Method for non-invasive evaluation of age-dependent metabolic characteristics of mouse oocytes

Abstract

The invention relates to a method for noninvasively evaluating age-dependent metabolic characteristics of mouse oocytes. The method comprises the following steps: S1, collecting oocytes of mice at different month ages; s2, treating the oocytes to obtain a sample capable of being used for spontaneous Raman spectroscopy and imaging; s3, performing spontaneous Raman spectroscopy and imaging of different oocytes; s4, processing and analyzing Raman spectrum data; s5, processing and analyzing two-dimensional and three-dimensional Raman imaging; s6, analyzing morphological and spectral characteristics of oocytes of mice of different age groups; and S7: determining the specific age-related change of the lipid content and distribution, the cytochrome c level and the metabolic profile in the mouse oocytes. Compared with the prior art, the method has the advantages that the metabolic change of the oocytes of mice with different maternal ages is analyzed by Raman spectrum for the first time, the age-dependent distribution change of lipid and cytochrome c is revealed through high-resolution subcellular mapping, and a new research scheme is provided for the biochemical mechanism of oocyte senescence.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology, and in particular relates to a method for non-invasively evaluating age-dependent metabolic characteristics of mouse oocytes. Background Art

[0002] Oocyte quality is a key factor affecting female fertility, embryonic development, and reproductive success. There is ample evidence that advanced age is a key risk factor for poor oocyte quality and low oocyte quantity. Studies have shown that advanced age is a major risk factor for decreased oocyte quality and quantity, and its mechanisms involve mitochondrial dysfunction and metabolic disorders, leading to reduced fertility, increased risk of aneuploidy, and worsened embryonic developmental outcomes. Traditional assessment methods (such as mass spectrometry and fluorescence microscopy) are often invasive, requiring complex sample processing or the use of dyes that may interfere with cell integrity. Therefore, there is an urgent need to develop non-destructive, label-free oocyte quality analysis technologies.

[0003] Although there are some studies on the relationship between oocyte morphology and embryo quality, traditional oocyte morphology assessment cannot yet freely provide predictors of oocyte quality.Even with some new microscopy tools such as fluorescence lifetime imaging microscopy (FLIM), optical coherence microscopy (OCM) and enhanced polarization microscopy, a comprehensive metabolic overview of oocyte age-related changes is still incompletely understood.

[0004] Raman spectroscopy, which generates molecular fingerprints of cells by analyzing biomolecular vibrations, has become a transformative tool in biological research. Unlike conventional methods, Raman spectroscopy enables high-resolution analysis without the need for chemical labeling or complex sample processing, making it particularly suitable for fragile systems such as oocytes. Advances in Raman imaging have further enhanced its ability to visualize subcellular metabolites. Recent advances in spectroscopy technology offer new opportunities for non-invasively studying the molecular composition of single cells.

[0005] Single-cell Raman spectroscopy provides a "molecular fingerprint" of a cell, providing information about the presence and relative abundance of the entire collection of biomolecules in that cell, including proteins, lipids, and nucleic acids, as a snapshot of the overall metabolic state. Through advanced Raman spectroscopic microscopy, the technique offers high spatial resolution, allowing detailed mapping of metabolic composition at the subcellular level. Raman spectroscopy has shown promise for assessing oocyte or embryo quality through single-cell assessment in a variety of models, including fish, mice, cattle, and humans. However, its application in studying the effects of age-related changes in oocyte quality remains limited.

[0006] Metaphase II (MII) is a critical stage in mouse oocyte maturation. After completing meiosis I (MI) and undergoing significant chromatin condensation and microtubule organization, oocytes become arrested while awaiting fertilization. This arrest at MII ensures that oocytes are ready for the final steps of fertilization and subsequent embryonic development.

[0007] The quality of oocytes used determines the effective application of assisted reproductive technologies. However, currently available methods for oocyte assessment are primarily qualitative and suffer from a lack of standardization.

[0008] Although Raman spectroscopy has been used to assess oocyte / embryo quality and culture medium composition, its application in studying age-related changes in oocyte quality has not been reported.

[0009] In summary, finding a non-invasive method to assess the age-dependent metabolic characteristics of mouse oocytes is urgent. Summary of the Invention

[0010] Currently, the age-dependent metabolic characteristics of mouse oocytes are assessed by invasive methods. There is currently a lack of solutions for using Raman spectroscopy technology to conduct non-invasive research on age-related changes in oocyte quality. This application provides a method for non-invasively assessing the age-dependent metabolic characteristics of mouse oocytes.

[0011] This application uses Raman spectroscopy and high-resolution imaging to non-invasively evaluate the age-dependent metabolic characteristics of mouse metaphase II (MII) oocytes of young (2 months old), normal reproductive age (4 months old) and advanced reproductive age (10 months old) mice, thereby laying the foundation for the non-invasive evaluation of the age-dependent metabolic characteristics of human oocytes.

[0012] To achieve the above purpose, the specific technical solutions of the present invention are as follows:

[0013] The present application provides a method for non-invasively evaluating age-dependent metabolic characteristics of mouse oocytes, comprising the following steps:

[0014] S1: Collect oocytes from mice of different ages;

[0015] S2: Process the oocytes to obtain samples that can be used for spontaneous Raman spectroscopy and imaging;

[0016] S3: Perform spontaneous Raman spectroscopy and imaging of different oocytes;

[0017] S4: Processing and analysis of Raman spectroscopy data;

[0018] S5: Processing and analysis of 2D and 3D Raman images;

[0019] S6: Morphological and spectral characteristics analysis of mouse oocytes in different age groups;

[0020] S7: Determine age-specific changes in lipid content and distribution, cytochrome c levels, and metabolic profiles in mouse oocytes.

[0021] In one embodiment of the present invention, in step S1, female C57BL / 6 mice aged 2, 4, and 10 months are injected with pregnant mare serum gonadotropin and human chorionic gonadotropin to establish superovulation model mice, and oocytes are collected.

[0022] In one embodiment of the present invention, in step S1, female mice are intraperitoneally injected with 10 IU of pregnant mare serum gonadotropin, the mice are superovulated, and 48 hours later, 10 IU of human chorionic gonadotropin is injected intraperitoneally; 14-16 hours after the hCG injection, the mice are euthanized by inhalation of carbon dioxide; the abdominal cavity is opened to expose the reproductive tract, the fallopian tubes are removed, and the cumulus ovary-oocyte complexes in the ampulla of the mouse fallopian tubes are collected under a stereomicroscope and released into M2 culture medium; in the M2 culture medium, 300 μg / mL hyaluronidase is used for treatment for 2-3 minutes to remove the cumulus cells; the exposed oocytes are washed in fresh M2 culture medium to obtain oocytes.

[0023] In one embodiment of the present invention, in step S2, after the oocyte is obtained, it is placed in a PBS saline solution. Before the machine is tested, 0.4% agarose is prepared and placed in a culture dish. Before the agarose solidifies, the oocyte obtained is sucked into the agarose liquid. After it is dried, the oocyte is wrapped therein, to be tested. Agarose can keep it from obtaining optical photographs of the oocyte and the shooting of Raman spectra under moist conditions. And because agarose is free of Raman signal interference, the oocyte is found to be shot under a light microscope to carry out Raman spectroscopy imaging.

[0024] In one embodiment of the present invention, in step S2, before detection, the oocytes are allowed to solidify in agarose at 4° C. for 10 minutes before Raman analysis is performed. This embedding process fixes the oocytes while maintaining their three-dimensional structure.

[0025] In one embodiment of the present invention, in step S3, the method for performing spontaneous Raman spectroscopy and imaging of different oocytes is as follows:

[0026] Place the prepared agarose-encapsulated oocyte sample under a 63x water objective lens. Fill the space between the water objective and the sample with water. The integration time is 5s-10s. Single spectrum data refers to the position information of a cell covered by a light spot.

[0027] The method for acquiring Raman data for oocyte 2D imaging is to select an XY axis area under the optical lens. The size of this area is based on the size of the oocyte. The single-point integration time and power, as well as the number of points or steps, are set. The number of points is the number of points in the area covered by the laser spot collected on the X axis multiplied by the number of rows collected on the Y axis when drawing a 2D image of an oocyte. The distance between the laser spot moving from the X(0,0)Y(0,0) position to the next position is called the step length, which means that a 2D Raman image of the oocyte is obtained.

[0028] Acquisition of 3D imaging data of oocytes: Acquisition of 3D data is to increase the Z-axis step size and distance settings based on the 2D settings, thereby obtaining 3D cell imaging data of oocytes.

[0029] In one embodiment of the present invention, in step S3, spontaneous Raman spectroscopy and imaging of different oocytes are performed using a Raman confocal microspectrometer, a 532 nm laser, and a 63× water immersion objective lens to collect spectra in the spectral range of 0-3600 cm -1 , integration time 0.1 s, step size 333 nm.

[0030] In one embodiment of the present invention, in step S4, the Raman spectroscopy data is processed and analyzed as follows:

[0031] Based on the original data, the image processing software WITec project five 5.2.4 was used for data preprocessing, including spectral range, cosmic ray removal, and baseline removal. The spectral data range was 400–3400 cm -1 , remove the silent noise area 1800~2700cm -1 , is one-dimensional floating point data;

[0032] The spectral data were processed by removing cosmic rays and background noise, using the analyze function in project and selecting the filter 15-20cm. -1 , obtain the peak position value picture of a single peak position, i.e., a 2D image, and attribute DNA, carbohydrates, lipids, and proteins with 786, 490, 1440, and 2928, respectively;

[0033] Image J was then used to perform 3D stacking on the peak position images to form a 3D image.

[0034] In one embodiment of the present invention, in step S4, all cell spectral data are baseline corrected by subtracting the minimum intensity and baseline correction with the rolling ball method; the spectra are then normalized to the water peak, 3300 to 3500 cm -1 To account for focus drift, the spectral data were then cropped to the wavenumber range of 400 to 3100 cm-1 .

[0035] In one embodiment of the present invention, in step S4, the average spectrum is calculated for each cell and group, and linear discriminant analysis (LDA) is used to distinguish based on the spectral data. The Raman band in the spectral data is determined using a peak detection method. The area under each identified peak is integrated to quantify the intensity of the specific spectral feature, and violin plots and box plots are used for visualization to compare the peak intensity distribution under different conditions. One-way analysis of variance and post hoc comparisons are performed using Tukey's honest significant difference (HSD) test to test the pairwise differences between groups.

[0036] In one embodiment of the present invention, in step S5, the processing and analysis method of the two-dimensional and three-dimensional Raman imaging is as follows:

[0037] Raman images were processed using WITec; version: 5.2.4 and Imaris 10.0;

[0038] First, all spectral data in the Raman image were extracted, and cosmic rays were automatically removed. The filter size was 4, the dynamic factor was 4.1, and lipids were detected at 2855 cm -1 Center, width 50cm -1 ; Unsaturated lipids: at 1655cm -1 Center, 75 cm wide -1 Protein: 2930 cm -1 Center, 50 cm wide -1 ; Cytochrome c: at 750 cm -1 Center, width 25cm -1 The color scale was set to keep the non-cellular background unchanged. The particle distribution of the lipid channel was calculated using ImageJ, and the three-dimensional imaging was visualized using Imaris Viewer.

[0039] In one embodiment of the present invention, in step S6, the morphological and spectral characteristics analysis of mouse oocytes of different age groups includes:

[0040] (1) Morphological observation of mouse oocytes of different age groups under bright-field microscope images;

[0041] (2) Analysis of different metabolic characteristics of mouse oocytes at different ages:

[0042] (3) 2D subcellular comparison of metabolic composition of young and old mouse oocytes;

[0043] (4) Comparison of 3D Raman images of metabolic components in young and old mouse oocytes.

[0044] In one embodiment of the present invention, when analyzing different metabolic characteristics of mouse oocytes of different age groups, the average Raman spectra of mouse oocytes of different age groups are used to demonstrate the presence and relative abundance of different biomolecules in the oocytes.

[0045] In one embodiment of the present invention, when performing a two-dimensional subcellular comparison of the metabolic components of young and old mouse oocytes, label-free Raman imaging technology is used to comprehensively analyze the subcellular distribution of key metabolic components in young and old mouse oocytes. Raman chemical imaging of mouse oocytes is used to display the total intensity and distribution of key metabolic components, and a histogram is used to display the relative abundance and distribution of each metabolic component, highlighting the differences between young and old oocytes, and displaying the pixel intensity distribution of lipids, unsaturated lipids, proteins, and cytochrome c in young and old mouse oocytes.

[0046] In one embodiment of the present invention, 3D Raman images of different components in mouse oocytes are used to provide the spatial organization of key metabolic components within oocytes and how they change with maternal age when comparing 3D Raman images of metabolic components of young and old mouse oocytes.

[0047] In one embodiment of the present invention, in step S7, for total lipids, the intensity decreases and the distribution becomes more uneven from 2 months to 10 months; unsaturated lipids are more significantly reduced and aggregated in 10-month oocytes; the cytochrome c signal decreases significantly from 2 months to 10 months, and 4-month oocytes show a moderate level.

[0048] Decreased oocyte quality with maternal aging poses a significant challenge to fertility and embryonic development. Traditional oocyte quality assessment methods rely on invasive techniques, limiting their application in clinical and research settings. This application proposal employs single-cell Raman spectroscopy and imaging for the first time to characterize age-related metabolic changes in metaphase II (MII) stage mouse oocytes. Raman spectroscopy provides high-resolution, label-free metabolic fingerprints, enabling detailed mapping of the spatial distribution of key biomolecules such as lipids, proteins, and cytochrome c.

[0049] This study found that lipid content, distribution patterns, and cytochrome c levels in aged oocytes undergo significant changes, consistent with mitochondrial dysfunction and impaired lipid metabolism. Three-dimensional Raman imaging further revealed these changes, highlighting the potential of this technique for visualizing subcellular metabolic dynamics. Linear discriminant analysis (LDA) successfully differentiated oocytes of different age groups based on their spectral signatures, validating the effectiveness of Raman spectroscopy as a non-invasive tool for oocyte quality assessment.

[0050] Compared with the prior art, the present invention has the following advantages:

[0051] The technical solution presented in this application demonstrates the unique ability of Raman spectroscopy to analyze the biochemical mechanisms of oocyte aging, providing a methodological framework for future applications in reproductive biology and assisted reproductive technology. As a platform technology, this solution lays the foundation for integrating Raman spectroscopy into oocyte quality assessment systems.

[0052] This application scheme uses Raman spectroscopy and high-resolution imaging for the first time to non-invasively evaluate the age-dependent metabolic characteristics of mouse metaphase II (MII) oocytes of young (2 months old), normal reproductive age (4 months old) and advanced reproductive age (10 months old) mice, thus laying the foundation for the non-invasive evaluation of the age-dependent metabolic characteristics of human oocytes.

[0053] Although there are reports in the prior art that Raman spectroscopy has been used to assess oocyte / embryo quality and culture medium composition, its application in the study of age-related changes in oocyte quality has not been reported.

[0054] This application proposal uses Raman spectroscopy for the first time to analyze metabolic changes in mouse oocytes of different maternal ages. High-resolution subcellular mapping reveals age-dependent distribution changes of lipids and cytochrome c, providing a new research plan for the biochemical mechanism of oocyte aging.

[0055] This application proposal not only introduces a new method for reproductive aging research but also demonstrates its broad potential for application in reproductive biology. By overcoming the limitations of existing technologies, Raman spectroscopy is expected to become a reliable tool for non-invasive and spatially resolved oocyte metabolic analysis, providing a foundation for the integration of assisted reproductive technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 , Schematic diagram of the workflow for morphological examination and metabolic profiling of individual oocytes using single-cell Raman spectroscopy and imaging;

[0057] Figure 2 , Morphological observation results of mouse oocytes of different age groups under bright-field microscope images, including 2-month-old mouse oocytes (upper row), 4-month-old mouse oocytes (middle row) and 10-month-old mouse oocytes (lower row);

[0058] Figure 3 , different metabolic characteristics of mouse oocytes at different ages, among which,

[0059] A is the average Raman spectrum of key biomolecules,

[0060] B is the linear discriminant analysis (LDA) graph,

[0061] C is the integrated band of key Raman spectral peaks of mouse oocytes at different ages;

[0062] The violin plots provide a detailed view of the integrated band regions of the key Raman peaks for the three age groups;

[0063] Figure 4 A two-dimensional comparison of the metabolic components of oocytes from young and old mice,

[0064] A is Raman chemical imaging,

[0065] B is a histogram (quantitative comparison of pixel intensity distribution of each metabolic component between young and old oocytes);

[0066] Figure 5 、 2 Three-dimensional Raman imaging of cellular components in mouse oocytes at 1 month (top), 4 months (middle), and 10 months (bottom).

[0067] in Figure 1-Figure 5 middle,

[0068] *, **, *** and **** represent P < 0.05, P < 0.01, P < 0.001 and P < 0.0001, respectively, indicating that the difference between the two groups was statistically significant. DETAILED DESCRIPTION

[0069] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0070] The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention. Unless otherwise specified, the technical means used in the examples are conventional means well known to those skilled in the art.

[0071] Unless otherwise specified, all materials and reagents in the following examples can be obtained from commercial sources.

[0072] The female C57BL / 6 mice used in the following examples are commonly used experimental mice and are commercially available.

[0073] The pregnant mare serum gonadotropin (PMSG) used in the following examples is a commonly used biomaterial and is commercially available. Human chorionic gonadotropin (hCG) is also a commonly used biomaterial and is commercially available.

[0074] In the following examples, the instruments used are:

[0075] The Raman confocal microspectrometer was selected from Witec confocal Raman microscope Alpha300 R.

[0076] A 63x water immersion objective lens is used to achieve imaging with different resolution requirements.

[0077] Example 1

[0078] Mouse superovulation model and oocyte collection at different ages

[0079] In this example, female C57BL / 6 mice (=9) were divided into three age groups: 2 months (=3), 4 months (=3) and 10 months (=3). All animal treatment procedures were approved by the Ethics Committee of Peking Union Medical College Hospital (XHDW-2023-169).

[0080] Mice were superovulated with an intraperitoneal injection of 10 IU of pregnant mare serum gonadotropin (PMSG), and 48 hours later, 10 IU of human chorionic gonadotropin (hCG) was injected intraperitoneally. Oocytes were collected 14–16 hours after hCG injection. Mice were euthanized by carbon dioxide inhalation. The abdominal cavity was opened to expose the reproductive tract. The oviducts were removed, and the cumulus-oocyte complexes from the ampulla of the oviduct were collected under a stereomicroscope and released into M2 medium. Cumulus cells were removed by incubation in M2 medium (containing 300 μg / mL hyaluronidase) at 37°C for 2–3 minutes. Denuded oocytes were washed three times in fresh M2 medium and then fixed in 4% paraformaldehyde (PFA) for 5 minutes at room temperature. After fixation, oocytes were washed three times in M2 medium.

[0081] Oocytes obtained from 2-month-old, 4-month-old, and 10-month-old mice are MII-stage mouse oocytes, representing the young adult age, primary reproductive age, and late reproductive age of mice, respectively.

[0082] In this example, only MII stage oocytes confirmed by bright-field microscopy were selected for analysis, and a total of 89 oocytes from 2-month-old mice, 49 oocytes from 4-month-old mice, and 13 oocytes from 10-month-old mice were collected.

[0083] Example 2

[0084] Single-cell Raman spectroscopy and high-resolution imaging reveal age-dependent metabolic signatures of mouse oocytes

[0085] This example uses single-cell Raman spectroscopy and high-resolution imaging to perform morphological examinations of individual mouse oocytes and obtain metabolic profiles. The workflow diagram is shown in the figure below. Figure 1 As shown;

[0086] Mouse oocytes collected from mice at three different age groups (2, 4, and 10 months) were subjected to bright-field morphological examination and single-cell Raman spectroscopy. High-resolution, label-free chemical imaging was used to analyze the metabolic composition of oocytes, with each pixel containing a Raman spectrum. Raman spectra from two cellular locations with different lipid content are shown.

[0087] The specific process involves collecting a single mouse oocyte. After retrieval, the oocyte is placed in a PBS saline solution. Prior to testing, 0.4% agarose is prepared and placed in a culture dish. Before the agarose solidifies, the retrieved oocyte is aspirated into the agarose liquid. Once the agarose dries, the oocyte is encapsulated within the agarose liquid and ready for testing. The agarose keeps the oocyte moist, allowing for optical imaging and Raman mapping of the oocyte. Because agarose eliminates Raman signal interference, the oocyte is located under a light microscope for Raman spectral imaging.

[0088] Prior to testing, oocytes were allowed to solidify in agarose at 4°C for 10 minutes before Raman analysis. This embedding process immobilizes the oocytes while maintaining their three-dimensional structure.

[0089] All experiments were repeated three times, and oocytes were randomly selected from each age group for analysis.

[0090] This application uses bright-field morphological examination and high-resolution, label-free chemical imaging to obtain hyperspectral images that can display the spatial distribution of various biomolecules within oocytes, with each pixel containing a complete Raman spectrum.

[0091] Specifically, in this embodiment, the spontaneous Raman spectroscopy and imaging method is as follows:

[0092] The prepared agarose-encapsulated oocyte sample is placed under a 63x water immersion lens. The space between the water immersion lens and the sample is filled with water. The integration time is 5-10 seconds. Single spectrum data refers to the location of a cell covered by a spot (size ~500nm). However, the size of an oocyte is around 100μm, and the coverage area of ​​a single spectrum is very small. Therefore, when the number of oocytes is very large, a single spectrum should measure as many cells as possible for statistical efficiency.

[0093] Acquiring 2D Oocyte Imaging Data: 2D Raman imaging data is acquired by selecting an XY-axis area under the optical lens. This area is based on the size of the oocyte. For example, if the oocyte diameter is 100 μm, the XY-axis range is 110 μm. The single-point integration time and power, as well as the number of points or steps, are set. The number of points is the area covered by the laser spot collected on the X axis multiplied by the number of rows collected on the Y axis when drawing a 2D image of an oocyte. The distance between the laser spot moving from the X(0,0)Y(0,0) position to the next position (called the step length) is the distance between the laser spot and the next position. This will produce a 2D Raman image of the oocyte.

[0094] Acquisition of 3D imaging data of oocytes: Acquisition of 3D data is based on 2D settings, with the addition of Z-axis step size and distance settings, thereby obtaining 3D cell imaging data of oocytes.

[0095] All spectra were acquired using a Raman confocal microspectrometer equipped with a piezoelectric stage, a 63x water-immersion objective, a green solid-state excitation laser (λ = 532 nm, 32 mW), and an imaging spectrometer equipped with a 600 grooves / mm grating and a thermoelectrically cooled (-60°C) charge-coupled detector (CCD), optically connected to the objective via a 10 μm diameter single-mode silicon fiber cable. The laser excitation spot size was ∼350 nm. This setup can operate in the range of 0–3600 cm -1 Spectral data were acquired over a wavenumber range of 100 nm. In all cases, the excitation laser intensity was kept constant between sample scans, with an integration time of 0.1 s and a step size of 333 nm for cell mapping.

[0096] In this embodiment, the Raman spectroscopy data is processed and analyzed as follows:

[0097] Based on the original data, the image processing software WITec project five 5.2.4 was used for data preprocessing, including: spectrum range, cosmic ray removal, baseline removal, etc. Specifically, the spectral data range is 400~3400cm -1 , remove the silent noise area 1800~2700cm -1 , is one-dimensional floating point data.

[0098] The spectral data were processed by removing cosmic rays and background noise, using the analyze function in project and selecting the filter 15-20cm. -1 , obtain the peak position value image of a single peak position, that is, a 2D image, such as DNA, carbohydrates, lipids, and proteins are attributed with 786, 490, 1440, and 2928 respectively.

[0099] Image J was then used to perform 3D stacking on the peak position images to form a 3D image.

[0100] Initial data preparation involved creating metadata and spectral datasets for three conditions, identifying oocytes from 2-month-old mice, 4-month-old mice, and 10-month-old mice, with three replicates for each condition.

[0101] In this embodiment, specifically, the stimulated Raman spectra obtained by analysis are taken as a set and the set is analyzed using the spectral analysis method in the related art (t-distributed Stochastic Neighbor Embedding (t-SNE) can be used for dimensionality reduction, and the k-means clustering algorithm (k-means) is used for clustering based on the obtained scatter distribution) to obtain a scatter plot of the cells to be detected, and the number of scatter points of the results of different cell types is counted to obtain the number of each type of cell. The difference in the stimulated Raman spectra of different cells is used to achieve the classification and statistics of the cells to be detected, thereby improving the accuracy of cell detection. When K-means clustering is applied to the Raman spectral data of a single cell, the background spectral cluster and the cell spectral cluster can be distinguished.

[0102] This preprocessing step is crucial for isolating the cellular regions of interest from the background and other non-cellular components. By focusing on relevant clusters identified during the clustering process, the resulting image more clearly highlights cellular structures.

[0103] All cell spectral data were baseline corrected by subtracting the minimum intensity and baseline correction with the rolling ball method. Spectra were then normalized to the water peak (3300 to 3500 cm -1 ) to account for focus drift. The spectral data were then cropped to the wavenumber range of 400 to 3100 cm -1 .

[0104] Average spectra were calculated for each cell and group. Linear discriminant analysis (LDA) was used to discriminate between spectral data. Raman bands within the spectral data were identified using peak detection. The area under each identified peak was integrated to quantify the intensity of specific spectral features and visualized using violin plots and box plots to compare peak intensity distributions across conditions. One-way ANOVA and post hoc comparisons using Tukey's honestly significant difference (HSD) test were performed to test pairwise differences between groups.

[0105] In this embodiment, the processing and analysis methods of two-dimensional and three-dimensional Raman imaging are as follows:

[0106] Raman images were processed using WITec version 5.2.4 and Imaris 10.0. All spectral data from the Raman images were first extracted, and cosmic rays were automatically removed (filter size: 4; dynamic factor: 4.1). Different color channels were created for total intensity and specific Raman bands: lipids: 2855 cm -1 Center, width 50cm -1 ; Unsaturated lipids: at 1655cm -1 Center, 75 cm wide -1 Protein: 2930 cm -1 Center, 50 cm wide -1 ; Cytochrome c: at 750 cm -1 Center, width 25cm -1 The color scale was set to keep the non-cellular background unchanged. The particle distribution of the lipid channel was calculated using ImageJ. 3D imaging was visualized using Imaris Viewer.

[0107] In this example, morphological and spectral characteristics of mouse oocytes in different age groups were analyzed.

[0108] (I) Morphological observation results of mouse oocytes of different age groups under bright-field microscope images

[0109] Bright field microscope images of mouse oocytes of different age groups Figure 2 Shown are bright-field images of single oocytes from mice of different age groups: 2 months (2M; top row), 4 months (4M (middle row), and 10 months (10M at bottom); each group contains 3 cells. Scale bar represents 30 μm.

[0110] Oocytes from 2-month-old (top row) and 4-month-old (middle row) mice were spherical with clear cell membranes. The spherical shape was particularly uniform in the 2-month-old group. The cytoplasm was uniform and granular, indicating that the organelles and cytoplasmic components were evenly distributed.

[0111] In contrast, oocytes from 10-month-old mice (bottom row) showed some striking differences. While they generally maintained a spherical shape, some irregularities in the cell membrane could be observed. The cytoplasm was less uniform and more granular, and some oocytes occasionally contained large inclusions or vacuoles.

[0112] The observed differences, particularly in the 10-month group, may indicate changes in cytoplasmic organization, organelle distribution, or accumulation of cellular components associated with age. These morphological alterations may be associated with the decreased developmental capacity and fertility observed in aged female mice.

[0113] (II) Different metabolic characteristics of mouse oocytes at different ages

[0114] Figure 3 Different metabolic characteristics of mouse oocytes at different ages, among which,

[0115] Average Raman spectra of oocytes from mice of different ages (green 2 months, purple 4 months, orange 10 months) Figure 3 As shown in A, key Raman shifts have been marked, and these peaks indicate the presence and relative abundance of different biomolecules within the oocyte;

[0116] like Figure 3 B Linear discriminant analysis (LDA) plot shows that oocytes from 2-month-old (green), 4-month-old (purple), and 10-month-old (orange) mice are clearly clustered together, highlighting age-related metabolic differences and illustrating the separation of Raman spectroscopy data based on oocyte samples. This shows that Raman spectroscopy data can effectively distinguish oocytes from different age groups, indicating different metabolic profiles associated with maternal aging. The clusters of 2 and 4 months are close to each other, while the clusters of 10 months are farther apart, confirming the morphological observations ( Figure 2 When all spectra of a cell were used (42,920 spectra per cell on average) rather than averaging them into a single spectrum, the LDA plot showed that group separation was still visible, but there was significant overlap in cellular composition between groups; this is because a single spectrum captures the inherent heterogeneity within each cell, reflecting the different molecular compositions of different cellular regions and organelles.

[0117] like Figure 3 The violin plots in C provide a detailed view of the integrated band regions of the key Raman peaks for the three age groups, with notable observations consistent with the average spectral trends ( Figure 3 A).

[0118] The key spectral peaks corresponding to different biomolecules were identified, including 1447 cm -1 、1658cm -1 lipids (overlapping with amide III) and 1744 cm -1 (ester); protein at 1003cm -1 (phenylalanine) and 1280cm -1 (Amide I); DNA at 1587 cm -1 , other significant peaks are at 603cm -1 (cholesterol) and 749cm -1 (cytochrome c);

[0119] The lipid-related peaks generally showed a downward trend with increasing maternal age, suggesting possible changes in lipid metabolism or storage;

[0120] DNA peaks varied slightly between age groups, which may indicate changes in nuclear content or chromatin structure;

[0121] Protein-related peaks showed subtle changes, which may reflect changes in protein composition or structure with age;

[0122] The cytochrome c peak decreased significantly in aged oocytes, which may indicate changes in mitochondrial function or content.

[0123] (III) Two-dimensional subcellular comparison of metabolic components in young and aged mouse oocytes

[0124] Using label-free Raman imaging, we comprehensively analyzed the subcellular distribution of key metabolic components in young and aged mouse oocytes;

[0125] like Figure 4 (A) Raman chemical images provide a visual representation of the spatial distribution of various metabolites in single oocytes from young and old mice; (A) Raman chemical images of oocytes from young (upper row) and old (lower row) mice; images show the total intensity and distribution of key metabolites: total intensity is the total Raman signal intensity, 2855 cm -1 Total lipids, 1655cm -1 Unsaturated lipids, 2930cm -1 of protein and 750cm -1 of cytochrome c, the scale bar represents 20 μm;

[0126] like Figure 4 As shown in B, the histogram can display the relative abundance and distribution of each metabolite, highlighting the differences between young and old oocytes, showing the pixel intensity distribution of lipids, unsaturated lipids, proteins and cytochrome c in young (green) and old (orange) mouse oocytes; providing a quantitative comparison of the pixel intensity distribution of each metabolite between young and old oocytes; obvious differences can also be observed in the distribution of specific components.

[0127] Young oocytes showed a higher overall lipid content, with a higher proportion of high-intensity pixels compared to older oocytes. The distribution of unsaturated lipids was similar in both age groups, but the proportion of high-intensity pixels was slightly higher in young oocytes, indicating a higher content of unsaturated lipids. Unsaturated lipids showed different localization patterns in both age groups, with a more dispersed pattern in young oocytes compared to more concentrated clusters in young oocytes. The lipid distribution in young oocytes was more uniform, richer, and more aggregated than in older oocytes, suggesting a possible decrease in lipid content.

[0128] Young oocytes showed a distinctly different protein distribution compared to aged oocytes, with a higher proportion of medium-intensity pixels, suggesting potential differences in protein content or organization.

[0129] In addition, the cytochrome c content of young oocytes was significantly more uniform, more intensely distributed, and the signal intensity was significantly reduced.

[0130] The above differences in lipid content, protein distribution, and cytochrome c content suggest that young oocytes have a higher overall metabolic intensity than old oocytes.

[0131] (IV) Comparison of 3D Raman images of metabolic components in young and old mouse oocytes

[0132] like Figure 5 As shown, the changes were further confirmed in 3D Raman images of different components in mouse oocytes at 2, 4, and 10 months of age. 3D imaging showed a gradual shift in metabolic composition with increasing maternal age.

[0133] For total lipids, the intensity decreased significantly from 2 to 10 months, and the distribution became more uneven, confirming that Figure 4 Unsaturated lipids also showed a similar trend, with a more obvious decrease and aggregation in 10-month-old oocytes.

[0134] The protein distribution was relatively uniform across all age groups, but showed subtle changes in 10-month-old oocytes, with a slight decrease in overall intensity and a more uneven distribution. Figure 4 Protein histogram in B Figure 1 Consistent with this, the image shows that older oocytes shift towards pixels of lower intensity.

[0135] Most strikingly, cytochrome c signaling showed a significant decrease from 2 to 10 months of age, with oocytes at 4 months showing intermediate levels. This progressive decline in cytochrome c content was consistent with the 2D imaging findings, further highlighting the potential mitochondrial dysfunction in aged oocytes.

[0136] These 3D imaging results provide insights into the spatial organization of key metabolic components within oocytes and how they change with maternal age, reinforcing and extending insights gained from 2D analyses.

[0137] 3. Conclusion

[0138] Using single-cell Raman spectroscopy and imaging, we identified specific age-related changes in lipid content and distribution, cytochrome c levels, and metabolic profiles in mouse oocytes.

[0139] For total lipids, the intensity decreased significantly from 2 to 10 months, and the distribution became more uneven;

[0140] Unsaturated lipids also showed a similar trend, with more obvious reduction and aggregation in 10-month oocytes;

[0141] Cytochrome c signaling decreased significantly from 2 to 10 months of age, with 4-month-old oocytes showing intermediate levels.

[0142] The above description of the embodiments is intended to facilitate understanding and use of the invention by those skilled in the art. It will be apparent that those skilled in the art can readily make various modifications to these embodiments and apply the general principles described herein to other embodiments without requiring inventive effort. Therefore, the present invention is not limited to the above-described embodiments. Improvements and modifications made by those skilled in the art based on the disclosure of the present invention, without departing from the scope of the present invention, should be within the scope of protection of the present invention.

Claims

1. A method for non-invasively assessing age-dependent metabolic characteristics of mouse oocytes, characterized in that: The following steps are involved: S1: Collect oocytes from mice of different ages; S2: Process the oocytes to obtain samples that can be used for spontaneous Raman spectroscopy and imaging; S3: Perform spontaneous Raman spectroscopy and imaging of different oocytes; S4: Processing and analysis of Raman spectroscopy data; S5: Processing and analysis of 2D and 3D Raman images; S6: Morphological and spectral characteristics analysis of mouse oocytes in different age groups; S7: Determine age-specific changes in lipid content and distribution, cytochrome c levels, and metabolic profiles in mouse oocytes.

2. The method for non-invasively evaluating age-dependent metabolic characteristics of mouse oocytes according to claim 1, wherein: In step S1, female C57BL / 6 mice aged 2 months, 4 months, and 10 months were injected with pregnant mare serum gonadotropin and human chorionic gonadotropin to establish superovulation model mice, and oocytes were collected.

3. The method for non-invasively evaluating age-dependent metabolic characteristics of mouse oocytes according to claim 1, wherein: In step S2, after the oocyte is obtained, it is placed in a PBS saline solution. Before being tested on the machine, 0.4% agarose is prepared and placed in a culture dish. Before the agarose solidifies, the obtained oocyte is sucked into the agarose liquid. After it dries, the oocyte is wrapped in it for testing.

4. The method for non-invasively evaluating age-dependent metabolic characteristics of mouse oocytes according to claim 1, wherein: In step S3, the method for performing spontaneous Raman spectroscopy of different oocytes and high-resolution cell imaging is as follows: Place the prepared agarose-encapsulated oocyte sample under a 63x water objective lens. Fill the space between the water objective and the sample with water. The integration time is 5s-10s. Single spectrum data refers to the position information of a cell covered by a light spot. The method for acquiring Raman data for oocyte 2D imaging is to select an XY axis area under the optical lens. The size of this area is based on the size of the oocyte. The single-point integration time and power, as well as the number of points or steps, are set. The number of points is the number of points in the area covered by the laser spot collected on the X axis multiplied by the number of rows collected on the Y axis when drawing a 2D image of an oocyte. The distance between the laser spot moving from the X(0,0)Y(0,0) position to the next position is called the step length, which means that a 2D Raman image of the oocyte is obtained. Acquisition of 3D imaging data of oocytes: Acquisition of 3D data is to increase the Z-axis step size and distance settings based on the 2D settings, thereby obtaining 3D cell imaging data of oocytes.

5. The method for non-invasively evaluating age-dependent metabolic characteristics of mouse oocytes according to claim 4, wherein: In step S3, spontaneous Raman spectroscopy and imaging of different oocytes were performed using a Raman confocal microspectrometer with a 532 nm laser and a 63× water immersion objective lens to collect spectra in the spectral range of 0–3600 cm -1 , integration time 0.1 s, step size 333 nm.

6. The method for non-invasively assessing age-dependent metabolic characteristics of mouse oocytes according to claim 1, characterized in that: In step S4, the Raman spectrum data is processed and analyzed as follows: Based on the original data, the image processing software WITec project five 5.2.4 was used for data preprocessing, including spectral range, cosmic ray removal, and baseline removal. The spectral data range was 400–3400 cm -1 , remove the silent noise area 1800~2700cm -1 , is one-dimensional floating point data; The spectral data were processed by removing cosmic rays and background noise, using the analyze function in project and selecting the filter 15-20cm. -1 , obtain the peak position value picture of a single peak position, i.e., a 2D image, and attribute DNA, carbohydrates, lipids, and proteins with 786, 490, 1440, and 2928, respectively; Image J was then used to perform 3D stacking on the peak position images to form a 3D image.

7. The method for non-invasively evaluating age-dependent metabolic characteristics of mouse oocytes according to claim 6, characterized in that: In step S4, all cell spectral data were baseline corrected by subtracting the minimum intensity and baseline correction with the rolling ball method; the spectra were then normalized to the water peak, 3300 to 3500 cm -1 To account for focus drift, the spectral data were then cropped to the wavenumber range of 400 to 3100 cm -1 ; In step S4, the average spectrum was calculated for each cell and group, and linear discriminant analysis was used to distinguish the spectral data. The Raman bands in the spectral data were determined using the peak detection method. The area under each identified peak was integrated to quantify the intensity of the specific spectral feature and visualized using violin plots and box plots to compare the peak intensity distribution under different conditions. One-way analysis of variance and post hoc comparisons were performed using Tukey's honestly significant difference test to test the pairwise differences between groups.

8. The method for non-invasively assessing age-dependent metabolic characteristics of mouse oocytes according to claim 1, wherein: In step S5, the processing and analysis methods of the two-dimensional and three-dimensional Raman imaging are as follows: Use WITec;version: 5.2.4 and Imaris 10.0 for processing Raman images; First, all spectral data in the Raman image were extracted, and cosmic rays were automatically removed. The filter size was 4, the dynamic factor was 4.1, and lipids were detected at 2855 cm -1 Center, width 50cm -1 ; Unsaturated lipids: at 1655cm -1 Center, 75 cm wide -1 Protein: 2930 cm -1 Center, 50 cm wide -1 ; Cytochrome c: at 750 cm -1 Center, width 25cm -1 The color scale was set to keep the non-cellular background unchanged. The particle distribution of the lipid channel was calculated using ImageJ, and the three-dimensional imaging was visualized using Imaris Viewer.

9. The method for non-invasively evaluating age-dependent metabolic characteristics of mouse oocytes according to claim 1, wherein: In step S6, the morphological and spectral characteristics of mouse oocytes of different age groups are analyzed, including: (1) Morphological observation of mouse oocytes of different age groups under bright-field microscope images; (2) Analysis of different metabolic characteristics of mouse oocytes at different ages: (3) 2D subcellular comparison of metabolic composition of young and old mouse oocytes; (4) Comparison of 3D Raman images of metabolic components in young and old mouse oocytes.

10. The method for non-invasively evaluating age-dependent metabolic characteristics of mouse oocytes according to claim 9, characterized in that: When analyzing the different metabolic characteristics of mouse oocytes at different ages, the average Raman spectra of mouse oocytes at different ages are used to demonstrate the presence and relative abundance of different biomolecules in the oocytes; In a two-dimensional subcellular comparison of the metabolic components of young and old mouse oocytes, label-free Raman imaging technology was used to comprehensively analyze the subcellular distribution of key metabolites in young and old mouse oocytes. Raman chemical imaging of mouse oocytes was used to display the total intensity and distribution of key metabolites, and histograms were used to display the relative abundance and distribution of each metabolite, highlighting the differences between young and old oocytes. The pixel intensity distribution of lipids, unsaturated lipids, proteins, and cytochrome c in young and old mouse oocytes was displayed; 3D Raman images of the metabolic composition of young and old mouse oocytes provide insights into the spatial organization of key metabolic components within oocytes and how they change with maternal age.

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