Spectral Library Search Improves Assignment of TMT Labeled MS/MS Spectra

JUMPlib: Integrative Search Tool Combining Fragment Ion Indexing with Comprehensive TMT Spectral Libraries

The identification of peptides is a cornerstone of mass spectrometry-based proteomics. Spectral library-based algorithms are well-established methods to enhance the identification efficiency of peptides during database searches in proteomics. However, these algorithms are not specifically tailored for tandem mass tag (TMT)-based proteomics due to the lack of high-quality TMT spectral libraries. Here, we introduce JUMPlib, a computational tool for a TMT-based spectral library search. JUMPlib comprises components for generating spectral libraries, conducting library searches, filtering peptide identifications, and quantifying peptides and proteins. Fragment ion indexing in the libraries increases the search speed and utilizing the experimental retention time of precursor ions improves peptide identification. We found that methionine oxidation is a major factor contributing to large shifts in peptide retention time. To test the JUMPlib program, we curated two comprehensive human libraries for the labeling of TMT6/10/11 and TMT16/18 reagents, with ∼286,000 precursor ions and ∼304,000 precursor ions, respectively. Our analyses demonstrate that JUMPlib, employing the fragment ion index strategy, enhances search speed and exhibits high sensitivity and specificity, achieving approximately a 25% increase in peptide-spectrum matches compared to other search tools. Overall, JUMPlib serves as a streamlined computational platform designed to enhance peptide identification in TMT-based proteomics. Both the JUMPlib source code and libraries are publicly available.

Spectral library search for improved TMTpro labelled peptide assignment in human plasma proteomics

Clinical biomarker discovery is often based on the analysis of human plasma samples. However, the high dynamic range and complexity of plasma pose significant challenges to mass spectrometry-based proteomics. Current methods for improving protein identifications require laborious pre-analytical sample preparation. In this study, we developed and evaluated a TMTpro-specific spectral library for improved protein identification in human plasma proteomics. The library was constructed by LC-MS/MS analysis of highly fractionated TMTpro-tagged human plasma, human cell lysates, and relevant arterial tissues. The library was curated using several quality filters to ensure reliable peptide identifications. Our results show that spectral library searching using the TMTpro spectral library improves the identification of proteins in plasma samples compared to conventional sequence database searching. Protein identifications made by the spectral library search engine demonstrated a high degree of complementarity with the sequence database search engine, indicating the feasibility of increasing the number of protein identifications without additional pre-analytical sample preparation. The TMTpro-specific spectral library provides a resource for future plasma proteomics research and optimization of search algorithms for greater accuracy and speed in protein identifications in human plasma proteomics, and is made publicly available to the research community via ProteomeXchange with identifier PXD042546.

Tetraspanin CD82 Correlates with and May Regulate S100A7 Expression in Oral Cancer

Many metastatic cancers with poor prognoses correlate to downregulated CD82, but exceptions exist. Understanding the context of this correlation is essential to CD82 as a prognostic biomarker and therapeutic target. Oral squamous cell carcinoma (OSCC) constitutes over 90% of oral cancer. We aimed to uncover the function and mechanism of CD82 in OSCC. We investigated CD82 in human OSCC cell lines, tissues, and healthy controls using the CRISPR-Cas9 gene knockout, transcriptomics, proteomics, etc. CD82 expression is elevated in CAL 27 cells. Knockout CD82 altered over 300 genes and proteins and inhibited cell migration. Furthermore, CD82 expression correlates with S100 proteins in CAL 27, CD82KO, SCC-25, and S-G cells and some OSCC tissues. The 37-50 kDa CD82 protein in CAL 27 cells is upregulated, glycosylated, and truncated. CD82 correlates with S100 proteins and may regulate their expression and cell migration. The truncated CD82 explains the invasive metastasis and poor outcome of the CAL 27 donor. OSCC with upregulated truncated CD82 and S100A7 may represent a distinct subtype with a poor prognosis. Differing alternatives from wild-type CD82 may elucidate the contradictory functions and pave the way for CD82 as a prognostic biomarker and therapeutic target.

Real-Time Spectral Library Matching for Sample Multiplexed Quantitative Proteomics

Sample multiplexed quantitative proteomics assays have proved to be a highly versatile means to assay molecular phenotypes. Yet, stochastic precursor selection and precursor coisolation can dramatically reduce the efficiency of data acquisition and quantitative accuracy. To address this, intelligent data acquisition (IDA) strategies have recently been developed to improve instrument efficiency and quantitative accuracy for both discovery and targeted methods. Toward this end, we sought to develop and implement a new real-time spectral library searching (RTLS) workflow that could enable intelligent scan triggering and peak selection within milliseconds of scan acquisition. To ensure ease of use and general applicability, we built an application to read in diverse spectral libraries and file types from both empirical and predicted spectral libraries. We demonstrate that RTLS methods enable improved quantitation of multiplexed samples, particularly with consideration for quantitation from chimeric fragment spectra. We used RTLS to profile proteome responses to small molecule perturbations and were able to quantify up to 15% more significantly regulated proteins in half the gradient time compared to traditional methods. Taken together, the development of RTLS expands the IDA toolbox to improve instrument efficiency and quantitative accuracy for sample multiplexed analyses.

Improving quantitation accuracy in isobaric-labeling mass spectrometry experiments with spectral library searching and feature-based peptide-spectrum match filter

Isobaric labeling relative quantitation is one of the dominating proteomic quantitation technologies. Traditional quantitation pipelines for isobaric-labeled mass spectrometry data are based on sequence database searching. In this study, we present a novel quantitation pipeline that integrates sequence database searching, spectral library searching, and a feature-based peptide-spectrum-match (PSM) filter using various spectral features for filtering. The combined database and spectral library searching results in larger quantitation coverage, and the filter removes PSMs with larger quantitation errors, retaining those with higher quantitation accuracy. Quantitation results show that the proposed pipeline can improve the overall quantitation accuracy at the PSM and protein levels. To our knowledge, this is the first study that utilizes spectral library searching to improve isobaric labeling-based quantitation. For users to conveniently perform the proposed pipeline, we have implemented the feature-based filter being executable on both Windows and Linux platforms; its executable files, user manual, and sample data sets are freely available at https://ms.iis.sinica.edu.tw/comics/Software_FPF.html . Furthermore, with the developed filter, the proposed pipeline is fully compatible with the Trans-Proteomic Pipeline.

Identification of Highly Sensitive Pleural Effusion Protein Biomarkers for Malignant Pleural Mesothelioma by Affinity-Based Quantitative Proteomics

Malignant pleural mesothelioma (MPM) is an asbestos-associated, highly aggressive cancer characterized by late-stage diagnosis and poor prognosis. Gold standards for diagnosis are pleural biopsy and cytology of pleural effusion (PE), both of which are limited by low sensitivity and markedly inter-observer variations. Therefore, the assessment of PE biomarkers is considered a viable and objective diagnostic tool for MPM diagnosis. We applied a novel affinity-enrichment mass spectrometry-based proteomics method for explorative analysis of pleural effusions from a prospective cohort of 84 patients referred for thoracoscopy due to clinical suspicion of MPM. Protein biomarkers with a high capability to discriminate MPM from non-MPM patients were identified, and a Random Forest algorithm was applied for building classification models. Immunohistology of pleural biopsies confirmed MPM in 40 patients and ruled out MPM in 44 patients. Proteomic analysis of pleural effusions identified panels of proteins with excellent diagnostic properties (90-100% sensitivities, 89-98% specificities, and AUC 0.97-0.99) depending on the specific protein combination. Diagnostic proteins associated with cancer growth included galactin-3 binding protein, testican-2, haptoglobin, Beta ig-h3, and protein AMBP. Moreover, we also confirmed previously reported diagnostic accuracies of the MPM markers fibulin-3 and mesothelin measured by two complementary mass spectrometry-based methods. In conclusion, a novel affinity-enrichment mass spectrometry-based proteomics identified panels of proteins in pleural effusion with extraordinary diagnostic accuracies, which are described here for the first time as biomarkers for MPM.

In-Depth Blood Proteome Profiling by Extensive Fractionation and Multiplexed Quantitative Mass Spectrometry

Blood in the circulatory system carries information of physiological and pathological status of the human body, so blood proteins are often used as biomarkers for diagnosis, prognosis, and therapy. Human blood proteome can be explored by the latest technologies in mass spectrometry (MS), creating an opportunity of discovering new disease biomarkers. The extreme dynamic range of protein concentrations in blood, however, poses a challenge to detect proteins of low abundance, namely, tissue leakage proteins. Here, we describe a strategy to directly analyze undepleted blood samples by extensive liquid chromatography (LC) fractionation and 18-plex tandem-mass-tag (TMT) mass spectrometry. The proteins in blood specimens (e.g., plasma or serum) are isolated by acetone precipitation and digested into peptides. The resulting peptides are TMT-labeled, separated by basic pH reverse-phase (RP) LC into at least 40 fractions, and analyzed by acidic pH RPLC and high-resolution MS/MS, leading to the quantification of ~3000 unique proteins. Further increase of basic pH RPLC fractions and adjustment of the fraction concatenation strategy can enhance the proteomic coverage (up to ~5000 proteins). Finally, the combination of multiple batches of TMT experiments allows the profiling of hundreds of blood samples. This TMT-MS-based method provides a powerful platform for deep proteome profiling of human blood samples.

Prosit-TMT: Deep Learning Boosts Identification of TMT-Labeled Peptides

The prediction of fragment ion intensities and retention time of peptides has gained significant attention over the past few years. However, the progress shown in the accurate prediction of such properties focused primarily on unlabeled peptides. Tandem mass tags (TMT) are chemical peptide labels that are coupled to free amine groups usually after protein digestion to enable the multiplexed analysis of multiple samples in bottom-up mass spectrometry. It is a standard workflow in proteomics ranging from single-cell to high-throughput proteomics. Particularly for TMT, increasing the number of confidently identified spectra is highly desirable as it provides identification and quantification information with every spectrum. Here, we report on the generation of an extensive resource of synthetic TMT-labeled peptides as part of the ProteomeTools project and present the extension of the deep learning model Prosit to accurately predict the retention time and fragment ion intensities of TMT-labeled peptides with high accuracy. Prosit-TMT supports CID and HCD fragmentation and ion trap and Orbitrap mass analyzers in a single model. Reanalysis of published TMT data sets show that this single model extracts substantial additional information. Applying Prosit-TMT, we discovered that the expression of many proteins in human breast milk follows a distinct daily cycle which may prime the newborn for nutritional or environmental cues.

Deephos: Predicted spectral database search for TMT-labeled phosphopeptides and its false discovery rate estimation

MOTIVATION

Tandem mass tag (TMT)-based tandem mass spectrometry (MS/MS) has become the method of choice for the quantification of post-translational modifications in complex mixtures. Many cancer proteogenomic studies have highlighted the importance of large-scale phosphopeptide quantification coupled with TMT labeling. Herein, we propose a predicted Spectral DataBase (pSDB) search strategy called Deephos that can improve both sensitivity and specificity in identifying MS/MS spectra of TMT-labeled phosphopeptides.

RESULTS

With deep learning-based fragment ion prediction, we compiled a pSDB of TMT-labeled phosphopeptides generated from ∼8,000 human phosphoproteins annotated in UniProt. Deep learning could successfully recognize the fragmentation patterns altered by both TMT labeling and phosphorylation. In addition, we discuss the decoy spectra for false discovery rate (FDR) estimation in the pSDB search. We show that FDR could be inaccurately estimated by the existing decoy spectra generation methods and propose an innovative method to generate decoy spectra for more accurate FDR estimation. The utilities of Deephos were demonstrated in multi-stage analyses (coupled with database searches) of glioblastoma, acute myeloid leukemia, and breast cancer phosphoproteomes.

AVAILABILITY

Deephos pSDB and the search software are available at https://github.com/seungjinna/deephos.

Plasma proteome profiling using tandem mass tag labeling technology reveals potential biomarkers for Parkinson's disease: a preliminary study

PURPOSE

Parkinson's disease (PD) is the second most frequently occurring progressive neurodegenerative disorder. Biomarkers are useful indicators for tracking disease progression, early diagnosis, and intervention of disease progression. We aimed to develop plasma biomarker panel which maybe aid to predict the onset and progression of PD.

EXPERIMENTAL DESIGN

Tandem mass tag (TMT) mass spectrometry was applied using an Orbitrap Lumos mass spectrometer to analyze plasma protein expression in patients diagnosed with PD and healthy controls.

RESULTS

In total, 555 proteins were quantified. Using a cut-off of p < 0.05 and a fold change of >1.2 for the variation in expression, 25 proteins were differentially expressed between the PD and control groups. Sixteen proteins were upregulated and nine were downregulated. Several proteins, including Chitinase-3-like protein 1 (CHI3L1) and thymosin beta-4 (TMSB4X) were implicated in PD pathogenesis.

CONCLUSIONS

The data from the TMT-based proteomic profiling of plasma samples in PD may help advance the understanding of the molecular mechanisms of PD and identify potential novel biomarkers of PD for further characterization.

Spleen proteome profiling of dairy goats infected with C. pseudotuberculosis by TMT-based quantitative proteomics approach

Corynebacterium pseudotuberculosis (C.pseudotuberculosis) is a zoonotic pathogen that can cause cheese lymphadenitis in goats. In order to obtain detailed information about the pathogenesis and host immune response of goats infected with C.pseudotuberculosis, we used tandem mass tag (TMT)-labeling proteomic analysis to detect differentially expressed proteins (DEPs) in dairy goats infected with C.pseudotuberculosis, and confirmed the altered proteins with western blot. A total of 6611 trusted proteins were identified, and 126 proteins were differentially abundant. Gene ontology (GO) analysis showed that all DEPs were annotated as biological processes, cell composition, and molecular functions. Biological processes mainly involve acute inflammation and immune response; cell components mainly involve extracellular areas and high-density lipoprotein particles; molecular functions are mainly antigen binding, ferric iron binding, and iron ion binding. KEGG analysis showed that a total of 102 pathways were significantly enriched, mainly lysosomes, phagosomes, and mineral absorption pathways. Our findings provided the relevant knowledge of spleen protein levels in goats infected with C.pseudotuberculosis and revealed the complex molecular pathways and immune response mechanisms in the process of C.pseudotuberculosis infection. SIGNIFICANCE: C.pseudotuberculosis is the most fatal infectious disease in dairy goats, causing huge economic losses. However, the molecular pathways and immune response mechanisms of C.pseudotuberculosis infection in goats remain unclear. Therefore, we conducted a comparative quantitative proteomics study on dairy goats infected with C.pseudotuberculosis. The results provide a basis for better understanding the complexity of C.pseudotuberculosis infection, reveal the complex molecular pathways and immune response mechanisms in C.pseudotuberculosis infection, and provide some clues for identifying potential therapeutic targets. This is the first report to show that C.pseudotuberculosis infection in dairy goats can disrupt the immune response mechanism and lead to massive immune cell death. The study provided new findings on the interaction between C.pseudotuberculosis and the host.

Basement membrane proteins in various arterial beds from individuals with and without type 2 diabetes mellitus: a proteome study

BACKGROUND

Basement membrane (BM) accumulation is a hallmark of micro-vessel disease in diabetes mellitus (DM). We previously reported marked upregulation of BM components in internal thoracic arteries (ITAs) from type 2 DM (T2DM) patients by mass spectrometry. Here, we first sought to determine if BM accumulation is a common feature of different arteries in T2DM, and second, to identify other effects of T2DM on the arterial proteome.

METHODS

Human arterial samples collected during heart and vascular surgery from well-characterized patients and stored in the Odense Artery Biobank were analysed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). We included ascending thoracic aortas (ATA) (n = 10 (type 2 DM, T2DM) and n = 10 (non-DM)); laser capture micro-dissected plaque- and media compartments from carotid plaques (n = 10 (T2DM) and n = 9 (non-DM)); and media- and adventitia compartments from ITAs (n = 9 (T2DM) and n = 7 (non-DM)).

RESULTS

We first extended our previous finding of BM accumulation in arteries from T2DM patients, as 7 of 12 pre-defined BM proteins were significantly upregulated in bulk ATAs consisting of > 90% media. Although less pronounced, BM components tended to be upregulated in the media of ITAs from T2DM patients, but not in the neighbouring adventitia. Overall, we did not detect effects on BM proteins in carotid plaques or in the plaque-associated media. Instead, complement factors, an RNA-binding protein and fibrinogens appeared to be regulated in these tissues from T2DM patients.

CONCLUSION

Our results suggest that accumulation of BM proteins is a general phenomenon in the medial layer of non-atherosclerotic arteries in patients with T2DM. Moreover, we identify additional T2DM-associated effects on the arterial proteome, which requires validation in future studies.

Proteomic Analysis of Hypoxia-Induced Senescence of Human Bone Marrow Mesenchymal Stem Cells

Methods

Hypoxia in hBMSCs was induced for 0, 4, and 12 hours, and cellular senescence was evaluated by senescence-associated β-galactosidase (SA-β-gal) staining. Tandem mass tag (TMT) labeling was combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) for differential proteomic analysis of hypoxia in hBMSCs. Parallel reaction monitoring (PRM) analysis was used to validate the candidate proteins. Verifications of signaling pathways were evaluated by western blotting. Cell apoptosis was evaluated using Annexin V/7-AAD staining by flow cytometry. The production of reactive oxygen species (ROS) was detected by the fluorescent probe 2,7-dichlorodihydrofluorescein diacetate (DCFH-DA).

Results

Cell senescence detected by SA-β-gal activity was higher in the 12-hour hypoxia-induced group. TMT analysis of 12-hour hypoxia-induced cells identified over 6000 proteins, including 686 differentially expressed proteins. Based on biological pathway analysis, we found that the senescence-associated proteins were predominantly enriched in the cancer pathways, PI3K-Akt pathway, and cellular senescence signaling pathways. CDK1, CDK2, and CCND1 were important nodes in PPI analyses. Moreover, the CCND1, UQCRH, and COX7C expressions were verified by PRM. Hypoxia induction for 12 hours in hBMSCs reduced CCND1 expression but promoted ROS production and cell apoptosis. Such effects were markedly reduced by the PI3K agonist, 740 Y-P, and attenuated by LY294002.

Conclusions

Hypoxia of hBMSCs inhibited CCND1 expression but promoted ROS production and cell apoptosis through activating the PI3K-dependent signaling pathway. These findings provided a detailed characterization of the proteomic profiles related to hypoxia-induced senescence of hBMSCs and facilitated our understanding of the molecular mechanisms leading to stem cell senescence.

Effects of long-term low dose saxitoxin exposure on nerve damage in mice

Saxitoxin (STX), as a type of paralytic shellfish poisoning (PSP), is gaining widespread attention due to its long existence in edible shellfish. However, the mechanism underlying STX chronic exposure-induced effect is not well understood. Here, we evaluated the neurotoxicity effects of long-term low-dose STX exposure on C57/BL mice by behavioral tests, pathology analysis, and hippocampal proteomics analysis. Several behavioral tests showed that mice were in a cognitive deficiency after treated with 0, 0.5, 1.5, or 4.5 μg STX equivalents/kg body weight in the drinking water for 3 months. Compared with control mice, STX-exposed mice exhibited brain neuronal damage characterized by decreasing neuronal cells and thinner pyramidal cell layers in the hippocampal CA1 region. A total of 29 proteins were significantly altered in different STX dose groups. Bioinformatics analysis showed that protein phosphatase 1 (Ppp1c) and arylsulfatase A (Arsa) were involved in the hippo signaling pathway and sphingolipid metabolism pathway. The decreased expression of Arsa indicates that long-term low doses of STX exposure can cause neuronal inhibition, which is a process related to spatial memory impairment. Taken together, our study provides a new understanding of the molecular mechanisms of STX neurotoxicity.

Use of a Recombinant Biomarker Protein DDA Library Increases DIA Coverage of Low Abundance Plasma Proteins

Credible detection and quantification of low abundance proteins from human blood plasma is a major challenge in precision medicine biomarker discovery when using mass spectrometry (MS). In this proof-of-concept study, we employed a mixture of selected recombinant proteins in DDA libraries to subsequently identify (not quantify) cancer-associated low abundance plasma proteins using SWATH/DIA. The exemplar DDA recombinant protein spectral library (rPSL) was derived from tryptic digestion of 36 recombinant human proteins that had been previously implicated as possible cancer biomarkers from both our own and other studies. The rPSL was then used to identify proteins from nondepleted colorectal cancer (CRC) EDTA plasmas by SWATH-MS. Most (32/36) of the proteins used in the rPSL were reliably identified from CRC plasma samples, including 8 proteins (i.e., BTC, CXCL10, IL1B, IL6, ITGB6, TGFα, TNF, TP53) not previously detected using high-stringency protein inference MS according to PeptideAtlas. The rPSL SWATH-MS protocol was compared to DDA-MS using MARS-depleted and postdigestion peptide fractionated plasmas (here referred to as a human plasma DDA library). Of the 32 proteins identified using rPSL SWATH, only 12 could be identified using DDA-MS. The 20 additional proteins exclusively identified using the rPSL SWATH approach were almost exclusively lower abundance (i.e., <10 ng/mL) proteins. To mitigate justified FDR concerns, and to replicate a more typical library creation approach, the DDA rPSL library was merged with a human plasma DDA library and SWATH identification repeated using such a merged library. The majority (33/36) of the low abundance plasma proteins added from the rPSL were still able to be identified using such a merged library when high-stringency HPP Guidelines v3.0 protein inference criteria were applied to our data set. The MS data set has been deposited to ProteomeXchange Consortium via the PRIDE partner repository (PXD022361).

Sample preparation approaches for qualitative and quantitative analysis of lipid-derived electrophile modified proteomes by mass spectrometry

Lipid-derived electrophile (LDE) modifications, which are covalent modifications of proteins by endogenous LDEs, are essential types of protein posttranslational modifications. LDE modifications alter the protein structure and regulate their biological processes in cells. LDE modifications of proteins are also closely associated with several diseases and function as potential biomarkers for clinical diagnosis. The crucial step in studying the LDE modifications is to enrich the LDE modified proteins/peptides from complex biological samples with high efficiency and high selectivity and quantify modified proteins/peptides with high accuracy. In this review, we summarize the recent progress in MS-based proteomic technologies to globally identify and quantify LDE modified proteomes, mainly focusing on discussing the qualitative and quantitative technologies.

Updated MS²PIP web server delivers fast and accurate MS² peak intensity prediction for multiple fragmentation methods, instruments and labeling techniques

MS²PIP is a data-driven tool that accurately predicts peak intensities for a given peptide's fragmentation mass spectrum. Since the release of the MS²PIP web server in 2015, we have brought significant updates to both the tool and the web server. In addition to the original models for CID and HCD fragmentation, we have added specialized models for the TripleTOF 5600+ mass spectrometer, for TMT-labeled peptides, for iTRAQ-labeled peptides, and for iTRAQ-labeled phosphopeptides. Because the fragmentation pattern is heavily altered in each of these cases, these additional models greatly improve the prediction accuracy for their corresponding data types. We have also substantially reduced the computational resources required to run MS²PIP, and have completely rebuilt the web server, which now allows predictions of up to 100 000 peptide sequences in a single request. The MS²PIP web server is freely available at https://iomics.ugent.be/ms2pip/.

Proteomics and Bioinformatics Analysis of Cartilage in Post-Traumatic Osteoarthritis in a Mini-Pig Model of Anterior Cruciate Ligament Repair

Background

Although osteoarthritis (OA) is a degenerative disease that is increasingly common with age, the pathogenesis of post-traumatic OA (PTOA) is poorly understood. This study aimed to undertake proteomics and bioinformatics analysis of cartilage in PTOA in a mini-pig model of anterior cruciate ligament repair (ACLR).

Material/Methods

The mini-pig model of PTOA involved autologous orthotopic ACLR. Screening and identification of differentially expressed proteins in the knee joint cartilage in the OA cartilage group were compared with the control group using tandem mass tag (TMT)-labeling liquid chromatography with tandem mass spectrometry (LC-MS-MS). A protein expression level >1.2 fold-change represented protein upregulation and <0.83 fold-change represented protein down-regulation Bioinformatics analysis included Gene Ontology (GO) functional annotation and the Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analysis to determine the biological functions and pathways of proteins showing altered expression profiles associated with OA.

Results

There were 2,950 proteins screened from the knee cartilage tissues of the OA model group using quantitative TMT-labeling LC-MS-MS. There were 491 proteins identified with altered expression profiles, 198 proteins were upregulated and 293 proteins were down-regulated in the OA cartilage group. GO function and KEGG pathway enrichment analysis of the 491 proteins identified their functions in cellular processes, metabolic processes, and biological regulation.

Conclusions

Proteomics and bioinformatics analysis of cartilage in PTOA in a mini-pig model of ACLR identified OA-related proteins.