Abstract
This review summarizes and exemplifies the current understanding of osteoarthritis in vitro models and describes their relevance for new insights in the future of osteoarthritis research. Our friend and highly appreciated colleague, Prof. Alan Grodzinsky has contributed greatly to the understanding of joint tissue biology and cartilage biomechanics. He frequently utilizes in vitro models and cartilage explant cultures, and recent work also includes proteomics studies. This review is dedicated to honor his 75-year birthday and will focus on recent proteomic in vitro studies related to osteoarthritis, and within this topic highlight some of his contributions to the field.
You have full access to this open access chapter, Download chapter PDF
Similar content being viewed by others
Keywords
1 Introduction
Osteoarthritis (OA), the most common degenerative joint disease, is a major source of pain, disability, and socioeconomic cost worldwide [17]. OA is a complex disease affecting the whole joint and multiple molecular and clinical phenotypes of OA seem to exist [38]. The pathologic changes seen in OA joints include degradation of the articular cartilage, thickening of the subchondral bone, osteophyte formation, variable degrees of synovial inflammation, degeneration of ligaments and, in the knee, the menisci, and hypertrophy of the joint capsule [30]. The degradation of articular cartilage is caused by an increased proteolytic activity of matrix degrading enzymes such as matrix metalloproteinases (MMPs) and aggrecanases. However, as the detailed molecular mechanisms involved in OA initiation and progression remain poorly understood, no effective Disease-Modifying OA Drugs (DMOADs) are currently available.
Human tissue samples for the study of osteoarthritis are often collected at the time of joint replacement when disease progression is in late stage, and limit researchers’ ability to study the early development factors that contribute to the disease. The inherent variability of OA disease progression and onset of symptoms between individuals also presents challenges for studying OA pathophysiology. To overcome these limitations, in vitro models have been extensively used to address queries related to pathological changes, drug-target interactions, molecular pathways and to investigate the roles of pro-inflammatory cytokines in certain conditions.
Proteomics is the analysis of the entire protein complement of a cell, tissue, or organism under a specific, defined set of conditions. Proteomics applications in the field of OA has become rather common [19] as the technology can analyze complex samples in a more discovery-based approach than traditional specific methods such as enzyme-linked immunosorbent assays (ELISA). In this review, we have limited the content and searched the literature for proteomics applications (by mass spectrometry) within OA using in vitro experiments that are further discussed below.
2 In Vitro Models in Osteoarthritis
One of the main challenges in osteoarthritis research is to find a model that accurately represents the mechanisms of the disease. Different models have been developed, each with its own advantages and disadvantages [12, 22, 49, 50], but there is currently no consensus or gold standard approach. The two most common in vitro models are cell cultures and explant cultures.
Cell culture models typically target the chondrocyte as the cell type of interest for OA research but also synoviocytes and other joint cells have been targeted. Monolayer cell culture models are inexpensive, easy to use and allow for many replicates to be made from a single source of tissue. The layout of cells as they are put on a flat surface in a culture flask exposes them to an equal amount of growth factors in the surrounding media. Typical applications of monolayer cultures are the effect of cytokine stimulation, osmotic pressure, or the role of synovium in OA [12]. Like monolayer cell culture models, co-culture models are also used to study the effect of cytokines and osmotic pressure but have the added benefit of allowing cell-cell interactions and crosstalk between cell types to be investigated. However, co-culture experiments can be costly, and are limited because different cell types can require different conditions and cells may de-differentiate depending on the co-culture system. A limitation of cell cultures is the potential loss of chondrogenic phenotype as the chondrocyte is isolated from the extracellular matrix, due to their sensitivity to their molecular environment [11, 22].
Explant models are derived directly from in vivo tissue and for the study of OA pathophysiology, they have the unmistakable advantage of being a better representation of the in vivo tissue compared to cell cultures, as the overall characteristics of the tissue are maintained. An advantage of explant models over in vivo models is that they, to a greater extent, allow standardization and controlled variables [56]. Explant models are relatively inexpensive and easy to set up and can be used to study both inflammatory processes and biomechanical loading of the tissue. Despite the benefits over cell culture models, the use of tissue explants also has disadvantages such as chondrocyte death at the edge of the explant and limited number of cells. Explant models are also limited in that few replicates are available from the tissue source [50, 56]. Due to the limitations in sample availability, much of the published work is performed using animals, which is a limitation as not all findings can be directly translated to humans [12].
3 Inflammatory Models
The development of inflammatory processes in OA involves the increased expression of catabolic proteins in chondrocytes following cytokine exposure [22]. Cytokines are signaling proteins involved in inflammation response and are produced by nearly all cell types [39]. The balance and interplay of cytokines is increasingly being recognized to have a central role in OA disease progression [60]. To study these effects, models of pro- and anti-inflammatory cytokines are implemented in cell culture or explant models. Some of the most important pro-inflammatory cytokines used for induction of OA-like biological changes in these models are IL-1α, IL-1β, TNF-α, IL-6, leukemia inhibitory factor (LIF), oncostatin-M (OSM), IL-15, IL-17, and IL-18 [18, 40, 60]. In contrast, the major anti-inflammatory cytokines, which are involved in OA pathogenesis by inhibiting actions of catabolic cytokines, are IL-4, IL-10, IL-11 and IL-13, IL-1 receptor antagonist (IL-1Ra), and interferon (IFN)- γ [18, 60].
Investigations to compare different pro-inflammatory cytokines were made in a cell culture model using RNA-seq [42]. In this study, the authors studied the effect of IFN-γ, IL-1β, IL-4 and IL-17 on gene expression in OA chondrocytes and found that 2800 genes were altered in chondrocytes treated with IL-1β. The mechanisms of IL-1β related to inflammation in OA were also studied in an in vitro cell culture model where human articular chondrocytes were cultured with or without recombinant IL-1β [24]. Known proteome changes following IL-1β stimulation, such as activation of the NFKB pathway, and subsequent synthesis of IL-1β, IL-6, IL-8, MMP-13, and ADAMTS-5, were validated by mass spectrometry on articular cartilage from three donors. After the cells had been stimulated with recombinant IL-1β for 20 h, IL-1β, IL-6, MMP-13 and IL-8 were upregulated. The authors also demonstrated that microRNA-140 inhibits the activation of the NFKB pathway, meaning a possible increase in cartilage repair and decrease of cartilage breakdown.
4 Mechanical Loading
In addition to biochemical factors, the development of osteoarthritis is affected by the biomechanical homeostasis of the joint. While mechanical loading is essential for the function and maintenance of healthy joints, mechanical overload induces molecular events similar to those stimulated by pro-inflammatory cytokines [13, 20]. It is becoming increasingly clear that impact-induced injuries not only cause cell damage, but also initiate progressive tissue damage and are recognized as a risk factor for OA. With this in mind, load-based models using tissue explants are effective systems for simulating the development of post-traumatic OA.
A study of post-traumatic OA has demonstrated a relationship between injury-induced oxidative damage and progressive matrix degradation [34]. Further, in vitro models have shown that while moderate intermittent compression have anti-catabolic effects on cartilage homeostasis, the cellular response to high compression involves degradation and decreased biosynthesis of ECM, and upregulation of pro-inflammatory enzymes [27, 29].
Due to variations in experimental protocols defined as injurious compression, direct comparisons of outcomes from models can be difficult. Indeed, mechanical injury can be induced in a multitude of ways; short repetitive loading, cyclic loading over a longer period of time, cartilage can be injured from a weight which drops down from a defined height, and explants can be compressed under confined conditions such that bulging of the explants do not occur [25].
5 Proteomics
The advances in mass spectrometry (MS) over the last decade has enabled this powerful technology to be frequently used in proteomics applications [1, 33]. The most common application “bottom-up proteomics” includes an enzymatic digestion of proteins e.g. extracted from cells, tissue or released into cell/tissue culture media. Proteins are digested with a sequence-specific enzyme like trypsin to generate a complex mixture of peptides. Peptides are usually separated by liquid chromatography, after which they enter a mass spectrometer where peptide ions are measured, to enable their identification and quantification. The data acquisition methods can be either discovery based or targeted against a pre-determined list of peptides. Both approaches have been utilized for in vitro studies in osteoarthritis research, see Table 4.1. The discovery approach has the advantage of not being biased against any particular proteins, although in practice there is some bias towards medium and high abundant proteins as the precursor selection for MSMS experiments are based on top N most intense peptides i.e. data-dependent acquisition (DDA), and thereby low abundant proteins can be missed out. There is another discovery approach using data-independent acquisition (DIA) where all precursors within a certain m/z range, sequential isolation windows of typical 20–25 Da, are selected for fragmentation to cover a typical mass range for proteolytic digests. This results in highly complex and data information-rich datasets that typically are matched against spectral libraries using a targeted analysis [61]. This latter approach combines an untargeted data collection with a targeted data analysis, reducing missing values and in addition enables data to be re-analyzed against more comprehensive libraries.
The traditional targeted approach, multiple reaction monitoring (MRM) is usually performed on triple quadrupole instruments using preset precursor mass filter (Q1), collision cell for fragmentation (Q2) and precursor fragment mass filter (Q3) with combined settings called peptide transitions which can be identified and optimized for highest sensitivity in pilot experiment [16]. The targeted approach has the advantage of high sensitivity, better reproducibility and fewer missing values and it is suitable for absolute quantification using heavy-isotope labeled standards while having limitations in a number of precursors as well as being limited to the pre-selection of targets missing out on novel findings (non-discovery). The development of parallel reaction monitoring (PRM) does increase the number of targets available and also results in full MSMS scans as the third quadrupole is replaced with a high-resolution mass analyzer (orbitrap) [43].
6 Proteomics Applications Using In Vitro Models
Mass spectrometry-based proteomics is a powerful technique that has increasingly enabled insights generated from OA in vitro experiments and has been used for characterizing biological elements relevant to OA pathophysiology, such as extracellular matrix components and structure. Cartilage is the most used tissue in OA in vitro studies and in proteomics application special considerations must be taken into account due to its physical characteristics. Articular cartilage is an avascular tissue with few cells and an extensive extracellular matrix (ECM) with major components being collagen (mainly type II) and proteoglycans (mainly aggrecan). The crossed-linked collagen network results in poor extractability with only a minor part (soluble fraction) being measured [41]. The high level of aggrecan can cause problems as the highly negatively charged glycosaminoglycan chains can interfere in the sample preparation steps ultimately affecting the chromatography performance [21]. However, despite these difficulties, more than 1000 additional proteins can be identified using current proteomics technologies [8].
Earlier in this review, we described a typical bottom-up proteomics workflow and here, we will demonstrate some of its applications using in vitro experiments related to OA. An experimental workflow for an in vitro model using proteomics is shown in Fig. 4.1.
In early proteomics studies, a commonly applied technique was two-dimensional gel electrophoresis (2D SDS-PAGE), which separated complex mixtures of a sample by isoelectric point (pI) and molecular weight. Following this, spots of interest and subsequent differentially expressed proteins could be identified by MS while quantification was usually performed using image analysis and intensities of matching protein spots [23]. Some limitations of this technique include low sensitivity, labor intensive work and poor ability for automation [48]. A simpler alternative was the more straightforward “salami” approach where sample lanes from 1D gels were cut into multiple bands followed by LC-MS analysis. However, due to these limitations and overall technological developments in the field, separation by liquid chromatography coupled with MS (gel-free proteomics) has been the preferred method in recent studies.
6.1 Targeted Proteomics Applications
6.1.1 Explant Cultures
A cartilage explant study by Melin-Fürst et al. [14] used MRM, a targeted approach, to characterize the inflammatory processes involved cartilage degradation, induced by IL-1α in bovine cartilage explants, and monitor interactions with the complement system. Following cytokine stimulation, the authors found a decrease in proteoglycan and collagen content in the cartilage, and activation of the complement.
MRM was also used in a study to characterize cartilage response to mechanical injury and cytokine treatment [58]. The targeted approach allowed the authors to monitor a predefined set of potential molecular biomarkers including cleavage neoepitopes, see also Fig. 4.2. In this work, both the explant culture media (individual time points) and the final explant, representing the sum of events during the entire culture period, were measured. The neo-epitope measurements give extra biological value as it represents an active proteolytic event being measured in addition to the overall protein release.
6.2 Discovery Proteomics Applications
6.2.1 Cell Cultures
Several comparative proteomic analyses that aim to describe the proteome of OA have been conducted as cell culture models analyzed using discovery-based acquisition methods. One such study used 2D SDS-PAGE and subsequent discovery MS to analyze the proteome of human articular chondrocytes and described the phosphorylation status of differentially expressed proteins in OA progression [26]. Another study reported on the role of hypertrophy-like alterations in chondrocytes in OA using high resolution MS [57].
Secretome analysis can provide information on the mechanisms behind remodeling of ECM in response to drug treatment or mechanical load and thereby provide insights into the pathogenesis of OA. The effect of chondroitin sulfate in the presence of IL-1β on proteins in chondrocyte secretome was examined in a study from 2012 by 2D SDS-PAGE and SILAC in a discovery workflow [9]. The study found 75 proteins in the secretome, 18 of which were modulated by chondroitin sulfate, and provided evidence of its anti-angiogenic, anti-inflammatory, and anti-catabolic properties.
Using bovine cartilage explant monoculture and cartilage-synovium co-culture, a recent study investigated the role of cartilage-synovium crosstalk in a discovery-based mass spectrometry experiment [37]. Sustained doses of IL-1Ra were shown to suppress cytokine-induced catabolism in cartilage more effectively in the presence of synovium, which was associated with endogenous production of anti-catabolic factors.
Though most of the discovery workflows relied on data dependent acquisition (DDA), one study, focusing on the difference in the phosphoproteome of OA and acute joint fracture in synovial tissue, used data independent acquisition (DIA) in an IL-1β–treated human synoviocyte (HS) in vitro model to verify their results [55]. The study found that IL-1β could induce HS to secrete proteins associated with the endosomal/vacuolar pathway, endoplasmic reticulum/Golgi secretion, complement activation, and collagen degradation.
6.2.2 Explant Cultures
Cartilage degradation is a well-established process in OA pathogenesis, and as such, a commonly studied model is the degradation of cartilage upon cytokine stimulation in explant culture. Several proteomic studies have employed this model to measure the effects of pro-inflammatory cytokines on articular cartilage. One study from 2016 investigated the chondrocyte response to IL-1α within native cartilage tissue and its secretome using discovery LC-MS and whole-genome expression profiling using microarray [59]. The study was the first to report on the effects of IL-1α in native cartilage and cartilage lacking the catalytic domain of ADAMTS5 (aggrecanase) and identified more than 150 proteins modulated by IL-1α. Additionally, the combined LC-MS and microarray analysis permitted the authors to differentiate between proteins modulated by IL-1α on gene expression level and those which were a product of ECM degradation.
Discovery LC-MS using DDA was used in a metabolomics and proteomics study published in 2020, where Anderson et al. studied OA pathogenesis using an equine cartilage explant model [2]. In the study, nine potential novel OA neoepitope peptides were discovered.
Unbiased labeled approaches such as Isobaric Tag for Relative and Absolute Quantitation (ITRAQ) were used in two studies. Stevens et al. used traumatic injury, IL-1β or TNF-α compared to control showing increased levels of MMPs and proteins of the innate immunity while the mechanical injury mainly led to an increased release of intracellular proteins [53]. Lourido et al. conducted a comparative profiling of proteins in early- versus late-stage OA, and subsequent clustering analysis and found upregulation of periostin and downregulation of osteoprotegerin in OA [32]. In a recent study a cartilage injury model was described using articular cartilage explants treated with cytokines and mechanical injury [7]. The addition of dexamethasone was shown to rescue the catabolic response but not the anabolic dysregulation. The treatment effects in this model are illustrated in a heatmap (Fig. 4.3).
7 Concluding Remarks and Future Perspectives
In this review, we have shown an increased use of proteomics applications within in vitro models during the last decade and this trend will most likely continue. The high sensitivity and selectivity offered by current and future MS instruments allow identification and quantification of a large number of proteins in highly complex samples. These information-rich data sets further allow much more detailed results to be obtained including individual peptide levels (relative abundances), post-translational modifications (e.g. measuring catabolic proteolytic events, phosphorylation states and protein synthesis), hence making this a very attractive technology. Novel developments in MS instrumentation, data acquisition methods as well as improved tools for data processing and evaluation tools will enable an increased depth of the proteome improving the bioinformatic evaluation of the data to identify proteins involved in specific functional pathways and interpretation of protein-protein association networks. Clustering analysis can also identify proteins following similar patterns e.g. grouping proteins according to their kinetic release profile [6].
In comparison to cell culture models, explant models better represent the extracellular matrix environment in vivo and also possess the ability to perform mechanical loading experiments. The kinetics of specific proteins release can easily be addressed as the culture medium needs to be replaced every 2–3 days creating a longitudinal experiment that can reflect both early-, mid- and late stages in the model. This is a great potential when healthy donors are available as early changes can be investigated. In vitro models are also well suited for molecular therapeutic interventions e.g. applying potential drug candidates for treatment purposes. One example is the glucorticoid dexamethasone that has been extensively studied with somewhat conflicting results in the past [5] but in explant culture, the combination of dexamethasone with insulin-like growth factor-1 inhibited both the loss of soluble proteoglycan (sGAG) and collagen, rescued the suppression of matrix biosynthesis and inhibited loss of chondrocyte viability induced by IL-1α treatment [28]. Drug delivery is an important aspect to this as without a carrier, intra-articular injections of the drug is rapidly cleared from the joint cavity. The Grodzinsky group has circumvented this drawback by linking the drug to a basic carrier enabling rapid uptake and sustained delivery both in vitro [3] and in vivo [4]. The Dex treatment effect was further investigated using proteomics in a post-traumatic in vitro model where it suppressed most of the proteins affected by cytokine+injury treatment versus control [6, 7].
The ground-breaking work of Prof. Grodzinsky and his contributions to the research community has been widely recognized by numerous awards. He is considered as a world leader in his field and in 2021, he received the most prestigious Lifetime Achievement Award by the Osteoarthritis Research Society International.
References
Aebersold R, Mann M (2016) Mass-spectrometric exploration of proteome structure and function. Nature 537:347–355. https://doi.org/10.1038/nature19949
Anderson JR, Phelan MM, Foddy L, Clegg PD, Peffers MJ (2020) Ex vivo equine cartilage explant osteoarthritis model: a metabolomics and proteomics study. J Proteome Res 19:3652–3667. https://doi.org/10.1021/acs.jproteome.0c00143
Bajpayee AG, Quadir MA, Hammond PT, Grodzinsky AJ (2016) Charge based intra-cartilage delivery of single dose dexamethasone using Avidin nano-carriers suppresses cytokine-induced catabolism long term. Osteoarthr Cartil 24:71–81. https://doi.org/10.1016/j.joca.2015.07.010
Bajpayee AG, Scheu M, Grodzinsky AJ, Porter RM (2014) Electrostatic interactions enable rapid penetration, enhanced uptake and retention of intra-articular injected avidin in rat knee joints. J Orthop Res 32:1044–1051. https://doi.org/10.1002/jor.22630
Black R, Grodzinsky A (2019) Dexamethasone: chondroprotective corticosteroid or catabolic killer? Eur Cell Mater 38:246–263. https://doi.org/10.22203/ecm.v038a17
Black RM, Wang Y, Struglics A, Lorenzo P, Chubinskaya S, Grodzinsky AJ, Önnerfjord P (2021) Proteomic clustering reveals the kinetics of disease biomarkers in bovine and human models of post-traumatic osteoarthritis. Osteoarthr Cartil Open 3:100191. https://doi.org/10.1016/j.ocarto.2021.100191
Black RM, Wang Y, Struglics A, Lorenzo P, Tillgren V, Rydén M, Grodzinsky AJ, Önnerfjord P (2020) Proteomic analysis reveals dexamethasone rescues matrix breakdown but not anabolic dysregulation in a cartilage injury model. Osteoarthr Cartil Open 2:100099. https://doi.org/10.1016/j.ocarto.2020.100099
Bundgaard L, Åhrman E, Malmström J, dem Keller UA, Walters M, Jacobsen S (2021) Effective protein extraction combined with data independent acquisition analysis reveals a comprehensive and quantifiable insight into the proteomes of articular cartilage and subchondral bone. Osteoarthr Cartil 30:137–146. https://doi.org/10.1016/j.joca.2021.09.006
Calamia V, Lourido L, Fernández-Puente P, Mateos J, Rocha B, Montell E, Vergés J, Ruiz-Romero C, Blanco FJ (2012) Secretome analysis of chondroitin sulfate-treated chondrocytes reveals anti-angiogenic, anti-inflammatory and anti-catabolic properties. Arthritis Res Ther 14:R202–R202. https://doi.org/10.1186/ar4040
Calamia V, Rocha B, Mateos J, Fernández-Puente P, Ruiz-Romero C, Blanco FJ (2011) Metabolic labeling of chondrocytes for the quantitative analysis of the Interleukin-1-beta-mediated modulation of their intracellular and extracellular proteomes. J Proteome Res 10:3701–3711. https://doi.org/10.1021/pr200331k
Caron MMJ, Emans PJ, Coolsen MME, Voss L, Surtel DAM, Cremers A, van Rhijn LW, Welting TJM (2012) Redifferentiation of dedifferentiated human articular chondrocytes: comparison of 2D and 3D cultures. Osteoarthr Cartil 20:1170–1178. https://doi.org/10.1016/j.joca.2012.06.016
Cope PJ, Ourradi K, Li Y, Sharif M (2019) Models of osteoarthritis: the good, the bad and the promising. Osteoarthr Cartil 27:230–239. https://doi.org/10.1016/j.joca.2018.09.016
Engstrøm A, Gillesberg FS, Groen SS, Frederiksen P, Bay-Jensen A-C, Karsdal MA, Thudium CS (2021) Intermittent dynamic compression confers anabolic effects in articular cartilage. Appl Sci 11:7469. https://doi.org/10.3390/app11167469
Fürst CM, Åhrman E, Bratteby K, Waldemarson S, Malmström J, Blom AM (2016) Quantitative mass spectrometry to study inflammatory cartilage degradation and resulting interactions with the complement system. J Immunol 197:3415–3424. https://doi.org/10.4049/jimmunol.1601006
Geurts J, Jurić D, Müller M, Schären S, Netzer C (2018) Novel ex vivo human osteochondral explant model of knee and spine osteoarthritis enables assessment of inflammatory and drug treatment responses. Int J Mol Sci 19:1314. https://doi.org/10.3390/ijms19051314
Gillet LC, Navarro P, Tate S, Röst H, Selevsek N, Reiter L, Bonner R, Aebersold R (2012) Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics 11(6). https://doi.org/10.1074/mcp.o111.016717
Gillette MA, Carr SA (2013) Quantitative analysis of peptides and proteins in biomedicine by targeted mass spectrometry. Nat Methods 10:28–34. https://doi.org/10.1038/nmeth.2309
Glyn-Jones S, Palmer AJR, Agricola R, Price AJ, Vincent TL, Weinans H, Carr AJ (2015) Osteoarthritis. Lancet 386:376–387. https://doi.org/10.1016/s0140-6736(14)60802-3
Goldring MB (2000) Osteoarthritis and cartilage: the role of cytokines. Curr Rheumatol Rep 2:459–465. https://doi.org/10.1007/s11926-000-0021-y
Haartmans MJJ, Emanuel KS, Tuijthof GJM, Heeren RMA, Emans PJ, Cillero-Pastor B (2021) Mass spectrometry-based biomarkers for knee osteoarthritis: a systematic review. Expert Rev Proteomics 18:693–706. https://doi.org/10.1080/14789450.2021.1952868
Henrotin Y, Pesesse L, Sanchez C (2011) Mechanical stretch and cytokines. Springer, pp 223–233. https://doi.org/10.1007/978-94-007-2004-6_10
Hsueh M-F, Khabut A, Kjellström S, Önnerfjord P, Kraus VB (2016) Elucidating the molecular composition of cartilage by proteomics. J Proteome Res 15:374–388. https://doi.org/10.1021/acs.jproteome.5b00946
Johnson CI, Argyle DJ, Clements DN (2016) In vitro models for the study of osteoarthritis. Vet J 209:40–49. https://doi.org/10.1016/j.tvjl.2015.07.011
Joshi K, Patil D (2017) Innovative approaches in drug discovery. Elsevier, pp 273–294. https://doi.org/10.1016/b978-0-12-801814-9.00009-x
Karlsen TA, de Souza GA, Ødegaard B, Engebretsen L, Brinchmann JE (2016) microRNA-140 inhibits inflammation and stimulates chondrogenesis in a model of interleukin 1β-induced osteoarthritis. Mol Ther Nucleic Acids 5:e373. https://doi.org/10.1038/mtna.2016.64
Kurz B, Lemke AK, Fay J, Pufe T, Grodzinsky AJ, Schünke M (2005) Pathomechanisms of cartilage destruction by mechanical injury. Ann Anat 187:473–485. https://doi.org/10.1016/j.aanat.2005.07.003
Lambrecht S, Verbruggen G, Verdonk PCM, Elewaut D, Deforce D (2008) Differential proteome analysis of normal and osteoarthritic chondrocytes reveals distortion of vimentin network in osteoarthritis. Osteoarthr Cartil 16:163–173. https://doi.org/10.1016/j.joca.2007.06.005
Li Y, Frank EH, Wang Y, Chubinskaya S, Huang H-H, Grodzinsky AJ (2013) Moderate dynamic compression inhibits pro-catabolic response of cartilage to mechanical injury, tumor necrosis factor-α and interleukin-6, but accentuates degradation above a strain threshold. Osteoarthr Cartil 21:1933–1941. https://doi.org/10.1016/j.joca.2013.08.021
Li Y, Wang Y, Chubinskaya S, Schoeberl B, Florine E, Kopesky P, Grodzinsky AJ (2015) Effects of insulin-like growth factor-1 and dexamethasone on cytokine-challenged cartilage: relevance to post-traumatic osteoarthritis. Osteoarthr Cartil 23:266–274. https://doi.org/10.1016/j.joca.2014.11.006
Lin W, Klein J (2021) Recent Progress in cartilage lubrication. Adv Mater 33:2005513. https://doi.org/10.1002/adma.202005513
Loeser RF, Goldring SR, Scanzello CR, Goldring MB (2012) Osteoarthritis: a disease of the joint as an organ. Arthritis Rheum 64:1697–1707. https://doi.org/10.1002/art.34453
Lourido L, Calamia V, Fernández-Puente P, Mateos J, Oreiro N, Blanco FJ, Ruiz-Romero C (2016) Secretome analysis of human articular chondrocytes unravels catabolic effects of nicotine on the joint. Proteomics Clin Appl 10:671–680. https://doi.org/10.1002/prca.201400186
Lourido L, Calamia V, Mateos J, Fernández-Puente P, Fernández-Tajes J, Blanco FJ, Ruiz-Romero C (2014) Quantitative proteomic profiling of human articular cartilage degradation in osteoarthritis. J Proteome Res 13:6096–6106. https://doi.org/10.1021/pr501024p
Ludwig C, Gillet L, Rosenberger G, Amon S, Collins BC, Aebersold R (2018) Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial. Mol Syst Biol 14:e8126. https://doi.org/10.15252/msb.20178126
Martin JA, McCabe D, Walter M, Buckwalter JA, McKinley TO (2009) N-acetylcysteine inhibits post-impact chondrocyte death in osteochondral explants. J Bone Joint Surg Am 91:1890–1897. https://doi.org/10.2106/jbjs.h.00545
Matta C, Fellows CR, Quasnichka H, Williams A, Jeremiasse B, Allaway D, Mobasheri A (2021) Clusterin secretion is attenuated by the proinflammatory cytokines interleukin-1β and tumor necrosis factor-α in models of cartilage degradation. J Orthop Res 39:1017–1029. https://doi.org/10.1002/jor.24814
McNulty AL, Rothfusz NE, Leddy HA, Guilak F (2013) Synovial fluid concentrations and relative potency of interleukin-1 alpha and beta in cartilage and meniscus degradation. J Orthop Res 31:1039–1045. https://doi.org/10.1002/jor.22334
Mehta S, Akhtar S, Porter RM, Önnerfjord P, Bajpayee AG (2019) Interleukin-1 receptor antagonist (IL-1Ra) is more effective in suppressing cytokine-induced catabolism in cartilage-synovium co-culture than in cartilage monoculture. Arthritis Res Ther 21:238. https://doi.org/10.1186/s13075-019-2003-y
Mobasheri A, Saarakkala S, Finnilä M, Karsdal MA, Bay-Jensen A-C, van Spil WE (2019) Recent advances in understanding the phenotypes of osteoarthritis. F1000Res 8:F1000 Faculty Rev-2091. https://doi.org/10.12688/f1000research.20575.1
Noakes PS, Michaelis LJ (2013) Diet, immunity and inflammation, Part I: Immunity and inflammation: an introduction. Elsevier, pp 3–33. https://doi.org/10.1533/9780857095749.1.3
Olson SA, Horne P, Furman B, Huebner J, AlRashid M, Kraus VB, Guilak F (2014) The role of cytokines in posttraumatic arthritis. J Am Acad Orthop Surg 22:29–37. https://doi.org/10.5435/jaaos-22-01-29
Önnerfjord P, Khabut A, Reinholt FP, Svensson O, Heinegård D (2012) Quantitative proteomic analysis of eight cartilaginous tissues reveals characteristic differences as well as similarities between subgroups*. J Biol Chem 287:18913–18924. https://doi.org/10.1074/jbc.m111.298968
Pemmari A, Leppänen T, Hämäläinen M, Moilanen T, Moilanen E (2021) Chondrocytes from osteoarthritis patients adopt distinct phenotypes in response to central TH1/TH2/TH17 cytokines. Int J Mol Sci 22:9463. https://doi.org/10.3390/ijms22179463
Peterson AC, Russell JD, Bailey DJ, Westphall MS, Coon JJ (2012) Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics*. Mol Cell Proteomics 11:1475–1488. https://doi.org/10.1074/mcp.o112.020131
Polacek M, Bruun J-A, Johansen O, Martinez I (2010) Differences in the secretome of cartilage explants and cultured chondrocytes unveiled by SILAC technology: chondrocytes secretome by SILAC. J Orthop Res 28:1040–1049. https://doi.org/10.1002/jor.21067
Ravindra KC, Ahrens CC, Wang Y, Ramseier JY, Wishnok JS, Griffith LG, Grodzinsky AJ, Tannenbaum SR (2018) Chemoproteomics of matrix metalloproteases in a model of cartilage degeneration suggests functional biomarkers associated with posttraumatic osteoarthritis. J Biol Chem 293:11459–11469. https://doi.org/10.1074/jbc.m117.818542
Rocha B, Calamia V, Casas V, Carrascal M, Blanco FJ, Ruiz-Romero C (2014) Secretome analysis of human mesenchymal stem cells undergoing Chondrogenic differentiation. J Proteome Res 13:1045–1054. https://doi.org/10.1021/pr401030n
Ruiz-Romero C, Carreira V, Rego I, Remeseiro S, López-Armada MJ, Blanco FJ (2008) Proteomic analysis of human osteoarthritic chondrocytes reveals protein changes in stress and glycolysis. Proteomics 8:495–507. https://doi.org/10.1002/pmic.200700249
Ruiz-Romero C, Fernández-Puente P, Calamia V, Blanco FJ (2015) Lessons from the proteomic study of osteoarthritis. Expert Rev Proteomics 12:433–443. https://doi.org/10.1586/14789450.2015.1065182
Salgado C, Jordan O, Allémann E (2021) Osteoarthritis in vitro models: applications and implications in development of intra-articular drug delivery systems. Pharmaceutics 13:60. https://doi.org/10.3390/pharmaceutics13010060
Samvelyan HJ, Hughes D, Stevens C, Staines KA (2021) Models of osteoarthritis: relevance and new insights. Calcif Tissue Int 109:243–256. https://doi.org/10.1007/s00223-020-00670-x
Sinkeviciute D, Aspberg A, He Y, Bay-Jensen A-C, Önnerfjord P (2020) Characterization of the interleukin-17 effect on articular cartilage in a translational model: an explorative study. BMC Rheumatol 4:30. https://doi.org/10.1186/s41927-020-00122-x
Stevens AL, Wishnok JS, Chai DH, Grodzinsky AJ, Tannenbaum SR (2008) A sodium dodecyl sulfate–polyacrylamide gel electrophoresis–liquid chromatography tandem mass spectrometry analysis of bovine cartilage tissue response to mechanical compression injury and the inflammatory cytokines tumor necrosis factor α and interleukin-1β. Arthritis Rheum 58:489–500. https://doi.org/10.1002/art.23120
Stevens AL, Wishnok JS, White FM, Grodzinsky AJ, Tannenbaum SR (2009) Mechanical injury and cytokines cause loss of cartilage integrity and upregulate proteins associated with catabolism, immunity, inflammation, and repair*. Mol Cell Proteomics 8:1475–1489. https://doi.org/10.1074/mcp.m800181-mcp200
Swan AL, Hillier KL, Smith JR, Allaway D, Liddell S, Bacardit J, Mobasheri A (2013) Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning. BMC Musculoskelet Disord 14:349. https://doi.org/10.1186/1471-2474-14-349
Tang S, Deng S, Guo J, Chen X, Zhang W, Cui Y, Luo Y, Yan Z, He Q-Y, Shen S, Wang T (2018) Deep coverage tissue and cellular proteomics revealed IL-1β can independently induce the secretion of TNF-associated proteins from human Synoviocytes. J Immunol 200:821–833. https://doi.org/10.4049/jimmunol.1700480
Tarafder S, Park G, Lee CH (2019) Explant models for meniscus metabolism, injury, repair, and healing. Connect Tissue Res 61:1–12. https://doi.org/10.1080/03008207.2019.1702031
Tsolis KC, Bei ES, Papathanasiou I, Kostopoulou F, Gkretsi V, Kalantzaki K, Malizos K, Zervakis M, Tsezou A, Economou A (2015) Comparative proteomic analysis of hypertrophic chondrocytes in osteoarthritis. Clin Proteomics 12:12. https://doi.org/10.1186/s12014-015-9085-6
Wang Y, Li Y, Khabut A, Chubinskaya S, Grodzinsky AJ, Önnerfjord P (2017) Quantitative proteomics analysis of cartilage response to mechanical injury and cytokine treatment. Matrix Biol 63:11–22. https://doi.org/10.1016/j.matbio.2016.12.004
Wilson R, Golub SB, Rowley L, Angelucci C, Karpievitch YV, Bateman JF, Fosang AJ (2016) Novel elements of the chondrocyte stress response identified using an in vitro model of mouse cartilage degradation. J Proteome Res 15:1033–1050. https://doi.org/10.1021/acs.jproteome.5b01115
Wojdasiewicz P, Poniatowski ŁA, Szukiewicz D (2014) The role of inflammatory and anti-inflammatory cytokines in the pathogenesis of osteoarthritis. Mediat Inflamm 2014:561459. https://doi.org/10.1155/2014/561459
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Copyright information
© 2023 The Author(s)
About this chapter
Cite this chapter
Rydén, M., Önnerfjord, P. (2023). In Vitro Models and Proteomics in Osteoarthritis Research. In: Connizzo, B.K., Han, L., Sah, R.L. (eds) Electromechanobiology of Cartilage and Osteoarthritis. Advances in Experimental Medicine and Biology, vol 1402. Springer, Cham. https://doi.org/10.1007/978-3-031-25588-5_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-25588-5_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-25587-8
Online ISBN: 978-3-031-25588-5
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)