Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications

Xiwei Fan , Antonia Rujia Sun , Reuben S. E. Young , Isaac O. Afara , Brett R. Hamilton , Louis Jun Ye Ong , Ross Crawford , Indira Prasadam

Bone Research ›› 2024, Vol. 12 ›› Issue (1) : 7

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Bone Research ›› 2024, Vol. 12 ›› Issue (1) : 7 DOI: 10.1038/s41413-023-00304-6
Review Article

Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications

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Abstract

Osteoarthritis (OA) is a debilitating degenerative disease affecting multiple joint tissues, including cartilage, bone, synovium, and adipose tissues. OA presents diverse clinical phenotypes and distinct molecular endotypes, including inflammatory, metabolic, mechanical, genetic, and synovial variants. Consequently, innovative technologies are needed to support the development of effective diagnostic and precision therapeutic approaches. Traditional analysis of bulk OA tissue extracts has limitations due to technical constraints, causing challenges in the differentiation between various physiological and pathological phenotypes in joint tissues. This issue has led to standardization difficulties and hindered the success of clinical trials. Gaining insights into the spatial variations of the cellular and molecular structures in OA tissues, encompassing DNA, RNA, metabolites, and proteins, as well as their chemical properties, elemental composition, and mechanical attributes, can contribute to a more comprehensive understanding of the disease subtypes. Spatially resolved biology enables biologists to investigate cells within the context of their tissue microenvironment, providing a more holistic view of cellular function. Recent advances in innovative spatial biology techniques now allow intact tissue sections to be examined using various -omics lenses, such as genomics, transcriptomics, proteomics, and metabolomics, with spatial data. This fusion of approaches provides researchers with critical insights into the molecular composition and functions of the cells and tissues at precise spatial coordinates. Furthermore, advanced imaging techniques, including high-resolution microscopy, hyperspectral imaging, and mass spectrometry imaging, enable the visualization and analysis of the spatial distribution of biomolecules, cells, and tissues. Linking these molecular imaging outputs to conventional tissue histology can facilitate a more comprehensive characterization of disease phenotypes. This review summarizes the recent advancements in the molecular imaging modalities and methodologies for in-depth spatial analysis. It explores their applications, challenges, and potential opportunities in the field of OA. Additionally, this review provides a perspective on the potential research directions for these contemporary approaches that can meet the requirements of clinical diagnoses and the establishment of therapeutic targets for OA.

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Xiwei Fan, Antonia Rujia Sun, Reuben S. E. Young, Isaac O. Afara, Brett R. Hamilton, Louis Jun Ye Ong, Ross Crawford, Indira Prasadam. Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications. Bone Research, 2024, 12(1): 7 DOI:10.1038/s41413-023-00304-6

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References

[1]

Hawker GA. Osteoarthritis is a serious disease. Clin. Exp. Rheumatol., 2019, 37 Suppl 120 3-6

[2]

Leifer VP, Katz JN, Losina E. The burden of OA-health services and economics. Osteoarthr. Cartil., 2022, 30: 10-16

[3]

Kraus VB, Blanco FJ, Englund M, Karsdal MA, Lohmander LS. Call for standardized definitions of osteoarthritis and risk stratification for clinical trials and clinical use. Osteoarthr. Cartil., 2015, 23: 1233-1241

[4]

Felson DT et al. The prevalence of knee osteoarthritis in the elderly. The Framingham Osteoarthritis Study. Arthritis Rheum., 1987, 30: 914-918

[5]

Jordan JM et al. Prevalence of knee symptoms and radiographic and symptomatic knee osteoarthritis in African Americans and Caucasians: the Johnston County Osteoarthritis Project. J. Rheumatol., 2007, 34: 172-180

[6]

Primorac D et al. Knee osteoarthritis: a review of pathogenesis and state-of-the-art non-operative therapeutic considerations. Genes, 2020, 11: 854

[7]

Zhang Y et al. Associations of dietary macroelements with knee joint structures, symptoms, quality of life, and comorbid conditions in people with symptomatic knee osteoarthritis. Nutrients, 2022, 14: 3576

[8]

Cicuttini FM, Wluka AE. Osteoarthritis: is OA a mechanical or systemic disease? Nat. Rev. Rheumatol., 2014, 10: 515-516

[9]

Kreitmaier P, Katsoula G, Zeggini E. Insights from multi-omics integration in complex disease primary tissues. Trends Genet., 2023, 39: 46-58

[10]

Kellgren JH, Lawrence JS. Radiological assessment of osteo-arthrosis. Ann. Rheum. Dis., 1957, 16: 494-502

[11]

Henrotin Y. Osteoarthritis in year 2021: biochemical markers. Osteoarthr. Cartil., 2022, 30: 237-248

[12]

Siaton BC, Hogans BH, Hochberg MC. Precision medicine in osteoarthritis: not yet ready for prime time. Expert Rev. Precis. Med. Drug Dev., 2021, 6: 5-8

[13]

Sawitzke AD. Personalized medicine for osteoarthritis: where are we now? Ther. Adv. Musculoskelet. Dis., 2013, 5: 67-75

[14]

Fan X, Wu X, Crawford R, Xiao Y, Prasadam I. Macro, micro, and molecular. changes of the osteochondral interface in osteoarthritis development. Front. Cell Dev. Biol., 2021, 9: 659654

[15]

Hofmann GO et al. Detection and evaluation of initial cartilage pathology in man: a comparison between MRT, arthroscopy and near-infrared spectroscopy (NIR) in their relation to initial knee pain. Pathophysiology, 2010, 17: 1-8

[16]

Sarin JK et al. Dataset on equine cartilage near infrared spectra, composition, and functional properties. Sci. Data, 2019, 6

[17]

Gamsjaeger S, Klaushofer K, Paschalis EP. Raman analysis of proteoglycans simultaneously in bone and cartilage. J. Raman Spectrosc., 2014, 45: 794-800

[18]

Eveque-Mourroux MR, Rocha B, Barré FPY, Heeren RMA, Cillero-Pastor B. Spatially resolved proteomics in osteoarthritis: state of the art and new perspectives. J. Proteom., 2020, 215: 103637

[19]

Carlberg K et al. Exploring inflammatory signatures in arthritic joint biopsies with spatial transcriptomics. Sci. Rep., 2019, 9

[20]

Cillero-Pastor B, Eijkel GB, Blanco FJ, Heeren RMA. Protein classification and distribution in osteoarthritic human synovial tissue by matrix-assisted laser desorption ionization mass spectrometry imaging. Anal. Bioanal. Chem., 2015, 407: 2213-2222

[21]

Hamilton BR et al. Mapping enzyme activity on tissue by functional mass spectrometry imaging. Angew. Chem. Int. Ed., 2020, 59: 3855-3858

[22]

Fan X et al. A technique for preparing undecalcified osteochondral fresh frozen sections for elemental mapping and understanding disease etiology. Histochem. Cell Biol., 2022, 158: 463-469

[23]

Stolz M et al. Early detection of aging cartilage and osteoarthritis in mice and patient samples using atomic force microscopy. Nat. Nanotechnol., 2009, 4: 186-192

[24]

Guo G et al. Automated annotation and visualisation of high-resolution spatial proteomic mass spectrometry imaging data using HIT-MAP. Nat. Commun., 2021, 12

[25]

Palmer A et al. FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry. Nat. Methods, 2017, 14: 57-60

[26]

Radtke AJ et al. IBEX: a versatile multiplex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues. Proc. Natl. Acad. Sci. USA, 2020, 117: 33455-33465

[27]

Lee YR et al. Mass spectrometry imaging as a potential tool to investigate human osteoarthritis at the tissue level. Int. J. Mol. Sci., 2020, 21: 6414

[28]

Afara IO et al. Characterization of connective tissues using near-infrared spectroscopy and imaging. Nat. Protoc., 2021, 16: 1297-1329

[29]

Yu C, Zhao B, Li Y, Zang H, Li L. Vibrational spectroscopy in assessment of early osteoarthritis-a narrative review. Int. J. Mol. Sci., 2021, 22: 5235

[30]

Baker MJ et al. Using fourier transform IR spectroscopy to analyze biological materials. Nat. Protoc., 2014, 9: 1771-1791

[31]

Santos MC, Nascimento YM, Araújo JM, Lima KM. ATR-FTIR spectroscopy coupled with multivariate analysis techniques for the identification of DENV-3 in different concentrations in blood and serum: a new approach. RSC Adv., 2017, 7: 25640-25649

[32]

Spahn G et al. Near-infrared spectroscopy for arthroscopic evaluation of cartilage lesions: results of a blinded, prospective, interobserver study. Am. J. Sports Med., 2010, 38: 2516-2521

[33]

Afara IO et al. Near infrared spectroscopy for rapid determination of Mankin score components: a potential tool for quantitative characterization of articular cartilage at surgery. Arthroscopy, 2014, 30: 1146-1155

[34]

Huck CW, Ozaki Y, Huck-Pezzei VA. Critical review upon the role and potential of fluorescence and near-infrared imaging and absorption spectroscopy in cancer related cells, serum, saliva, urine and tissue analysis. Curr. Med. Chem., 2016, 23: 3052-3077

[35]

Laimer J et al. Amalgam tattoo versus melanocytic neoplasm—differential diagnosis of dark pigmented oral mucosa lesions using infrared spectroscopy. PLoS One, 2018, 13: e0207026

[36]

Palukuru UP et al. Near infrared spectroscopic imaging assessment of cartilage composition: validation with mid infrared imaging spectroscopy. Anal. Chim. Acta, 2016, 926: 79-87

[37]

Afara IO, Prasadam I, Arabshahi Z, Xiao Y, Oloyede A. Monitoring osteoarthritis progression using near infrared (NIR) spectroscopy. Sci. Rep., 2017, 7

[38]

Afara IO et al. Machine learning classification of articular cartilage integrity using near infrared spectroscopy. Cell Mol. Bioeng., 2020, 13: 219-228

[39]

Maddams, W. & Willis, H. The Principles and Applications of Mathematical Peak Finding Procedures in Vibrational Spectra. Vol. 0917 SIR (SPIE, 1988).

[40]

Tiernan H, Byrne B, Kazarian SG. ATR-FTIR spectroscopy and spectroscopic imaging for the analysis of biopharmaceuticals. Spectrochim. Acta A Mol. Biomol. Spectrosc., 2020, 241: 118636

[41]

Bunaciu AA, Hoang VD, Aboul-Enein HY. Vibrational micro-spectroscopy of human tissues analysis: review. Crit. Rev. Anal. Chem., 2017, 47: 194-203

[42]

Huth F et al. Nano-FTIR absorption spectroscopy of molecular fingerprints at 20 nm spatial resolution. Nano Lett., 2012, 12: 3973-3978

[43]

Oinas J et al. Imaging of osteoarthritic human articular cartilage using fourier transform infrared microspectroscopy combined with multivariate and univariate analysis. Sci. Rep., 2016, 6

[44]

Mao Z-H, Zhang X-X, Wu Y-C, Yin J-H, Xia Y. Fourier transform infrared microscopic imaging and fisher discriminant analysis for identification of healthy and degenerated articular cartilage. Chin. J. Anal. Chem., 2015, 43: 518-522

[45]

Zhang X-X, Yin J-H, Mao Z-H, Xia Y. Discrimination of healthy and osteoarthritic articular cartilages by Fourier transform infrared imaging and partial least squares-discriminant analysis. J. Biomed. Opt., 2015, 20: 060501

[46]

Mao Z-H, Wu Y-C, Zhang X-X, Gao H, Yin J-H. Comparative study on identification of healthy and osteoarthritic articular cartilages by Fourier transform infrared imaging and chemometrics methods. J. Innov. Opt. Health Sci., 2017, 10: 1650054

[47]

Rieppo L et al. Application of second derivative spectroscopy for increasing molecular specificity of Fourier transform infrared spectroscopic imaging of articular cartilage. Osteoarthr. Cartil., 2012, 20: 451-459

[48]

Rieppo L, Saarakkala S, Jurvelin JS, Rieppo J. Optimal variable selection for Fourier transform infrared spectroscopic analysis of articular cartilage composition. J. Biomed. Opt., 2014, 19: 027003

[49]

Yin J, Xia Y, Xiao Z. Comparison of macromolecular component distributions in osteoarthritic and healthy cartilages by fourier transform infrared imaging. J. Innov. Opt. Health Sci., 2013, 06: 1350048

[50]

David-Vaudey E et al. Fourier transform infrared Imaging of focal lesions in human osteoarthritic cartilage. Eur. Cell Mater., 2005, 10: 51-60

[51]

Das Gupta S et al. Raman microspectroscopic analysis of the tissue-specific composition of the human osteochondral junction in osteoarthritis: a pilot study. Acta Biomaterialia, 2020, 106: 145-155

[52]

Stack J, McCarthy GM. Cartilage calcification and osteoarthritis: a pathological association? Osteoarthr. Cartil., 2020, 28: 1301-1302

[53]

Bergholt MS et al. Raman spectroscopy reveals new insights into the zonal organization of native and tissue-engineered articular cartilage. ACS Cent. Sci., 2016, 2: 885-895

[54]

Albro MB et al. Raman spectroscopic imaging for quantification of depth-dependent and local heterogeneities in native and engineered cartilage. NPJ Regen. Med, 2018, 3: 3

[55]

Gaifulina R et al. Intra-operative Raman spectroscopy and ex vivo Raman mapping for assessment of cartilage degradation. Clin. Spectrosc., 2021, 3: 100012

[56]

Kerns JG et al. Evidence from Raman spectroscopy of a putative link between inherent bone matrix chemistry and degenerative joint disease. Arthritis Rheumatol., 2014, 66: 1237-1246

[57]

Bocsa CD et al. Knee osteoarthritis grading by resonant Raman and surface-enhanced Raman scattering (SERS) analysis of synovial fluid. Nanomedicine, 2019, 20: 102012

[58]

Casal-Beiroa P, González P, Blanco FJ, Magalhães J. Molecular analysis of the destruction of articular joint tissues by Raman spectroscopy. Expert Rev. Mol. Diagn., 2020, 20: 789-802

[59]

Vandereyken K, Sifrim A, Thienpont B, Voet T. Methods and applications for single-cell and spatial multi-omics. Nat. Rev. Genet., 2023, 24: 494-515

[60]

Zhao C et al. Molecular network strategy in multi-omics and mass spectrometry imaging. Curr. Opin. Chem. Biol., 2022, 70: 102199

[61]

Tan X et al. A robust platform for integrative spatial multi-omics analysis to map immune responses to SARS-CoV-2 infection in lung tissues. Immunology, 2023, 170: 401-418

[62]

Marx V. Method of the year: spatially resolved transcriptomics. Nat. Methods, 2021, 18: 9-14

[63]

Williams CG, Lee HJ, Asatsuma T, Vento-Tormo R, Haque A. An introduction to spatial transcriptomics for biomedical research. Genome Med., 2022, 14

[64]

Ji Q et al. Single-cell RNA-seq analysis reveals the progression of human osteoarthritis. Ann. Rheum. Dis., 2019, 78: 100-110

[65]

Andersson A et al. Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Commun. Biol., 2020, 3: 565

[66]

Wang M et al. Knee fibrosis is associated with the development of osteoarthritis in a murine model of tibial compression. J. Orthop. Res., 2021, 39: 1030-1040

[67]

Sanjurjo-Rodríguez C et al. Characterization and miRNA profiling of extracellular vesicles from human osteoarthritic subchondral bone multipotential stromal cells (MSCs). Stem Cells Int., 2021, 2021: 7232773

[68]

Chen A et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell, 2022, 185: 1777-1792.e1721

[69]

Rodriques SG et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science, 2019, 363: 1463-1467

[70]

Kulzhanova G, Hansen V, Shammas H, Reuter JM, Wu CL. Spatial transcriptomics reveal unique molecular fingerprints of chondrogenesis during embryonic limb development. Osteoarthr. Cartil., 2022, 30: S49-S50

[71]

Vickovic S et al. Three-dimensional spatial transcriptomics uncovers cell type localizations in the human rheumatoid arthritis synovium. Commun. Biol., 2022, 5: 129

[72]

Reuter, J. et al. Poster 116: integrated scRNA-seq and spatial transcriptomics analysis uncovers distinct cellular populations and transcriptomes in human hip synovium between patients with femoroacetabular impingement and osteoarthritis. Orthop. J. Sports Med. 11, https://doi.org/10.1177/2325967123s00106 (2023).

[73]

Kawamoto T, Kawamoto K. Preparation of thin frozen sections from nonfixed and undecalcified hard tissues using Kawamoto’s film method (2020). Methods Mol. Biol., 2021, 2230: 259-281

[74]

Lundberg E, Borner GHH. Spatial proteomics: a powerful discovery tool for cell biology. Nat. Rev. Mol. Cell Biol., 2019, 20: 285-302

[75]

Angel, P. M., Mehta, A., Norris-Caneda, K. & Drake, R. R. in Tissue Proteomics: Methods and Protocols (eds Minnie M. Sarwal & Tara K. Sigdel) 225–241 (Springer New York, 2018).

[76]

Cillero-Pastor B, Heeren RM. Matrix-assisted laser desorption ionization mass spectrometry imaging for peptide and protein analyses: a critical review of on-tissue digestion. J. Proteome Res., 2014, 13: 325-335

[77]

Lu P, Takai K, Weaver VM, Werb Z. Extracellular matrix degradation and remodeling in development and disease. Cold Spring Harb. Perspect. Biol., 2011, 3: a005058

[78]

Reed KSM et al. Transcriptional response of human articular chondrocytes treated with fibronectin fragments: an in vitro model of the osteoarthritis phenotype. Osteoarthr. Cartil., 2021, 29: 235-247

[79]

Cillero-Pastor B, Eijkel GB, Kiss A, Blanco FJ, Heeren RM. Matrix-assisted laser desorption ionization-imaging mass spectrometry: a new methodology to study human osteoarthritic cartilage. Arthritis Rheum., 2013, 65: 710-720

[80]

Matsuhashi T et al. Alteration of N-glycans related to articular cartilage deterioration after anterior cruciate ligament transection in rabbits. Osteoarthr. Cartil., 2008, 16: 772-778

[81]

Briggs MT et al. MALDI mass spectrometry imaging of N-glycans on tibial cartilage and subchondral bone proteins in knee osteoarthritis. Proteomics, 2016, 16: 1736-1741

[82]

Bien T, Bessler S, Dreisewerd K, Soltwisch J. Transmission-mode MALDI mass spectrometry imaging of single cells: optimizing sample preparation protocols. Anal. Chem., 2021, 93: 4513-4520

[83]

Merritt CR et al. Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat. Biotechnol., 2020, 38: 586-599

[84]

Gerdes MJ et al. Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue. Proc. Natl. Acad. Sci. USA, 2013, 110: 11982-11987

[85]

Lomeli G, Bosse M, Bendall SC, Angelo M, Herr AE. Multiplexed ion beam imaging readout of single-cell immunoblotting. Anal. Chem., 2021, 93: 8517-8525

[86]

Rost S et al. Multiplexed ion beam imaging analysis for quantitation of protein expression in cancer tissue sections. Lab. Investig., 2017, 97: 992-1003

[87]

Giesen C et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods, 2014, 11: 417-422

[88]

Galeano Niño JL et al. Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer. Nature, 2022, 611: 810-817

[89]

Han S et al. Single-cell profiling of microenvironment components by spatial localization in pancreatic ductal adenocarcinoma. Theranostics, 2022, 12: 4980-4992

[90]

Hillert R et al. Large molecular systems landscape uncovers T cell trapping in human skin cancer. Sci. Rep., 2016, 6

[91]

Swinnen JV, Dehairs J. A beginner’s guide to lipidomics. Biochemist., 2022, 44: 20-24

[92]

Yang K, Han X. Lipidomics: techniques, applications, and outcomes related to biomedical sciences. Trends Biochem. Sci., 2016, 41: 954-969

[93]

Villalvilla A, Gómez R, Largo R, Herrero-Beaumont G. Lipid transport and metabolism in healthy and osteoarthritic cartilage. Int. J. Mol. Sci., 2013, 14: 20793-20808

[94]

van Gastel N et al. Lipid availability determines fate of skeletal progenitor cells via SOX9. Nature, 2020, 579: 111-117

[95]

Cillero-Pastor B, Eijkel G, Kiss A, Blanco FJ, Heeren RM. Time-of-flight secondary ion mass spectrometry-based molecular distribution distinguishing healthy and osteoarthritic human cartilage. Anal. Chem., 2012, 84: 8909-8916

[96]

Rocha B et al. Identification of a distinct lipidomic profile in the osteoarthritic synovial membrane by mass spectrometry imaging. Osteoarthr. Cartil., 2021, 29: 750-761

[97]

Eveque-Mourroux MR et al. Heterogeneity of lipid and protein cartilage profiles associated with human osteoarthritis with or without type 2 diabetes mellitus. J. Proteome Res., 2021, 20: 2973-2982

[98]

Haartmans MJJ et al. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) reveals potential lipid markers between infrapatellar fat pad biopsies of osteoarthritis and cartilage defect patients. Anal. Bioanal. Chem., 2023, 415: 5997-6007

[99]

Urita A et al. Alterations of high-mannose type N-glycosylation in human and mouse osteoarthritis cartilage. Arthritis Rheum., 2011, 63: 3428-3438

[100]

Heijs B et al. Multimodal mass spectrometry imaging of N-glycans and proteins from the same tissue section. Anal. Chem., 2016, 88: 7745-7753

[101]

Smith RL. Degradative enzymes in osteoarthritis. Front. Biosci., 1999, 4: D704-D712

[102]

Meszaros E, Malemud CJ. Prospects for treating osteoarthritis: enzyme-protein interactions regulating matrix metalloproteinase activity. Ther. Adv. Chronic Dis., 2012, 3: 219-229

[103]

Klein O, Haeckel A, Reimer U, Nebrich G, Schellenberger E. Multiplex enzyme activity imaging by MALDI-IMS of substrate library conversions. Sci. Rep., 2020, 10

[104]

Fan X et al. Functional mass spectrometry imaging maps phospholipase-A2 enzyme activity during osteoarthritis progression. Theranostics, 2023, 13: 4636-4649

[105]

Carlson CS, Loeser RF, Purser CB, Gardin JF, Jerome CP. Osteoarthritis in cynomolgus macaques. III: effects of age, gender, and subchondral bone thickness on the severity of disease. J. Bone Min. Res., 1996, 11: 1209-1217

[106]

Fan X et al. The deterioration of calcified cartilage integrity reflects the severity of osteoarthritis—a structural, molecular, and biochemical analysis. FASEB j., 2022, 36: e22142

[107]

Hackett MJ et al. Chemical alterations to murine brain tissue induced by formalin fixation: implications for biospectroscopic imaging and mapping studies of disease pathogenesis. Analyst, 2011, 136: 2941-2952

[108]

Zuo Q et al. Characterization of nano-structural and nano-mechanical properties of osteoarthritic subchondral bone. BMC Musculoskelet. Disord., 2016, 17

[109]

Sindhupakorn B, Thienpratharn S, Kidkhunthod P. A structural study of bone changes in knee osteoarthritis by synchrotron-based X-ray fluorescence and X-ray absorption spectroscopy techniques. J. Mol. Struct., 2017, 1146: 254-258

[110]

Jung Y-K et al. Calcium-phosphate complex increased during subchondral bone remodeling affects earlystage osteoarthritis. Sci. Rep., 2018, 8

[111]

Herrmann AM et al. Nano-scale secondary ion mass spectrometry—a new analytical tool in biogeochemistry and soil ecology: a review article. Soil Biol. Biochem., 2007, 39: 1835-1850

[112]

de Rezende MU, de Campos GC. Is osteoarthritis a mechanical or inflammatory disease? Rev. Bras. Ortop., 2013, 48: 471-474

[113]

Griffin TM, Guilak F. The role of mechanical loading in the onset and progression of osteoarthritis. Exerc. Sport Sci. Rev., 2005, 33: 195-200

[114]

Dall’Ara E, Ohman C, Baleani M, Viceconti M. Reduced tissue hardness of trabecular bone is associated with severe osteoarthritis. J. Biomech., 2011, 44: 1593-1598

[115]

Gardner-Morse MG, Tacy NJ, Beynnon BD, Roemhildt ML. In situ microindentation for determining local subchondral bone compressive modulus. J. Biomech. Eng., 2010, 132: 094502

[116]

Miller GJ, Morgan EF. Use of microindentation to characterize the mechanical properties of articular cartilage: comparison of biphasic material properties across length scales. Osteoarthr. Cartil., 2010, 18: 1051-1057

[117]

Marchi G et al. Fiberoptic microindentation technique for early osteoarthritis diagnosis: an in vitro study on human cartilage. Biomed. Microdevices, 2019, 21

[118]

Hartmann B et al. Early detection of cartilage degeneration: a comparison of histology, fiber Bragg grating-based micro-indentation, and atomic force microscopy-based nano-indentation. Int. J. Mol. Sci., 2020, 21: 7384

[119]

Liu B et al. Etoricoxib decreases subchondral bone mass and attenuates biomechanical properties at the early stage of osteoarthritis in a mouse model. Biomed. Pharmacother., 2020, 127: 110144

[120]

Ihnatouski M, Pauk J, Karev B, Karev D. Nanomechanical properties of articular cartilage due to the PRP injection in experimental osteoarthritis in rabbits. Molecules, 2020, 25: 3734

[121]

Fleischhauer L et al. Nano-scale mechanical properties of the articular cartilage zones in a mouse model of post-traumatic osteoarthritis. Appl. Sci., 2022, 12: 2596

[122]

Tschaikowsky M et al. Hybrid fluorescence-AFM explores articular surface degeneration in early osteoarthritis across length scales. Acta Biomater., 2021, 126: 315-325

[123]

Ståhl PL et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science, 2016, 353: 78-82

[124]

Kooijman PC et al. Increased throughput and ultra-high mass resolution in DESI FT-ICR MS imaging through new-generation external data acquisition system and advanced data processing approaches. Sci. Rep., 2019, 9

[125]

Poad BL et al. Ozone-induced dissociation on a modified tandem linear ion-trap: observations of different reactivity for isomeric lipids. J. Am. Soc. Mass Spectrom., 2010, 21: 1989-1999

[126]

Haartmans MJJ et al. Mass spectrometry-based biomarkers for knee osteoarthritis: a systematic review. Expert Rev. Proteom., 2021, 18: 693-706

[127]

Serowoky MA, Patel DD, Hsieh JW, Mariani FV. The use of commercially available adhesive tapes to preserve cartilage and bone tissue integrity during cryosectioning. Biotechniques, 2018, 65: 191-196

[128]

Piehowski PD et al. Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution. Nat. Commun., 2020, 11

[129]

Ellis SR et al. Automated, parallel mass spectrometry imaging and structural identification of lipids. Nat. Methods, 2018, 15: 515-518

[130]

Tortorella S et al. LipostarMSI: comprehensive, vendor-neutral software for visualization, data analysis, and automated molecular identification in mass spectrometry imaging. J. Am. Soc. Mass Spectrom., 2020, 31: 155-163

[131]

Bond NJ, Koulman A, Griffin JL, Hall Z. massPix: an R package for annotation and interpretation of mass spectrometry imaging data for lipidomics. Metabolomics, 2017, 13

[132]

Janda M et al. Determination of abundant metabolite matrix adducts illuminates the dark metabolome of MALDI-Mass spectrometry imaging datasets. Anal. Chem., 2021, 93: 8399-8407

[133]

Khader A, Alquran H. Automated prediction of osteoarthritis level in human osteochondral tissue using histopathological images. Bioengineering, 2023, 10: 764

[134]

Neubauer M et al. Artificial-intelligence-aided radiographic diagnostic of knee osteoarthritis leads to a higher association of clinical findings with diagnostic ratings. J. Clin. Med., 2023, 12: 744

[135]

Calivà F et al. Studying osteoarthritis with artificial intelligence applied to magnetic resonance imaging. Nat. Rev. Rheumatol., 2022, 18: 112-121

[136]

Kim K, Park H. Machine-learning models predicting osteoarthritis associated with the lead blood level. Environ. Sci. Pollut. Res. Int., 2021, 28: 44079-44084

[137]

Lourido L et al. Quantitative proteomic profiling of human articular cartilage degradation in osteoarthritis. J. Proteome Res., 2014, 13: 6096-6106

[138]

Boris Chan PM, Zhu L, Wen CY, Chiu KY. Subchondral bone proteomics in osteoarthritis: current status and perspectives. J. Orthop. Transl., 2015, 3: 71-77

[139]

Ali N et al. Proteomics profiling of human synovial fluid suggests increased protein interplay in early-osteoarthritis (OA) that is lost in late-stage OA. Mol. Cell Proteom., 2022, 21: 100200

[140]

Uniprot Consortium. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 49, D480-D489 (2021).

[141]

McDonnell LA, Walch A, Stoeckli M, Corthals GL. MSiMass list: a public database of identifications for protein MALDI MS imaging. J. Proteome Res., 2014, 13: 1138-1142

[142]

Maier SK et al. Comprehensive identification of proteins from MALDI imaging. Mol. Cell. Proteom., 2013, 12: 2901-2910

[143]

Fahy E et al. Update of the LIPID MAPS comprehensive classification system for lipids. J. Lipid Res., 2009, 50 Suppl S9-S14

[144]

Taguchi R, Nishijima M, Shimizu T. Basic analytical systems for lipidomics by mass spectrometry in Japan. Methods Enzymol., 2007, 432: 185-211

[145]

Yasugi E, Watanabe K. LIPIDBANK for Web, the newly developed lipid database. Tanpakushitsu Kakusan Koso, 2002, 47: 837-841

[146]

Wishart DS et al. HMDB 5.0: the human metabolome database for 2022. Nucleic Acids Res., 2022, 50: D622-d631

[147]

Wishart DS et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res., 2018, 46: D608-d617

[148]

Sajed T et al. ECMDB 2.0: a richer resource for understanding the biochemistry of E. coli. Nucleic Acids Res., 2016, 44: D495-D501

[149]

Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res., 2000, 28: 27-30

[150]

Smith CA et al. METLIN: a metabolite mass spectral database. Ther. Drug Monit., 2005, 27: 747-751

[151]

Mobasheri A, Kapoor M, Ali SA, Lang A, Madry H. The future of deep phenotyping in osteoarthritis: how can high throughput omics technologies advance our understanding of the cellular and molecular taxonomy of the disease? Osteoarthr. Cartil. Open, 2021, 3: 100144

[152]

Chen L et al. Horizontal fissuring at the osteochondral interface: a novel and unique pathological feature in patients with obesity-related osteoarthritis. Ann. Rheum. Dis., 2020, 79: 811-818

[153]

Papathanasiou I, Anastasopoulou L, Tsezou A. Cholesterol metabolism related genes in osteoarthritis. Bone, 2021, 152: 116076

[154]

Cannata F et al. Osteoarthritis and type 2 diabetes: from pathogenetic factors to therapeutic intervention. Diabetes Metab. Res Rev., 2020, 36: e3254

[155]

Linus A et al. Visible and near-infrared spectroscopy enables differentiation of normal and early osteoarthritic human knee joint articular cartilage. Ann. Biomed. Eng., 2023, 51: 2245-2257

[156]

Sarin JK et al. Arthroscopic near infrared spectroscopy enables simultaneous quantitative evaluation of articular cartilage and subchondral bone in vivo. Sci. Rep., 2018, 8

[157]

de Souza RA et al. Raman spectroscopy detection of molecular changes associated with two experimental models of osteoarthritis in rats. Lasers Med. Sci., 2014, 29: 797-804

[158]

Buchberger AR, DeLaney K, Johnson J, Li L. Mass spectrometry imaging: a review of emerging advancements and future insights. Anal. Chem., 2018, 90: 240-265

[159]

Nguyen DT, van Horssen P, Derriks H, van de Giessen M, van Leeuwen T. Autofluorescence imaging for improved visualization of joint structures during arthroscopic surgery. J. Exp. Orthop., 2017, 4

[160]

Boer CG et al. Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations. Cell, 2021, 184: 4784-4818.e4717

[161]

Bay-Jensen AC, Mobasheri A, Thudium CS, Kraus VB, Karsdal MA. Blood and urine biomarkers in osteoarthritis—an update on cartilage associated type II collagen and aggrecan markers. Curr. Opin. Rheumatol., 2022, 34: 54-60

[162]

Mirzaii-Dizgah MR, Mirzaii-Dizgah MH, Mirzaii-Dizgah I, Karami M, Forogh B. Osteoprotegerin changes in saliva and serum of patients with knee osteoarthritis. Rev. Esp. Cir. Ortop. Traumatol., 2022, 66: 47-51

[163]

Mirzaii-Dizgah MR, Mirzaii-Dizgah MH, Mirzaii-Dizgah I. Elevation of urate in saliva and serum of patients with knee osteoarthritis. Gerontology, 2021, 67: 87-90

[164]

Wang G et al. Analyzing cell-type-specific dynamics of metabolism in kidney repair. Nat. Metab., 2022, 4: 1109-1118

[165]

Schwaiger-Haber M et al. Using mass spectrometry imaging to map fluxes quantitatively in the tumor ecosystem. Nat. Commun., 2023, 14

[166]

Barré FP et al. Distribution, quantification and effects of triamcinolone acetonide in human osteoarthritic cartilage. Osteoarthr. Cartil., 2018, 26: S284-S285

[167]

Barré FPY et al. Enhanced sensitivity using MALDI imaging coupled with laser postionization (MALDI-2) for pharmaceutical research. Anal. Chem., 2019, 91: 10840-10848

[168]

Niehaus M, Soltwisch J, Belov ME, Dreisewerd K. Transmission-mode MALDI-2 mass spectrometry imaging of cells and tissues at subcellular resolution. Nat. Methods, 2019, 16: 925-931

[169]

Seeley EH, Caprioli RM. 3D imaging by mass spectrometry: a new frontier. Anal. Chem., 2012, 84: 2105-2110

[170]

Lanekoff I et al. Three-dimensional imaging of lipids and metabolites in tissues by nanospray desorption electrospray ionization mass spectrometry. Anal. Bioanal. Chem., 2015, 407: 2063-2071

[171]

Ratneswaran A, Rockel JS, Kapoor M. Understanding osteoarthritis pathogenesis: a multiomics system-based approach. Curr. Opin. Rheumatol., 2020, 32: 80-91

[172]

Jackson, M., Wagnieres, G. & Mantsch, H. H. in Encyclopedia of Spectroscopy and Spectrometry (Third Edition) (eds John C. Lindon, George E. Tranter, & David W. Koppenaal) 479–487 (Academic Press, 2017).

[173]

Afara IO, Moody H, Singh S, Prasadam I, Oloyede A. Spatial mapping of proteoglycan content in articular cartilage using near-infrared (NIR) spectroscopy. Biomed. Opt. Express, 2015, 6: 144-154

[174]

Lasch P, Naumann D. Spatial resolution in infrared microspectroscopic imaging of tissues. Biochim. Biophys. Acta (BBA) - Biomembr., 2006, 1758: 814-829

[175]

Bodzon-Kulakowska A, Suder P. Imaging mass spectrometry: instrumentation, applications, and combination with other visualization techniques. Mass Spectrom. Rev., 2016, 35: 147-169

[176]

Kriegsmann M et al. MALDI MS imaging as a powerful tool for investigating synovial tissue. Scand. J. Rheumatol., 2012, 41: 305-309

[177]

Elaldi R et al. High dimensional imaging mass cytometry panel to visualize the tumor immune microenvironment contexture. Front. Immunol., 2021, 12: 666233

[178]

Keren L et al. MIBI-TOF: a multiplexed imaging platform relates cellular phenotypes and tissue structure. Sci. Adv., 2019, 5: eaax5851

[179]

Rocha B, Cillero-Pastor B, Ruiz-Romero C, Heeren R, Blanco F. MALDI-MSI analysis revealed an increment of lipid candidate biomarkers in oa synovium. Osteoarthr. Cartil., 2018, 26: S41-S42

[180]

Eveque-Mourroux MR et al. Spatially resolved endogenous improved metabolite detection in human osteoarthritis cartilage by matrix assisted laser desorption ionization mass spectrometry imaging. Analyst, 2019, 144: 5953-5958

[181]

Turyanskaya A et al. Correlation of μXRF and LA-ICP-MS in the analysis of a human bone-cartilage sample. J. Anal. At. Spectrom., 2021, 36: 1512-1523

Funding

Department of Health | National Health and Medical Research Council (NHMRC)(APP1176298)

Academy of Finland (Suomen Akatemia)(315820)

Jane ja Aatos Erkon Säätiö (Jane and Aatos Erkko Foundation)(190001)

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