A method for deep proteomics analysis of trace samples and its application

Abstract

A method and application for deep proteomics analysis of trace samples, which is used for high-depth and high-sensitivity proteomics analysis of the retinal pigment epithelium / choroid complex, and is also applicable to the deep proteomics testing of other trace samples, with wide applicability and flexibility. By systematically optimizing the LC-FAIMS-MS / MS parameters, the present invention overcomes the technical bottlenecks of low coverage of trace tissue proteome and poor quantitative stability, providing a highly sensitive solution for disease mechanism research and biomarker discovery. This method increases the number of protein identifications by 28%, expands the dynamic range to six orders of magnitude, improves the quantitative repeatability, and reduces the proportion of missing values to 6%; it can detect AMD-related low-abundance proteins and 10 cell type markers, helping with disease mechanism research.

Description

Technical Field

[0001] The present invention relates to the field of bioanalysis technology, and particularly relates to a method and application for deep proteomics analysis of trace samples. Background Art

[0002] The retinal pigment epithelium / choroid complex (RC complex) plays a key role in maintaining retinal homeostasis and is closely related to various vision-threatening eye diseases. However, due to limited sample sizes, especially in mouse models, comprehensive characterization of the functional proteins in the RC complex faces great challenges. In traditional proteomics analysis, conventional liquid chromatography-tandem mass spectrometry (LC-MS / MS) methods have low resolution and insufficient coverage due to peptide complexity in the testing of trace samples (such as the RC complex). Although existing high-field asymmetric waveform ion mobility spectrometry (FAIMS) technology can improve the peptide separation efficiency, its parameters (such as carrier gas flow rate and compensation voltage), the corresponding chromatographic conditions, and mass spectrometry scanning conditions are not optimized for trace samples, resulting in a limited dynamic range and a low detection rate of low-abundance proteins. In addition, existing methods have defects in quantitative repeatability and background noise suppression, affecting the accurate identification of disease-related proteins (such as age-related macular degeneration (AMD) risk gene products). Summary of the Invention

[0003] To solve the technical defects existing in the prior art, the present invention provides a method and application for deep proteomics analysis of trace samples, which is used for high-depth and high-sensitivity proteomics analysis of the retinal pigment epithelium / choroid complex (RC complex). This method is also applicable to the deep proteomics testing of other trace samples and has wide applicability and flexibility.

[0004] The technical solution adopted by the present invention is: a method for deep proteomics analysis of trace samples, which uses a liquid chromatography-tandem mass spectrometry (LC-MS / MS) method optimized by high-field asymmetric waveform ion mobility spectrometry (FAIMS) to perform deep proteomics analysis on trace samples, including the following steps:

[0005] S1. Sample treatment: After digesting a trace tissue sample with a mixture of trypsin / lysyl endopeptidase (LYS-C), take the peptide segments for loading.

[0006] S2. Liquid chromatography separation: Separate the treated sample through an optimized liquid chromatography system.

[0007] S3. FAIMS separation: Further separate and enrich the separated ions using optimized FAIMS parameters.

[0008] S4, Mass spectrometry detection: High-resolution mass spectrometry analysis is performed using a mass spectrometer, combined with an ion trap as a tandem mass spectrometry (MS2) analyzer to obtain proteomic data with high coverage and high dynamic range.

[0009] Preferably, in step S1, the sample loading amount of the peptide segments is 0.5 micrograms.

[0010] Preferably, in the optimized liquid chromatography system of step S2, the Acclaim PepMap™ RSLC analytical column is 75 µm × 25 cm, mobile phase A is 0.1% formic acid in water by volume percentage, mobile phase B is 80% acetonitrile solution containing 0.1% formic acid by volume percentage, the chromatographic gradient time is 180 minutes, the column temperature is 40 °C, and the flow rate is 300 nl / min.

[0011] Preferably, in the optimized liquid chromatography system of step S2, the peak capacity is 152; the final concentration of acetonitrile by volume is 55%, and the elution of hydrophobic peptide segments is increased, so that the proportion of the total eluted peptide segments is increased to more than 97%.

[0012] Preferably, in the FAIMS separation of step S3, three compensation voltage (CV) combinations of -40 V, -55 V, -75 V or -45 V, -60 V, -80 V are used to achieve the best separation of multiply charged peptide segments and protein identification effect, the carrier gas flow rate is 4.6 L / min, and the internal / external electrode temperature is 100 °C.

[0013] Preferably, in step S4, the mass spectrometry detection is specifically performed using a Thermo Scientific™ OrbitrapFusion™ Lumos™ Tribrid™ mass spectrometer. In the full scan mode, the Orbitrap resolution is 60000, and the mass-to-charge ratio (m / z) scanning range is 350 - 1500; in the data-dependent tandem fragmentation scan (DDA-MS²) mode, 30% energy high-energy collision dissociation (HCD) fragmentation is used, ion trap detection is performed, the automatic gain control target value (AGC target) is 1E4, the maximum injection time is 25 milliseconds, the dynamic exclusion time is 50 seconds, and the cycle time is 1.2 seconds.

[0014] Preferably, the chromatographic gradient conditions in step S2 are specifically as follows: 0 - 2 minutes, mobile phase B linearly increases from 3% to 8%; 2 - 35 minutes, mobile phase B linearly increases from 8% to 10%; 35 - 57 minutes, mobile phase B linearly increases from 10% to 15%; 57 - 91 minutes, mobile phase B linearly increases from 15% to 25%; 91 - 164 minutes, mobile phase B linearly increases from 25% to 69%; 164 - 170 minutes, mobile phase B linearly increases from 69% to 100%; 170 - 180 minutes, mobile phase B remains at 100%.

[0015] Preferably, the trace sample is a biological tissue or cell sample including a retinal pigment epithelium / choroid complex.

[0016] Application of the analysis method in high-depth and high-sensitivity proteomic analysis and detection of a retinal pigment epithelium / choroid complex.

[0017] The high-depth and high-sensitivity proteomic analysis and detection of the retinal pigment epithelium / choroid complex includes proteomic analysis and detection of age-related macular degeneration (AMD) risk gene products.

[0018] The beneficial effects of the present invention are as follows: A method and application for deep proteomic analysis of trace samples, which are used for high-depth and high-sensitivity proteomic analysis of a retinal pigment epithelium / choroid complex (RC complex), and are also applicable to deep proteomic testing of other trace samples, having wide applicability and flexibility. The present invention overcomes the technical bottlenecks of low coverage of trace tissue proteome and poor quantitative stability by systematically optimizing LC-FAIMS-MS / MS parameters, provides a highly sensitive solution for disease mechanism research and biomarker discovery. This method increases the number of protein identifications by 28%, expands the dynamic range to six orders of magnitude, improves the quantitative repeatability (CV median 8.6% vs. 11.1%), and reduces the proportion of missing values to 6%; it can detect low-abundance proteins related to AMD (such as high-temperature requirement serine protease A1, Vav family protein 1) and 10 cell type markers, facilitating disease mechanism research. Description of the Drawings

[0019] Figure 1 It is a curve showing the relationship between gradient time and the number of peptide identifications and the number of protein identifications.

[0020] Figure 2 It is a comparison chart of CV combination optimization.

[0021] Figure 3 It is a detection comparison of the same RC complex sample by conventional LC-MS / MS, FAIMS combined with Orbitrap and ion trap as two secondary fragment detectors.

[0022] Figure 4 It is the coefficient of variation of the normalized abundance of quantifiable proteins when the optimized LC-FAIMS-MS / MS method and the conventional LC-MS / MS method are used in label-free quantitative proteome (LFQ) testing.

[0023] Figure 5 It is the ranking of proteins detected by the optimized LC-FAIMS-MS / MS method (orange) and the LC-MS / MS method (green) according to the normalized abundance. Detailed implementation manners

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0025] Optimization of liquid chromatography conditions:

[0026] Use the Thermo Scientific™ Easy-nLC™ 1200 system, which provides excellent separation performance at pressures up to 1200 bar.

[0027] Use an Acclaim PepMap™ 100 C18 capture column (75 µm × 2 cm) for desalting and pre-concentration of the sample, and an Acclaim PepMap™ RSLC analytical column (75 µm × 25 cm) for reverse-phase separation.

[0028] Optimized sample loading amount: 0.5 micrograms of peptides is the optimal sample loading amount, balancing chromatographic performance and detection limit, suitable for high-coverage analysis of a single mouse RC complex.

[0029] Chromatographic gradient conditions: Mobile phase A is 0.1% formic acid in water by volume, mobile phase B is an 80% acetonitrile solution containing 0.1% formic acid by volume. The chromatographic gradient time is extended from 60 minutes to 180 minutes proportionally to increase the peak capacity from 77 to 152; the final concentration of acetonitrile by volume is increased to 55% to increase the elution of hydrophobic peptides and increase the proportion of total eluted peptides to over 97%; the optimal column temperature of the chromatographic column is set at 40°C to balance the number of protein identifications and system pressure.

[0030] FAIMS parameter optimization:

[0031] Adopt the latest FAIMS instrument (FAIMS Pro Duo) of Thermo Scientific.

[0032] The carrier gas flow rate is 4.6 L / min, and the inner / outer electrode temperature is set at 100°C to improve the separation resolution.

[0033] Adopt 3 combinations of compensation voltages (CV) to achieve the best separation of multi-charged peptides and protein identification effects. The compensation voltage (CV) combinations are -40 V, -55 V, -75 V or -45 V, -60 V, -80 V to maximize the ion transmission efficiency.

[0034] Optimization of Mass Spectrometry Detection:

[0035] Using a Thermo Scientific™ Orbitrap Fusion™ Lumos™ Tribrid™ mass spectrometer, synchronous detection of Orbitrap and ion trap, cycle time of 1.2 seconds, MS² injection time of 25 milliseconds, and dynamic exclusion time of 50 seconds to improve the detection sensitivity of low-abundance peptides.

[0036] Full scan: Orbitrap resolution of 60000, mass-to-charge ratio (m / z) scan range of 350 - 1500.

[0037] Data Analysis Method:

[0038] The raw data was analyzed using Proteome Discoverer (PD) 2.5.0.400 (Thermo Scientific) and searched through the Sequest HT node in PD with the SwissProt database of Mus musculus (version 2023 - 09 - 13, containing 17,157 sequences and 9,751,818 amino acid residues) as a reference.

[0039] Trypsin was set as the protease, and the maximum number of missed cleavage sites was 2.

[0040] Carbamidomethyl of cysteine (C) was set as a fixed modification.

[0041] The precursor ion mass deviation was set to 10 ppm, while the fragment ion mass deviations were 0.02 Da for Orbitrap and 0.8 Da for the ion trap, respectively.

[0042] The Precursor Ions Quantifier node in PD was used for label-free quantification (LFQ). Data normalization was performed based on the total peptide amount, and the protein abundance was determined by calculating the average of the top 3 different peptide groups for each protein.

[0043] Relationship between Chromatographic Gradient Time and the Number of Peptide Identifications and Protein Identifications in Example 1

[0044] 1. For wild-type normal mice, mechanically isolate the RC complex

[0045] 2. The RC complex was lysed using T-PER buffer; the protein extract was quantified for protein using the BCA method; the protein was digested using a trypsin / LYS-C mixture, and after digestion, the peptide segments were desalted and purified, and 0.5 micrograms of peptide segments were loaded for detection.

[0046] 3. Chromatographic conditions: The analytical column was Acclaim PepMap™ RSLC (75 μm×25 cm); the mobile phases were phase A (0.1% formic acid in water) and phase B (80% acetonitrile solution containing 0.1% formic acid); the chromatographic gradients for different durations are shown in Table 1.

[0047] Table 1. Proportion of phase B under chromatographic gradients of different durations

[0048]

[0049] 4. Mass spectrometry parameters: Scanning was performed in the data-dependent acquisition (DDA) mode; the mass-to-charge ratio (m / z) range for the full scan (Full MS) was from 350 to 1500, and it was acquired with a resolution of 60,000 by the Orbitrap detector, and the automatic gain control (AGC) target value was 4×10 5 , and the maximum injection time (MIT) was 50 milliseconds. The secondary mass spectrometry (MS²) acquisition was performed in the fastest cycle mode with a cycle time of 2 seconds. The precursor ions were isolated within a window of m / z 1.6 and fragmented by high-energy collision dissociation (HCD) with a normalized energy of 30%. The Orbitrap detector was used to detect the fragment ions, and its AGC target value was 5×10 4 , and the maximum ion injection time (MIT) was 25 milliseconds, and the resolution was 15,000.

[0050] 5. The raw data was analyzed using Proteome Discoverer (PD) 2.5.0.400 (Thermo Scientific), and searched with reference to the SwissProt database of Mus musculus (version 2023-09-13, containing 17,157 sequences and 9,751,818 amino acid residues) through the Sequest HT node in PD; Trypsin was set as the protease for digestion, and the maximum number of missed cleavage sites was 2; the carboxymethylation of cysteine (Carbamidomethyl of cysteine, C) was set as a fixed modification; the mass deviation of the precursor ions was set to 10 ppm, and the mass deviation of the fragment ions was 0.02 Da.

[0051] 6. The implementation results are as Figure 1As shown, when the chromatographic gradient time was increased from 60 minutes to 180 minutes, the peak capacity of the chromatographic column increased significantly, from 77 to 152; as the gradient time was extended, the peptide identification and protein identification effects were both improved. Compared with the 60-minute gradient time, when the gradient time was extended to 180 minutes, the number of identified peptides and proteins increased by 83% and 48.3% respectively, significantly improving the identification depth of the proteome.

[0052] Example 2 Optimization of Compensation Voltage (CV) Combinations in LC-FAIMS-MS / MS Analysis

[0053] 1. The acquisition of the RC complex was the same as in Example 1.

[0054] 2. The protein extraction, digestion, and loading conditions of the RC complex were the same as in Example 1.

[0055] 3. The chromatographic conditions were the same as in Example 1, where the chromatographic gradient was a 180-minute chromatographic gradient.

[0056] 4. FAIMS parameters: Different CV combinations were set, as shown in Figure 2 ; the carrier gas flow rate was 4.6 L / min, and the inner / outer electrode temperature was set to 100 °C.

[0057] 5. Mass spectrometry parameters: The cycle time was 1 second, and the other parameters were the same as in Example 1.

[0058] 6. The original data analysis steps were the same as in Example 1.

[0059] 7. The present invention applied five different CV combinations in the 180-minute gradient time. As can be seen from Figure 2 , as the initial voltage decreased in gradient, the number of identified peptides gradually decreased, while the number of identified proteins showed a trend of first decreasing, then increasing, and then decreasing; by using the CV combinations of -40 V, -55 V, -75 V and -45 V, -60 V, -80 V, the largest number of protein identifications was obtained. In addition, compared with the combination of -45 V, -60 V, -80 V, the number of detected peptides in the combination of -40 V, -55 V, -75 V increased by more than 8%. Thus, it can be seen that these two CV combinations have significantly improved the peptide identification and protein identification effects.

[0060] Example 3 Using an Ion Trap as a Second-Fragment Detector to Further Improve the LC-FAIMS-MS / MS Detection Effect

[0061] 1. The acquisition of the RC complex was the same as in Example 1.

[0062] 2. The protein extraction, digestion, and loading conditions of the RC complex were the same as in Example 1.

[0063] 3. The chromatographic conditions were the same as those in Example 2.

[0064] 4. FAIMS parameters: CV was set to -40 V, -55 V, -75 V; the carrier gas flow rate was 4.6 L / min, and the inner / outer electrode temperature was set to 100 °C.

[0065] 5. Mass spectrometry parameters: The cycle time was 1.2 seconds, and the dynamic exclusion time was 50 seconds; when the ion trap was used as the fragment ion detector, the AGC target value was set to 1x10 4 ; the other parameters were the same as those in Example 1.

[0066] 6. Raw data analysis parameters: When the ion trap was used as the fragment ion detector, the mass deviation of the fragment ions was 0.8 Da, and the other analysis parameters were set the same as those in Example 1.

[0067] 7. The experimental results were as Figure 3 shown. For the same RC complex samples, under the same chromatographic conditions and mass spectrometry scanning parameter conditions, adding FAIMS significantly improved the number of peptide identifications and the number of protein identifications, from 30253 to 34537 and from 4711 to 5291, respectively; and after converting the secondary fragment detector from orbitrap to ion trap, the number of peptide identifications and the number of protein identifications were further improved to 37345 and 6112.

[0068] Evaluation of the Quantitative Data Repeatability of the Optimized LC-FAIMS-MS / MS Method in Example 4 for LFQ Testing

[0069] 1. The acquisition of the RC complex was the same as that in Example 1.

[0070] 2. The protein extraction, digestion, and sample loading conditions of the RC complex were the same as those in Example 1.

[0071] 3. The chromatographic conditions were the same as those in Example 2.

[0072] 4. The FAIMS parameters were the same as those in Example 3.

[0073] 5. Mass spectrometry parameters: The ion trap was used as the secondary fragment ion detector, and the maximum injection time of the tandem mass spectrometry (MS2) was 20 milliseconds. The other parameters were the same as those in Example 3.

[0074] 6. Raw data analysis: The precursor ions quantifier node in PD was used for label-free quantification (LFQ); data normalization was performed based on the total peptide amount, and the abundance of each protein was determined by calculating the average value of the first 3 different peptide groups of each protein; the other parameters were the same as those in Example 3.

[0075] 7. In this experiment, the optimized LC-FAIMS-MS / MS method and the conventional LC-MS / MS method were respectively compared, and the same RC complex sample was injected continuously nine times. In the conventional method, 552 out of 4914 quantifiable proteomes had missing values, accounting for 11%. In the optimized FAIMS method, only 414 out of 6532 quantifiable proteomes had missing values, accounting for only 6%. Therefore, the optimized FAIMS method improved data integrity, thereby enhancing the reliability of quantitative results. In the optimized FAIMS method, the correlation coefficient of LFQ measurements between every two injections was 0.98 to 0.99, while in the non-FAIMS method it was 0.97 to 0.98. The FAIMS method observed slightly higher correlations, indicating that the synergistic effect of the FAIMS technology and the ion trap enhanced the consistency of proteomic analysis between different injections. It should be noted that the median coefficient of variation of the normalized abundances of quantifiable proteins was 8.6% in the FAIMS method and 11.1% in the conventional method, with a P value < 0.001 ( Figure 4 ), which further confirmed that the optimized FAIMS technology improved the quality and repeatability of quantitative data in LFQ measurements.

[0076] Example 5 Application of the Optimized LC-FAIMS-MS / MS Method in an Animal Model of Age-Related Macular Degeneration (AMD)

[0077] 1. Obtaining the RC complex of normal mice was the same as in Example 1.

[0078] 2. Obtaining the RC complex of disease model mice: Laser-induced choroidal neovascularization (CNV) model mice were obtained. After verifying the successful construction of the model by fundus fluorescein angiography (FFA), the RC complex was mechanically separated.

[0079] 3. Protein extraction, digestion, and loading conditions of the RC complex were the same as in Example 1.

[0080] 4. Chromatographic conditions were the same as in Example 2.

[0081] 5. FAIMS parameters were the same as in Example 3.

[0082] 6. Mass spectrometry parameters: The maximum injection time for MS2 was 25 milliseconds, and the other parameters were the same as in Example 4.

[0083] 7. Raw data analysis parameters were the same as in Example 4.

[0084] 8. A total of 7,047 proteins containing at least one unique peptide were detected by the optimized LC-FAIMS-MS / MS, while only 5,500 proteins were detected by traditional LC-MS / MS. Among them, 5,193 proteins were commonly identified by both methods, while 1,854 and 307 proteins were identified only by LC-FAIMS-MS / MS and LC-MS / MS, respectively.

[0085] 9. The normalized abundance range of proteins detected by the LC-FAIMS-MS / MS method was from 5.4×10 3 to 1.2×10 9 , while that of the traditional method was from 7.2×10 4 to 1.7×10 9 . According to the distribution of the normalized abundance, the optimized LC-FAIMS-MS / MS method achieved a nearly six-order-of-magnitude wider dynamic range ( Figure 5 ) in the proteomic analysis of RC samples in the CNV mouse model, thus significantly improving the sensitivity of protein detection, and then identifying AMD risk proteins (such as high-temperature requirement serine protease A1) and cell markers (apoptosis-inducing factor 1, fibronectin 1), revealing the molecular mechanism of the CNV disease model.

[0086] In summary, the present invention overcomes the technical bottlenecks of low coverage of trace tissue proteome and poor quantitative stability by systematically optimizing the LC-FAIMS-MS / MS parameters, providing a highly sensitive solution for disease mechanism research and biomarker discovery.

[0087] All technical personnel should note that although the present invention has been explained through the above specific embodiments, the inventive concept of the present invention is not limited thereto. Any improvement or variation based on the inventive concept of the present invention falls within the protection scope of the patent right of the present invention.

[0088] The above is only the preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments and experimental examples. Any technical solution that follows the concept of the present invention is included in the protection scope of the present invention. It should be emphasized that for those of ordinary skill in the art, any modification or equivalent replacement should be regarded as part of the protection scope of the present invention without departing from the purpose and scope of the present invention.

Claims

1. A method for deep proteomics analysis of trace samples, characterized in that: The deep proteomics analysis of trace samples was performed using a liquid chromatography-tandem mass spectrometry (LC-MS / MS) method optimized by high-field asymmetric waveform ion mobility spectrometry (FAIMS), which includes the following steps: S1. Sample processing: digest the trace tissue sample with a trypsin / lysine protease LYS-C mixture, and then take the peptide fragments for loading. The trace sample is a biological tissue or cell sample including a retinal pigment epithelium / choroid complex; S2. Liquid chromatography separation: The treated samples were separated by an optimized liquid chromatography system. The analytical column in the liquid chromatography system was 75 µm × 25 cm, the mobile phase A was 0.1% by volume formic acid in water, the mobile phase B was 80% by volume acetonitrile solution containing 0.1% by volume formic acid, the chromatographic gradient time was 180 minutes, the column temperature was 40°C, the flow rate was 300 nl / min, and the peak capacity was 152; the final volume concentration of acetonitrile was 55%, and the elution of hydrophobic peptides was increased to increase the proportion of the total eluted peptides to more than 97%; S3, FAIMS separation: The separated ions were further separated and enriched using the optimized FAIMS parameters. In FAIMS separation, three compensation voltage CV combinations of -40 V, -55 V, -75 V or -45 V, -60 V, -80 V were used to achieve the best multi-charged peptide separation and protein identification effects. The carrier gas flow rate was 4.6 L / min, and the inner / outer electrode temperature was 100 °C. S4. Mass spectrometry detection: High-resolution mass spectrometry analysis was performed by mass spectrometer, combined with ion trap as secondary mass spectrometry MS2 analyzer to obtain proteomic data with high coverage and high dynamic range. The specific mass spectrometry detection was Orbitrap resolution 60000 in full scan mode, mass-to-charge ratio m / z scanning range of 350-1500; in data-dependent secondary fragmentation scanning DDA-MS² mode, 30% energy high-energy collision dissociation HCD fragmentation was used, ion trap detection, automatic gain control target value AGC target of 1E4, maximum injection time of 25 milliseconds, dynamic exclusion time of 50 seconds, and cycle time of 1.2 seconds.

2. A method for deep proteomics analysis of trace samples according to claim 1, characterized in that: In step S1, the amount of peptide to be loaded is 0.5 μg.

3. A method for deep proteomics analysis of trace samples according to claim 1, characterized in that: The chromatographic gradient conditions in step S2 are specifically as follows: 0-2 minutes, phase B increases linearly from 3% to 8%; 2-35 minutes, phase B increases linearly from 8% to 10%; 35-57 minutes, phase B increases linearly from 10% to 15%; 57-91 minutes, phase B increases linearly from 15% to 25%; 91-164 minutes, phase B increases linearly from 25% to 69%; 164-170 minutes, phase B increases linearly from 69% to 100%; 170-180 minutes, phase B remains at 100%.

4. An application of the analytical method according to claim 1 in high-depth and high-sensitivity proteomic analysis of the retinal pigment epithelium / choroid complex.

5. The use according to claim 4, characterized in that: The high-depth and high-sensitivity proteomic analysis and detection of the retinal pigment epithelium / choroid complex includes the proteomic analysis and detection of age-related macular degeneration AMD risk gene products.

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