4 expression in the patient's tumor can only be obtained by magnetic resonance scanning and data analysis. Figure 6 shows the quantitative imaging results of intratumoral AQP4 expression levels in a patient with low-grade glioma (bottom left) and a patient with high-grade glioma (bottom right), as well as the corresponding thin-section enhanced T1-weighted magnetic resonance images (top).

[0074] The magnetic resonance imaging marker measurement system for glioma provided by the present invention comprises: a pre-processing module for performing step 1, step 2, and step 3; an image extraction module for performing steps 4 and 5; an image processing module for performing step 6; a post-processing module for combining steps 7, 8, and 9 to perform steps 10 and 11; and a prediction module for performing step 12.

[0075] Evaluation of AQP4 expression levels and k as a magnetic resonance imaging marker for gliomas in the preparation of products for predicting the sensitivity of gliomas to radiotherapy and chemotherapy io To further verify the application of the present invention, the present invention uses the following experimental method.

[0076] Cell culture and construction of TMZ-resistant cell models This invention uses the glioma cell lines C6 and U87MG from the American Type Culture Collection (ATCC, HTB14™). Taking C6 cell culture as an example, the method is as follows: C6 cells are cultured in Dulbecco's modified Eagle's medium (DMEM, Sigma-Aldrich, D6429-500ML), which contains 10% fetal bovine serum (FBS, Biological Industries, 04-002-1A) and 1% penicillin and streptomycin double antibody (P / S, Gibco, Thermo Fisher Scientific, 10378016), which is a complete medium. The culture environment is a humidified incubator at 37°C with 5% CO2 and air. The medium is changed twice a week, and the cells are passaged in the logarithmic growth phase (phase II).

[0077] C6 cells were treated with TMZ (50 μM) in complete medium containing 0.1% DMSO (control group) or 50 μM TMZ in 0.1% DMSO. Cellular magnetic resonance parameters k io and physiological information (such as cell morphology, AQP4 expression, cell activity, and cell migration distance) were obtained on the 3rd and 7th days of TMZ (50 μM) treatment, respectively.

[0078] For primary cell culture, cut glioma biopsy tissue into 1 mm pieces and mix with 10 ml of trypsin-EDTA (Gibco, 25200056) at 37 °C for 10-15 min until most of the pieces are digested into a single cell suspension. Then, culture the cell suspension using the same method as for glioma cell lines (flow cytometry results in Figure 8).

[0079] Cell samples for MRI testing and protocols for responding to specific inhibitors To obtain cell samples for MRI, we first cultured U87MG (0.5-1 × 10 5 ) and C6(1-2×10 6 ) Cell lines were washed with DPBS and dissociated by adding 1 mL of 0.25% trypsin-EDTA solution to a 60 mm Petri dish. The cells were incubated for 0.5–1.0 min and then resuspended in 2 mL of PBS (or PBS with 5 mM gadoteridol). The cell samples were centrifuged at 150 g for 5 min at 4 °C. The samples were then resuspended in 250–300 μL of PBS (or PBS with 5 mM gadoteridol). Next, the sample tubes were centrifuged at 300 g for 2 min at 4 °C before MRI. For AQP4-specific inhibition experiments, the PBS in the above step should be replaced with PBS plus inhibitor; i.e., the cell samples were preincubated for 15 min in PBS or PBS containing 6.4 μM TGN020 (Axon, CAS 51987-99-6) prior to detection (Figures 7c and d, Figure 8a).

[0080] cell k io A benchtop MRI system for measuring in vitro cell culture A 0.5T benchtop MRI measurement system for in vitro cell culture (Pure Devices GmbH, Germany) includes the following: an MRI system mounted on a vibration-isolated table, an autonomous NMR tube containing live cell pellets from an incubator, 5% CO2 + 95% O2 and PBS supernatant to ensure high viability, and an attached optical fiber for temperature monitoring. All MRI measurements were performed at room temperature (23.5 + / - 1 °C).

[0081] Before measuring water exchange DCE-MRI, diffusion-weighted imaging (DWI) was performed to identify the location of cell layers with low apparent diffusivity using the following parameters: single-layer acquisition, 5 mm slice thickness, and FOV = 12.8 × 12.8 mm. 2 , matrix size 64 × 64, average value of 16 pieces, and 10 seconds / mm 2 and 2000 sec / mm 2 The water-exchange DCE-MRI uses an inversion-recovery-prepared turbine spin echo (IR-TSE) sequence and the Gd-based contrast agent gadoteridol (Prohance™, Bracco Diagnostics, Princeton, NJ). The scan parameters used for the water-exchange DCE-MRI were: echo time (TE) 3 ms, turbo factor = 16, FOV = 12.8 × 12.8 mm. 2 The matrix size is 32 × 32. For water-exchange DCE-MRI, two contrast agent (CA) concentrations (0 mM and 5 mM) are used. For [CA] = 5 mM, 13 IR delays (10 ms, 30 ms, 50 ms, 70 ms, 90 ms, 150 ms, 200 ms, 400 ms, 600 ms, 800 ms, 1 s, 5 s, 5 s) are used with a repetition time TR (TR = IR delay + 5 s), with one repetition of each IR delay. For [CA] = 0 mM, the longest IR delay is extended to 10 s, and the TR is simultaneously extended (TR = IR delay + 10 s) to ensure complete recovery of longitudinal magnetization for each TR. The scan times for monolayer acquisitions with [CA] = 0 mM and 5 mM are 10 min and 5.5 min, respectively.

[0082] The IR-TSE signal (M) for each IR delay is obtained from the average signal of the cell ROI. The signal is then subtracted and normalized by the equilibrium magnetization (M0, the M with the longest IR delay). Define JPEG0007761973000008.jpg8139 to be the normalized signal analyzed by the water exchange model.

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[0083] Here, the two-site exchange (2SX) SS model is used. Briefly, the MR signal is considered to be the sum of signals from two water sites, intracellular water and extracellular water, each with a similar longitudinal recovery rate R 1 It is assumed that p i and p o are the molar fractions of water molecules in the intracellular and extracellular spaces, respectively, and p i +p o = 1. Next, the normalized IR-TSE signal JPEG0007761973000010.jpg8145 can be expressed as a double exponential function,

number

[0084] where R 1、sm and R 1、lar are small R1 and large R1, respectively, and p sm is R 1、sm The three parameters R 1、sm , R 1、lar and p sm is the intracellular water efflux rate constant (k io ), intracellular water R1 (R 1i ), and CA-dependent extracellular water R1 (R 1o ) are determined by three physical parameters. For each case (e.g., C6 on days 0, 3, and 7 of TMZ), at least three samples are measured using IR-TS as baseline data when [CA] = 0 mM. 1i is determined in advance by fitting the IR-TSE signals acquired at two different CA concentrations to more than three samples.

[0085] Immunofluorescence staining, visualization, and quantification of AQP4 in vitro Immediately after MRI, cell line samples were fixed in 4% paraformaldehyde (PFA) for 20 minutes at room temperature and then stored in 0.5% PFA at 4°C until further molecular biology experiments. For immunofluorescence (IF) studies, the samples were (1) blocked with 10% goat lysate (Beyotime, C0265) for 1 hour at room temperature, (2) incubated with AQP4 primary antibody (1:200) overnight at 4°C, followed by incubation with AQP4 secondary antibody (1:500) for 1 hour at room temperature, (3) stained with DAPI (4',6-diamidino-2-phenylindole, Sigma-Aldrich, 1:1000) for 5 minutes at room temperature, and (4) washed three times with PBS. Fluorescence quantification was achieved using a microvolume fluorescence spectrophotometer (DFX, Denovix, Wilmington). Data were collected from two fluorescence channels. One is to characterize DAPI at excitation / emission wavelengths of 375 nm / 435-485 nm, and the other is collected in the blue channel (excitation / emission 470 nm / 514-567 nm). A blank control is used for all measurements to avoid false positives. Both measurements are performed independently by two researchers before processing the DCE-MRI results (right side of Figure 7a).

[0086] Characterization of cell viability upon TMZ treatment On days 3 and 7 of TMZ or DMSO treatment, 1% CCK-8 (Cell Counting Kit-8, Beyotime, C0037) was added to the 96-well plate for cell viability assay. After 1 hour of incubation, the supernatant was used for optical density testing using a microspectrophotometer (Ultrafine Photometer, NanoDrop 2000, Thermo, Waltham) (Figure 7e).

[0087] Assessment of cell migration Culture cells into a monolayer and use a standard 200 µl pipette tip to draw a strip-shaped scratch 300–500 µm wide from the bottom of a cell culture dish. Then, after 24 h of incubation under corresponding conditions, fix the sample with 4% PFA for 30 min and stain with DAPI. For cell migration calculations, only count the cells in the scratch area (Figure 3b).

[0088] Classification methods for fast cycling cell (FCC) and slow cycling cell (SCC) markers The present invention uses a cell tracer method to distinguish FCC from SCC. Here, OG (Oregon Green 488 Carboxylic Acid Diacetate, succinimidyl ester) and CTV (CellTrace™ Violet Reagent, ThermoFisher Scientific, Invitrogen C34557) are used to distinguish FCC (low OG or CTV intensity) from SCC (high OG or CTV intensity, e.g., CTV fluorescence intensity >104 in the C6 cell line and primary human glioma cells, respectively). The principle is to distinguish and separate FCC and SCC cell populations based on their different abilities to retain OG or CTV within the same period during the cell culture process. The specific method is as follows: First, cells are suspended in PBS containing 25 μM OG and incubated for 10 minutes (in a cell culture incubator). After incubation, the cell sample is washed with DMEM to remove residual dye and returned to culture medium until microscopic imaging. Prior to fluorescence imaging, samples were fixed with 4% PFA for 30 minutes and stained with DAPI. The percentage of SCC was defined as the percentage of OG-positive cells among DAPI-positive cells. Primary human glioma cells were stained with 5 μM CTV (same method as for OG) for 3 days. Cell fluorescence intensity statistics and counting were then simultaneously measured by fluorescence flow cytometry (FACS) (LSRFortessa X-20, BD, USA).

[0089] The present invention also uses EdU (5-ethynyl-2'-deoxyuridine), a biomarker that marks proliferating cell subtypes by marking newly synthesized DNA, to identify FCC (high EdU) and SCC (low EdU). The specific method is as follows: A C6 cell suspension is obtained and incubated with 50 μM EdU solution (containing Cell-Light™ EdU Apollo567, Ribobio C10338-1, EdU solution, and Apoll Fluorescence Solution) at 37°C for 2 hours, followed by AQP4 marking according to the IF method described above. After AQP4 staining, the sample is incubated in Apollo Fluorescence Solution for 30 minutes to fluorescently mark EdU, and then transferred for FACS analysis.

[0090] The cell morphology and fluorescence images of the present invention are taken using a fluorescence inverted microscope system (cellSensV1.13, Olympus, Japan) or a confocal laser scanning microscope (fv1200, Olympus, Japan).

[0091] Two rat models of glioma and magnetic resonance imaging All animal studies were approved by the Zhejiang University Animal Care and Use Committee. Adult (7-8 weeks old) male Sprague-Dawley (SD) rats were obtained from the Zhejiang University Experimental Animal Center. For the glioma cell transduction process, rats were anesthetized with 2% (v / v) isoflurane (R500IE, RWD Life Science Co., Ltd.) and 5 × 10 cells were injected. 6 100 μl of PBS containing 100 C6 cells and 1% antibiotics was slowly injected subcutaneously into the right leg. 7–9 days after tumor implantation, the animals were subjected to 7T MRI. After MRI, the tumors were rapidly excised, fixed in 4% PFA for 24 h, and stored in 0.5% PFA until IHC.

[0092] For the orthotopic glioma model, inject 10 μl of C6 cell suspension (0.5 × 10) into the right caudate putamen of the brain using a microinjection needle. 5In addition, the injection coordinates were located in the brain region 0.8 mm from the anterior arcuate suture, 2 mm to the right of the sagittal suture, and 4.5 mm deep. Two weeks after tumor inoculation, animals were subjected to 9.4T MRI examination, fixed in 4% PFA for 24 hours immediately after MRI, and preserved in 0.5% PFA until IHC. The tumor volume in all animal models was 4000 mm. 3 does not exceed.

[0093] Specific pharmacological inhibition of AQP4 in a rat glioma model Here, we use TGN020 to inhibit AQP4 in a rat glioma model. For the TGN020 group, each animal is intravenously administered TGN020 (3 mg / kg, 4 ml / kg body weight) via the tail vein 15 minutes before water-exchange DCE-MRI. To enhance solubility, TGN020 is dispersed in 37°C saline (0.9% NaCl) with repeated sonication and vortexing to promote dissolution before intravenous injection. One day before TGN020 treatment, the same animals are treated with saline (4 ml / kg) and then subjected to water-exchange DCE-MRI data acquisition. In the control group, animals are treated with the same volume of saline on both the first and second days. Here, a post-infusion flush is required after a small injection volume to ensure rat safety and therapeutic efficacy.

[0094] Histology and immunohistochemistry IHC was performed on rat gliomas using paraffin-embedded thin sections. Briefly, immediately after the MRI experiment, rats were euthanized with 5% isoflurane, and tumor tissue was excised and fixed in 4% PFA for 24 hours. Next, after carefully registering the tumor tissue to the MRI data, tissue sections (approximately 4 μm thick) were cut along the MRI scan plane. AQP4-IHC was performed as follows: (1) incubation with anti-AQP4 rabbit polyclonal antibody at 4°C overnight, followed by (2) incubation with goat secondary antibody HRP (horseradish peroxidase) at room temperature for 1 hour. Hematoxylin was also used for nuclear staining. Finally, the tissue sections were completely scanned at 20x magnification using a microscope slide scanning system (VS120, Olympus, Japan).

[0095] The process for quantitative analysis of immunohistochemical profiles is as follows. First, histological imaging images are stained and separated using ImageJ (open source Fiji v1.53c, plugin: Color deconvolution). Further analysis is performed using MATLAB® 2018 (MathWorks, Natick, MA, USA) to remove background and quantify the number of nuclei and AQP4 staining (grayscale intensity). Next, the average AQP4 grayscale intensity of all cells in each slide is calculated. At the same time, several ROIs showing the highest AQP4 expression in all slides are manually selected by two experienced pathologists and are defined as 100% AQP4 positive (AQP4 + Finally, the AQP4 in the ROI is considered + AQP4 for each slide by normalizing to the mean grayscale density in % + The percentage of AQP4 is further calculated as the mean AQP4 greyscale density of the slice. A double-blind principle is followed for IHC and AQP4 quantification.

[0096] Region of interest (ROI) selection for rat glioma model. Because most subcutaneous glioma tumors exhibit a ring-like structure with high AQP4 expression, concentric ring-shaped ROIs were used to divide the MRI tumor area and corresponding tissue image into six ROIs. First, the minimum circumscribing rectangle of the entire tumor was calculated, and the center coordinates (XR, YR), length (LR), and width (WR) of the rectangle were determined. Next, a series of concentric elliptical curves were automatically drawn by the program to divide the tumor into six ROIs. The major axis (am) and minor axis (bm) of the concentric elliptical curves were calculated as follows:

number

[0097] where m is the sequence number of each concentric elliptic curve (from the outer (m=1) to the inner (m=6)), and q=0.75.

[0098] Water exchange DCE-MR and stereotactic biopsy of human brain glioma (Step 7, using a biopsy planning system to identify intratumoral k io Based on the images, optimal biopsy point coordinates are obtained in combination with clinical factors).

[0099] The samples obtained in stereotactic biopsy are typically small. Nevertheless, the present invention incorporates several modifications in specimen processing practices to accommodate the full range of histological, immunocytochemical, and ultrastructural studies. For specimens 5–10 mm in length, the fine tip is removed from the biopsy needle to prevent drying and transferred to the laboratory in a glass bottle soaked in saline. The biopsy specimen is then divided into multiple smaller samples for various purposes; for example, one can be selected for cryopreservation, while another can be fixed with glutaraldehyde for electron microscopy. The remaining specimen is fixed, embedded, and processed for routine IHC and HE staining. This method allows specialized staining procedures and IHC studies to be performed on comparable consecutive samples. Each sample is specific for specific kDa. ioAfter tissues were histologically identified as glioma by a neuropathologist, AQP4 and ZEB1 staining and image digital analysis were performed according to the same IHC protocol as in the rat glioma model.

[0100] For IEM, immunogold-silver labeling was used to detect AQP4, and the steps are as follows: (1) Biopsy tissues were rapidly fixed in IEM buffer (a PBS solution containing 0.2% glutaraldehyde and 2% paraformaldehyde) and incubated at room temperature for 2 hours, followed by incubation in PBS containing 50 mM glycine for 15 minutes at room temperature. (2) Samples were blocked and permeabilized for 40 minutes in PBS containing 5% goat serum, 1% Triton, and 1% fish collagen. (3) Samples and primary antibodies were prepared in PBS containing 1% fish collagen and incubated overnight at 4°C. (4) Post-embedding immunogold labeling was performed using a gold-conjugated secondary antibody in PBS containing 1% goat serum and 1% Triton and incubated overnight at 4°C. (5) Silver enhancement was performed in the dark using an HQ Silver Enhancement Assay Kit (Nanoprobes, 2012-45ML) to observe AQP4 immunoreactivity. (6) Samples were washed several times with deionized water before and after the silver enhancement step. (7) Immunolabeled specimens were fixed in PBS containing 0.2% OSO4 for 2 hours, stained with 0.5% uranyl acetate for 1 hour, dehydrated in graded ethanol, and then flattened and embedded in Epon 812 medium. Ultrathin sections (70 nm thick) were then observed under a Jeol-1200 electron microscope (JEOL Ltd., Tokyo, Japan). The above process followed the rules of a double-blind experiment.

[0101] The above experiment verifies the following:

[0102] k io Accurately detected the dynamic expression of AQP4 in C6 cell line during temozolomide (TMZ) treatment To evaluate the ability of water-exchange DCE-MRI to detect dynamic changes in AQP4 expression levels during TMZ treatment of glioma, we used the above-described TMZ incubation to induce C6 cells to establish a glioma treatment model. As shown in Figure 7(a), confocal fluorescence images indicate that AQP4 expression continues to decrease with increasing TMZ treatment time, until it almost completely disappears on day 7 of TMZ treatment, with AQP4 only detected in a few cell membrane uropods (Figure 7(a) and Figure 8(a, b, c)). Using a microfluorescence spectrophotometer (QFX, Denovix, Wilmington), AQP4 expression levels, i.e., fluorescence intensity (RFU), were further quantified. The results showed a 42.5% (p = 0.0013) and 85.9% (p < 0.0001) decrease on days 3 and 7 of TMZ treatment (Figure 7(a), right), respectively, while no significant changes were observed in the control group (Figure 9(b)). As expected, cell migration (scratch assay, Fig. 7b), proliferation rate (Fig. 9e), Ki-67 expression level (Fig. 9d), and cell viability and proliferation index obtained by CCK-8 (Cell Count Kit-8)-mediated cell migration (Fig. 7e) all gradually decreased with increasing TMZ treatment time.

[0103] The control C6 cell line was treated with k before TMZ treatment. io = 10.9 ± 0.7 s (n = 10, Fig. 7c), and k io was 6.7±0.8 seconds on the third day of TMZ treatment. -1 (37.3% decrease, p=0.0008, n=6) and by day 7, the mean age at onset was 5.8±0.2 seconds. -1 (45.5% decrease, p<0.0001, n=7) (c in Figure 7). io The magnitude of the decrease (10.9-5.8=4.9 seconds) -1 ) corresponds well with the magnitude of the 85% reduction in AQP4 expression, i.e., 85% AQP4 regulation k io =85%*5.8 seconds -1 =4.9 seconds 1 , which is the k during TMZ treatment ioThis indicates that the dynamic changes in glial cell AQP4 are mainly controlled specifically by the AQP4 pathway. This explanation is further confirmed by the results of glial cell AQP4 specific inhibition using TGN020 with 7 days of TMZ, which showed that on the 7th day of TMZ, AQP4 expression was barely observed, and TGN020 was able to inhibit k io It can be seen that the value of β cannot be further reduced (d in FIG. 7).

[0104] Here, a specific description of FIGS. 7 to 9 is as follows.

[0105] Figure 7 shows the k io The dynamic expression of AQP4 in the C6 cell line during TMZ treatment can be accurately detected. a) Images (confocal microscopy) of AQP4 (red) expression and distribution after TMZ treatment of C6 cells. On the right, quantitative rfu results (measured with a microfluorescence spectrophotometer) of AQP4 and DAPI in the C6 cell line after TMZ treatment are shown. n = (4, 3, 3) from left to right. Scale: 25 μm. b) Measurement results of the migration ability of C6 cells at various times during TMZ treatment. Here, cell nuclei marked with DAPI (4',6-diamidino-2-phenylindole) are shown in green in the scratch area. The bar graph on the right shows the total migration distance of all cells within the scratch area. (n = (13, 4, 7) from left to right) Normalized to the results of the control group. Scale: 200 μm. c) C6 cell k during TMZ chemotherapy. io Dynamic change trend of n = (10, 6, 7), (G-I: The numbers at the bottom represent the control, day 3 of TMZ, and day 7 of TMZ from left to right). d: On day 7 of TMZ treatment of C6 cells, the specific inhibition of AQP4 by TGN020 (marked as "+" and without TGN020 marked as "-") further increased k io e Calculation of cell viability by CCK-8 optical density (OD) (n = (4, 3, 3). Data are shown as mean ± SEM. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, ns = not significant.

[0106] Figure 8 shows the k of C6 cell line after TMZ treatment. io , AQP4 and other parameters are shown, and in a-f, a is the k between the treatment group and the control group. io a) Change in AQP4 between treatment and control groups, n = (4, 6, 4, 6), p = 0.0099, p = 0.0006; b) Change in AQP4 between treatment and control groups, AQP4(rfu) / DAPI(rfu), n = (3, 5, 3, 3), p = 0.0328, p < 0.0001; c) Migration distance between treatment and control groups (normalized by control group), n = (5, 4, 8, 8), p = 0.0836, p = 0.0001; d) Ki-67(rfu) / DAPI(rfu) between treatment and control groups, n = (3, 6, 3, 6), p = 0.1041, p = 0.0133; e) Cell proliferation rate between treatment and control groups. n = (6, 15, 3, 34, 13), p = 0.0505, p = 0.0001. f: Percentage of SCC (OG+ cells / total cells). n = (4, 3, 3, 3). Here, in the control group, C6 cell line was incubated with DMSO only. e: Results for the AQP4 KO group are also shown. Data are presented as mean ± standard deviation. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, ns: not significant.

[0107] Figure 9 shows confocal microscopy results of AQP4 expression and distribution in the C6 cell line after TMZ treatment. (a) Confocal microscopy images of AQP4 and DAPI cellular morphology in the C6 cell line. Scale: 50 μm. (b) and (c) Results of stereotactic analysis of AQP4 expression levels along the white dotted line in (a), i.e., quantitative analysis of dynamic changes in AQP4 and nuclear fluorescence intensity at different locations far from the core of the cell nucleus. The results show that after 7 days of TMZ, AQP4 expression levels decreased while the highest peak of AQP4 expression was located far from the core of the cell nucleus, and nuclear fluorescence appeared bright and irregular in some areas.

[0108] quantitative k io accurately predicted the dynamic regulation of AQP4 during the cell proliferation cycle Biomarker k to detect dynamic AQP4 regulation associated with glioma cell proliferation io To evaluate the ability of AQP4 expression, we performed SS-DCE-MRI measurements at different stages of the U87MG growth curve. The cells exhibited different proliferation states, as well as different cell densities and morphologies (Figure 10a). A typical S-shaped curve of cell growth and proliferation was observed, which can be divided into: (I) an adaptation phase (0-48 hours) during which cells adapt to the culture conditions without dividing; (II) a logarithmic (log) growth phase (72-96 hours) during which cells rapidly proliferate and their cell numbers increase exponentially; and (III) a stationary and death phase (starting at 120 hours) during which overall cell proliferation slows. AQP4 expression levels peaked in phase II, decreased by 60.4% in phase I (p<0.0001), and decreased by 66.0% in phase III (p<0.05) (Figure 10b,c). Interestingly, but not surprisingly, k io shows a similar trend to the growth cycle. io also reached a peak in logarithmic phase II (6.7 ± 0.8 seconds) -1 , n=27, smaller than previous results using 0.1% DMSO), Phase I (5.04±0.68 seconds -1 , p<0.0001) and Phase III (4.6±0.9 seconds) -1 , p<0.0001) (d in Figure 10). In the first stage, the inhibition by TGN020 was io (e in Fig. 10), which indicates that the k io Most of the activity was lost in the first step, which explains why the values ​​are smaller than those of the control group.

[0109] Figure 10 shows the biomarker k io(a) shows the accurate tracking of the dynamic regulation of AQP4 in U87MG cell line during the growth cycle. (b) shows the morphology of U87MG cell line in various growth phases, including adaptive phase I, logarithmic phase II, stationary phase, and decline phase III. (c) shows the typical microscopic images of the changes in AQP4 (red) in U87MG cell line. (d) shows the dynamic regulation of AQP4 expression and kappa-like activity in three cell cycle stages. io e indicates that AQP4 inhibition by TGN020 (n=6) was significantly higher than that in Phase I. io In c-e, bar height and error bar width indicate mean and standard deviation, respectively; *p<0.05, ***p<0.001; ns indicates not significant; in c, p=0.7767; in d, p=0.2207; in e, p=0.4031. In c-e, data points (e.g., dot charts) are overlaid on corresponding boxes; two-tailed unpaired t-tests were performed. f shows k using data from U87MG and C6 cell lines. io The aim was to perform a correlation analysis between C6 and AQP4 expression. The same symbols are used for each cell line (triangles for C6 and points for U87MG), the solid line reflects the linear regression, and the area between the two dashed lines reflects the 95% confidence interval.

[0110] Biomarker k io is linearly correlated with AQP4 expression in C6 and U87M cell lines Quantifying AQP4 expression io To further evaluate the ability of k io A direct correlation analysis is performed between k and AQP4 expression (for RFU values ​​and detection methods, see AQP4 Fluorescence Spectrophotometer Detection). io showed a significant positive correlation with AQP4 (correlation coefficient R = 0.92, p = 0.01), which indicates that k io We demonstrate that this method can accurately represent the dynamic expression of AQP4 even in glioma cell types.

[0111] In vivo k obtained from water-exchange DCE-MRIio The map accurately reveals intra- and intertumor AQP4 heterogeneity in a rat glioma model To further demonstrate the accuracy of water-exchange DCE-MRI in detecting AQP4 in vivo, we establish a glioma animal model by subcutaneously implanting the C6 cell line into the right leg of Sprague Dawley (SD) rats. In vivo water-exchange DCE-MRI is achieved using a clinically used Gd-based CA (gadopentetate dimethylamine, Guangzhou, China). io To better estimate k, the present invention uses a method of injecting CA twice, which reduces k compared to the conventional method of injecting contrast agent once. io The accuracy of the estimation is improved by 10 times. A steady-state multi-gradient echo (MGE) sequence is used to overcome potential T2* artifacts caused by contrast agents. Furthermore, in combination with numerical simulation, the MGE sequence parameters are optimized (e.g., optimal parameters: repetition time (TR) = 100 ms, flip angle (FA) = 20°). Finally, a careful error analysis is performed on the SS model fitting to identify k cases with large fitting errors. io In short, these steps remove k within the physiological range ([0 s -1, 10 s -1]). io Ensure the accuracy of water exchange DCE-MRI in estimating β (c in Figure 11).

[0112] As expected, the in vivo SS model fit was excellent for most tumor voxels. io The image (Fig. 11d) and AQP4 immunohistochemistry (IHC) staining images (Fig. 11e, f) show similar spatial distributions. Note that strong intratumoral heterogeneity in AQP4 expression was observed in most animals, with high expression at the tumor margin (infiltrated area) and low expression in the tumor center (Fig. 11e, f). io The map shows high k at the tumor margin in the tissue slice shown in the schematic. io , low k in the tumor center ioThese show the same expression pattern as AQP4. To verify the accuracy of water-exchange DCE-MRI quantitative assessment of AQP4 expression in vivo, combined with the ring-like distribution of AQP4 in most subcutaneous glioma animal models, a series of concentric ring-shaped ROIs were used to divide the tumor slice into six regions (Methods, Figure 11, d, e). Next, the average k of the ROIs was calculated. io value and AQP4 positivity (AQP4 + ) fraction. The results are compared with the k obtained from water-exchange DCE-MRI. io The figure shows the AQP4 of each animal. + The results show that there is a strong linear correlation between the rate and the mean (i in Figure 11, correlation coefficient R>0.80 for each animal, Figure 12), with an average R=0.82 (p<0.0001, g in the figure, n=10). io This indicates that the method can accurately detect intratumoral AQP4 expression heterogeneity in gliomas in vivo. Linear regression analysis (Fig. 11g) further confirmed the AQP4-related k io takes 10.5 seconds -1 whereas the non-AQP4 pathway k io takes just 0.4 seconds -1 The results show that AQP4 in each ROI (60 ROIs in total) + Percentage of cells (AQP4 + %) follows the linear relationship:

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[0113] As shown in Figure 11, the k obtained by water-exchange DCE-MRI io The maps can accurately demonstrate the heterogeneity of AQP4 in vivo. a) T2-weighted TSE image of a glioma tumor xenograft. The red and blue rectangles indicate the FOV in b and d, respectively. b) K-weighted TSE image overlaid on the T2-weighted TSE image. trans Fig. 1c shows that the Monte Carlo simulations show that optimization of MRI parameters (FA = 20 and TR = 100 ms) ensures water-exchange DCE-MRI within the physiological range ([0 s-1, 10 s-1]). The dotted line indicates the input kio (ground truth). The points and error bars represent the estimated k io , n = (100, 100) represents the mean and standard deviation. d represents the k overlaid on the T2-weighted TSE image. io (d, e) shows the IHC results of the same slice d, overlaid on a T2-weighted image (left) and not (right). Here, AQP4 and cell nuclei are marked with different colors. For each animal, six contour lines are used to divide the tumor slice into six concentric ring-shaped ROIs. (f) shows the enlarged IHC from the tumor ring pixels (top of f) and tumor core pixels (bottom of f), the positions of which are shown in (d, e). From left to right, they are the combined image, cell nuclei, and AQP4. (g) shows the ROI average k for 60 ROIs. io The linear correlation observed between the mean k and AQP4-positive fraction (6 ROIs per animal, n = 10; data from the same animal are shown with the same symbols). h is the linear correlation observed between the mean k across the tumor and the AQP4-positive fraction (6 ROIs per animal, n = 10; data from the same animal are shown with the same symbols). io and AQP4-positive rate (n = 10, each animal uses the same symbol as in g). In g and h, the solid line reflects the linear regression, and the area between the two dashed lines reflects the 95% confidence interval. i represents the k of each animal. io Proportion and AQP4 + Statistics of correlation coefficients in intratumor correlation analysis between the ratio and the tumor size (n = 10, black points). Bar height and error bar width represent the mean and standard deviation, respectively. ****p < 0.0001, two-tailed t-test of Z-transformed correlation coefficients.

[0114] Mean k across tumors per animal io and AQP4 expression, when analyzed at the intertumor level, k io This linear relationship between α and AQP4 expression still exists (h in Figure 11) and k in Figure 12 io When combined with , the intratumoral AQP4 expression level of each rat glioma model can be accurately displayed (good correlation), which is io We demonstrate that this method can accurately measure intra- and intertumor AQP4 heterogeneity.

[0115] As shown in Figure 2, k io can accurately represent the intratumoral AQP4 expression level of each rat glioma model, and a~j are the k values ​​in each experimental animal. io Value and AQP4 + A significant linear correlation was found between the percentage of AQP4 and the mean k of the six regions for each rat. Here, considering the ring-like distribution of AQP4, a series of concentric elliptical ROIs were used to divide each tumor slice (including tissue slices and MRI scan sections) into six concentric ring-like regions. io values ​​and their corresponding six AQP4 + Correlation analysis is performed using percentages. For k, the mean k across tumors in the TGN020-specific inhibition control group (Figure 12) was io showed no significant change between the two days of saline injection. Paired t-test, ns not significant, p=0.9588. Bar height and error bar width represent the mean and standard deviation of the mean, respectively. n=4.

[0116] We also constructed an orthotopic glioma model by implanting C6 cell lines into the right caudate putamen of SD rats and acquired water exchange DCE-MRI data. Because the tumor size of the orthotopic glioma model (Fig. 13a and b) is much smaller than that of the subcutaneous glioma model, and the spatial resolution of MRI is limited, we performed a water exchange DCE-MRI study. io The correlation analysis between α and AQP4 expression is performed only among rats with orthotopic gliomas. As expected, the mean k across the tumors io A linear correlation was observed between the AQP4 expression level and the AQP4 control (R = 0.92, p < 0.01, n = 7, Fig. 13c). io 13.0 seconds respectively -1 and 1.3 seconds -1 These results demonstrate the potential of AQP4 expression as an imaging biomarker in gliomas. io This further demonstrates the robustness of

[0117] As shown in Figure 13, in a rat orthotopic model of C6 glioma, kio A linear correlation between α and AQP4 expression was observed, and a, b indicates a low k io (a) and high k io (b) k of AQP4 from two animals io Examples of profiles and typical IHC results. From top to bottom, a contrast-enhanced T1-weighted image (tumor location indicated by a white dotted circle) and a k-weighted image overlaid on the T1-weighted image. io Typical IHC results for AQP4 at the location of the map and white arrow. MRI scale: 2 mm, IHC scale: 25 μm. c) Mean k in the whole tumor in seven rats with orthotopic glioma. io Proportion and AQP4 + A linear correlation is observed between the rate and the solid line reflects the linear regression analysis, and the two dashed lines represent the 95% confidence interval. n=7.

[0118] Specific pharmacological inhibition of AQP4 inhibits kappa glioma in a rat glioma model io Decreased To further verify that water-exchange DCE-MRI is a highly sensitive imaging method related to AQP4 expression in vivo, TGN020 was further used to specifically inhibit AQP4 function in a rat subcutaneous glioma model. As shown in Figure 14, TGN020 (3 mg / kg, dissolved in saline) was injected via the tail vein of each animal (n=9) 15 minutes before water-exchange DCE-MRI scanning. The injection time of TGN020 was selected so that TGN020 achieved and maintained a stable collection level during water-exchange DCE-MRI acquisition. As a control group, water-exchange DCE-MRI measurements were performed on the same animals using the same volume of saline instead of TGN020, compared with the day before TGN020 treatment. The results showed that TGN020-treated glioma tumors showed a significant improvement in kJ / kg. io shows a significant decrease in the value of k io As shown in the map (Fig. 14b), the average k io is 6.3±0.6 seconds -1 (Saline injection group) to 0.5 ± 0.7 seconds -1(TGN020-injected group) (43%, n=9, p=0.0246, c in FIG. 14). Note that TGN020 also significantly reduced the mean K trans 0.060±0.015 minutes -1 (Saline) to 0.045±0.024 minutes -1 (TGN020), most tumor voxels still passed the error analysis and showed reliable k io Since measurements can be generated, this means that k io In the control group, each animal (n=4) was treated with saline on days 1 and 2, but there was no significant effect on the k values ​​on these two days (see example in Figure 14b). io No change is shown (k in Figure 12). The dose of TGN020 used here is relatively low due to safety concerns, but since TGN020 only partially inhibits AQP4, k io However, the present qualitative results suggest that AQP4 expression levels in gliomas may be a useful biomarker for the treatment of gliomas with gliomas. io The sensitivity and specificity of the test can be fully demonstrated.

[0119] Figure 14 shows the kDa of specific pharmacological inhibition of AQP4 in a rat subcutaneous glioma model. io a) shows the effect of AQP4-specific inhibitor (TGN020) on k in vivo. io In the TGN020 group, the same animals are injected with saline on day 1 and with TGN020 on day 2. In the control group, animals are injected with saline on both days. b) K registered to T2-weighted TSE images. io The map shows the effect of AQP4 inhibition on glioma (C6) in the TGN020 group. Scale: 2 mm. c shows the mean k across the tumor after AQP4 inhibition with TGN020. iodecreases by 43% (from 6.3 ± 0.6 s-1 to 3.5 ± 0.7 s-1). Two-tailed paired t-test, *p<0.05, where p=0.0246. Data points cover the corresponding boxes, bar heights and error bar widths represent the mean and standard deviation, respectively, n=9.

[0120] Human glioma k io By guided stereotactic biopsy, io Further verify the linear correlation between and AQP4 expression Water-exchange DCE-MRI was performed using a bolus injection of a clinically approved contrast agent (Gd-DTPA, 0.1 mmol / kg body weight) to visualize the kinematics of human gliomas. io The present invention discloses intratumoral heterogeneity of AQP4. Instead of AQP4 spatial distribution, k is used to guide stereotactic biopsy. io The aim of this study is to use a frameless neuronavigation technique. It is known that intraoperative brain transduction can affect the sampling accuracy of frameless neuronavigation techniques. This may not affect the spatial accuracy of radiopathological correlation. The present invention avoids the influence of brain metastases on the time from preoperative MRI to tumor sampling by frameless stereotactic biopsy techniques. Therefore, k io Quantitative analysis of images and biopsy specimens is expected to provide reliable assessments that allow accurate interpretation of radiological-histopathological correlation results at the voxel level.

[0121] This observational study was approved by the Institutional Review Board (IRB) of Shandong Provincial Hospital Affiliated to Shandong First Medical University, and written informed consent was obtained from each subject. Between May 2019 and August 2021, 21 patients with suspected glioma (both histological and molecular diagnoses confirmed by biopsy) were recruited according to the IRB's inclusion criteria using a Leksell® Model G stereotactic frame system (Elekta AB, Stockholm, Sweden). Prior to biopsy, water-exchange DCE-MRI was performed. io The map is obtained (c in Figure 15). Next, k ioThe values ​​are integrated into the structural images used to guide stereotactic biopsy, and each patient has a different k io Multiple ROIs with values ​​are collected (Figure 15a). After excluding two patients with insufficient image registration, the final analysis uses 45 biopsies from 19 patients (6 WHO I-II and 13 WHO III-IV, 10 of which had recurrent gliomas).

[0122] Typical K in the same section of the patient trans and k io The parameter diagrams are shown in Figure 15b and c, where K trans and k io The d in Figure 15 shows the results of AQP4-IHC of a typical biopsy sample, while k shows the spatial distribution of the 2D and 3D regions, representing different pathophysiological conditions. io =10.0 seconds -1 The sample of k io <0.5 seconds -1 The results of in situ ultrastructural detection using a combination of IHC and transmission electron microscopy (TEM)-based immunoelectron microscopy (IEM) with an AQP4 antibody also showed higher AQP4 expression than the samples from the control group. io The results show a high density of AQP4-positive gold nano-labeled spots in the biopsy samples with high k (Fig. 15e), which supports the conclusions of this paper at the subcellular level. The AQP4+ fraction in each biopsy was further quantified, and then summed across all 45 biopsy samples. io Cellular percentage and AQP4 + A significant linear correlation was found between the cell ratio and the cellular ratio (R=0.97, p<0.0001) (FIG. 15 f). The linear regression equation is as follows:

number

[0123] This is due to the AQP4-related io (14.9 seconds -1 ) are non-AQP4-related k io (i.e., k io Baseline, 0.2 seconds -1), suggesting that kio is primarily regulated by the AQP4-regulated pathway in human gliomas. io This linear relationship between α and AQP4 expression persists across multiple biopsy samples taken from the same patient, as shown in Figure 15f, where data from each patient are marked with different symbols.

[0124] As shown in Figure 15, the k obtained from water-exchange DCE-MRI io The map discloses the intratumoral AQP4 distribution of human gliomas, a, k io k in human gliomas using guided stereotactic biopsy io The purpose of this study was to verify the relationship between AQP4 and glioma. io Multiple biopsies with varying signal intensity are acquired. Shown here is a schematic map of two points acquired using the same needle (overlaid on frame 70 of water-exchange DCE-MRI). io (b, c) are photographs of the K of the same patient shown in a. trans Maps b and k io Map c is registered to the water-exchange DCE-MRI image (frame 70) (a, b, c, staff: 20 mm). (d, e) are high (left) and low (right) k, respectively. io Typical examples of AQP4 IHC (d, 100 μm scale) and IEM (e, 0.1 μm scale) from stereotactic biopsy. In IHC, AQP4 is marked brown, and cell nuclei are marked blue. In IEM, AQP4 is marked with gold particles (black arrows). The red arrows indicate glioma microfibrils. These two results demonstrate that k io Tissues with higher AQP4 expression have higher AQP4 expression, and vice versa. io Cellular percentage and AQP4 + A linear correlation between the cellularity and the cellularity was observed in 45 stereotactic biopsy sites from 19 glioma patients. Data for each patient are marked with a different symbol. The solid line reflects the linear regression analysis, and the two dashed lines represent the 95% confidence interval.

[0125] Figure 16 also shows the special case of a patient with recurrent glioblastoma who underwent multi-needle radiofrequency ablation due to the large tumor area. In this particular case, the physician safely collects 10 biopsy samples along the planned trajectory. The present invention also demonstrates that the 10 biopsy samples are k io We observed a good linear correlation between k and AQP4 (R = 0.96), which indicates that k is a reliable predictor of AQP4 in single case applications. io We further prove the accuracy of . Note that when all biopsy data for each patient are averaged, the average k of the biopsy samples is io It can be seen that a good correlation is maintained between the mean AQP4 and the mean AQP4 (R=0.98, d in FIG. 16).

[0126] Figure 16 shows the average statistical data for a special case of a glioma patient. This is a patient with recurrent glioblastoma who underwent several biopsy surgeries due to the large tumor area. In this particular case, 10 biopsy samples are safely collected along the planned trajectory of the biopsy needle. a is k io a) Map and examples of locations where biopsy samples were taken (white arrows). b) Examples of AQP4 IHC results for the three biopsy sites shown in a). Scale: 25 μm. c) 10 stereotactic biopsy sites from this patient. io Cell fractionation and AQP4 + A linear correlation between the cell percentage and the mean k io and AQP4 + The linear correlation between the cell percentages does not change across 19 data points for 19 patients. Here, for patients with multiple biopsies, the average result from all biopsies for that patient is used as the final representative biopsy result for that patient. The solid line reflects the linear regression analysis, and the two dashed lines represent the 95% confidence interval.

[0127] In summary, the above experiments demonstrated the efficacy of glioma treatment in vitro using cultured cells, in vivo using two rat models of glioma, and in clinical cases of human glioma. io We demonstrate that IL-16 is a powerful and sensitive imaging biomarker of AQP4 in human glioma.

[0128] low k io reflects glioma treatment resistance Tumor recurrence is common after radiochemotherapy for glioma. io These results suggest that low-AQP4 (i.e., low-AQP4) cells may represent a treatment-resistant cell subtype in glioma. A closer look at the image texture of C6 cell nuclei after TMZ treatment reveals the following: While some cells exhibit nuclear damage after TMZ treatment, others exhibit essentially intact and uniform nuclear structures, indicating that this cell subtype is resistant to the nuclear-damaging anticancer drug TMZ (Figure 17(a)). The AQP4 expression of these TMZ chemotherapy-resistant cells was significantly lower than that of cells sensitive to TMZ treatment (Figure 17(c)). Meanwhile, an increase in the number of quiescent, slowly proliferating cells (SCCs) was also observed during TMZ treatment, indicating that these cells are more resistant to TMZ than fast-proliferating cells (FCCs) (Figure 17(b)). Here, SCCs were marked by the cell fluorescence tracking method described above (see Methods), and positive cells (OG) were identified when SCCs were marked with CellTrace reagents (Oregon Green (OG) for the C6 cell line, CellTrace Violet (CTV) for primary human glioma cells). + and CTV +The reason for using TMZ is that SCCs do not divide or proliferate and retain more OG and CTV fluorescent dyes, resulting in brighter images under the microscope. On days 3 and 7 of TMZ treatment, the SCC rate increased from 6.0 (±0.4)% before TMZ treatment to 16.0 (±0.1)% (p<0.0001) and 28.9 (±0.3)%, respectively (Figure 17(d) and Figure 9(f)). To further demonstrate the close relationship between AQP4 expression and the cell cycle, we used EdU (5-ethynyl-2'-deoxyuridine) to rapidly label newly synthesized DNA and mark proliferative C6 cells on day 7 of TMZ chemotherapy, while double-staining for AQP4. Corresponding flow cytometry results showed that AQP4 expression in SCC (low EdU) cells was lower than that in FCC cells (Figure 17(e)), and vice versa. In the cytomorphological image of C6 cells on day 7 after TMZ treatment, several new long tail structures or "pseudopodia" (red arrows) (Fig. 17b) were found, and these features are often considered to be stem cell-like properties of treatment resistance. io (i.e., AQP4) tend to be associated with more SCC subtypes, slow proliferation rates, and a treatment-resistant phenotype (Fig. 17 e, f).

[0129] More importantly, the treatment-resistant status of clinical glioma biopsy samples was further characterized using the biomarker ZEB1 (zinc finger enhancer-binding protein 1, a transcription factor that regulates DNA damage) (the immunohistochemical and quantitative methods are the same as those for AQP4), and ZEB1 was used as a marker of treatment resistance in glioma. We investigated the low kappa expression of gliomas from recurrent gliomas. io Biopsy samples were collected from the same subject with high k io We found that the ZEB1 expression was higher in the biopsy sample than in the control sample (f in Figure 17), and that the low k io This conclusion is further supported by flow cytometry results (Figure 17g) of primary cells from biopsy samples (see Methods), which showed that low k ioCells from the sample were analyzed using high-k io A higher proportion of cells from the sample exhibit the SCC (or CTV) phenotype.

[0130] As shown in Figure 17, low-k io (AQP4) reflects the phenotype of therapy-resistant glioma. (a) Confocal microscopy images of AQP4 (red) and DAPI (blue) in C6 cells on day 3 after TMZ treatment. Some of these cells have damaged nuclei (long-tailed arrows), while others exhibit TMZ resistance and have more intact nuclear structures (short-tailed dashed arrows). From left to right, the scales are 25 μm and 5 μm, respectively. (b) Cell tracking results for OG-marked cells. Slow-cycling cells (SCCs) show high signal intensity in the fluorescent image (long-tailed arrows). (c) Quantitative results of AQP4 expression in therapy-sensitive and therapy-resistant C6 cells on day 3 of TMZ treatment. Data are shown as mean ± standard deviation. *p<0.05, where p=0.0465, n=(4, 8). d, Bar graph showing the increase in the proportion of SCC (OG+) after TMZ treatment; ****p<0.0001, n=(7, 3, 3). e, Flow cytometry is used to characterize the AQP4 and EdU expression results on day 7 of TMZ treatment. The two-dimensional scatter plot shows two distinct cell phenotypes marked as FCC and SCC. Slowly proliferating glioma cells (SCC, i.e., low EdU) show lower AQP4 expression (red) than fast-proliferating cells (FCC). f, ZEB1 results obtained from stereotactic biopsy samples from patients with recurrent glioma, showing low k io Sample (right k io <0.5 seconds -1 ) is a high-k io Sample(k io >0.5 seconds -1 ) shows a higher ZEB1 expression level than that of the control. Scale: 50 μm. As can be seen from the histogram statistics, k io For biopsy sites <0.5 sec-1 (n=5), k ioMore abundant ZEB1 expression (ZEB1+ fraction) is shown compared to >0.5 s-1 (n=5). Data for cell nuclei (blue) and ZEB1 (brown) are shown as mean ± SE. *pM 0.05, where p<0.0260. In c, d, and f, data points (blue point plots) cover the corresponding boxes (i.e., two-tailed unpaired t-test). g, CTV flow sorting results for primary cells from a clinical glioma biopsy sample obtained by stereotaxy, showing low k io Primary cells obtained from the sample (right) were cultured in high-k io The samples show a much higher percentage of SCC (CTV+) than primary cells (left). In e and g, the black lines represent the gating selection for flow cytometry.

[0131] TMZ inhibits proliferation by inducing DNA double-strand breaks in glioma cells, killing FCC subtypes while sparing SCC subtypes. TMZ treatment of the C6 cell line exhibits similar phenomena, including decreased proliferation and an increased SCC rate. Furthermore, cell subtypes with low AQP4 expression levels and long tail or pseudopodia morphology at day 7 of TMZ treatment are often considered to represent treatment-resistant stem cell-like cells (GSCs) in GBM. Similarly, the U87MG cell line exhibits decreased proliferation and downregulation of AQP4 during quiescence and death upon nutrient and oxygen deprivation. In these two microenvironments, the k obtained from water-exchange DCE-MRI was io Dynamic regulation of AQP4 expression is closely related to TMZ treatment. A proportional increase in the number of SCCs with low AQP4 expression levels was observed in TMZ-treated (C6) and quiescent, reduced-proliferation (U87MG) SCCs. This phenomenon is reasonable because SCCs are a low-proliferation cell subtype and AQP4 expression levels are related to cell proliferation. More importantly, low AQP4 expression may slow the rate of transport across the cell membrane, protecting SCCs from TMZ and other treatments. Indeed, the C6 cell line, which has low AQP4 expression, was observed to show little or no damage to the cell nucleus under TMZ treatment, indicating its TMZ-resistant state. Furthermore, low-kAQP4 expression from human gliomas was observed in TMZ-treated and TMZ-resistant SCCs. io(i.e., low AQP4) biopsy samples also show high expression of the treatment resistance biomarker ZEB1 and a high rate of SCC. Collectively, these results suggest that kappa B as a potential method for imaging SCC and predicting treatment response. io Prove that.

[0132] The spatial and temporal heterogeneity of AQP4 expression level profiles in gliomas has great potential to facilitate precision therapy and predict therapeutic response. AQP4 expression correlates with the degree of treatment resistance, and therefore, k io The heterogeneity of AQP4 expression may indicate differences in treatment resistance across the glioma region. This AQP4 expression profile has great potential in assessing the pathological status of recurrent gliomas, further influencing therapeutic strategies and leading to many alternative methods for the treatment of recurrent gliomas. For example, if recurrent gliomas express high levels of kappa io If the tumor shows high AQP4 expression levels, secondary radiotherapy and chemotherapy are recommended, as high levels of AQP4 expression indicate that the tumor cells are sensitive to damage caused by radiotherapy and chemotherapy. io If measured, surgical intervention is recommended as these tumor cells are resistant to treatment with radiotherapy or chemotherapy.

[0133] The present invention uses the water-exchange DCE-MRI efflux rate constant k of intracellular molecules across the cell membrane as a highly sensitive MRI biomarker of AQP4 expression level. ioBy specifically and quantitatively measuring AQP4, we can accurately measure AQP4 heterogeneity within and between glioma tumors. Previous studies have shown that ADC on DWI also demonstrates sensitivity to RNA-mediated AQP4 silencing, but subsequent results have cast doubt on this finding. While water exchange can contribute to ADC, the use of ADC lacks the specificity of detecting transmembrane water exchange in vivo, raising further questions because many biophysical mechanisms could potentially cause changes in ADC, including cell swelling or shrinkage, changes in cell density and shape, and gap geometry. Specifically, ADC has long been shown to primarily reflect cell density in human gliomas, further hindering its use as a specific biomarker of water exchange in gliomas. In clinical practice, by adding a water-exchange DCE-MRI sequence during contrast agent (CA) injection, water-exchange DCE-MRI can be used in parallel with conventional MRI scans for glioma diagnosis without any additional financial or time costs. Conventional MRI can also be used to identify glioma tumors from normal or other tissues, and then water-exchange DCE-MRI can be used to further analyze intratumoral AQP4. Because individual differences may still be observed in rats ( Figure 12 ) and human gliomas, the water-exchange DCE-MRI scanning protocol used in this invention may need to be further optimized to achieve k io The accuracy of estimation and AQP4 detection can be improved (f in Figure 15). Areas that need improvement include, but are not limited to, optimization of MRI sequences, CA injection, and SS model analysis. Aquaporin 4 (AQP4) plays a key role in determining the fate of gliomas, including tumor migration, proliferation, and therapeutic resistance. While tissue biopsies can quantitatively characterize AQP4 expression levels in vivo, they cannot provide information about the heterogeneous distribution of AQP4 throughout the tumor. The present invention provides a non-invasive magnetic resonance biomarker (MRI) for detecting and mapping AQP4 in gliomas in vivo as a biomarker sensitive to AQP4 expression levels. ioAQP4 is visualized by MRI by quantitatively measuring AQP4-regulated water exchange across the cell membrane. AQP4 is the primary pathway regulating water exchange across the cell membrane, and k io We demonstrate for the first time that water-exchange DCE-MRI is a highly sensitive biomarker of AQP4 expression in glioma. We then demonstrate that water-exchange DCE-MRI accurately detects dynamic changes in AQP4 expression and function during various stages of glioma growth, temozolomide (TMZ) treatment, gene knockout, and AQP4 inhibition by TGN020, and captures the spatial heterogeneity of AQP4 expression within rat glioma models and human gliomas. io "The cells showed greater resistance to therapy, suggesting the potential value of AQP4 profiling in assessing therapy resistance in gliomas. More importantly, this method allows for easy radiological diagnosis and evaluation of gliomas on whole-tumor MRI, and this technology may significantly improve the accurate evaluation and treatment of human gliomas."

[0134] The above specific embodiments have described in detail the technical solutions and advantageous effects of the present invention, and it should be understood that the above content is merely the most preferred embodiment of the present invention and does not limit the present invention, and any modifications, additions, equivalent replacements, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. 1. A system for measuring a glioma magnetic resonance imaging marker, the system comprising: Dynamic contrast-enhanced magnetic resonance imaging was used to measure the steady-state flow rate of water molecules across the cell membrane, k, which is the steady-state flow rate of tissue water molecules from cells to the interstitium. ｉｏ an image extraction and processing module for quantitatively measuring k obtained by the image extraction and processing module ｉｏ and a post-processing module for stereotactically taking biopsy tissue using the biopsy planning system and establishing a linear relationship between the AQP4 expression level of the tissue and the AQP4 expression level of the quantified biopsy tissue. The tissue to be measured is subjected to an image extraction and processing module. ｉｏ and a prediction module for predicting the AQP4 expression level of the tissue to be measured based on the linear relationship of the post-processing module.

2. The measurement system includes: a pre-processing module for setting dynamic contrast-enhanced magnetic resonance imaging parameters, measuring a noise level during dynamic contrast-enhanced magnetic resonance scanning, and optimizing a flip angle in the dynamic contrast-enhanced magnetic resonance imaging DCE-MRI acquisition parameters through Monte Carlo simulation to reset the flip angle for dynamic contrast-enhanced magnetic resonance imaging; an image extraction module for scanning quantitative T1 magnetic resonance imaging and for scanning dynamic contrast-enhanced magnetic resonance imaging; Full shutter speed model SSM ｆｕｌｌ Pixel analysis was performed for each pixel point within the tumor region using the steady-state flow rate coefficient k of water molecules from the cell to the interstitium at each pixel point. ｉｏ an image processing module for obtaining the k obtained by the image processing module ｉｏ a post-processing module for establishing a linear relationship between the AQP4 expression level of the biopsied tissue and the α-amino acid sequence; The tissue to be measured is subjected to an image extraction module and an image processing module. ｉｏ and a prediction module for predicting the AQP4 expression level of the tissue to be measured based on the linear relationship of the post-processing module; The full shutter speed model (SSM) divides water molecules into three compartments: blood vessels (b), interstitium (o), and intracellular space (i), and divides them into two water exchange processes: water exchange between blood and interstitium and interstitial water exchange between cells.

2. The measurement system for glioma magnetic resonance imaging markers of claim 1, wherein the full shutter speed model SSM full is composed of five independent physiologically relevant parameters, including the molar fraction of water in blood vessels (p b ), the molar fraction of water in interstitial space (p o ), the steady-state flow rate of water molecules from blood vessels to the interstitium (k bo ), the steady-state flow rate of water molecules from intracellular space to interstitial space (k io ), and the vascular leakage rate of contrast agent in the tumor region (K trans ), wherein the vascular leakage rate of contrast agent in the tumor region (K trans ) is obtained by multiplying the contrast agent (CA) extravasation rate constant (K pe ) and the plasma volume fraction (v p ) (K trans = K pe * v p ).

3. A method for measuring the flux rate of water molecules across a cell membrane for non-disease diagnostic purposes as a magnetic resonance imaging marker for glioma to evaluate the expression level of AQP4, wherein the measurement method is carried out using the measurement system for glioma magnetic resonance imaging markers according to claim 1, (1) The image extraction and processing module of the measurement system uses dynamic contrast-enhanced magnetic resonance imaging to obtain the steady-state flow rate of water molecules across the cell membrane, k, which is the steady-state flow rate of water molecules in the tissue from the cells to the interstitium. ｉｏ Quantitatively measuring the (2) a post-processing module of the measurement system uses a biopsy planning system to acquire an AQP4 immunohistochemistry photograph of the biopsy tissue and quantify the AQP4 expression level in the tissue; (3) a post-processing module of the measurement system ｉｏ and establishing a linear relationship between the two based on the expression levels of AQP4. (4) The image extraction and processing module of the measurement system uses dynamic contrast-enhanced magnetic resonance imaging to measure the steady-state water molecule flux rate k across the cell membrane of the tissue water molecules being measured. ｉｏ and a step in which a prediction module of the measurement system obtains the AQP4 expression level of the tissue to be measured based on the linear relationship of step (3).

4. The measurement method is carried out using the glioma magnetic resonance imaging marker measurement system according to claim 2, (1) setting dynamic contrast-enhanced magnetic resonance imaging parameters by the pre-processing module to measure a noise level during dynamic contrast-enhanced magnetic resonance scanning; (2) optimizing a flip angle in the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquisition parameters through Monte Carlo simulation by the pre-processing module, and resetting the flip angle for dynamic contrast-enhanced magnetic resonance imaging; (3) scanning quantitative T1 magnetic resonance imaging by the image extraction module; (4) scanning dynamic contrast-enhanced magnetic resonance imaging by the image extraction module; (5) The image processing module performs full shutter speed model SSM. ｆｕｌｌ Pixel analysis was performed for each pixel point within the tumor region using the steady-state flow rate coefficient k of water molecules from the cell to the interstitium at each pixel point. ｉｏ obtaining a (6) using a biopsy planning system based on dynamic contrast-enhanced magnetic resonance imaging to obtain AQP4 immunohistochemistry photographs of the biopsy tissue and quantify the AQP4 expression level of the tissue by the post-processing module; (7) The post-processing module ｉｏ A linear regression analysis was performed on the AQP4 expression level and k ｉｏ obtaining a linear equation for (8) Repeat steps (3) to (5) for the tissue to be measured, and use the prediction module to calculate k according to the linear equation of step (7). ｉｏ and converting the image into an AQP4 expression level image to achieve intratumoral AQP4 expression imaging.

5. In step (2), the measured vascular contrast agent concentration C ｐ Then, by simulating the situation of real tissues and scanning parameters, DCE-MRI data are synthesized under different flip angles, and the time series signal of the synthesized DCE-MRI data is obtained. is a full shutter speed model SSM ｆｕｌｌ and based on the noise level estimated by the DCE-MRI experiment, Add white noise of the same noise level to the full shutter speed model SSM ｆｕｌｌ Noise was added using A nonlinear least-squares fitting is performed on the k ｉｏ Finally, k ｉｏ The flip angle for which the fitting result is closest to the predetermined value of the simulation and the variance is the smallest is the optimal flip angle. In step (2), additional quantitative measurements of the spatial distribution of the actual flip angles are performed under ultra-high magnetic fields to optimize the flip angles; 5. The method for measuring the flux rate of water molecules across cell membranes for non-disease diagnostic purposes as a magnetic resonance imaging marker for assessing AQP4 expression levels in gliomas according to claim 4, characterized in that in step (3), quantitative T1 magnetic resonance imaging uses a short, repeated time series of multiple flip angles.

6. In step (5), the automatic shutter speed analysis method using the full shutter speed model SSM full is used to calculate the vascular leakage rate K of the contrast agent in the tumor region. ｔｒａｎｓ and furthermore, SSM ｆｕｌｌ Model fitting ｔｒａｎｓ >0.01 minutes －１ The steady-state water molecule flux coefficient k from the cell to the interstitium at each pixel point is calculated only in the tumor region. ｉｏ 5. A method for measuring the flux rate of water molecules across a cell membrane for non-disease diagnostic purposes as a magnetic resonance imaging marker for glioma to evaluate the AQP4 expression level according to claim 4, characterized by obtaining:

7. In step (7), the AQP4 expression level and k ｉｏ The linear relationship between the AQP4 cell positivity rate and the ｉｏ -A) / B, In the formula, A is 0.1 to 0.2 seconds －１ , B is 13.07 to 15.04 seconds －１ 5. A method for measuring the flux rate of water molecules across a cell membrane for non-disease diagnostic purposes as a magnetic resonance imaging marker for evaluating AQP4 expression level according to claim 4, characterized in that:

8. A method for evaluating AQP4 expression levels by quantitative imaging of AQP4 expression using the measurement method of claim 3.

9. The linear relationship between the water molecule flux rate kio across the cell membrane for non-disease diagnostic purposes as a magnetic resonance imaging marker of glioma to evaluate the AQP4 expression level and the AQP4 expression level is expressed as AQP4 cell positivity rate = (k ｉｏ -A) / B, In the formula, A is 0.1 to 0.2 seconds －１ , B is 13.07 to 15.04 seconds －１ The method according to claim 8, wherein

10. A method for preparing a product for predicting the sensitivity of glioma to radiation therapy and chemotherapy using the measurement method described in claim 3, wherein the efflux rate of water molecules across the cell membrane is used as a magnetic resonance imaging marker for glioma to prepare a product for predicting the sensitivity of glioma to radiation therapy and chemotherapy.

11. 11. The method according to claim 10, wherein the drug used in radiotherapy and chemotherapy is temozolomide.

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