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
Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications
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.
| [1] |
|
| [2] |
|
| [3] |
|
| [4] |
|
| [5] |
|
| [6] |
|
| [7] |
|
| [8] |
|
| [9] |
|
| [10] |
|
| [11] |
|
| [12] |
|
| [13] |
|
| [14] |
|
| [15] |
|
| [16] |
|
| [17] |
|
| [18] |
|
| [19] |
|
| [20] |
|
| [21] |
|
| [22] |
|
| [23] |
|
| [24] |
|
| [25] |
|
| [26] |
|
| [27] |
|
| [28] |
|
| [29] |
|
| [30] |
|
| [31] |
|
| [32] |
|
| [33] |
|
| [34] |
|
| [35] |
|
| [36] |
|
| [37] |
|
| [38] |
|
| [39] |
Maddams, W. & Willis, H. The Principles and Applications of Mathematical Peak Finding Procedures in Vibrational Spectra. Vol. 0917 SIR (SPIE, 1988). |
| [40] |
|
| [41] |
|
| [42] |
|
| [43] |
|
| [44] |
|
| [45] |
|
| [46] |
|
| [47] |
|
| [48] |
|
| [49] |
|
| [50] |
|
| [51] |
|
| [52] |
|
| [53] |
|
| [54] |
|
| [55] |
|
| [56] |
|
| [57] |
|
| [58] |
|
| [59] |
|
| [60] |
|
| [61] |
|
| [62] |
|
| [63] |
|
| [64] |
|
| [65] |
|
| [66] |
|
| [67] |
|
| [68] |
|
| [69] |
|
| [70] |
|
| [71] |
|
| [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] |
|
| [74] |
|
| [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] |
|
| [77] |
|
| [78] |
|
| [79] |
|
| [80] |
|
| [81] |
|
| [82] |
|
| [83] |
|
| [84] |
|
| [85] |
|
| [86] |
|
| [87] |
|
| [88] |
|
| [89] |
|
| [90] |
|
| [91] |
|
| [92] |
|
| [93] |
|
| [94] |
|
| [95] |
|
| [96] |
|
| [97] |
|
| [98] |
|
| [99] |
|
| [100] |
|
| [101] |
|
| [102] |
|
| [103] |
|
| [104] |
|
| [105] |
|
| [106] |
|
| [107] |
|
| [108] |
|
| [109] |
|
| [110] |
|
| [111] |
|
| [112] |
|
| [113] |
|
| [114] |
|
| [115] |
|
| [116] |
|
| [117] |
|
| [118] |
|
| [119] |
|
| [120] |
|
| [121] |
|
| [122] |
|
| [123] |
|
| [124] |
|
| [125] |
|
| [126] |
|
| [127] |
|
| [128] |
|
| [129] |
|
| [130] |
|
| [131] |
|
| [132] |
|
| [133] |
|
| [134] |
|
| [135] |
|
| [136] |
|
| [137] |
|
| [138] |
|
| [139] |
|
| [140] |
Uniprot Consortium. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 49, D480-D489 (2021). |
| [141] |
|
| [142] |
|
| [143] |
|
| [144] |
|
| [145] |
|
| [146] |
|
| [147] |
|
| [148] |
|
| [149] |
|
| [150] |
|
| [151] |
|
| [152] |
|
| [153] |
|
| [154] |
|
| [155] |
|
| [156] |
|
| [157] |
|
| [158] |
|
| [159] |
|
| [160] |
|
| [161] |
|
| [162] |
|
| [163] |
|
| [164] |
|
| [165] |
|
| [166] |
|
| [167] |
|
| [168] |
|
| [169] |
|
| [170] |
|
| [171] |
|
| [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] |
|
| [174] |
|
| [175] |
|
| [176] |
|
| [177] |
|
| [178] |
|
| [179] |
|
| [180] |
|
| [181] |
|
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)
/
| 〈 |
|
〉 |