A method for detecting spatial proteomics and spatial phosphorylation modification proteomics

Abstract

This invention provides a method for detecting spatial proteomics and spatial phosphorylation modification omics. The method includes the following steps: (1) obtaining two consecutive sections of the target tissue and staining the sections with hematoxylin-eosin to obtain stained sections; (2) using a 3D printing mold to cut the stained sections into continuous, gapless strip-shaped subunits; (3) extracting peptides from the strip-shaped subunits and enriching the peptides by phosphorylation modification; (4) performing chromatographic separation and mass spectrometry detection on the phosphorylation-enriched peptides to obtain one-dimensional omics information of each strip-shaped subunit; (5) using the image of the stained sections as a spatial reference, reconstructing the one-dimensional omics information into a two-dimensional spatial distribution to obtain the spatial distribution of the target omics biomolecules. The method of this invention only requires two tissue sections to simultaneously obtain two-dimensional spatial proteomics and spatial modification omics results, which is friendly to small clinical samples.

Description

Technical Field

[0001] This invention relates to the fields of biomaterials and bioinformatics, and in particular to a detection method for spatial proteomics and spatial phosphorylation modification proteomics. Background Technology

[0002] Space omics technologies aim to elucidate the spatial distribution of biomolecules in situ within tissues to reveal cellular heterogeneity and function. Existing space omics detection technologies are mainly divided into three categories: space transcriptomics, space proteomics, and space metabolomics. Space transcriptomics technologies, based on microscopic imaging methods (such as MERFISH and ISS), achieve single-molecule RNA localization through multiplex fluorescence in situ hybridization, but rely on high-resolution microscopes, resulting in low throughput and high cost. Methods based on space nucleic acid tag arrays, such as 10x Visium and Stereo-seq, use barcode probes on a glass slide to capture mRNA, but their resolution is limited (typically 50-100 μm) and they cannot directly couple proteomic or metabolomic information. Space proteomics technologies are mainly divided into targeted antibody methods and non-targeted laser cutting methods. Targeted antibody methods, such as CODEX technology, can bind DNA barcodes to antibodies, but the types of antibodies are limited (usually only a few dozen), expensive, and cannot cover post-translational modifications. Non-targeted laser dissection methods, such as laser microdissection combined with mass spectrometry (e.g., Deep Visual Proteomics, DVP), can overcome the limitations of traditional immunofluorescence labeling, covering over 10,000 proteins in a single detection. Laser microdissection (LCM) is used to target cells, and mass spectrometry provides in-depth analysis of spatial protein networks from single cells to tissue sections. However, these methods also have drawbacks, including low sample throughput and the need for thousands of dissections and mass spectrometry detections for large tissue areas. Spatial metabolomics technologies primarily rely on mass spectrometry imaging (MSI), such as matrix-assisted laser desorption / ionization imaging (MALDI-MSI) or electrospray desorption / ionization imaging (DESI-MSI). These methods do not require tissue dissection but have limitations: they cannot accurately identify compounds; metabolites are not chromatographically separated after ionization, making it difficult to distinguish isomers or obtain secondary structure information. They also place high demands on the mass spectrometer: they are incompatible with ordinary LC-MS equipment.

[0003] Post-translational modifications (PDMs) are chemical group modifications that occur after protein translation. They precisely regulate protein activity, subcellular localization, spatial folding, and interactions between biomolecules, playing a central role in the regulation of life activities and the development of diseases. Conventional proteomics can only detect the abundance of protein expression, while PDMs can reflect the true functional activation state of proteins and represent an important research direction independent of proteomics.

[0004] Current technologies such as spatial proteomics, metabolomics, and multi-omics analysis typically rely on large sample sizes and complex equipment (e.g., laser microdissection or mass spectrometry imaging). For example, excessive sample size: traditional non-targeted spatial proteomics requires thousands of tissue cuts and mass spectrometry analyses, resulting in low efficiency and high costs (e.g., analyzing a 5mm × 5mm tissue at 0.1mm resolution requires 2500 samples). Low resolution: limited by detection sensitivity, existing methods struggle to achieve resolutions below 100μm (e.g., the smallest tissue block cut by laser is 100μm), failing to meet the needs of cellular-level spatial analysis. Existing methods such as DBiT-seq integrate transcriptomics and targeted proteomics using orthogonal arrays. This technology uses orthogonal arrays to spatially combine tags and deliver nucleotide-labeled antibodies to the same tissue sample, but it can only detect a small number of proteins (≤25) and cannot obtain non-targeted metabolomics or modifier information. While existing novel high-resolution spatial proteomics methods can significantly reduce the number of tissue sections used and improve spatial reconstruction accuracy, they still have significant technical drawbacks: these techniques require at least three consecutive tissue sections, with reference and detection sections spaced apart, resulting in significant differences in morphological deformation between sections, directly reducing the accuracy of spatial reconstruction; furthermore, these techniques rely on microfluidic precision segmentation equipment, which is complex, cumbersome to operate, and has high maintenance costs; the microfluidic segmentation channels have significant gaps, resulting in intermittent sampling and the loss of a large amount of tissue spatial information. More importantly, these technical processes are completely unsuitable for post-translational modification assays.

[0005] Post-translational modifications of proteins are extremely rare, typically less than one ten-thousandth of the total protein abundance. They are easily overwhelmed by a large number of unmodified protein signals, necessitating specific enrichment steps for effective detection. The extremely small amount of tissue sample required for spatial detection further significantly increases the difficulty of detecting spatial modifications. The meager amount of tissue protein obtained from laser cutting is insufficient to support stable post-translational modification enrichment, making it impossible to effectively identify modification sites such as non-targeted spatial phosphorylation.

[0006] Therefore, how to achieve high-resolution, high-sensitivity non-targeted protein post-translational modification spatial omics detection under low sample size conditions while maintaining tissue spatial information is a technical problem that urgently needs to be solved. Summary of the Invention

[0007] To address the shortcomings of existing spatial omics detection methods in achieving high-resolution, low-cost, high-accuracy, and high-depth acquisition of non-targeted modification omics spatial information distribution, this invention proposes a detection method for spatial proteomics and spatial phosphorylation modification omics, combining spatial multi-omics technology with high-resolution non-targeted proteomics and modification group detection through mechanical cutting, non-destructive detection by HE staining, and ultra-micro modification enrichment.

[0008] This invention provides a method for detecting spatial proteomics and spatial phosphorylation modification proteomics, comprising the following steps: (1) Obtain two consecutive sections of the target tissue, and stain the sections with hematoxylin-eosin to obtain stained sections; (2) The stained slices are cut into continuous, gapless strip subunits using a 3D cutting mold; (3) Extract the peptide segments of the strip-shaped subunits and enrich the peptide segments by phosphorylation modification; (4) The phosphorylated peptides were separated by chromatography and detected by mass spectrometry to obtain one-dimensional omics information of each strip subunit; (5) Using the image of the stained section as a spatial reference, the one-dimensional omics information is reconstructed into a two-dimensional spatial distribution.

[0009] In some embodiments, step (1) includes the following steps: eluting the stained sections with an acidic organic phase; the acidic organic phase is an acetonitrile solution containing acetic acid.

[0010] This invention effectively removes HE dye by adding an acidic organic phase elution step, resulting in a normal trend in the peptide quantification curve.

[0011] In some embodiments, the volume fraction of acetonitrile in the acidic organic phase is 50% to 80%.

[0012] In some embodiments, the elution time of the acidic organic phase is 5-15 min, and the elution temperature is 20-30°C.

[0013] In some implementations, the thickness of the continuous slice in step (1) is 4~20 μm.

[0014] In some implementations, the width of the strip sub-unit in step (2) is 10~200 μm.

[0015] In some embodiments, the phosphorylation modification enrichment in step (3) is carried out using the immobilized metal ion affinity chromatography (IMAC) enrichment method; the present invention utilizes the specific binding of immobilized metal ions to phosphorylated peptides to achieve the enrichment of low-abundance phosphorylated peptides.

[0016] In some implementations, the mass spectrometry detection in step (4) employs a data-independent scanning (DIA) mode.

[0017] In summary, compared with the prior art, the present invention achieves the following technical effects: (1) The detection method of the present invention only requires two tissue sections to obtain two-dimensional spatial proteomics and spatial modification omics results simultaneously, which is friendly to trace clinical samples. By further optimizing the unique HE staining compatible preparation method, the HE image is 100% matched with the actual detection section, thereby improving the accuracy and precision of detection.

[0018] (2) To address the problem of extremely low abundance of modified proteins such as spatial phosphorylation, this invention has successfully reconstructed spatial modification omics data by optimizing preparation conditions. On average, more than 5,500 proteins were identified by spatial phosphorylation modification, and more than 11,000 modification sites were identified, enabling in-depth and comprehensive analysis of spatial post-translational modifications.

[0019] (3) The present invention directly uses the stained image of the corresponding slice for in situ detection by mass spectrometry as the original information of two-dimensional space, which results in higher accuracy of reconstruction results. The correlation of two-dimensional space proteome reconstruction reaches more than 0.93, which is far better than the traditional 3-slice method (only 0.69~0.87). The spatial distribution restoration degree and the reliability of the results are significantly improved. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating the technical route of the present invention.

[0022] Figure 2 The diagram and physical image show the 3D printed cutting mold of Embodiment 1 of the present invention.

[0023] Figure 3 This is a depth map for identifying spatially reconstructed phosphorylated peptides in Example 1 of the present invention.

[0024] Figure 4 This is a spatially reconstructed phosphorylated protein and its physiological distribution diagram for Embodiment 1 of the present invention.

[0025] Figure 5 This is a graph showing the effect of HE staining on peptide quantification in Example 2 of the present invention.

[0026] Figure 6 The figure shows the effect of HE staining on the mass spectrometry signal in Example 2 of this invention.

[0027] Figure 7 This is a comparison diagram of mass spectrometry identification depth in Example 2 of the present invention.

[0028] Figure 8 This is a graph showing the results of verifying the reconstruction accuracy in Embodiment 3 of the present invention.

[0029] Figure 9 This is a comparison image of AI reconstruction and IHC staining in Embodiment 3 of the present invention.

[0030] Figure 10 This is a graph showing the accuracy verification results of the reconstruction in the 2-piece and 3-piece modes of Embodiment 3 of the present invention. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0032] Unless otherwise specified, the experimental methods used in the following examples are conventional methods. Unless otherwise specified, all materials and reagents used are commercially available.

[0033] Example 1 1. Sample preparation Technical processes such as Figure 1 As shown, after collecting fresh tissue samples, blot away any blood or water stains with lint-free paper. Then, take an appropriately sized tissue embedding cassette and label it with sample information and tissue orientation. Next, inject a small amount of OCT pre-cooled with wet ice into the embedding mold, taking care to avoid air bubbles, and place the frozen tissue block horizontally at the correct angle in the tissue embedding cassette. Continue injecting OCT embedding solution until the entire tissue block is completely embedded. After embedding, fix the tissue in the embedding cassette, transfer it to a resealable bag, seal it, label the sample information, and finally, it can be temporarily stored on dry ice or stored long-term in a -80°C freezer.

[0034] 2. Tissue sections After removing the sample from the -80℃ freezer, equilibrate it in a microtome for 30 minutes. After cutting to the target area, cut two tissue sections with a thickness of 10-20 μm. The tissue sections are then mounted on a glass slide.

[0035] 3. HE staining and cutting For sections fixed with anhydrous methanol, they were first rinsed three times in PBS solution to remove residual OCT. Then, hematoxylin staining was performed for 5 minutes, followed by immersion in a blueing solution for 1 minute. After blueing, the sections were incubated in eosin working solution for 1 minute. After staining, rinsing and air drying completed the preparation. Finally, the stained images of the sections, analyzed by mass spectrometry, were used as a tissue spatial reference to reduce analytical errors caused by morphological differences between different sections. A custom-made 3D-printed mold was used to fix and position the tissue sections to be tested. Guided by the mold, the tissue sections were precisely cut into continuous strip-shaped tissue units with a width of 100 μm (see...). Figure 2 This method ensures uniform band width and stable, controllable cutting positions, providing tissue samples with uniform size and well-defined spatial locations for subsequent spatial proteomics sample preparation and mass spectrometry analysis.

[0036] 4. Modification and enrichment The extracted peptides were dissolved in enrichment buffer (50% acetonitrile / 0.5% acetic acid), and the supernatant was transferred to pre-washed IMAC material. The material was then incubated on a rotary shaker with gentle shaking. After incubation, the material was washed three times sequentially with buffer solutions of 50% acetonitrile / 0.5% acetic acid and 30% acetonitrile / 0.1% trifluoroacetic acid. Finally, the phosphopeptides were eluted with 10% ammonia, and the eluent was collected and freeze-dried under vacuum. After drying, the eluent was desalted using C18 ZipTips, freeze-dried under vacuum, and then used for LC-MS / MS analysis.

[0037] 5. Mass spectrometry detection Peptides were separated by an ultra-high performance liquid chromatography (UHPLC) system and then injected into an NSI ion source for ionization before being analyzed by an Orbitrap Astral mass spectrometer (Thermo Fisher). The ion source voltage was set to 1900 V. The precursor peptide ion was detected and analyzed using an Orbitrap detector, while secondary fragment ions were detected and analyzed using an Astral detector. The primary mass spectrometry scan range was set to 350–1250 m / z with a scan resolution of 240,000 m / z; the secondary mass spectrometry scan range had a fixed starting point of 150 m / z and a secondary scan resolution of 80,000 m / z. Data acquisition was performed using a data-independent scan (DIA) procedure, where peptide ions from multiple consecutive m / z windows were followed by fragmentation in an HCD collision cell after the primary scan, using 25% of the fragmentation energy, and then analyzed sequentially by secondary mass spectrometry. To improve the efficiency of the mass spectrometry, automatic gain control (AGC) was set to 800%, and the maximum injection time was set to 4 ms. Protein annotation and quantification were performed using Spectronaut (Biognosys) software.

[0038] 6. Two-dimensional modified proteomics To further evaluate the application potential of this spatial reconstruction method in resolving the distribution of modifications in complex tissues, this study selected mouse cerebellum and medulla oblongata tissues for spatial modalomics validation. Two serial tissue sections were used in the experiment, and one-dimensional phosphorylated proteomics information (channel width 100 μm) was obtained through 3D mold cutting combined with mass spectrometry. On average, more than 5500 phosphorylation modifications were detected in each microchannel, with a cumulative identification of over 11000 phosphorylation sites (see...). Figure 3 The phosphorylation modification distribution of brain region-specific marker proteins is highly consistent with the theoretical distribution. Figure 4 This provides a solid foundation for in-depth exploration of tissue spatial phosphorylation modification.

[0039] Example 2 (1) Optimization of destaining conditions after HE staining: The destaining conditions after hematoxylin-eosin (HE) staining were optimized to reduce the interference of HE staining on subsequent detection. Specifically, HE-stained sections were eluted with an acidic organic phase of 60% acetonitrile-0.5% acetic acid at 25°C for 10 min, and the elution was repeated twice. After each elution, the sections were rinsed with ultrapure water, air-dried, and then cut and extracted.

[0040] Peptide content was detected using a Nanodrop micro-volume spectrophotometer: 1 μL of peptide solution was loaded, with the corresponding elution buffer as a blank control. Absorbance values ​​were measured at 60 nm and 280 nm wavelengths, respectively. Peptide concentration was calculated based on the A280 absorbance, and the A260 / A280 ratio was recorded to assess purity. Three biological replicates were performed for each group. The mass spectrometry detection procedure was followed as described in Example 1.

[0041] The results are as follows Figure 5 As shown, by adding an acidic organic phase elution step, HE dye can be effectively removed, resulting in a normal trend in the peptide quantification curve, with the UV absorption peak mainly concentrated at 280 nm. Meanwhile, mass spectrometry results indicate that the signal intensity of the untreated sample is lower than that of the destained sample (see...). Figure 6 This indicates that HE dye significantly interferes with the detection of peptide mass spectrometry signals.

[0042] (2) Effects of HE staining on the spatial proteome and the modified proteome: HE-stained sections, treated with acidic organic phase destaining and those without destaining treatment, were simultaneously subjected to protein extraction, phosphorylation enrichment, and mass spectrometry detection. The quantities of proteins, peptides, and modified peptides in the two groups were compared. Results are as follows: Figure 7 As shown.

[0043] The results show that by adding an acidic organic phase elution step, this embodiment significantly improved the mass spectrometry identification depth. Specifically, the number of proteins identified, the number of peptides identified, and the number of modified peptides identified were all significantly improved, further confirming that HE dye has an adverse effect on the mass spectrometry identification of spatial proteomes and modified proteomes.

[0044] Example 3 This embodiment verifies the accuracy of the reconstruction results: The steps of the traditional laser capture microdissection (LCM) point sampling method are as follows: Obtain a single tissue section of the target tissue, and stain the section with hematoxylin and eosin to obtain stained sections; then perform spot cutting with LCM, 1mm. A total of 100 100μm sections were cut from a 1mm slice. A 100μm dotted grid was used to extract peptides from each strip subunit; the peptides were then subjected to chromatographic separation and mass spectrometry detection to obtain true two-dimensional spatial expression information.

[0045] The correlation between the method of this invention and the traditional laser capture microdissection (LCM) point sampling method was verified. The Pearson correlation coefficient between the two methods was calculated using the protein expression matrix of 100 points obtained.

[0046] The results showed that the Pearson correlation coefficient between the two in terms of protein spatial distribution reached 0.93 (see...). Figure 8 This indicates that the data obtained by this method has high reliability and good reproducibility.

[0047] Meanwhile, the reconstruction results of the method of the present invention are compared with the distribution of immunohistochemical (IHC) staining.

[0048] The immunohistochemical (IHC) staining procedure is as follows: First, fix the slides at room temperature, wash with PBS, then block endogenous peroxidase with 3% hydrogen peroxide, block non-specific sites with BSA at room temperature, add primary antibody and incubate in a humidified chamber at 4°C, warm to room temperature, then incubate with HRP-labeled secondary antibody at room temperature, develop with DAB, counterstain with hematoxylin, wash with water and air dry before microscopic examination for comparison and verification of spatial reconstruction results.

[0049] The spatial localization of MBP-tagged proteins is highly consistent and uniform (see...). Figure 9 This further confirms that the method can accurately reflect the true spatial distribution characteristics of the target protein in the tissue.

[0050] To verify the accuracy of the two-piece segmentation pattern, this embodiment simultaneously compared the results of the two-piece and three-piece patterns with those of holographic segmentation: 3-slice mode: Obtain more than 3 consecutive slices of the target sample, and divide 2 of the 3 slices into strip-shaped sub-organisms at different angles; obtain two-dimensional spatial distribution information from the third adjacent reference slice; train the learning model based on the two-dimensional spatial distribution information of biomolecules of adjacent reference omics, and then use the trained learning model and the one-dimensional information of biomolecules of the target omics to predict the two-dimensional spatial information of the target omics molecules.

[0051] Two-slice mode: Obtain two consecutive tissue slices of the target tissue, stain the slices with hematoxylin and eosin to obtain stained slices; then cut the stained slices into continuous strip-shaped sub-units without gaps; train a learning model based on the two-dimensional spatial distribution information of biomolecules in any one of the slices, and then use the trained learning model and the one-dimensional information of biomolecules in the target omics to predict the two-dimensional spatial information of the target omics molecules.

[0052] As can be seen from the Pearson correlation coefficient heatmap (see...) Figure 10 The proposed method employs both 3-segment and 2-segment segmentation techniques, both of which demonstrate high correlation with the holographic segmentation method. Specifically, the correlation coefficient between 3-segment segmentation and holographic segmentation has an overall average value close to 0.8. The correlation coefficient between 2-segment segmentation and holographic segmentation has an overall average value close to 0.9, indicating even higher correlation and better data matching. The 2-segment mode ensures 100% matching between the HE image and the actual detected slice, thereby improving detection accuracy and precision. These results demonstrate that the segmentation segmentation scheme of this invention can achieve detection results similar to holographic segmentation while ensuring data reliability, exhibiting good practicality and substitution value.

[0053] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for detecting spatial proteomics and spatial phosphorylation modification proteomics, characterized in that, Includes the following steps: (1) Obtain two consecutive sections of the target tissue, and stain the sections with hematoxylin-eosin to obtain stained sections; (2) The stained slices are cut into continuous, gapless strip subunits using a 3D cutting mold; (3) Extract the peptide segments of the strip-shaped subunits and enrich the peptide segments by phosphorylation modification; (4) The phosphorylated peptides were separated by chromatography and detected by mass spectrometry to obtain one-dimensional omics information of each strip subunit; (5) Using the image of the stained section as a spatial reference, the one-dimensional omics information is reconstructed into a two-dimensional spatial distribution; Step (1) includes the following steps: eluting the stained sections with an acidic organic phase; the acidic organic phase is an acetonitrile solution containing acetic acid.

2. The detection method according to claim 1, characterized in that, The volume fraction of acetonitrile in the acidic organic phase is 50% to 80%.

3. The detection method according to claim 2, characterized in that, The elution time of the acidic organic phase is 5-15 min, and the elution temperature is 20-30℃.

4. The detection method according to claim 1, characterized in that, The thickness of the continuous slices in step (1) is 4~20μm.

5. The detection method according to claim 1, characterized in that, The width of the strip-shaped subunit in step (2) is 10~200 μm.

6. The detection method according to claim 1, characterized in that, The phosphorylation modification enrichment in step (3) is performed using the immobilized metal ion affinity chromatography (IMAC) enrichment method.

7. The detection method according to claim 1, characterized in that, The mass spectrometry detection in step (4) adopts a data-independent scanning mode.

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