Use of biomarkers in assessing degree of platelet contamination

Abstract

The application provides application of biomarkers in platelet contamination degree evaluation, the markers include IDH2, LIMS1, MLEC, DIAPH1, GNAI2, ATP5F1B, RAB14, TBXAS1, JAM3, VDAC1, ATP2A3, ARHGAP6, VDAC3, ESAM, SLC25A5, ITGA6, PDLIM7, PF4V1, HSP90B1, HSPD1, RDH11, ATP2A2, VDAC2, RAB35, TREML1, GP5, PRDX3, ITGB1, MDH2 and GNB1. Based on the marker combination, platelet contamination samples can be accurately identified and excluded, and it is also beneficial to the large-scale landing of plasma proteomics to clinical scenes such as early screening of cancer and personalized medicine.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of biomedical technology, in particular to the application of biomarkers in the evaluation of platelet contamination degree. BACKGROUND

[0002] As a core technology for non-invasive disease biomarker discovery, circulating blood proteomics has significantly improved the depth of plasma protein detection in recent years, driven by mass spectrometry (MS) and nanoparticle (NP) enrichment methods. In previous literature reports, about 54% of blood proteomics studies have residual platelet contamination, and the contaminants have non-specifically adsorbed and artificially increased the number of protein identifications (an average increase of 38%).

[0003] Existing methods for evaluating platelet contamination of plasma samples rely on subjective experience or a single marker (such as PF4) to evaluate contamination, which cannot quantify the degree of contamination or correct data bias, resulting in analysis results that are disconnected from the true biological effect. Moreover, there are reports that the proteins in platelets are more than those in plasma, and it can be expected that if samples with platelet contamination are treated by nanoparticle enrichment methods, it may be more likely to cause platelet contamination of plasma proteins.

[0004] In terms of clinical translation, biomarkers for evaluating diseases screened using existing NP technology often have specific proteins related to platelets. If proteins related to contamination are screened as biomarkers for diseases, it will increase the risk of errors, specifically manifested as an increase in false positive rate, a decrease in specificity, and failure in clinical validation. For example, platelet contamination marker PF4 (Platelet Factor 4) may be misjudged as a potential biomarker for cardiovascular diseases, while in fact its abundance changes only reflect the degree of platelet activation during sample processing, leading to incorrect association of disease mechanisms. Therefore, providing a marker that can accurately evaluate platelet contamination in plasma proteomics samples is a problem that needs to be solved in the art. SUMMARY

[0005] In view of the above-mentioned shortcomings of the prior art, the purpose of the present application is to provide a marker combination for evaluating the degree of platelet contamination, to solve the problems in the prior art.

[0006] To achieve the above-mentioned purposes and other related purposes, the present application is obtained by the following technical solutions.

[0007] In a first aspect of the present application, a marker combination for assessing the degree of platelet contamination is provided, the marker combination comprising IDH2, LIMS1, MLEC, DIAPH1, GNAI2, ATP5F1B, RAB14, TBXAS1, JAM3, VDAC1, ATP2A3, ARHGAP6, VDAC3, ESAM, SLC25A5, ITGA6, PDLIM7, PF4V1, HSP90B1, HSPD1, RDH11, ATP2A2, VDAC2, RAB35, TREML1, GP5, PRDX3, ITGB1, MDH2, and GNB1.

[0008] In a second aspect of the present application, a reagent for quantitatively detecting a marker combination in a sample is provided for use in at least one of the following, the combination comprising IDH2, LIMS1, MLEC, DIAPH1, GNAI2, ATP5F1B, RAB14, TBXAS1, JAM3, VDAC1, ATP2A3, ARHGAP6, VDAC3, ESAM, SLC25A5, ITGA6, PDLIM7, PF4V1, HSP90B1, HSPD1, RDH11, ATP2A2, VDAC2, RAB35, TREML1, GP5, PRDX3, ITGB1, MDH2, and GNB1.

[0009] (1) assessing the degree of platelet contamination;

[0010] (2) preparing a product for assessing the degree of platelet contamination;

[0011] (3) calculating the number of platelets;

[0012] (4) preparing a product for calculating the number of platelets.

[0013] In some embodiments of the present application, the reagent is quantitatively detected by at least one of mass spectrometry, chromatography, surface-enhanced Raman spectroscopy, Western blotting, flow cytometry, protein array, immunoprecipitation, and immunoadsorption.

[0014] In some embodiments of the present application, the reagent is selected from one or more of a substance specific to the marker, a probe specific to the marker, and a protein chip.

[0015] In some embodiments of the present application, the substance specific to the marker comprises an antibody, a ligand protein, a polypeptide, a non-protein compound, and / or a nucleic acid aptamer.

[0016] In some embodiments of the present application, the sample comprises at least one of plasma, serum, tissue, and cells.

[0017] In some embodiments of the present invention, the sample includes an enriched sample obtained through pretreatment, the pretreatment including pretreatment methods involving or not involving nanoparticles.

[0018] In some embodiments of the present invention, the product comprises reagents, reagent kits, test strips, chips, devices, or detection systems.

[0019] A third aspect of the invention provides a product comprising a reagent for detecting the combination of markers described above for assessing the degree of platelet contamination.

[0020] In some embodiments of the present invention, the reagent is quantitatively detected by at least one of the following methods: mass spectrometry, chromatography, surface-enhanced Raman spectroscopy, Western blotting, flow cytometry, protein array, immunoprecipitation, and immunoadsorption.

[0021] In some embodiments of the present invention, the reagent is selected from one or more of substances specific to the marker, marker-specific probes, and protein chips.

[0022] In some embodiments of the present invention, the substances that are specific to the marker include antibodies, ligand proteins, polypeptides, non-protein compounds and / or nucleic acid aptamers.

[0023] In some embodiments of the present invention, the product comprises reagents, reagent kits, test strips, chips, devices, or detection systems.

[0024] In some embodiments of the present invention, the product further includes a protein abundance auxiliary detection reagent.

[0025] In some embodiments of the present invention, the test sample includes at least one of plasma, serum, tissue, and cells.

[0026] In some embodiments of the present invention, the sample includes an enriched sample obtained through pretreatment, the pretreatment including pretreatment methods involving or not involving nanoparticles.

[0027] A fourth aspect of the present invention provides a method for assessing the degree of platelet contamination, comprising the following steps:

[0028] 1) Collect the samples to be tested and determine the abundance of the markers in the above-mentioned combination of markers used to assess the degree of platelet contamination;

[0029] 2) Calculate the contamination index and / or platelet count of the sample to be tested. The formula for calculating the contamination index is as follows:

[0030] Pollution index = (IDH2 abundance + LIMS1 abundance + MLEC abundance + DIAPH1 abundance + GNAI2 abundance)

[0031] +ATP5F1B abundance +RAB14 abundance +TBXAS1 abundance +JAM3 abundance +VDAC1 abundance +ATP2A3 abundance +ARHGAP6 abundance +VDAC3 abundance +ESAM abundance +SLC25A5 abundance +ITGA6 abundance +PDLIM7 abundance +PF4V1 abundance +HSP90B1 abundance +HSPD1 abundance +RDH11 abundance +ATP2A2 abundance +VDAC2 abundance +RAB35 abundance +TREML1 abundance +GP5 abundance +PRDX3 abundance +ITGB1 abundance +MDH2 abundance

[0032] +GNB1 abundance) / total protein abundance;

[0033] The platelet count is calculated by mapping the contamination index to the actual cell count using a regression model.

[0034] 3) The contamination level of the sample is predicted based on the calculated contamination index and / or platelet count: the lower the contamination index and / or platelet count of the sample, the lower the degree of contamination; the higher the contamination index and / or platelet count of the sample, the higher the degree of contamination.

[0035] In some embodiments of the present invention, the pollution index is compared with a threshold value. If the index is higher than the threshold value, the pollution level is higher; if the index is lower than the threshold value, the pollution level is lower.

[0036] In some embodiments of the present invention, the threshold value is 0.006. If the threshold value is higher than 0.006, the sample is considered to be platelet contaminated and needs to be excluded from subsequent proteomics analysis.

[0037] In some embodiments of the present invention, the regression model includes: polynomial regression, support vector regression, regression tree, and multivariate adaptive regression splines or spline regression; preferably spline regression.

[0038] In some embodiments of the present invention, the sample includes at least one of plasma, serum, tissue, and cells.

[0039] In some embodiments of the present invention, the sample includes an enriched sample obtained through pretreatment, the pretreatment including pretreatment methods involving or not involving nanoparticles.

[0040] In some embodiments of the present invention, the preprocessing specifically includes the following steps:

[0041] The sample to be tested is incubated with nanoparticles. After incubation, it is washed, denatured, and enzymatically digested. In the pretreatment, the plasma sample is incubated with nanoparticles. The plasma sample and nanoparticles combine to form protein crowns (including soft protein crowns and hard protein crowns). The soft protein crowns are removed by washing, and the high-affinity hard protein crowns are retained. Subsequently, the sample can be used for proteomics analysis by denaturation and enzymatic digestion.

[0042] In some embodiments of the present invention, the incubation temperature is 20–40°C; it can also be 20–25°C, 25–30°C, 30–35°C or 35–40°C, or it can be 26°C, 27°C, 28°C, 29°C, 30°C, 31°C, 32°C, 33°C or 34°C.

[0043] In some embodiments of the present invention, the incubation time is 30-120 min; it can also be 30-120 min, 30-120 min, 30-120 min, 30-120 min, 30-120 min, 30-120 min, or 30-120 min.

[0044] In some embodiments of the present invention, the incubation rotation speed is 100 to 500 rpm; it can also be 100 rpm, 200 rpm, 300 rpm, 400 rpm or 500 rpm.

[0045] In some embodiments of the present invention, the materials of the nanoparticles include, but are not limited to, silica, molecular sieves, Fe3O4, liposomes, polymer nanoparticles, etc.

[0046] In some embodiments of the present invention, the types of nanoparticles include, but are not limited to, solid spheres, mesoporous structures, hollow mesoporous structures, and hierarchical porous structures.

[0047] In some embodiments of the present invention, the particle size of the nanoparticles is 300nm to 1000nm; it can also be 400nm, 500nm, 600nm, 700nm, 800nm, 900nm or 1000nm.

[0048] In some embodiments of the present invention, the mass-to-volume ratio of the nanoparticles to the sample to be tested is 3–15 mg / mL; it may also be 3–6 mg / mL, 6–9 mg / mL, 9–12 mg / mL or 12–15 mg / mL.

[0049] In some embodiments of the present invention, the sample to be tested is a diluted sample, specifically, the sample to be tested is diluted with a diluent.

[0050] In some embodiments of the present invention, the diluent includes an ionic surfactant and a buffer solution.

[0051] In some embodiments of the present invention, the ionic surfactant includes 3-[(3-cholamidopropyl)dimethylammonium]-1-propanesulfonate (CHAPS), 3-[(3-cholamidopropyl)dimethylammonium]-2-hydroxy-1-propanesulfonate (CHAPSO), hexadecyltrimethylammonium bromide (CTAB), sodium dodecyl sulfate (SDS), sodium sarkosyl dodecyl creatine (sarkosyl) or / and dodecyltrimethylammonium bromide (DTAB); preferably 3-[(3-cholamidopropyl)dimethylammonium]-1-propanesulfonate (CHAPS).

[0052] In some embodiments of the present invention, the concentration of the ionic surfactant is 0.01 to 0.1 w / w, and may also be 0.03 w / w%, 0.04 w / w%, 0.05 w / w%, 0.06 w / w%, or 0.07 w / w.

[0053] In some embodiments of the present invention, the buffer solution includes PBS buffer, Tris buffer, etc.

[0054] In some embodiments of the present invention, the diluent further includes a pH adjuster, such as ammonia, sodium hydroxide, sodium bicarbonate, sodium carbonate, etc., for adjusting the pH value of the diluent.

[0055] In some embodiments of the present invention, the pH value of the diluent is 7-11, and may also be 7-8, 8-9, 9-10 or 10-11, preferably 10-11.

[0056] In some embodiments of the present invention, the dilution factor is 2 to 8, wherein the dilution factor is the ratio of the volume after dilution to the volume before dilution; the dilution factor may also be 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5 or 8.

[0057] In some embodiments of the invention, the washing includes first washing the soft corona layer with a buffer solution, and then centrifuging to remove the soft protein crown.

[0058] In some embodiments of the present invention, the number of centrifugations is 2 to 5.

[0059] In some embodiments of the present invention, the conditions for a single centrifugation are 2000-10000g and 5-15min; wherein the centrifugal force can also be 2000-4000g, 4000-6000g, 6000-8000g or 8000-10000g; and the centrifugation time can also be 5-8min, 8-12min or 12-15min.

[0060] In some embodiments of the present invention, the pretreatment further includes a purification step; the purification method includes, but is not limited to, using commercial purification kits; and can be column purification reagents, magnetic bead purification reagents, gel electrophoresis reagents, etc.

[0061] A fifth aspect of the present invention provides a system for assessing the degree of platelet contamination, comprising the following modules:

[0062] a) Data collection module: Collects the samples to be tested and determines the abundance of the markers in the above-mentioned combination of markers used to assess the degree of platelet contamination;

[0063] b) Model calculation module: Calculates the contamination index and / or platelet count of the sample to be tested. The formula for calculating the contamination index is as follows:

[0064] Pollution index = (IDH2 abundance + LIMS1 abundance + MLEC abundance + DIAPH1 abundance + GNAI2 abundance + ATP5F1B abundance + RAB14 abundance + TBXAS1 abundance + JAM3 abundance + VDAC1 abundance + ATP2A3 abundance + ARHGAP6 abundance + VDAC3 abundance + ESAM abundance + SLC25A5 abundance + ITGA6 abundance + PDLIM7 abundance + PF4V1 abundance + HSP90B1 abundance + HSPD1 abundance + RDH11 abundance + ATP2A2 abundance + VDAC2 abundance + RAB35 abundance + TREML1 abundance + GP5 abundance + PRDX3 abundance + ITGB1 abundance + MDH2 abundance + GNB1 abundance) / total protein abundance;

[0065] The platelet count is calculated by mapping the contamination index to the actual cell count using a regression model.

[0066] c) Output prediction module: Predicts the contamination status of the sample based on the calculated contamination index and / or platelet count of the sample to be tested: the lower the contamination index and / or platelet count of the sample to be tested, the lower the degree of contamination; the higher the contamination index and / or platelet count of the sample to be tested, the higher the degree of contamination.

[0067] In some embodiments of the present invention, the pollution index is compared with a threshold value. If the index is higher than the threshold value, the output pollution level is higher; if the index is lower than the threshold value, the output pollution level is lower.

[0068] In some embodiments of the present invention, the threshold value is 0.006. If the threshold value is higher than 0.006, the sample is considered to be platelet contaminated and needs to be excluded from subsequent proteomics analysis.

[0069] In some embodiments of the present invention, the regression model includes: polynomial regression, support vector regression, regression tree, and multivariate adaptive regression splines or spline regression; preferably spline regression.

[0070] A sixth aspect of the present invention provides a computing device comprising:

[0071] At least one processing unit; and

[0072] At least one memory coupled to the processing unit and storing a program for execution by the processing unit, which, when executed by the processor, causes the processor to perform an assessment of platelet contamination levels.

[0073] A seventh aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method described above.

[0074] It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.

[0075] Beneficial effects:

[0076] To address the frequent issue of platelet contamination in proteomics samples, this invention discloses for the first time a combination of biomarkers for assessing platelet contamination, comprising the following biomarkers: IDH2, LIMS1, MLEC, DIAPH1, GNAI2, ATP5F1B, RAB14, TBXAS1, JAM3, VDAC1, ATP2A3, ARHGAP6, VDAC3, ESAM, SLC25A5, ITGA6, PDLIM7, PF4V1, HSP90B1, HSPD1, RDH11, ATP2A2, VDAC2, RAB35, TR EML1, GP5, PRDX3, ITGB1, MDH2, and GNB1; this biomarker combination can accurately identify platelet contamination indices and quantify platelet counts in samples, excluding platelet-contaminated samples obtained through pretreatment in proteomics and other methods. Furthermore, this biomarker combination is universal and unaffected by the type of nanoparticle in nanoparticle-based pretreatment methods, exhibiting high sensitivity and specificity. It can be used to identify platelet-contaminated samples obtained through various enrichment methods; it also facilitates the large-scale application of plasma proteomics in clinical scenarios such as early cancer screening and personalized medicine. Attached Figure Description

[0077] Figure 1 The development process for platelet contamination quality control biomarkers includes: (a) screening scheme for platelet-related biomarkers; (b) number of peptide precursors identified in the discovery dataset; (c) number of proteomes; (d) Spearman correlation analysis of platelet biomarkers; (e) design of biomarker validation experiments; (f) correlation between platelet count and contamination index; (g) application scenarios of biomarkers; and (h) calculation of contamination index for PRP and PPP samples (PRP: platelet-rich plasma, PPP: platelet-poor plasma).

[0078] Figure 2 Correlation statistics of 30 proteins used to assess the degree of blood cell contamination during pretreatment with NaY-type zeolite (a) and iron oxide nanoparticles (b).

[0079] Figure 3 Platelet contamination index for the lung cancer cohort. Detailed Implementation

[0080] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification.

[0081] Before further describing specific embodiments of the present invention, it should be understood that the scope of protection of the present invention is not limited to the specific embodiments described below; it should also be understood that the terminology used in the embodiments of the present invention is for describing specific embodiments and not for limiting the scope of protection of the present invention. Test methods in the following embodiments that do not specify specific conditions are generally performed under conventional conditions or as recommended by the respective manufacturers.

[0082] When numerical ranges are given in the embodiments, it should be understood that, unless otherwise stated in the present invention, both endpoints of each numerical range and any value between the two endpoints may be selected. Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. In addition to the specific methods, apparatus, and materials used in the embodiments, based on the knowledge of the prior art possessed by one of ordinary skill in the art and the description of this invention, any prior art methods, apparatus, and materials similar to or equivalent to those described, apparatus, and materials in the embodiments of this invention may be used to implement the present invention.

[0083] Example 1: Screening and Construction of a Plasma Proteomics Pretreatment Process Based on Nanoparticle Enrichment

[0084] The pretreatment process for plasma proteomics based on nanoparticles includes: plasma sample and nanoparticles binding to form protein corona (soft and hard corona), removal of soft corona, protein denaturation and enzymatic digestion, etc.

[0085] This embodiment uses silica nanoparticles to enrich low-abundance proteins in plasma. The specific steps are as follows:

[0086] First, take 15-30 μL of plasma sample and dilute it with 75-100 μL of 1× phosphate buffer (PBS, referred to as buffer 2, pH 10-11) containing 0.05 w / w% 3-[(3-cholamidopropyl)dimethylamino]-1-propanesulfonic acid and 0.02 v / v% ammonia. Then add 0.3-1.0 mg of solid spherical silica nanoparticles (NP, particle size 300-1000 nm) solution and incubate at 100-500 rpm for 30-120 minutes in a 30°C isothermal shaker. After incubation, wash the soft corona layer with buffer 2 diluted to 33 v / v%, then centrifuge at 2500-10000g for 10 minutes, discard the supernatant, add diluted buffer 2 to the precipitate, and centrifuge again. Repeat the centrifugation and washing three times to remove the soft corona.

[0087] The enriched samples were then subjected to protein denaturation, enzymatic digestion, and desalting purification. The specific procedure is as follows: First, protein denaturation was achieved using 50 μL of 8M urea and 2M thiourea. Then, under light-protected conditions, Tris(2-carboxyethyl)phosphine (TCEP) and 40 mM iodoacetamide (IAA) were added to a final concentration of 10 mM, and the reaction was carried out for 30–60 minutes to complete reduction and alkylation. Next, the urea concentration was reduced to below 1.2 M using 100 mM ABB dilution buffer, and 0.5 μg–2 μg of trypsin was added at a mass ratio of 1:10–1:25 for overnight enzymatic digestion. Finally, 30–50 μL of 10% trifluoroacetic acid (TFA) was added to terminate the reaction. The peptides generated from the enzymatic digestion were purified using a peptide desalting column and then vacuum dried.

[0088] Finally, the plasma samples, after pretreatment, underwent 24-minute Astral mass spectrometry (nDIA) analysis. The mass spectrometry parameters were as follows: approximately 200–400 ng of peptides were first loaded onto the trap column, followed by separation using a custom-designed analytical column (75 μm inner diameter × 15 cm length, 1.9 μm particle size) via 24-minute LC-MS. The initial LC gradient conditions were: 8% buffer B (Buffer B: 80% acetonitrile containing 0.1% formic acid (v / v); Buffer A: 0.1% formic acid (v / v) dissolved in mass spectrometry-grade ultrapure water), increasing to 10% B within 1.5 minutes, then to 30% B within 16 minutes, and finally to 40% B within 2 minutes. Each run included a 4.3-minute column washing and equilibration step. Eluted peptides were analyzed using an Orbitrap Astral mass spectrometer with the following parameters: FAIMS voltage -42V, full scan resolution 240,000, mass-to-charge ratio scan range 380–980Th; MS / MS scan range was the same as the full scan, using DIA mode (data independent of acquisition), with an isolation window width of 2Da.

[0089] The results showed that 3,000–6,000 proteomes could be stably identified per sample, with a batch coefficient of variation (CV) of less than 12%.

[0090] Example 2: Discovery and Validation of Platelet Contamination Biomarkers

[0091] Based on the established nanoparticle-based plasma protein pretreatment method, and combined with a plasma proteomics spectral library, experiments were designed to establish biomarkers and algorithms for assessing platelet contamination of plasma samples.

[0092] First, platelet-rich plasma (PRP) and platelet-free plasma (PPP) samples were collected and mixed in a certain ratio to obtain samples containing different degrees of platelet contamination. Figure 1a) After enrichment with nanoparticles, plasma samples with different platelet contamination were annotated with peptides and proteins using commonly used proteomics software combined with a spectral library (https: / / www.uniprot.org / ). The results showed that plasma samples with different platelet contamination levels achieved a greater amount of protein identification as the platelet count gradually increased.

[0093] The results showed that an average of 4580 proteomes were identified in PRP samples and an average of 2492 proteomes were identified in PPP samples. Figure 1 bc).

[0094] Proteins with a missing rate of less than 50% in all samples were then screened. Mfuzz clustering analysis was used to select the top 100 proteins with the highest correlation to platelet concentration (Spearman r > 0.95). Then, based on protein abundance, the top 30 abundant proteins were selected as biomarkers for evaluating platelet contamination, including IDH2, LIMS1, MLEC, DIAPH1, GNAI2, ATP5F1B, RAB14, TBXAS1, JAM3, VDAC1, ATP2A3, ARHGAP6, VDAC3, ESAM, SLC25A5, ITGA6, PDLIM7, PF4V1, HSP90B1, HSPD1, RDH11, ATP2A2, VDAC2, RAB35, TREML1, GP5, PRDX3, ITGB1, MDH2, and GNB1. The median Spearman correlation of these 30 proteins was 0.94. Figure 1 d). The peptide sequences corresponding to these proteins are detailed in the table below.

[0095] Table 1

[0096]

[0097]

[0098] This invention establishes a contamination index, which is the ratio of the sum of the abundance of 30 biomarkers in each sample to the abundance of all proteins in that sample. Specifically, it is called the contamination index (platelet contamination index = Σ biomarker abundance / total protein abundance). The contamination index is used to assess the degree of platelet contamination in a sample, with a cutoff value of 0.006. A contamination index higher than 0.006 can be considered a contaminated sample and needs to be excluded. The higher the contamination index, the more severe the platelet contamination.

[0099] The feasibility of using another dataset was then verified on the 30 proteins selected for evaluating platelet contamination.

[0100] Twelve groups of pure platelet and pure PPP samples were collected. Each sample was then counted using a platelet counter to determine the blood cell distribution and purity. Platelet and PPP samples were then mixed in groups of three to obtain four groups of pure platelet and pure PPP samples. The four groups of samples were then subjected to 10-stage dilutions for both platelet and PPP. Figure 1 e) Then, a platelet counter is used to count the platelets in each sample to determine the absolute platelet count for each sample.

[0101] Correlation analysis between platelet concentration and the abundance of 30 biomarkers showed good results. Furthermore, spline regression was used to fit the absolute platelet content and contamination index, resulting in a positive R-value. 2 =0.95, indicating a good correlation ( Figure 1 f).

[0102] A spline regression model was used to map the index to the actual cell count. The model was constructed as follows: Based on 10-level dilution data of 4 groups of samples in the validation set (each dilution gradient contained 3 biological replicates, and after removing 3 outlier samples, a total of 117 samples), a univariate spline function was used for fitting and modeling. The contamination index was used as the independent variable and the actual platelet count as the dependent variable. The number of nodes was set to k=3, and the smoothing coefficient s=50 (through multiple cross-validations, this parameter setting maximizes the balance between flexibility and smoothness in the model). Finally, the platelet count prediction curve (R²) was obtained. 2 =0.95), the model expression is: platelet count = Spline (contamination index), where the coefficients (coeffs) and nodes (knots) of the spline function are as follows: coeffs[6.20722104,6.21479977,6.71490564,7.23311078] / knots[0.00935505,0.0804042).

[0103] Finally, spline regression was applied to 11 pairs of PPP and PRP samples. Figure 1 g), the results show that the model demonstrates good discriminative ability for both types of samples. Figure 1 h). This indicates that the contamination index can be used to assess the number of platelets in a sample and the degree of platelet contamination.

[0104] Subsequently, a new set of plasma samples was selected, and the enriched plasma was obtained using the nanoparticle-based enrichment process for low-abundance plasma proteins described in Example 1. The contaminated samples were then detected using the aforementioned platelet contamination assessment method, and ROC curves were plotted. The results showed that it had high sensitivity and specificity.

[0105] Example 3: Universality of Platelet Pollution Biomarkers

[0106] To verify the applicability of the screened biomarkers to samples treated with other types of nanoparticles, two high-frequency nanoparticles reported in the literature were selected: NaY-type zeolite (particle size 300–700 nm) and silanol-functionalized iron(III) oxide (particle size 400–700 nm). PPP (platelet-rich plasma) and PRP (platelet-rich plasma) samples were prepared by collecting donor blood. 100 μL of each sample was mixed and then serially diluted in 10 steps. The diluted plasma samples were then treated with NP74 and NP81 nanoparticles, respectively, and nDIA analysis was performed.

[0107] The results showed that with increasing platelet concentration, the number of peptide precursors and proteomes identified in both types of nanoparticle-treated samples significantly increased, with over 6000 proteomes detected in a single injection. Thirty platelet-related biomarkers showed a high correlation with the degree of platelet contamination in both NaY-type zeolite-treated samples (median: 0.95, range 0.89–0.96 between the abundance of 30 proteins evaluating platelet contamination and the platelet count) and iron oxide-treated samples (median: 0.93, range 0.87–0.94). Figure 2 ).

[0108] This also demonstrates that the platelet contamination assessment method of the present invention is compatible with various nanoparticles such as molecular sieves and Fe3O4, and the correlation of biomarkers is maintained at 0.87-0.96, providing a unified framework for cross-laboratory data standardization.

[0109] Example 4 Application Case

[0110] To evaluate the effectiveness of nanoparticle-based techniques for enriching low-abundance plasma proteins and assessing platelet contamination, this study included 193 participants, including 42 patients with benign pulmonary nodules and 151 patients with early-stage malignant tumors.

[0111] All plasma samples were collected using EDTA vacuum blood collection tubes. Some patients underwent secondary sampling to evaluate the stability of the pretreatment; samples from repeated processing were not included in subsequent modeling. Blood was centrifuged (3000g, 4°C, 15 minutes) to collect plasma, which was then stored at 80°C. Peptide samples were then obtained using the nanoparticle-based enrichment process for low-abundance plasma proteins described in Example 1, and analyzed using an Astral instrument for quantitative analysis of peptides and proteins. Proteomics analysis showed an average of 4413 proteomes identified per plasma sample, with stable coefficients of variation in both biological and technical replicates. Platelet counts in the samples were assessed using the aforementioned contamination assessment methods, and one sample was found to contain detectable contamination markers (…). Figure 3 These contaminated samples were excluded from subsequent analysis, which is beneficial for the subsequent screening of benign lung nodules / lung cancer markers.

[0112] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A combination of biomarkers for assessing the degree of platelet contamination, said biomarker combination comprising IDH2, LIMS1, MLEC, DIAPH1, GNAI2, ATP5F1B, RAB14, TBXAS1, JAM3, VDAC1, ATP2A3, ARHGAP6, VDAC3, ESAM, SLC25A5, ITGA6, PDLIM7, PF4V1, HSP90B1, HSPD1, RDH11, ATP2A2, VDAC2, RAB35, TREML1, GP5, PRDX3, ITGB1, MDH2, and GNB1.

2. The use of the biomarker combination of claim 1 or the reagent for detecting the biomarker combination of claim 1 in at least one of the following: 1) assessing the degree of platelet contamination; 2) preparing a product for assessing the degree of platelet contamination; 3) calculating the platelet count; 4) preparing a product for calculating the platelet count.

3. The application according to claim 2, characterized in that, The reagents are detected by at least one of the following methods: mass spectrometry, chromatography, surface-enhanced Raman spectroscopy, Western blotting, flow cytometry, protein array, immunoprecipitation, and immunoadsorption.

4. The application according to claim 2, characterized in that, The reagents include one or more of the following: substances specific to the marker, marker-specific probes, and protein chips.

5. The application according to claim 4, characterized in that, The substances that are specific to the biomarker include antibodies, ligand proteins, peptides, non-protein compounds, and / or nucleic acid aptamers.

6. A product characterized in that, The product includes a reagent for detecting the combination of markers as described in claim 1.

7. The product according to claim 6, characterized in that, The reagents are detected by at least one of the following methods: mass spectrometry, chromatography, surface-enhanced Raman spectroscopy, Western blotting, flow cytometry, protein array, immunoprecipitation, and immunoadsorption.

8. The product according to claim 6, characterized in that, The reagents include one or more of the following: substances specific to the marker, marker-specific probes, and protein chips.

9. The product according to claim 8, characterized in that, The substances that are specific to the biomarker include antibodies, ligand proteins, peptides, non-protein compounds, and / or nucleic acid aptamers.

10. The product according to claim 6, characterized in that, The products include reagents, reagent kits, test strips, chips, devices, and detection systems.

11. A method for assessing the degree of platelet contamination, comprising the following steps: a) Collect the sample to be tested and determine the abundance of the markers in the marker combination of claim 1; b) Calculate the contamination index and / or platelet count of the sample to be tested, wherein the contamination index is calculated as follows: Contamination index = (IDH2 abundance + LIMS1 abundance + MLEC abundance + DIAPH1 abundance + GNAI2 abundance + ATP5F1B abundance + RAB14 abundance + TBXAS1 abundance + JAM3 abundance + VDAC1 abundance + ATP2A3 abundance + ARHGAP6 abundance + VDAC3 abundance + ESAM abundance + SLC25A5 abundance + ITGA6 abundance + PDLIM7 abundance + PF4V1 abundance + HSP90B1 abundance + HSPD1 abundance + RDH11 abundance + ATP2A2 abundance + VDAC2 abundance + RAB35 abundance + TREML1 abundance + GP5 abundance + PRDX3 Abundance + ITGB1 Abundance + MDH2 Abundance + GNB1 Abundance) / Total Protein Abundance; The platelet count is calculated by mapping the contamination index to the actual cell count through a regression model; 3) The contamination status of the sample is predicted based on the calculated contamination index and / or platelet count: the lower the contamination index and / or platelet count of the sample, the lower the platelet contamination level of the sample; the higher the contamination index and / or platelet count of the sample, the higher the platelet contamination level of the sample.

12. The method according to claim 11, characterized in that, The samples are plasma, tissue, cell and / or serum samples.

13. The method according to claim 11, characterized in that, The samples include those obtained after pretreatment, wherein the pretreatment may or may not include the step of incubation with nanoparticles.

14. The method according to claim 13, characterized in that, The pretreatment specifically includes the following steps: incubating the plasma sample with nanoparticles, followed by washing, denaturation, and enzymatic hydrolysis after incubation.

15. A system for assessing the degree of platelet contamination, comprising the following modules: a) a data collection module: collecting the sample to be tested and determining the abundance of the markers in the marker combination of claim 1; b) a model calculation module: calculating the contamination index and / or platelet count of the sample to be tested, wherein the contamination index is calculated using the following formula: Contamination Index = (IDH2 abundance + LIMS1 abundance + MLEC abundance + DIAPH1 abundance + GNAI2 abundance + ATP5F1B abundance + RAB14 abundance + TBXAS1 abundance + JAM3 abundance + VDAC1 abundance + ATP2A3 abundance + ARHGAP6 abundance + VDAC3 abundance + ESAM abundance + SLC25A5 abundance + ITGA6 abundance + PDLIM7 abundance + PF4V1 abundance + HSP90B1 abundance + HSPD1 abundance + RDH11 abundance + ATP2A2 abundance + VDAC2 Abundance + RAB35 Abundance + TREML1 Abundance + GP5 Abundance + PRDX3 Abundance + ITGB1 Abundance + MDH2 Abundance + GNB1 Abundance) / Total Protein Abundance; The platelet count is calculated by mapping the contamination index to the actual cell count through a regression model; c) Output prediction module: predicts the contamination status of the sample based on the calculated contamination index and / or platelet count of the sample to be tested: the lower the contamination index and / or platelet count of the sample to be tested, the lower the degree of contamination; the higher the contamination index and / or platelet count of the sample to be tested, the higher the degree of contamination.

16. A computing device comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 11 to 14.

17. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 11 to 14.

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