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

Xiwei Fan1,2, Antonia Rujia Sun1,2, Reuben S. E. Young3,4, Isaac O. Afara5,6, Brett R. Hamilton7, Louis Jun Ye Ong1,2, Ross Crawford1,8, Indira Prasadam1,2

Bone Research ›› 2024, Vol. 12 ›› Issue (0) : 7. DOI: 10.1038/s41413-023-00304-6

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

  • Xiwei Fan1,2, Antonia Rujia Sun1,2, Reuben S. E. Young3,4, Isaac O. Afara5,6, Brett R. Hamilton7, Louis Jun Ye Ong1,2, Ross Crawford1,8, Indira Prasadam1,2
Author information +
History +

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.

Cite this article

Download citation ▾
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(0): 7 https://doi.org/10.1038/s41413-023-00304-6

References

1. Hawker G. A.Osteoarthritis is a serious disease. Clin. Exp. Rheumatol. 37(Suppl 120), 3-6 (2019).
2. Leifer, V. P., Katz, J. N.& Losina, E. The burden of OA-health services and economics. Osteoarthr. Cartil. 30, 10-16 (2022).
3. Kraus V. B., Blanco F. J., Englund M., Karsdal M. A.& Lohmander, L. S. Call for standardized definitions of osteoarthritis and risk stratification for clinical trials and clinical use. Osteoarthr. Cartil. 23, 1233-1241 (2015).
4. Felson, D. T.et al.The prevalence of knee osteoarthritis in the elderly. The Framingham Osteoarthritis Study. Arthritis Rheum. 30, 914-918 (1987).
5. Jordan, J. M.et al.Prevalence of knee symptoms and radiographic and symptomatic knee osteoarthritis in African Americans and Caucasians: the Johnston County Osteoarthritis Project. J. Rheumatol. 34, 172-180 (2007).
6. Primorac, D.et al.Knee osteoarthritis: a review of pathogenesis and state-of-theart non-operative therapeutic considerations. Genes 11, 854 (2020).
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 14, 3576 (2022).
8. Cicuttini F. M.& Wluka, A. E. Osteoarthritis: is OA a mechanical or systemic disease? Nat. Rev. Rheumatol. 10, 515-516 (2014).
9. Kreitmaier, P., Katsoula, G.& Zeggini, E. Insights from multi-omics integration in complex disease primary tissues. Trends Genet. 39, 46-58 (2023).
10. Kellgren J. H.& Lawrence, J. S. Radiological assessment of osteo-arthrosis. Ann. Rheum. Dis. 16, 494-502 (1957).
11. Henrotin Y.Osteoarthritis in year 2021: biochemical markers. Osteoarthr. Cartil. 30, 237-248 (2022).
12. Siaton, B. C., Hogans, B. H.& Hochberg, M. C. Precision medicine in osteoarthritis: not yet ready for prime time. Expert Rev. Precis. Med. Drug Dev. 6, 5-8 (2021).
13. Sawitzke A. D.Personalized medicine for osteoarthritis: where are we now? Ther. Adv. Musculoskelet. Dis. 5, 67-75 (2013).
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. 9, 659654(2021).
15. Hofmann, G. O.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 17, 1-8 (2010).
16. Sarin, J. K.et al.Dataset on equine cartilage near infrared spectra, composition, and functional properties. Sci. Data 6, 164 (2019).
17. Gamsjaeger, S., Klaushofer, K.& Paschalis, E. P. Raman analysis of proteoglycans simultaneously in bone and cartilage. J. Raman Spectrosc. 45, 794-800 (2014).
18. Eveque-Mourroux, M. R., Rocha, B., Barré, F. P. Y., Heeren, R. M. A. & Cillero-Pastor, B. Spatially resolved proteomics in osteoarthritis: state of the art and new perspectives. J. Proteom. 215, 103637(2020).
19. Carlberg, K.et al.Exploring inflammatory signatures in arthritic joint biopsies with spatial transcriptomics. Sci. Rep. 9, 18975(2019).
20. Cillero-Pastor, B., Eijkel, G. B., Blanco, F. J. & Heeren, R. M. A. Protein classification and distribution in osteoarthritic human synovial tissue by matrix-assisted laser desorption ionization mass spectrometry imaging. Anal. Bioanal. Chem. 407, 2213-2222 (2015).
21. Hamilton, B. R.et al.Mapping enzyme activity on tissue by functional mass spectrometry imaging. Angew. Chem. Int. Ed. 59, 3855-3858 (2020).
22. Fan, X.et al.A technique for preparing undecalcified osteochondral fresh frozen sections for elemental mapping and understanding disease etiology. Histochem. Cell Biol. 158, 463-469 (2022).
23. Stolz, M.et al.Early detection of aging cartilage and osteoarthritis in mice and patient samples using atomic force microscopy. Nat. Nanotechnol. 4, 186-192 (2009).
24. Guo, G.et al.Automated annotation and visualisation of high-resolution spatial proteomic mass spectrometry imaging data using HIT-MAP. Nat. Commun. 12, 3241(2021).
25. Palmer, A.et al. FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry. Nat. Methods 14, 57-60 (2017).
26. Radtke, A. J.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 117, 33455-33465 (2020).
27. Lee, Y. R.et al.Mass spectrometry imaging as a potential tool to investigate human osteoarthritis at the tissue level. Int. J. Mol. Sci. 21, 6414(2020).
28. Afara, I. O.et al.Characterization of connective tissues using near-infrared spectroscopy and imaging. Nat. Protoc. 16, 1297-1329 (2021).
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. 22, 5235(2021).
30. Baker, M. J.et al.Using fourier transform IR spectroscopy to analyze biological materials. Nat. Protoc. 9, 1771-1791 (2014).
31. Santos M. C., Nascimento Y. M., Araújo J. M.& Lima, K. M. 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. 7, 25640-25649 (2017).
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. 38, 2516-2521 (2010).
33. Afara, I. O.et al. Near infrared spectroscopy for rapid determination of Mankin score components: a potential tool for quantitative characterization of articular cartilage at surgery. Arthroscopy 30, 1146-1155 (2014).
34. Huck, C. W., Ozaki, Y.& Huck-Pezzei, V. A. 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. 23, 3052-3077 (2016).
35. Laimer, J.et al.Amalgam tattoo versus melanocytic neoplasm—differential diagnosis of dark pigmented oral mucosa lesions using infrared spectroscopy. PLoS One 13, e0207026 (2018).
36. Palukuru, U. P.et al. Near infrared spectroscopic imaging assessment of cartilage composition: validation with mid infrared imaging spectroscopy. Anal. Chim. Acta 926, 79-87 (2016).
37. Afara I. O., Prasadam I., Arabshahi Z., Xiao Y.& Oloyede, A. Monitoring osteoarthritis progression using near infrared (NIR) spectroscopy. Sci. Rep. 7, 11463(2017).
38. Afara, I. O.et al.Machine learning classification of articular cartilage integrity using near infrared spectroscopy. Cell Mol. Bioeng. 13, 219-228 (2020).
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, S. G. ATR-FTIR spectroscopy and spectroscopic imaging for the analysis of biopharmaceuticals. Spectrochim. Acta A Mol. Biomol. Spectrosc. 241, 118636(2020).
41. Bunaciu, A. A., Hoang, V. D.& Aboul-Enein, H. Y. Vibrational micro-spectroscopy of human tissues analysis: review. Crit. Rev. Anal. Chem. 47, 194-203 (2017).
42. Huth, F.et al.Nano-FTIR absorption spectroscopy of molecular fingerprints at 20 nm spatial resolution. Nano Lett. 12, 3973-3978 (2012).
43. Oinas, J.et al.Imaging of osteoarthritic human articular cartilage using fourier transform infrared microspectroscopy combined with multivariate and univariate analysis. Sci. Rep. 6, 30008(2016).
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. 43, 518-522 (2015).
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. 20, 060501(2015).
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. 10, 1650054(2017).
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. 20, 451-459 (2012).
48. Rieppo L., Saarakkala S., Jurvelin J. S.& Rieppo, J. Optimal variable selection for Fourier transform infrared spectroscopic analysis of articular cartilage composition. J. Biomed. Opt. 19, 027003(2014).
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. 06, 1350048(2013).
50. David-Vaudey, E. et al. Fourier transform infrared Imaging of focal lesions in human osteoarthritic cartilage. Eur. Cell Mater. 10, 51-60 (2005).
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 106, 145-155 (2020).
52. Stack J.& McCarthy, G. M. Cartilage calcification and osteoarthritis: a pathological association? Osteoarthr. Cartil. 28, 1301-1302 (2020).
53. Bergholt, M. S.et al.Raman spectroscopy reveals new insights into the zonal organization of native and tissue-engineered articular cartilage. ACS Cent. Sci. 2, 885-895 (2016).
54. Albro, M. B.et al.Raman spectroscopic imaging for quantification of depthdependent and local heterogeneities in native and engineered cartilage. NPJ Regen. Med 3, 3 (2018).
55. Gaifulina, R.et al.Intra-operative Raman spectroscopy and ex vivo Raman mapping for assessment of cartilage degradation. Clin. Spectrosc. 3, 100012(2021).
56. Kerns, J. G.et al.Evidence from Raman spectroscopy of a putative link between inherent bone matrix chemistry and degenerative joint disease. Arthritis Rheumatol. 66, 1237-1246 (2014).
57. Bocsa, C. D.et al.Knee osteoarthritis grading by resonant Raman and surfaceenhanced Raman scattering (SERS) analysis of synovial fluid. Nanomedicine 20, 102012 (2019).
58. Casal-Beiroa, P., González, P., Blanco, F. J. & Magalhães, J. Molecular analysis of the destruction of articular joint tissues by Raman spectroscopy. Expert Rev. Mol. Diagn. 20, 789-802 (2020).
59. Vandereyken K., Sifrim A., Thienpont B.& Voet, T. Methods and applications for single-cell and spatial multi-omics. Nat. Rev. Genet. 24, 494-515 (2023).
60. Zhao, C.et al.Molecular network strategy in multi-omics and mass spectrometry imaging. Curr. Opin. Chem. Biol. 70, 102199(2022).
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 170, 401-418 (2023).
62. Marx, V. Method of the year: spatially resolved transcriptomics. Nat. Methods 18, 9-14 (2021).
63. Williams C. G., Lee H. J., Asatsuma T., Vento-Tormo, R. & Haque, A. An introduction to spatial transcriptomics for biomedical research. Genome Med. 14, 68(2022).
64. Ji, Q.et al.Single-cell RNA-seq analysis reveals the progression of human osteoarthritis. Ann. Rheum. Dis. 78, 100-110 (2019).
65. Andersson, A.et al.Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Commun. Biol. 3, 565(2020).
66. Wang, M.et al.Knee fibrosis is associated with the development of osteoarthritis in a murine model of tibial compression. J. Orthop. Res. 39, 1030-1040 (2021).
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, 7232773(2021).
68. Chen, A.et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777-1792.e1721 (2022).
69. Rodriques, S. G.et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463-1467 (2019).
70. Kulzhanova G., Hansen V., Shammas H., Reuter J. M.& Wu, C. L. Spatial transcriptomics reveal unique molecular fingerprints of chondrogenesis during embryonic limb development. Osteoarthr. Cartil. 30, S49-S50 (2022).
71. Vickovic, S.et al.Three-dimensional spatial transcriptomics uncovers cell type localizations in the human rheumatoid arthritis synovium. Commun. Biol. 5, 129(2022).
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. 2230, 259-281 (2021).
74. Lundberg E.& Borner, G. H. H. Spatial proteomics: a powerful discovery tool for cell biology. Nat. Rev. Mol. Cell Biol. 20, 285-302 (2019).
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, R. M. Matrix-assisted laser desorption ionization mass spectrometry imaging for peptide and protein analyses: a critical review of ontissue digestion. J. Proteome Res. 13, 325-335 (2014).
77. Lu P., Takai K., Weaver V. M.& Werb, Z. Extracellular matrix degradation and remodeling in development and disease. Cold Spring Harb. Perspect. Biol. 3, a005058(2011).
78. Reed K. S.M. et al. Transcriptional response of human articular chondrocytes treated with fibronectin fragments: an in vitro model of the osteoarthritis phenotype. Osteoarthr. Cartil. 29, 235-247 (2021).
79. Cillero-Pastor, B., Eijkel, G. B., Kiss, A., Blanco, F. J. & Heeren, R. M. Matrixassisted laser desorption ionization-imaging mass spectrometry: a new methodology to study human osteoarthritic cartilage. Arthritis Rheum. 65, 710-720 (2013).
80. Matsuhashi, T.et al.Alteration of N-glycans related to articular cartilage deterioration after anterior cruciate ligament transection in rabbits. Osteoarthr. Cartil. 16, 772-778 (2008).
81. Briggs, M. T.et al. MALDI mass spectrometry imaging of N-glycans on tibial cartilage and subchondral bone proteins in knee osteoarthritis. Proteomics 16, 1736-1741 (2016).
82. Bien T., Bessler S., Dreisewerd K.& Soltwisch, J. Transmission-mode MALDI mass spectrometry imaging of single cells: optimizing sample preparation protocols. Anal. Chem. 93, 4513-4520 (2021).
83. Merritt, C. R.et al.Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat. Biotechnol. 38, 586-599 (2020).
84. Gerdes, M. J.et al. Highly multiplexed single-cell analysis of formalin-fixed, paraffinembedded cancer tissue. Proc. Natl. Acad. Sci. USA 110, 11982-11987 (2013).
85. Lomeli G., Bosse M., Bendall S. C., Angelo M.& Herr, A. E. Multiplexed ion beam imaging readout of single-cell immunoblotting. Anal. Chem. 93, 8517-8525 (2021).
86. Rost, S.et al.Multiplexed ion beam imaging analysis for quantitation of protein expression in cancer tissue sections. Lab. Investig. 97, 992-1003 (2017).
87. Giesen, C.et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417-422 (2014).
88. Galeano Niño, J. L.et al. Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer. Nature 611, 810-817 (2022).
89. Han, S.et al. Single-cell profiling of microenvironment components by spatial localization in pancreatic ductal adenocarcinoma. Theranostics 12, 4980-4992 (2022).
90. Hillert, R.et al.Large molecular systems landscape uncovers T cell trapping in human skin cancer. Sci. Rep. 6, 19012(2016).
91. Swinnen J. V.& Dehairs, J. A beginner’s guide to lipidomics. Biochemist. 44, 20-24 (2022).
92. Yang K.& Han, X. Lipidomics: techniques, applications, and outcomes related to biomedical sciences. Trends Biochem. Sci. 41, 954-969 (2016).
93. Villalvilla A., Gómez R., Largo R.& Herrero-Beaumont, G. Lipid transport and metabolism in healthy and osteoarthritic cartilage. Int. J. Mol. Sci. 14, 20793-20808 (2013).
94. van Gastel, N.et al. Lipid availability determines fate of skeletal progenitor cells via SOX9. Nature 579, 111-117 (2020).
95. Cillero-Pastor, B., Eijkel, G., Kiss, A., Blanco, F. J. & Heeren, R. M. Time-of-flight secondary ion mass spectrometry-based molecular distribution distinguishing healthy and osteoarthritic human cartilage. Anal. Chem. 84, 8909-8916 (2012).
96. Rocha, B.et al.Identification of a distinct lipidomic profile in the osteoarthritic synovial membrane by mass spectrometry imaging. Osteoarthr. Cartil. 29, 750-761 (2021).
97. Eveque-Mourroux, M. R. et al. Heterogeneity of lipid and protein cartilage profiles associated with human osteoarthritis with or without type 2 diabetes mellitus. J. Proteome Res. 20, 2973-2982 (2021).
98. Haartmans M. J.J. 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. 415, 5997-6007 (2023).
99. Urita, A.et al.Alterations of high-mannose type N-glycosylation in human and mouse osteoarthritis cartilage. Arthritis Rheum. 63, 3428-3438 (2011).
100. Heijs, B.et al.Multimodal mass spectrometry imaging of N-glycans and proteins from the same tissue section. Anal. Chem. 88, 7745-7753 (2016).
101. Smith R. L.Degradative enzymes in osteoarthritis. Front. Biosci. 4, D704-D712 (1999).
102. Meszaros E.& Malemud, C. J. Prospects for treating osteoarthritis: enzymeprotein interactions regulating matrix metalloproteinase activity. Ther. Adv. Chronic Dis. 3, 219-229 (2012).
103. Klein O., Haeckel A., Reimer U., Nebrich G.& Schellenberger, E. Multiplex enzyme activity imaging by MALDI-IMS of substrate library conversions. Sci. Rep. 10, 15522(2020).
104. Fan, X.et al. Functional mass spectrometry imaging maps phospholipase-A2 enzyme activity during osteoarthritis progression. Theranostics 13, 4636-4649 (2023).
105. Carlson C. S., Loeser R. F., Purser C. B., Gardin J. F.& Jerome, C. P. Osteoarthritis in cynomolgus macaques. III: effects of age, gender, and subchondral bone thickness on the severity of disease. J. Bone Min. Res. 11, 1209-1217 (1996).
106. Fan, X.et al.The deterioration of calcified cartilage integrity reflects the severity of osteoarthritis—a structural, molecular, and biochemical analysis. FASEB j. 36, e22142(2022).
107. Hackett, M. J.et al. Chemical alterations to murine brain tissue induced by formalin fixation: implications for biospectroscopic imaging and mapping studies of disease pathogenesis. Analyst 136, 2941-2952 (2011).
108. Zuo, Q.et al.Characterization of nano-structural and nano-mechanical properties of osteoarthritic subchondral bone. BMC Musculoskelet. Disord. 17, 367(2016).
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. 1146, 254-258 (2017).
110. Jung, Y.-K.et al.Calcium-phosphate complex increased during subchondral bone remodeling affects earlystage osteoarthritis. Sci. Rep. 8, 487(2018).
111. Herrmann, A. M.et al.Nano-scale secondary ion mass spectrometry—a new analytical tool in biogeochemistry and soil ecology: a review article. Soil Biol. Biochem. 39, 1835-1850 (2007).
112. de Rezende, M. U. & de Campos, G. C. Is osteoarthritis a mechanical or inflammatory disease? Rev. Bras. Ortop. 48, 471-474 (2013).
113. Griffin T. M.& Guilak, F. The role of mechanical loading in the onset and progression of osteoarthritis. Exerc. Sport Sci. Rev. 33, 195-200 (2005).
114. Dall’Ara, E., Ohman, C., Baleani, M. & Viceconti, M. Reduced tissue hardness of trabecular bone is associated with severe osteoarthritis. J. Biomech. 44, 1593-1598 (2011).
115. Gardner-Morse, M. G., Tacy, N. J., Beynnon, B. D. & Roemhildt, M. L. In situ microindentation for determining local subchondral bone compressive modulus. J. Biomech. Eng. 132, 094502(2010).
116. Miller G. J.& Morgan, E. F. Use of microindentation to characterize the mechanical properties of articular cartilage: comparison of biphasic material properties across length scales. Osteoarthr. Cartil. 18, 1051-1057 (2010).
117. Marchi, G.et al.Fiberoptic microindentation technique for early osteoarthritis diagnosis: an in vitro study on human cartilage. Biomed. Microdevices 21, 11 (2019).
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. 21, 7384(2020).
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. 127, 110144(2020).
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 25, 3734 (2020).
121. Fleischhauer, L.et al.Nano-scale mechanical properties of the articular cartilage zones in a mouse model of post-traumatic osteoarthritis. Appl. Sci. 12, 2596(2022).
122. Tschaikowsky, M.et al.Hybrid fluorescence-AFM explores articular surface degeneration in early osteoarthritis across length scales. Acta Biomater. 126, 315-325 (2021).
123. Ståhl, P. L.et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78-82 (2016).
124. Kooijman, P. C.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. 9, 8(2019).
125. Poad, B. L.et al.Ozone-induced dissociation on a modified tandem linear iontrap: observations of different reactivity for isomeric lipids. J. Am. Soc. Mass Spectrom. 21, 1989-1999 (2010).
126. Haartmans M. J.J. et al. Mass spectrometry-based biomarkers for knee osteoarthritis: a systematic review. Expert Rev. Proteom. 18, 693-706 (2021).
127. Serowoky M. A., Patel D. D., Hsieh, J. W. & Mariani, F. V. The use of commercially available adhesive tapes to preserve cartilage and bone tissue integrity during cryosectioning. Biotechniques 65, 191-196 (2018).
128. Piehowski, P. D.et al.Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution. Nat. Commun. 11, 8(2020).
129. Ellis, S. R.et al. Automated, parallel mass spectrometry imaging and structural identification of lipids. Nat. Methods 15, 515-518 (2018).
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. 31, 155-163 (2020).
131. Bond N. J., Koulman A., Griffin J. L.& Hall, Z. massPix: an R package for annotation and interpretation of mass spectrometry imaging data for lipidomics. Metabolomics 13, 128 (2017).
132. Janda, M.et al.Determination of abundant metabolite matrix adducts illuminates the dark metabolome of MALDI-Mass spectrometry imaging datasets. Anal. Chem. 93, 8399-8407 (2021).
133. Khader A.& Alquran, H. Automated prediction of osteoarthritis level in human osteochondral tissue using histopathological images. Bioengineering 10, 764 (2023).
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. 12, 744(2023).
135. Calivà, F.et al.Studying osteoarthritis with artificial intelligence applied to magnetic resonance imaging. Nat. Rev. Rheumatol. 18, 112-121 (2022).
136. Kim K.& Park, H. Machine-learning models predicting osteoarthritis associated with the lead blood level. Environ. Sci. Pollut. Res. Int. 28, 44079-44084 (2021).
137. Lourido, L.et al.Quantitative proteomic profiling of human articular cartilage degradation in osteoarthritis. J. Proteome Res. 13, 6096-6106 (2014).
138. Boris Chan, P. M., Zhu, L., Wen, C. Y. & Chiu, K. Y. Subchondral bone proteomics in osteoarthritis: current status and perspectives. J. Orthop. Transl. 3, 71-77 (2015).
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. 21, 100200(2022).
140. Uniprot Consortium.UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 49, D480-D489 (2021).
141. McDonnell, L. A., Walch, A., Stoeckli, M. & Corthals, G. L. MSiMass list: a public database of identifications for protein MALDI MS imaging. J. Proteome Res. 13, 1138-1142 (2014).
142. Maier, S. K.et al.Comprehensive identification of proteins from MALDI imaging. Mol. Cell. Proteom. 12, 2901-2910 (2013).
143. Fahy, E.et al.Update of the LIPID MAPS comprehensive classification system for lipids. J. Lipid Res. 50(Suppl), S9-S14 (2009).
144. Taguchi, R., Nishijima, M.& Shimizu, T. Basic analytical systems for lipidomics by mass spectrometry in Japan. Methods Enzymol. 432, 185-211 (2007).
145. Yasugi, E. & Watanabe, K. LIPIDBANK for Web, the newly developed lipid database. Tanpakushitsu Kakusan Koso 47, 837-841 (2002).
146. Wishart, D. S.et al.HMDB 5.0: the human metabolome database for 2022. Nucleic Acids Res. 50, D622-d631 (2022).
147. Wishart, D. S.et al.HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 46, D608-d617 (2018).
148. Sajed, T.et al.ECMDB 2.0: a richer resource for understanding the biochemistry of E. coli. Nucleic Acids Res. 44, D495-D501 (2016).
149. Kanehisa M.& Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27-30 (2000).
150. Smith, C. A.et al.METLIN: a metabolite mass spectral database. Ther. Drug Monit. 27, 747-751 (2005).
151. Mobasheri A., Kapoor M., Ali S. A., 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 3, 100144 (2021).
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. 79, 811-818 (2020).
153. Papathanasiou, I., Anastasopoulou, L.& Tsezou, A. Cholesterol metabolism related genes in osteoarthritis. Bone 152, 116076 (2021).
154. Cannata, F.et al.Osteoarthritis and type 2 diabetes: from pathogenetic factors to therapeutic intervention. Diabetes Metab. Res Rev. 36, e3254(2020).
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. 51, 2245-2257 (2023).
156. Sarin, J. K.et al.Arthroscopic near infrared spectroscopy enables simultaneous quantitative evaluation of articular cartilage and subchondral bone in vivo. Sci. Rep. 8, 13409(2018).
157. de Souza, R. A.et al. Raman spectroscopy detection of molecular changes associated with two experimental models of osteoarthritis in rats. Lasers Med. Sci. 29, 797-804 (2014).
158. Buchberger A. R.,DeLaney, K., Johnson, J. & Li, L. Mass spectrometry imaging: a review of emerging advancements and future insights. Anal. Chem. 90, 240-265 (2018).
159. Nguyen D. T., 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. 4, 19(2017).
160. Boer, C. G.et al. Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations. Cell 184, 4784-4818.e4717 (2021).
161. Bay-Jensen, A. C., Mobasheri, A., Thudium, C. S., Kraus, V. B. & Karsdal, M. A. Blood and urine biomarkers in osteoarthritis—an update on cartilage associated type II collagen and aggrecan markers. Curr. Opin. Rheumatol. 34, 54-60 (2022).
162. Mirzaii-Dizgah, M. R., Mirzaii-Dizgah, M. H., Mirzaii-Dizgah, I., Karami, M. & Forogh, B. Osteoprotegerin changes in saliva and serum of patients with knee osteoarthritis. Rev. Esp. Cir. Ortop. Traumatol. 66, 47-51 (2022).
163. Mirzaii-Dizgah, M. R., Mirzaii-Dizgah, M. H. & Mirzaii-Dizgah, I. Elevation of urate in saliva and serum of patients with knee osteoarthritis. Gerontology 67, 87-90 (2021).
164. Wang, G.et al.Analyzing cell-type-specific dynamics of metabolism in kidney repair. Nat. Metab. 4, 1109-1118 (2022).
165. Schwaiger-Haber, M. et al. Using mass spectrometry imaging to map fluxes quantitatively in the tumor ecosystem. Nat. Commun. 14, 2876(2023).
166. Barré, F. P.et al.Distribution, quantification and effects of triamcinolone acetonide in human osteoarthritic cartilage. Osteoarthr. Cartil. 26, S284-S285 (2018).
167. Barré F. P.Y. et al. Enhanced sensitivity using MALDI imaging coupled with laser postionization (MALDI-2) for pharmaceutical research. Anal. Chem. 91, 10840-10848 (2019).
168. Niehaus M., Soltwisch J., Belov, M. E. & Dreisewerd, K. Transmission-mode MALDI-2 mass spectrometry imaging of cells and tissues at subcellular resolution. Nat. Methods 16, 925-931 (2019).
169. Seeley E. H.& Caprioli, R. M. 3D imaging by mass spectrometry: a new frontier. Anal. Chem. 84, 2105-2110 (2012).
170. Lanekoff, I.et al.Three-dimensional imaging of lipids and metabolites in tissues by nanospray desorption electrospray ionization mass spectrometry. Anal. Bioanal. Chem. 407, 2063-2071 (2015).
171. Ratneswaran, A., Rockel, J. S.& Kapoor, M. Understanding osteoarthritis pathogenesis: a multiomics system-based approach. Curr. Opin. Rheumatol. 32, 80-91 (2020).
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 I. O., Moody H., Singh S., Prasadam, I. & Oloyede, A. Spatial mapping of proteoglycan content in articular cartilage using near-infrared (NIR) spectroscopy. Biomed. Opt. Express 6, 144-154 (2015).
174. Lasch P.& Naumann, D. Spatial resolution in infrared microspectroscopic imaging of tissues. Biochim. Biophys. Acta (BBA) - Biomembr. 1758, 814-829 (2006).
175. Bodzon-Kulakowska, A. & Suder, P. Imaging mass spectrometry: instrumentation, applications, and combination with other visualization techniques. Mass Spectrom. Rev. 35, 147-169 (2016).
[176
Funding
Indira Prasadam (i.prasadam@qut.edu.au)

Accesses

Citations

Detail

Sections
Recommended

/