Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Yu Zhao
 -- 4498 2023-02-05 05:33:49 |
2 format
Jason Zhu
 + 1 word(s) 4498 2023-02-06 06:34:22 |

Video Upload Options

We provide professional Video Production Services to translate complex research into visually appealing presentations. Would you like to try it?
No, upload directly Yes

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Feng, S.;  Li, J.;  Tian, J.;  Lu, S.;  Zhao, Y. Single-Cell and Spatial Omics Studies of Musculoskeletal Diseases. Encyclopedia. Available online: https://encyclopedia.pub/entry/40836 (accessed on 16 November 2024).
Feng S,  Li J,  Tian J,  Lu S,  Zhao Y. Single-Cell and Spatial Omics Studies of Musculoskeletal Diseases. Encyclopedia. Available at: https://encyclopedia.pub/entry/40836. Accessed November 16, 2024.
Feng, Site, Jiahao Li, Jingjing Tian, Sheng Lu, Yu Zhao. "Single-Cell and Spatial Omics Studies of Musculoskeletal Diseases" Encyclopedia, https://encyclopedia.pub/entry/40836 (accessed November 16, 2024).
Feng, S.,  Li, J.,  Tian, J.,  Lu, S., & Zhao, Y. (2023, February 05). Single-Cell and Spatial Omics Studies of Musculoskeletal Diseases. In Encyclopedia. https://encyclopedia.pub/entry/40836
Feng, Site, et al. "Single-Cell and Spatial Omics Studies of Musculoskeletal Diseases." Encyclopedia. Web. 05 February, 2023.
Single-Cell and Spatial Omics Studies of Musculoskeletal Diseases
Edit
This entry is adapted from the peer-reviewed paper 10.3390/ijms24032271

Musculoskeletal disorders, including fractures, scoliosis, heterotopic ossification, osteoporosis, osteoarthritis, disc degeneration, and muscular injury, etc., can occur at any stage of human life. Understanding the occurrence and development mechanism of musculoskeletal disorders, as well as the changes in tissues and cells during therapy, might help us find targeted treatment methods. Single-cell techniques provide excellent tools for studying alterations at the cellular level of disorders. However, the application of these techniques in research on musculoskeletal disorders is still limited. 

single cell spatial omics musculoskeletal disorders fracture

1. Introduction

The musculoskeletal system, consisting of the bones, muscles, cartilage, tendons, ligaments, and other connective tissues, supports the body, allows motion, and protects the vital organs [1]. Although we already know that bones develop to form the skeleton through a series of synchronized events [2], the cell populations involved in the development of skeletal, muscle, and connective tissue disorders are incompletely understood. Obviously, abnormal musculoskeletal conditions occur throughout the life course, from childhood to older age. They can be either short-term (fractures, sprains, and strains), which cause pain and functional restrictions, or long-term (osteoarthritis (OA) and chronic primary lower back pain). Many cell populations are implicated in the recovery progression of these disorders. The function, expression, and molecular signaling pathways of different cell populations in the recovery process can provide targets for treatment [2][3][4][5][6].
In addition, implants such as artificial bones and artificial joints are routinely used in the surgical treatment of skeletal diseases. Less understood, but equally important, are the interactions between implants and living cells and tissues [7]. Understanding the characteristics of the peri-implant cell population can reveal the causes of implant-related complications, including aseptic inflammation, loosening, and other problems. It provides a theoretical basis for researchers to select implant materials and design structures. However, due to the complicated anatomical nature and such a complex meshwork of different cell types, progress on these issues has been hampered by limitations inherited from traditional histological analysis methods for a long time [8]. Otherwise, stem cells and drugs for the treatment of musculoskeletal system diseases are also a major focus of current research [9][10][11].
Since the first description of scRNA-seq in 2009 [12], single-cell technology has contributed to significant advances both in the discovery of new cell types and groundbreaking insights into a variety of disorders. Spatial omics techniques were later developed to decode spatial context of tissues and cells. In recent years, knowledge about single-cell isolation and identification for the musculoskeletal system has continuously accumulated [13][14]. By offering crucial insights into the processes that maintain and regenerate the musculoskeletal system during homeostasis and repair, single-cell and spatial omics technologies allow researchers to observe accurate delineation of cellular activity and destiny throughout musculoskeletal system growth and repair.

2. Bone-Related Disorders

2.1. Bone Injury

The body’s capacity to heal bones via regeneration, returning them to their completely functioning and pre-injury form, demonstrates the importance of bones to mammalian physiology [2]. Bone fracture healing is a complex process that consecutively undergoes the inflammatory phase, two healing phases of soft and hard callus formation, and a remodeling phase [15]. During the process, the two main cellular components of bones, bone-forming osteoblasts, and bone-resorbing osteoclasts, exhibit a controlled balance of activities [2]. Research gaps still need to be filled in identifying regeneration-driving clusters and molecular mechanisms in different stages and locations, and single-cell analysis still needs to be fully applied.
Regarding the ribs, single-cell RNA sequencing of soft callus tissue generated after rib injury in mice with defective Hedgehog signaling revealed a decrease in Cxcl12-expressing cells, which indicates a failure to attract Cxcl12-expressing skeletal stem and progenitor cells (SSPCs) during the regenerative response [16]. Regarding the long bones in the leg, scRNA-seq profiling of non-hematopoietic stromal cells was performed on mice at post-fracture day 14, compared to age-matched, unfractured controls. The cell proportions of chondrocytes, fibroblasts, and Fabp5+ Mmp9+ septoclasts (SCs) [17][18][19] increased in fractured bones, while that of diaphyseal MSCs (dpMSCs) decreased. Trajectory analysis further suggests the conversion of dpMSCs into mpMSCs, which leads to the expansion of osteoblast lineage cells, fibroblasts, chondrocytes, and SCs [20].
In cranial bone injuries, Xu et al. [21]. found that nerve growth factor (NGF), a previously identified factor inducing skeletal reinnervation [22][23], is expressed in cranial bone injuries. NGF signals via p75 (Ngfr) [24] in resident mesenchymal osteogenic precursors to affect their migration into the damaged tissue. The researchers collected the injured frontal bones of mice with p75 conditionally knockout in mesenchymal cells. Single-cell transcriptomics identified repair- and inflammatory-response-related clusters that are affected by p75. A subcluster of mesenchymal cells that expressed high amounts of Itgb1 was enriched for specific GO pathways, including cell adhesion, modulation of actin cytoskeleton structure, and cell migration. Genes involved in receptor-mediated extracellular matrix interaction and cytokine-receptor-mediated signaling, both of which decreased in p75-deficient people, were significantly enriched in the clusters. Another crucial mesenchymal subcluster expressed a significant quantity of inflammatory regulators, such as Il1a, Il10, and Tnf, whose expression decreased in cells produced from mice lacking p75. Pathway analysis of the whole mesenchymal cell populations revealed that osteogenesis-related signaling pathways, including Notch, bone morphogenetic protein, transforming growth factor, fibroblast growth factor, and Hippo signaling, were suppressed in p75-deficient cells [21].

2.2. Skeletal Dysplasia

The developmental malformations of the musculoskeletal system contain congenital anomalies and post-natal diseases, including the abnormal development of the flat bone or long bone, joint dysplasia, and spinal deformity [25]. In normal congenital bone development, two types of bone ossification, intramembranous and endochondral, occur at different locations. Although these processes start with mesenchymal stem cells (MSC), their transformations into bones are distinct. Intramembranous ossification transforms mesenchymal tissue directly into the bone and forms the flat bones of the skull, clavicle, and the majority of the cranial bones. Endochondral ossification in long bones starts with the transformation of mesenchymal tissue into hypertrophic cartilage, which is thereafter replaced by bone [2][26][27]. These processes are followed by forming the axial skeleton and the long bones in post-natal development [2][26][28]. Although plenty of single-cell analyses have characterized normal skeletal developmental processes in mice [29], only a limited number of studies used single-cell or spatial omics to propose the potential pathology in skeletal dysplasia.
Congenital skeletal dysplasia is caused by genetic mutations that impair the pattern, structure, and development of bones [25]. This dysplasia appears as one or more phenotypes that impact the form and size of specific skeletal locations, such as short, stubby fingers, duplications of fingers or toes, clubfeet, missing bones, fragile bones, or curved spines [25]. Current single-cell studies to the characterization of mice hind limbs and calvarium have focused on molecules, pathways, and even the temporal and spatial nature that cause malformations.
From E15.5 hind limb tissues, Wang et al. [30] predicted the crucial function of IHH signaling in the transformation from cartilage to bone. Hypertrophic chondrocytes (HCs) were identified by the expression of early or late HC-specific markers, including Col10a1, Ihh, Mmp13, Vegfa, etc., and crucial transcriptional regulators such as Zbtb20 and Runx2. They used pseudo-temporal cell trajectory analysis to determine transcriptional patterns in HCs and Col1a1+ Runx2+ osteoblasts, with HCs positioned at the start point of the pseudo-time trajectory and Col1a1+ Runx2+ osteoblasts at the end of the trajectory. Analysis of Hedgehog gene expression through the trajectory showed that Ptch1 was expressed momentarily and was independent of Gli1-3 expression [31]. These findings, taken together, point to a distinctive pattern of hedgehog-related transcriptome states at the single-cell level during the simulated cartilage-to-bone transition. Further histological images on the mice model verified that deleting Ptch1 in HCs affected the formation of primary spongiosa and HC-derived osteogenic cells, which led to the bony bulges in adult mutant animals.
Through spatial transcriptomic data from murine calvarium, Tower et al. [32] used an approach similar to the scRNAseq computational technique of inferring pseudo-time, referred to herein as “spatial time” to compare changes in gene expression between tropomyosin receptor kinase A (TrkA) mutant and control mice. This methodology measures and normalizes the distance between each spatial place in a suture and a manually chosen central anchor point in the midline to the suture width. The midline suture, in particular, showed a low expression of numerous previously known factors, including Gli1, Twist1, and noggin, which were expressed and/or implicated in maintaining suture patency. The BMP/TGF signaling pathway components were dysregulated in the calvariae of TrkA mutants, according to the pathway analysis. BMP activation was specifically enriched within the osteogenic front (OF), with little activation within the suture midline, according to the modular scoring used to evaluate the expression of BMP activators and inhibitors; BMP inhibitory scores revealed the opposite spatial localization. Similar outcomes were shown for TGF signaling. These findings, to some extent, indicated that some extracellular factors might have affected the molecular pathways of cells, and several secreted factors were later identified, including FSTL1 [32].
Idiopathic scoliosis is a prevalent developmental deformity of the spine [33]. The etiology of idiopathic scoliosis is not yet understood but is likely to relate to skeletal and paravertebral muscle abnormalities. Potential causes of adolescent idiopathic scoliosis (AIS) include genetic, environmental, endocrinological, metabolic, biochemical, neurological, and asymmetric growth variables [34][35], suggesting that both congenital and post-natal factors might contribute to this abnormality.
Yang et al. [34] acquired scRNA-seq data from healthy and diseased samples to demonstrate the features of MSC, chondrocyte progenitor cells (CPC), and osteoblasts (OC). In the three identified MSC subtypes, IGFBP5-expressing MSC (MSC-IGFBP5) was identified as an AIS-specific MSC subtype. The cell number of osteoblasts was significantly decreased in AIS subjects, possibly due to the failure of MSC-IGFBP5’s differentiation into osteoblasts, the decreased potential of cell proliferation, and increased cell death. Regarding CPC, PCNA-expressing CPC was the specific subtype recognized only in AIS patients. The cell counts of BIRC3-expressing OC in AIS were less than that in the controls. Pseudo-time analysis suggested distinct patterns of osteoclast differentiation. In AIS, monocytes differentiated into CRISP3-expressing OC, which is different from the trajectory of the control subjects [34].
Due to the difficulty in obtaining clinical tissues of developmental malformations, most of the current single-cell studies are based on animal models, and their conclusions are usually further verified by histological or immunofluorescence imaging. In addition, existing studies mainly focus on the malformation of the skeletal system. Although the normal developmental processes of cartilage and joints have been characterized by several studies in mice [36][37], there are currently no single-cell studies on abnormal cartilage and joint development being reported. Future research can apply the single-cell methods to the characterization of other developmental diseases in other organs.

2.3. Heterotopic Ossification

Heterotopic ossification (HO) refers to ectopic endochondral bone growth and accumulation inside or around connective tissues, muscles, joints, blood vessels, and other locations, which is a frequent and potentially disabling acquired condition [38][39][40]. Tendon ossification, ossification of the ligamentum flavum (OLF), and ossification of the posterior longitudinal ligament (OPLL) are common HO of tendon and ligament (HOTL) diseases that disable patients. The HO of the above diseases occurs in the spinal cord or nerves, causing pain, paralysis, or even death due to compression [41][42]. Metabolomics studies have identified changes in certain cellular pathways in HO [43][44]. HO is an abnormal change in local tissues, and single-cell techniques are helpful for researchers to find the changes in cell expression in local tissues.
By implanting rhBMP2–Matrigel mixtures subcutaneously into mice and injecting a pre-immune anti-body (BMP2/IgG) or an activin A neutralizing antibody (BMP2/nActA.Ab), and ordering cells in a continuous two-dimensional pseudo-time trajectory, Mundy et al. revealed that activin A (encoded by Inhba), a member of the TGF-β superfamily, stimulated HO development in muscles [45]. Fewer cells in the early stages of the pseudo-time trajectory and more in the terminal (Acan- and Col2a1-expressing) segment were seen in the BMP2/IgG samples than in the BMP2/nActA samples. Furthermore, progenitors expressing Inhba in the BMP2/IgG and BMP2/nActA.Ab samples filled the latter part of the trajectory, while Matrigel-only samples were restricted to the early part. These findings suggest that activin A may promote heterotopic ossification and that activin-A-neutralizing antibody therapy prevents the formation of intramuscular HO [45].
A fraction of tendon stem/progenitor cells (TSPCs) expressing lubricin (PRG4) is believed to contribute to tissue repair [46]. The processes underlying ectopic ossification and their connections with TSPC, however, are yet unknown. Tachibana et al. explored the features of Prg4+ cells and discovered that a unique Prg4+ TSPC cluster expresses R-spondin 2 (RSPO2), a WNT activator. Pseudo-time trajectory characterized the Rspo2+ cluster as an undifferentiated one, and predicted that the cluster might differentiate into downstream tenogenic and chondro/osteogenic clusters [47].

2.4. Osteoporosis

Osteoporosis is a prevalent disorder characterized by a decreased bone mineral density and an increased risk of osteoporotic fractures [48][49]. Importantly, bone homeostasis disturbance plays a critical role in the etiology of osteoporosis. As a highly metabolically active tissue, bones involve a continual cycle of bone formation by osteoblasts and bone resorption by osteoclasts [48]. Notably, the regulation of bone mineral density is highly heritable, and disorders in gene expression result in osteoporosis and osteoporotic fracture; nevertheless, decoding the underlying genomic and molecular mechanisms of osteoporosis in vivo in humans remains problematic [48][49].
Samples of osteoporosis subjects can hardly be collected from the surgeries in the current studies. Wang et al. [50] performed single-cell RNA analysis on primary human femoral head tissue cells (FHTCs) from healthy subjects. Predictions were acquired from the pseudo-time trajectory of osteoclast formation and the enrichment of possible transcript biomarkers and signaling pathways that probably contribute to osteoporosis. Expression of potential critical genes, such as OLMF4, RPL39, H3F3B, and SAT1, altered dramatically throughout the progressive trajectory of OC formation. The transcriptional factors, including HMGB2, HMGB1, MEF2C, ID1, ID3, LITAF, and CREM, associated with immune cell proliferation and differentiation, were increasingly dysregulated as the trajectory differentiation process progressed. The zinc finger protein ZFP36L1 (encoding the zinc finger protein) and DEFA3 (encoding defensin) were discovered as new genes involved in bone metabolism. RETN-CAP1 was discovered to participate in the interaction between immune cells and osteoclasts, showing that the osteoimmunology microenvironment significantly contributed to the pathogenesis of osteoporosis or osteopenia [50].
Due to the lack of human samples, further research should be undertaken in animal models to study osteoporosis. The primary purpose of murine model construction is to simulate the significant types of osteoporosis in humans, including post-menopausal osteoporosis, disuse osteoporosis, and glucocorticoid-induced osteoporosis [51]. As estrogen receptor α (Esr1) conditional knockout mice verified that estrogen promotes bone resorption and impairs osteoblast function [52][53][54][55][56][57][58], animal models are usually generated by ovariectomy, which eliminates estrogen secretion. Animal models for simulating disuse osteoporosis are generated by unloading the limb, specifically tail suspension or hind limb immobilization. In humans and large animals [59][60][61][62][63], but not always in rodents, glucocorticoid therapy significantly reduces cancellous bones, suppresses bone production, and promotes bone resorption. Future research on these animal models may fill the gap in the molecular mechanism of osteoporosis.

3. Cartilage- or Joint-Related Disorders

3.1. Cartilage Injury

Cartilage has a limited intrinsic healing capacity due to the poor vascularization of tissue [64]. Identifying and characterizing regeneration-driving clusters and molecular mechanisms remain as research gaps. Previous research has shown that BMP9 induces a chondrogenic response in neonatal and adult amputations [65]. According to another pieces of research [66], fibroblasts are recognized as the most prevalent non-inflammatory mesenchymal cell types in amputation wounds. Together, these data suggest that a fibroblast is the sort of cell that responds to BMP9 and undergoes chondrogenesis during amputation regeneration. Using single-cell RNA datasets, Yu et al. confirmed the chondrogenic potential of a subgroup of amputation-wound-derived fibroblasts [67]. However, whether this result can be replicated in patients with simple cartilage injuries remains to be further explored.

3.2. Osteoarthritis

Osteoarthritis (OA) has long been regarded as a degenerative illness that causes cartilage loss [68]. OA was formerly believed to be the result of any process that raised strain on a joint, such as loading on weight-bearing joints and anatomical joint incongruence, or fragility of the cartilage matrix caused by genetic modifications [68]. It took a decade for synovitis to be recognized as a crucial aspect of OA [68][69][70]. The application of scRNA-seq provided new discoveries in chondrocyte classification and provided connections between cartilage and synovitis.
Ji et al. [71] discovered seven molecularly characterized groups of chondrocytes in human OA cartilage, including four traditional populations named as proliferative chondrocytes (ProCs), fibrocartilage chondrocytes (FCs), prehypertrophic chondrocytes (preHTCs), and hypertrophic chondrocytes (HTCs), together with three new populations named as regulatory chondrocytes (RegCs), homeostatic chondrocytes (HomCs), and effector chondrocytes (ECs) [72][73][74]. Favorable genes were predominantly expressed in RegCs, HomCs, and ECs, but unfavorable genes were expressed in a high percentage of ProCs, FCs, and preHTCs. These findings indicate that the cell populations of the former may inhibit the development of OA, whereas the cell populations of the latter may promote OA development [71]. New biomarkers of cartilage progenitor cells (CPCs), a cell type that potentially differentiates into FC, were also identified [75]. Chou et al. [75] identified cell groupings in both synovium and articular cartilage, including twelve different cell types from synovium and seven from cartilage. Similar to Ji et al., cell types such as HomC, HTCs, preHTCs, RegCs, and FCs were identified. In addition, the researchers identified two unique subgroups of chondrocytes, reparative chondrocytes (RepCs) and prefibrochondrocytes (preFCs), according to their gene expression patterns. HomC or HTC chondrocyte cell types were enriched in intact cartilage, whereas FC, preFC, RegC, RepC, and preHTC chondrocyte cell types were enriched in injured cartilage. FCs were the major source of numerous OA-related proteases. HomCs and HTCs were the major sources of MMP3 and SERPINA1 in damaged cartilage, respectively. Wang et al. [76] aimed to compare OA with Kashin–Beck disease (KBD), a chronic, endemic osteochondropathy in which chondrocyte necrosis occurs in the growth plate and articular surface and results in growth retardation and secondary osteoarthritis [77]. The RegC population, markedly expanded in OA, could be identified by the known markers CHI3L1, AEBP1, PLIN2, and STEAP1. Two unique chondrocyte populations, HomCs and the novel MTCs marked by mitochondrial electron transport and the response to hydroperoxide, expanded in KBD samples relative to normal or OA samples [76]. Lv et al. identified cell clusters including the HomCs, RegCs, the stressed chondrocytes (StrCs), and the degenerative chondrocytes (DegCs). In injured cartilage, the cell proportion of HomCs decreased and that of StrCs and RegCs increased. Within StrCs, researchers found a chondrocyte cluster, namely the ferroptotic chondrocyte cluster, characterized by preferential expression of ferroptotic hallmarks and genes. Comprehensive GSVA recognized TRPV1 as an anti-ferroptosis biomarker in human OA cartilage, which was supported by further experiments revealing the murine OA model [78].
Apart from chondrogenic cells, Chou et al. [75] also detected synovium cell composition and articular cartilage cell–cell interactions and identified possible upstream growth factors and cytokines that might regulate the chondrocyte genes. Twelve cytokines, including TNF, IL6, IL1B, IL1A, etc., were exclusively expressed by synoviocytes but not chondrocytes, demonstrating that synovium is the origin of a large number of signals that regulate chondrocyte transcription in the progression of OA, instead of the cartilage.
Several single-cell experiments have described cell subsets in the OA synovial tissues, including synovial fibroblasts (SFs) and synovial immune cells [79][80][81]. In addition, mice models were applied to several studies on OA single-cell characterization [82][83][84][85][86]. The relationship between synovitis and OA is expected to be further studied with the help of single cell technology in the future.

3.3. Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a progressive and aggressive immunological condition that may result in decreased mobility and handicap. It is characterized by chronic synovitis, pannus development, joint degeneration, and nearby bone erosion. The pathogenic underpinning of rheumatoid arthritis is synovitis; thus, key target cells of RA are synovial fibroblasts (SFs) and synovial immune cells, and the activities of these cells result in the loss of articular cartilage and bone [87]. Recent advances in scRNA-seq technology have facilitated the characterization of the two distinct kinds of synovial cells mentioned above, especially in immune cells [88][89][90].
Stephenson et al. [91] distinguished the differences between CD55+ synovial fibroblasts and the CD90+ subpopulation. CD55+ fibroblasts localize to the intimal lining and are responsible for the production and turnover of synovial fluid. GO enrichment showed that CD55+ fibroblasts are involved in endothelial cell proliferation and reactive oxygen species’ responses. The majority of CD90+ fibroblasts are located in the lower synovial sublining layer, which is rich in modules associated with metallopeptidase activity and the formation of the extracellular matrix [91].
Several cutting-edge techniques and animal models have also been used in the study of RA. As a proof-of-concept method for objective mRNA analyses at the site of inflammation in these chronic inflammatory illnesses, spatial transcriptomics has been applied to profile synovial samples from RA and spondyloarthritis (SpA) patients [92]. A scRNA-seq characterization of the mouse model of antigen-induced arthritis (AIA) was discussed by Gawel et al. [93].

3.4. IVD Degeneration (IVDD)

The intervertebral disc (IVD), a joint with limited movement, is the segregation of vertebrae in the spine [94]. The disc consists of three parts: the nucleus pulposus (NP), which is located in the center of the disc and is highly hydrated, the annulus fibrosus (AF), which is elastic and fibrous, and the cartilaginous endplate (CEP), which connects the disc to the vertebral bodies. Intervertebral disc degeneration is a frequent cause of lower back pain, a major cause of disability, and a common finding in spine imaging that becomes more prevalent with age [94]. Currently, a number of single-cell studies have characterized the virtual clusters participating in the pathological process, as well as differences in different parts of the disc.
Zhang et al. [95] identified novel chondrocyte subsets in the nucleus pulposus. GSEA revealed upregulated ferroptosis signaling in two novel chondrocyte clusters, CPCs and HomCs, in the IVDD group compared to the controls. The ferroptosis pathway might participate in disc degeneration pathogenesis and serve as a new target for intervening IVDD [95]. Ling et al. [96] revealed the heterogeneity of NP cells in IVD, and thus observed that the inflammatory response NP cells and fibrocartilaginous NP cells make up a significant component of NP cells in the late stage of IVDD. The inflammatory response NP cells significantly expressed ERG1 and FGF1 and are shown to undergo inflammatory and endoplasmic reticulum (ER) responses, as well as fibrocartilaginous activity. Cell–cell interaction analysis between NP cells and immune cells also revealed that macrophages interact with NP cells by macrophage migration inhibitory factor and NF-kB signaling pathways [96]. Cherif et al. [97] analyzed differential gene expression among degenerative and non-degenerative AF and NP [97]. In addition, using healthy samples, Gan et al. [98] identified potential cell lineages in healthy IVD tissue that might promote the regeneration process, which could rescue degeneration. They identified four NP progenitor cell subsets, together with several chondrocytes or fibroblast clusters. Several clusters expressed protective or regenerative genes such as EGF and were involved in pathways such as the GAS signaling pathway [98]. These findings may have important implications for cell selection and design in tissue engineering or stem cell therapy for the treatment of disc degeneration [98].

4. Muscle- or Tendon-Related Disorders

4.1. Tendon Injury

Like cartilage, tendons have a limited intrinsic healing capacity due to the poor vascularization of tissue, although might be affected by ectopic materials [99]. Research gaps remain in identifying and characterizing regeneration-driving clusters and molecular mechanisms. Using single-cell transcriptomics, Harvey et al. [100] identified a Tppp3+ cell population in which a fraction expresses Pdfgra. Injection of PDGF-AA protein stimulates the formation of new tenocytes, while Pdgfra inactivation contributes to the opposite effect, demonstrating that Tppp3+Pdgfra+ cells are tendon stem cells. Interestingly, Tppp3− Pdgfra+ fibro-adipogenic progenitor (FAP) simultaneously exists in the tendon stem cell niche and generates fibrotic cells, indicating a potential genesis of fibrotic scarring in regenerating tendons. These findings explain why fibrosis arises in wounded tendons and highlight therapeutic difficulties for enhancing tendon regeneration without concurrently increasing fibrosis by ectopic PDGF. [100]

4.2. Tendinopathy

Tendinopathy is a tendon degenerative disease accounting for over 30% of primary care consultations [101][102][103]. Resident tenocytes maintain the tendon matrix, and tendinopathy is connected with alterations in remodeling activity. [102] Developing therapies to address this difficulty requires an understanding of the crucial tenocytes, stem cells, and other cell subsets participating in tendinopathy.
Kendal et al. [101] used CITE-seq, which integrated surface proteomics with the gene expression modality of cells from tendinopathic and healthy human tendons. In response to chronic tendinopathy, tenocytes linked with microfibrils had higher expression of pro-inflammatory markers and PDPN (Podoplanin). Increased expression of IL-33, together with other chemokine and alarmin genes, was performed in diseased endothelium [101]. Garcia-Melchor et al. [104] profiled single-cell transcriptomes in tendinopathic tissues compared with healthy ones. They mainly focused on the interaction between T-cells and tenocytes, which increases T-cell activation through an auto-regulatory feedback loop, stimulates tenocytes to produce inflammatory cytokines and chemokines, changes collagen composition in favor of collagen 3, and modifies collagen composition [104].

4.3. Muscle Injury

Skeletal muscle injuries are prevalent disorders, particularly among athletes [105]. Mechanical injuries result from myofiber necrosis, hematomas, and inflammation and entail the disruption of connective tissues [106][107]. Inflammation and regeneration of muscles are contingent on the type, extent, and severity of the damage [106]. Muscle stem cells (MuSCs) are crucial for the lifelong maintenance of skeletal muscle homeostasis and regeneration [108]. The network among MuSC and immune cells, endothelial cells, and FAPs orchestrates the regeneration process [109]. Single-cell analysis recognized several muscle cell subtypes, especially in MuSCs [110], and this technique was applied to decode muscle injury and regeneration.
De Micheli et al. administered scRNA-seq and CyTOF to the muscles of adult mice at different time points after injury. They discovered that myogenic stem/progenitor cells exhibited heterogeneous expression of various syndecan proteins in cycling myogenic cells, indicating that syndecans may coordinate myogenic destiny control [111]. Dell’Orso et al. [112] compared scRNA-seq datasets from uninjured and regenerative MuSCs. After muscle injury, MuSCs were isolated by FACS 60 h later [112]. As a result, three unique clusters of MuSCs were identified in the injured muscle, with increased expression of metabolism-related genes [112]. Oprescu et al. [113] identified regeneration-related characteristics in three kinds of cells. Firstly, they found immune cells demonstrated early pro- and anti-inflammatory infiltration. Secondly, they found a MuSC cluster enriched for gene expression associated with immune cell complement activation, major histocompatibility class II antigens, and cathepsin family members. They also discovered the expression of myogenic genes in the muscle that was regenerating. In addition, pseudo-time trajectory suggests that activated, chemokine-expressing FAPs transform into Dpp4+ and Cxcl14+ cells in non-injured tissues [113].

4.4. Muscular Dystrophy

Multinucleated muscle fibers, as well as MuSC, non-myogenic mesenchymal progenitors (such as FAPs), immune cells, endothelial cells, and other mononuclear cells make up the skeletal muscle, which is a complex heterogeneous tissue [114][115]. Dysfunction of these cell types leads to various diseases. Muscular dystrophies are hereditary, myogenic disorders characterized by gradual muscular atrophy and varying degrees of muscle weakness. Duchenne muscular dystrophy (DMD), the best-known form of muscular dystrophy, is a severe, progressive muscle-wasting illness. The disease is caused by mutations in DMD which abolish the production of dystrophin in muscle [116][117]. Although this disease has been well-characterized, single-cell methods may still add new knowledge in the future. Using single-cell level qPCR in mice hind limbs, Malecova et al. [118] evaluated subclusters in FAPs based on differential Tie2 and Vcam1 expression levels. Under further experimental measurements, the diaphragms of the duchenne muscular dystrophy (DMD) mouse model displayed a substantial decrease in the number of Tie2high subFAPs and an increase in the fraction of Vcam1+ subFAPs. [118]

References

  1. Gilliland, K.O.; Kernick, E.T. Musculoskeletal tissues and anatomy. In Clinical Foundations of Musculoskeletal Medicine: A Manual for Medical Students; Esther, R.J., Ed.; Springer International Publishing: Cham, Switzerland, 2021; pp. 11–21. ISBN 978-3-030-42893-8.
  2. Salhotra, A.; Shah, H.N.; Levi, B.; Longaker, M.T. Mechanisms of bone development and repair. Nat. Rev. Mol. Cell Biol. 2020, 21, 696–711.
  3. Heath, J.R.; Ribas, A.; Mischel, P.S. Single-cell analysis tools for drug discovery and development. Nat. Rev. Drug Discov. 2016, 15, 204–216.
  4. Mobasheri, A. Intersection of inflammation and herbal medicine in the treatment of osteoarthritis. Curr. Rheumatol. Rep. 2012, 14, 604–616.
  5. Crevenna, R.; Mickel, M.; Schuhfried, O.; Gesslbauer, C.; Zdravkovic, A.; Keilani, M. Focused extracorporeal shockwave therapy in physical medicine and rehabilitation. Curr. Phys. Med. Rehabil. Rep. 2021, 9, 1–10.
  6. Richards, J.B.; Zheng, H.-F.; Spector, T.D. Genetics of osteoporosis from genome-wide association studies: Advances and challenges. Nat. Rev. Genet. 2012, 13, 576–588.
  7. Wang, W.; Ouyang, Y.; Poh, C.K. Orthopaedic implant technology: Biomaterials from past to future. Ann. Acad. Med. Singap. 2011, 40, 237–244.
  8. Greenblatt, M.B.; Ono, N.; Ayturk, U.M.; Debnath, S.; Lalani, S. The Unmixing Problem: A Guide to Applying Single-Cell RNA Sequencing to Bone. J. Bone Miner. Res. 2019, 34, 1207–1219.
  9. Ambrosi, T.H.; Longaker, M.T.; Chan, C.K.F. A revised perspective of skeletal stem cell biology. Front. Cell Dev. Biol. 2019, 7, 189.
  10. Aboul-Soud, M.A.M.; Alzahrani, A.J.; Mahmoud, A. Induced Pluripotent Stem Cells (iPSCs)-Roles in Regenerative Therapies, Disease Modelling and Drug Screening. Cells 2021, 10, 2319.
  11. Cai, G.; Liu, W.; He, Y.; Huang, J.; Duan, L.; Xiong, J.; Liu, L.; Wang, D. Recent advances in kartogenin for cartilage regeneration. J. Drug Target. 2019, 27, 28–32.
  12. Tang, F.; Barbacioru, C.; Wang, Y.; Nordman, E.; Lee, C.; Xu, N.; Wang, X.; Bodeau, J.; Tuch, B.B.; Siddiqui, A.; et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 2009, 6, 377–382.
  13. Zhang, Y.; Wang, J.; Yu, C.; Xia, K.; Yang, B.; Zhang, Y.; Ying, L.; Wang, C.; Huang, X.; Chen, Q.; et al. Advances in single-cell sequencing and its application to musculoskeletal system research. Cell Prolif. 2022, 55, e13161.
  14. Chai, R.C. Single-Cell RNA Sequencing: Unravelling the Bone One Cell at a Time. Curr. Osteoporos. Rep. 2022, 20, 356–362.
  15. Wubneh, A.; Tsekoura, E.K.; Ayranci, C.; Uludağ, H. Current state of fabrication technologies and materials for bone tissue engineering. Acta Biomater. 2018, 80, 1–30.
  16. Serowoky, M.A.; Kuwahara, S.T.; Liu, S.; Vakhshori, V.; Lieberman, J.R.; Mariani, F.V. A murine model of large-scale bone regeneration reveals a selective requirement for Sonic Hedgehog. NPJ Regen. Med. 2022, 7, 30.
  17. Gartland, A.; Mason-Savas, A.; Yang, M.; MacKay, C.A.; Birnbaum, M.J.; Odgren, P.R. Septoclast deficiency accompanies postnatal growth plate chondrodysplasia in the toothless (tl) osteopetrotic, colony-stimulating factor-1 (CSF-1)-deficient rat and is partially responsive to CSF-1 injections. Am. J. Pathol. 2009, 175, 2668–2675.
  18. Lee, E.R.; Lamplugh, L.; Shepard, N.L.; Mort, J.S. The septoclast, a cathepsin B-rich cell involved in the resorption of growth plate cartilage. J. Histochem. Cytochem. 1995, 43, 525–536.
  19. Bando, Y.; Sakashita, H.; Taira, F.; Miyake, G.; Ogasawara, Y.; Sakiyama, K.; Owada, Y.; Amano, O. Origin and development of septoclasts in endochondral ossification of mice. Histochem. Cell Biol. 2018, 149, 645–654.
  20. Sivaraj, K.K.; Majev, P.-G.; Jeong, H.-W.; Dharmalingam, B.; Zeuschner, D.; Schröder, S.; Bixel, M.G.; Timmen, M.; Stange, R.; Adams, R.H. Mesenchymal stromal cell-derived septoclasts resorb cartilage during developmental ossification and fracture healing. Nat. Commun. 2022, 13, 571.
  21. Xu, J.; Li, Z.; Tower, R.J.; Negri, S.; Wang, Y.; Meyers, C.A.; Sono, T.; Qin, Q.; Lu, A.; Xing, X.; et al. NGF-p75 signaling coordinates skeletal cell migration during bone repair. Sci. Adv. 2022, 8, eabl5716.
  22. Li, Z.; Meyers, C.A.; Chang, L.; Lee, S.; Li, Z.; Tomlinson, R.; Hoke, A.; Clemens, T.L.; James, A.W. Fracture repair requires TrkA signaling by skeletal sensory nerves. J. Clin. Investig. 2019, 129, 5137–5150.
  23. Tomlinson, R.E.; Li, Z.; Zhang, Q.; Goh, B.C.; Li, Z.; Thorek, D.L.J.; Rajbhandari, L.; Brushart, T.M.; Minichiello, L.; Zhou, F.; et al. NGF-TrkA Signaling by Sensory Nerves Coordinates the Vascularization and Ossification of Developing Endochondral Bone. Cell Rep. 2016, 16, 2723–2735.
  24. Álvarez-Viejo, M.; Menéndez-Menéndez, Y.; Otero-Hernández, J. CD271 as a marker to identify mesenchymal stem cells from diverse sources before culture. World J. Stem Cells 2015, 7, 470–476.
  25. Yip, R.K.H.; Chan, D.; Cheah, K.S.E. Mechanistic insights into skeletal development gained from genetic disorders. Curr. Top. Dev. Biol. 2019, 133, 343–385.
  26. Aghajanian, P.; Mohan, S. The art of building bone: Emerging role of chondrocyte-to-osteoblast transdifferentiation in endochondral ossification. Bone Res. 2018, 6, 19.
  27. Breeland, G.; Sinkler, M.A.; Menezes, R.G. Embryology, Bone Ossification. In StatPearls; StatPearls Publishing: Treasure Island, FL, USA, 2022.
  28. El-Farrash, R.A.; Ali, R.H.; Barakat, N.M. Post-natal bone physiology. Semin. Fetal Neonatal Med. 2020, 25, 101077.
  29. Kelly, N.H.; Huynh, N.P.T.; Guilak, F. Single cell RNA-sequencing reveals cellular heterogeneity and trajectories of lineage specification during murine embryonic limb development. Matrix Biol. 2020, 89, 1–10.
  30. Wang, H.; Zheng, C.; Lu, W.; He, T.; Fan, J.; Wang, C.; Jie, Q.; Chan, D.; Cheah, K.S.E.; Yang, L. Hedgehog signaling orchestrates cartilage-to-bone transition independently of Smoothened. Matrix Biol. 2022, 110, 76–90.
  31. Cohen, M.; Kicheva, A.; Ribeiro, A.; Blassberg, R.; Page, K.M.; Barnes, C.P.; Briscoe, J. Ptch1 and Gli regulate Shh signalling dynamics via multiple mechanisms. Nat. Commun. 2015, 6, 6709.
  32. Tower, R.J.; Li, Z.; Cheng, Y.-H.; Wang, X.-W.; Rajbhandari, L.; Zhang, Q.; Negri, S.; Uytingco, C.R.; Venkatesan, A.; Zhou, F.-Q.; et al. Spatial transcriptomics reveals a role for sensory nerves in preserving cranial suture patency through modulation of BMP/TGF-β signaling. Proc. Natl. Acad. Sci. USA 2021, 118, e2103087118.
  33. Lv, X.; Xu, J.; Jiang, J.; Wu, P.; Tan, R.; Wang, B. Genetic animal models of scoliosis: A systematical review. Bone 2021, 152, 116075.
  34. Yang, Y.; Yang, M.; Shi, D.; Chen, K.; Zhao, J.; He, S.; Bai, Y.; Shen, P.; Ni, H. Single-cell RNA Seq reveals cellular landscape-specific characteristics and potential etiologies for adolescent idiopathic scoliosis. JOR Spine 2021, 4, e1184.
  35. Latalski, M.; Danielewicz-Bromberek, A.; Fatyga, M.; Latalska, M.; Kröber, M.; Zwolak, P. Current insights into the aetiology of adolescent idiopathic scoliosis. Arch. Orthop. Trauma Surg. 2017, 137, 1327–1333.
  36. Feng, C.; Chan, W.C.W.; Lam, Y.; Wang, X.; Chen, P.; Niu, B.; Ng, V.C.W.; Yeo, J.C.; Stricker, S.; Cheah, K.S.E.; et al. Lgr5 and Col22a1 Mark Progenitor Cells in the Lineage toward Juvenile Articular Chondrocytes. Stem Cell Rep. 2019, 13, 713–729.
  37. Bian, Q.; Cheng, Y.-H.; Wilson, J.P.; Su, E.Y.; Kim, D.W.; Wang, H.; Yoo, S.; Blackshaw, S.; Cahan, P. A single cell transcriptional atlas of early synovial joint development. Development 2020, 147, dev185777.
  38. Meyers, C.; Lisiecki, J.; Miller, S.; Levin, A.; Fayad, L.; Ding, C.; Sono, T.; McCarthy, E.; Levi, B.; James, A.W. Heterotopic ossification: A comprehensive review. JBMR PLUS 2019, 3, e10172.
  39. McCarthy, E.F.; Sundaram, M. Heterotopic ossification: A review. Skelet. Radiol. 2005, 34, 609–619.
  40. Łęgosz, P.; Drela, K.; Pulik, Ł.; Sarzyńska, S.; Małdyk, P. Challenges of heterotopic ossification-Molecular background and current treatment strategies. Clin. Exp. Pharmacol. Physiol. 2018, 45, 1229–1235.
  41. Zhang, Q.; Zhou, D.; Wang, H.; Tan, J. Heterotopic ossification of tendon and ligament. J. Cell. Mol. Med. 2020, 24, 5428–5437.
  42. Yu, L.; Li, B.; Yu, Y.; Li, W.; Qiu, G.; Zhao, Y. The relationship between dural ossification and spinal stenosis in thoracic ossification of the ligamentum flavum. J. Bone Jt. Surg. Am. 2019, 101, 606–612.
  43. Li, B.; Guo, S.; Qiu, G.; Li, W.; Liu, Y.; Zhao, Y. A potential mechanism of dural ossification in ossification of ligamentum flavum. Med. Hypotheses 2016, 92, 1–2.
  44. Li, J.; Yu, L.; Guo, S.; Zhao, Y. Identification of the molecular mechanism and diagnostic biomarkers in the thoracic ossification of the ligamentum flavum using metabolomics and transcriptomics. BMC Mol. Cell Biol. 2020, 21, 37.
  45. Mundy, C.; Yao, L.; Sinha, S.; Chung, J.; Rux, D.; Catheline, S.E.; Koyama, E.; Qin, L.; Pacifici, M. Activin A promotes the development of acquired heterotopic ossification and is an effective target for disease attenuation in mice. Sci. Signal. 2021, 14, eabd0536.
  46. Kozhemyakina, E.; Zhang, M.; Ionescu, A.; Ayturk, U.M.; Ono, N.; Kobayashi, A.; Kronenberg, H.; Warman, M.L.; Lassar, A.B. Identification of a Prg4-expressing articular cartilage progenitor cell population in mice. Arthritis Rheumatol. 2015, 67, 1261–1273.
  47. Tachibana, N.; Chijimatsu, R.; Okada, H.; Oichi, T.; Taniguchi, Y.; Maenohara, Y.; Miyahara, J.; Ishikura, H.; Iwanaga, Y.; Arino, Y.; et al. RSPO2 defines a distinct undifferentiated progenitor in the tendon/ligament and suppresses ectopic ossification. Sci. Adv. 2022, 8, eabn2138.
  48. Yang, T.-L.; Shen, H.; Liu, A.; Dong, S.-S.; Zhang, L.; Deng, F.-Y.; Zhao, Q.; Deng, H.-W. A road map for understanding molecular and genetic determinants of osteoporosis. Nat. Rev. Endocrinol. 2020, 16, 91–103.
  49. Karasik, D.; Rivadeneira, F.; Johnson, M.L. The genetics of bone mass and susceptibility to bone diseases. Nat. Rev. Rheumatol. 2016, 12, 323–334.
  50. Wang, Y.; Wang, Q.; Xu, Q.; Li, J.; Zhao, F. Single-cell RNA sequencing analysis dissected the osteo-immunology microenvironment and revealed key regulators in osteoporosis. Int. Immunopharmacol. 2022, 113, 109302.
  51. Komori, T. Animal models for osteoporosis. Eur. J. Pharmacol. 2015, 759, 287–294.
  52. Windahl, S.H.; Börjesson, A.E.; Farman, H.H.; Engdahl, C.; Movérare-Skrtic, S.; Sjögren, K.; Lagerquist, M.K.; Kindblom, J.M.; Koskela, A.; Tuukkanen, J.; et al. Estrogen receptor-α in osteocytes is important for trabecular bone formation in male mice. Proc. Natl. Acad. Sci. USA 2013, 110, 2294–2299.
  53. Kondoh, S.; Inoue, K.; Igarashi, K.; Sugizaki, H.; Shirode-Fukuda, Y.; Inoue, E.; Yu, T.; Takeuchi, J.K.; Kanno, J.; Bonewald, L.F.; et al. Estrogen receptor α in osteocytes regulates trabecular bone formation in female mice. Bone 2014, 60, 68–77.
  54. Melville, K.M.; Kelly, N.H.; Khan, S.A.; Schimenti, J.C.; Ross, F.P.; Main, R.P.; van der Meulen, M.C.H. Female mice lacking estrogen receptor-alpha in osteoblasts have compromised bone mass and strength. J. Bone Miner. Res. 2014, 29, 370–379.
  55. Määttä, J.A.; Büki, K.G.; Gu, G.; Alanne, M.H.; Vääräniemi, J.; Liljenbäck, H.; Poutanen, M.; Härkönen, P.; Väänänen, K. Inactivation of estrogen receptor α in bone-forming cells induces bone loss in female mice. FASEB J. 2013, 27, 478–488.
  56. Almeida, M.; Iyer, S.; Martin-Millan, M.; Bartell, S.M.; Han, L.; Ambrogini, E.; Onal, M.; Xiong, J.; Weinstein, R.S.; Jilka, R.L.; et al. Estrogen receptor-α signaling in osteoblast progenitors stimulates cortical bone accrual. J. Clin. Investig. 2013, 123, 394–404.
  57. Martin-Millan, M.; Almeida, M.; Ambrogini, E.; Han, L.; Zhao, H.; Weinstein, R.S.; Jilka, R.L.; O’Brien, C.A.; Manolagas, S.C. The estrogen receptor-alpha in osteoclasts mediates the protective effects of estrogens on cancellous but not cortical bone. Mol. Endocrinol. 2010, 24, 323–334.
  58. Nakamura, T.; Imai, Y.; Matsumoto, T.; Sato, S.; Takeuchi, K.; Igarashi, K.; Harada, Y.; Azuma, Y.; Krust, A.; Yamamoto, Y.; et al. Estrogen prevents bone loss via estrogen receptor alpha and induction of Fas ligand in osteoclasts. Cell 2007, 130, 811–823.
  59. Chavassieux, P.; Buffet, A.; Vergnaud, P.; Garnero, P.; Meunier, P.J. Short-term effects of corticosteroids on trabecular bone remodeling in old ewes. Bone 1997, 20, 451–455.
  60. Quarles, L.D. Prednisone-induced osteopenia in beagles: Variable effects mediated by differential suppression of bone formation. Am. J. Physiol. 1992, 263, E136–E141.
  61. Baofeng, L.; Zhi, Y.; Bei, C.; Guolin, M.; Qingshui, Y.; Jian, L. Characterization of a rabbit osteoporosis model induced by ovariectomy and glucocorticoid. Acta Orthop. 2010, 81, 396–401.
  62. Grardel, B.; Sutter, B.; Flautre, B.; Viguier, E.; Lavaste, F.; Hardouin, P. Effects of glucocorticoids on skeletal growth in rabbits evaluated by dual-photon absorptiometry, microscopic connectivity and vertebral compressive strength. Osteoporos. Int. 1994, 4, 204–210.
  63. Ding, M.; Cheng, L.; Bollen, P.; Schwarz, P.; Overgaard, S. Glucocorticoid induced osteopenia in cancellous bone of sheep: Validation of large animal model for spine fusion and biomaterial research. Spine 2010, 35, 363–370.
  64. Vega, S.L.; Kwon, M.Y.; Burdick, J.A. Recent advances in hydrogels for cartilage tissue engineering. Eur. Cell. Mater. 2017, 33, 59–75.
  65. Yu, L.; Dawson, L.A.; Yan, M.; Zimmel, K.; Lin, Y.-L.; Dolan, C.P.; Han, M.; Muneoka, K. BMP9 stimulates joint regeneration at digit amputation wounds in mice. Nat. Commun. 2019, 10, 424.
  66. Storer, M.A.; Mahmud, N.; Karamboulas, K.; Borrett, M.J.; Yuzwa, S.A.; Gont, A.; Androschuk, A.; Sefton, M.V.; Kaplan, D.R.; Miller, F.D. Acquisition of a Unique Mesenchymal Precursor-like Blastema State Underlies Successful Adult Mammalian Digit Tip Regeneration. Dev. Cell 2020, 52, 509–524.e9.
  67. Yu, L.; Lin, Y.-L.; Yan, M.; Li, T.; Wu, E.Y.; Zimmel, K.; Qureshi, O.; Falck, A.; Sherman, K.M.; Huggins, S.S.; et al. Hyaline cartilage differentiation of fibroblasts in regeneration and regenerative medicine. Development 2022, 149, dev200249.
  68. Berenbaum, F. Osteoarthritis as an inflammatory disease (osteoarthritis is not osteoarthrosis!). Osteoarthr. Cartil. 2013, 21, 16–21.
  69. Goldring, M.B.; Otero, M. Inflammation in osteoarthritis. Curr. Opin. Rheumatol. 2011, 23, 471–478.
  70. Heinegård, D.; Saxne, T. The role of the cartilage matrix in osteoarthritis. Nat. Rev. Rheumatol. 2011, 7, 50–56.
  71. Ji, Q.; Zheng, Y.; Zhang, G.; Hu, Y.; Fan, X.; Hou, Y.; Wen, L.; Li, L.; Xu, Y.; Wang, Y.; et al. Single-cell RNA-seq analysis reveals the progression of human osteoarthritis. Ann. Rheum. Dis. 2019, 78, 100–110.
  72. Saito, T.; Fukai, A.; Mabuchi, A.; Ikeda, T.; Yano, F.; Ohba, S.; Nishida, N.; Akune, T.; Yoshimura, N.; Nakagawa, T.; et al. Transcriptional regulation of endochondral ossification by HIF-2alpha during skeletal growth and osteoarthritis development. Nat. Med. 2010, 16, 678–686.
  73. St-Jacques, B.; Hammerschmidt, M.; McMahon, A.P. Indian hedgehog signaling regulates proliferation and differentiation of chondrocytes and is essential for bone formation. Genes Dev. 1999, 13, 2072–2086.
  74. Prein, C.; Warmbold, N.; Farkas, Z.; Schieker, M.; Aszodi, A.; Clausen-Schaumann, H. Structural and mechanical properties of the proliferative zone of the developing murine growth plate cartilage assessed by atomic force microscopy. Matrix Biol. 2016, 50, 1–15.
  75. Chou, C.-H.; Jain, V.; Gibson, J.; Attarian, D.E.; Haraden, C.A.; Yohn, C.B.; Laberge, R.-M.; Gregory, S.; Kraus, V.B. Synovial cell cross-talk with cartilage plays a major role in the pathogenesis of osteoarthritis. Sci. Rep. 2020, 10, 10868.
  76. Wang, X.; Ning, Y.; Zhang, P.; Poulet, B.; Huang, R.; Gong, Y.; Hu, M.; Li, C.; Zhou, R.; Lammi, M.J.; et al. Comparison of the major cell populations among osteoarthritis, Kashin-Beck disease and healthy chondrocytes by single-cell RNA-seq analysis. Cell Death Dis. 2021, 12, 551.
  77. Yao, Y.; Pei, F.; Kang, P. Selenium, iodine, and the relation with Kashin-Beck disease. Nutrition 2011, 27, 1095–1100.
  78. Lv, Z.; Han, J.; Li, J.; Guo, H.; Fei, Y.; Sun, Z.; Dong, J.; Wang, M.; Fan, C.; Li, W.; et al. Single cell RNA-seq analysis identifies ferroptotic chondrocyte cluster and reveals TRPV1 as an anti-ferroptotic target in osteoarthritis. EBioMedicine 2022, 84, 104258.
  79. Liu, W.; Chen, Y.; Zeng, G.; Yang, S.; Yang, T.; Ma, M.; Song, W. Single-Cell Profiles of Age-Related Osteoarthritis Uncover Underlying Heterogeneity Associated With Disease Progression. Front. Mol. Biosci. 2021, 8, 748360.
  80. Zhao, X.; Younis, S.; Shi, H.; Hu, S.; Zia, A.; Wong, H.H.; Elliott, E.E.; Chang, T.; Bloom, M.S.; Zhang, W.; et al. RNA-seq characterization of histamine-releasing mast cells as potential therapeutic target of osteoarthritis. Clin. Immunol. 2022, 244, 109117.
  81. Nanus, D.E.; Badoume, A.; Wijesinghe, S.N.; Halsey, A.M.; Hurley, P.; Ahmed, Z.; Botchu, R.; Davis, E.T.; Lindsay, M.A.; Jones, S.W. Synovial tissue from sites of joint pain in knee osteoarthritis patients exhibits a differential phenotype with distinct fibroblast subsets. EBioMedicine 2021, 72, 103618.
  82. Zhang, Y.; Li, S.; Jin, P.; Shang, T.; Sun, R.; Lu, L.; Guo, K.; Liu, J.; Tong, Y.; Wang, J.; et al. Dual functions of microRNA-17 in maintaining cartilage homeostasis and protection against osteoarthritis. Nat. Commun. 2022, 13, 2447.
  83. Sunkara, V.; Heinz, G.A.; Heinrich, F.F.; Durek, P.; Mobasheri, A.; Mashreghi, M.F.; Lang, A. Combining segmental bulk- and single-cell RNA-sequencing to define the chondrocyte gene expression signature in the murine knee joint. Osteoarthr. Cartil. 2021, 29, 905–914.
  84. Knights, A.J.; Farrell, E.C.; Ellis, O.M.; Lammlin, L.; Junginger, L.M.; Rzeczycki, P.M.; Bergman, R.F.; Pervez, R.; Cruz, M.; Knight, E.; et al. Synovial fibroblasts assume distinct functional identities and secrete R-spondin 2 in osteoarthritis. Ann. Rheum. Dis. 2022.
  85. Sebastian, A.; McCool, J.L.; Hum, N.R.; Murugesh, D.K.; Wilson, S.P.; Christiansen, B.A.; Loots, G.G. Single-Cell RNA-Seq Reveals Transcriptomic Heterogeneity and Post-Traumatic Osteoarthritis-Associated Early Molecular Changes in Mouse Articular Chondrocytes. Cells 2021, 10, 1462.
  86. Sebastian, A.; Hum, N.R.; McCool, J.L.; Wilson, S.P.; Murugesh, D.K.; Martin, K.A.; Rios-Arce, N.D.; Amiri, B.; Christiansen, B.A.; Loots, G.G. Single-cell RNA-Seq reveals changes in immune landscape in post-traumatic osteoarthritis. Front. Immunol. 2022, 13, 938075.
  87. Cheng, L.; Wang, Y.; Wu, R.; Ding, T.; Xue, H.; Gao, C.; Li, X.; Wang, C. New Insights From Single-Cell Sequencing Data: Synovial Fibroblasts and Synovial Macrophages in Rheumatoid Arthritis. Front. Immunol. 2021, 12, 709178.
  88. Hardt, U.; Carlberg, K.; Af Klint, E.; Sahlström, P.; Larsson, L.; van Vollenhoven, A.; Hernandez Machado, S.; Israelsson, L.; Amara, K.; Chemin, K.; et al. Integrated single cell and spatial transcriptomics reveal autoreactive differentiated B cells in joints of early rheumatoid arthritis. Sci. Rep. 2022, 12, 11876.
  89. Argyriou, A.; Wadsworth, M.H.; Lendvai, A.; Christensen, S.M.; Hensvold, A.H.; Gerstner, C.; van Vollenhoven, A.; Kravarik, K.; Winkler, A.; Malmström, V.; et al. Single cell sequencing identifies clonally expanded synovial CD4+ TPH cells expressing GPR56 in rheumatoid arthritis. Nat. Commun. 2022, 13, 4046.
  90. Wu, X.; Liu, Y.; Jin, S.; Wang, M.; Jiao, Y.; Yang, B.; Lu, X.; Ji, X.; Fei, Y.; Yang, H.; et al. Single-cell sequencing of immune cells from anticitrullinated peptide antibody positive and negative rheumatoid arthritis. Nat. Commun. 2021, 12, 4977.
  91. Stephenson, W.; Donlin, L.T.; Butler, A.; Rozo, C.; Bracken, B.; Rashidfarrokhi, A.; Goodman, S.M.; Ivashkiv, L.B.; Bykerk, V.P.; Orange, D.E.; et al. Single-cell RNA-seq of rheumatoid arthritis synovial tissue using low-cost microfluidic instrumentation. Nat. Commun. 2018, 9, 791.
  92. Carlberg, K.; Korotkova, M.; Larsson, L.; Catrina, A.I.; Ståhl, P.L.; Malmström, V. Exploring inflammatory signatures in arthritic joint biopsies with Spatial Transcriptomics. Sci. Rep. 2019, 9, 18975.
  93. Gawel, D.R.; Serra-Musach, J.; Lilja, S.; Aagesen, J.; Arenas, A.; Asking, B.; Bengnér, M.; Björkander, J.; Biggs, S.; Ernerudh, J.; et al. A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases. Genome Med. 2019, 11, 47.
  94. Francisco, V.; Pino, J.; González-Gay, M.Á.; Lago, F.; Karppinen, J.; Tervonen, O.; Mobasheri, A.; Gualillo, O. A new immunometabolic perspective of intervertebral disc degeneration. Nat. Rev. Rheumatol. 2022, 18, 47–60.
  95. Zhang, Y.; Han, S.; Kong, M.; Tu, Q.; Zhang, L.; Ma, X. Single-cell RNA-seq analysis identifies unique chondrocyte subsets and reveals involvement of ferroptosis in human intervertebral disc degeneration. Osteoarthr. Cartil. 2021, 29, 1324–1334.
  96. Ling, Z.; Liu, Y.; Wang, Z.; Zhang, Z.; Chen, B.; Yang, J.; Zeng, B.; Gao, Y.; Jiang, C.; Huang, Y.; et al. Single-Cell RNA-Seq Analysis Reveals Macrophage Involved in the Progression of Human Intervertebral Disc Degeneration. Front. Cell Dev. Biol. 2021, 9, 833420.
  97. Cherif, H.; Mannarino, M.; Pacis, A.S.; Ragoussis, J.; Rabau, O.; Ouellet, J.A.; Haglund, L. Single-Cell RNA-Seq Analysis of Cells from Degenerating and Non-Degenerating Intervertebral Discs from the Same Individual Reveals New Biomarkers for Intervertebral Disc Degeneration. Int. J. Mol. Sci. 2022, 23, 3993.
  98. Gan, Y.; He, J.; Zhu, J.; Xu, Z.; Wang, Z.; Yan, J.; Hu, O.; Bai, Z.; Chen, L.; Xie, Y.; et al. Spatially defined single-cell transcriptional profiling characterizes diverse chondrocyte subtypes and nucleus pulposus progenitors in human intervertebral discs. Bone Res. 2021, 9, 37.
  99. Howell, K.; Chien, C.; Bell, R.; Laudier, D.; Tufa, S.F.; Keene, D.R.; Andarawis-Puri, N.; Huang, A.H. Novel model of tendon regeneration reveals distinct cell mechanisms underlying regenerative and fibrotic tendon healing. Sci. Rep. 2017, 7, 45238.
  100. Harvey, T.; Flamenco, S.; Fan, C.-M. A Tppp3+Pdgfra+ tendon stem cell population contributes to regeneration and reveals a shared role for PDGF signalling in regeneration and fibrosis. Nat. Cell Biol. 2019, 21, 1490–1503.
  101. Kendal, A.R.; Layton, T.; Al-Mossawi, H.; Appleton, L.; Dakin, S.; Brown, R.; Loizou, C.; Rogers, M.; Sharp, R.; Carr, A. Multi-omic single cell analysis resolves novel stromal cell populations in healthy and diseased human tendon. Sci. Rep. 2020, 10, 13939.
  102. Riley, G. Tendinopathy--from basic science to treatment. Nat. Clin. Pract. Rheumatol. 2008, 4, 82–89.
  103. Forde, M.S.; Punnett, L.; Wegman, D.H. Prevalence of musculoskeletal disorders in union ironworkers. J. Occup. Environ. Hyg. 2005, 2, 203–212.
  104. Garcia-Melchor, E.; Cafaro, G.; MacDonald, L.; Crowe, L.A.N.; Sood, S.; McLean, M.; Fazzi, U.G.; McInnes, I.B.; Akbar, M.; Millar, N.L. Novel self-amplificatory loop between T cells and tenocytes as a driver of chronicity in tendon disease. Ann. Rheum. Dis. 2021, 80, 1075–1085.
  105. Bayer, M.L.; Järvinen, T.A.H. Basic muscle physiology in relation to hamstring injury and repair. In Prevention and Rehabilitation of Hamstring Injuries; Thorborg, K., Opar, D., Shield, A., Eds.; Springer International Publishing: Cham, Switzerland, 2020; pp. 31–63. ISBN 978-3-030-31637-2.
  106. Järvinen, T.A.H.; Järvinen, T.L.N.; Kääriäinen, M.; Aärimaa, V.; Vaittinen, S.; Kalimo, H.; Järvinen, M. Muscle injuries: Optimising recovery. Best Pract. Res. Clin. Rheumatol. 2007, 21, 317–331.
  107. Quintero, A.J.; Wright, V.J.; Fu, F.H.; Huard, J. Stem cells for the treatment of skeletal muscle injury. Clin. Sports Med. 2009, 28, 1–11.
  108. Blau, H.M.; Cosgrove, B.D.; Ho, A.T.V. The central role of muscle stem cells in regenerative failure with aging. Nat. Med. 2015, 21, 854–862.
  109. Wosczyna, M.N.; Rando, T.A. A muscle stem cell support group: Coordinated cellular responses in muscle regeneration. Dev. Cell 2018, 46, 135–143.
  110. Tabula Muris Consortium; Overall coordination; Logistical coordination; Organ collection and processing; Library preparation and sequencing; Computational data analysis; Cell type annotation; Writing group; Supplemental text writing group; Principal investigators. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 2018, 562, 367–372.
  111. De Micheli, A.J.; Laurilliard, E.J.; Heinke, C.L.; Ravichandran, H.; Fraczek, P.; Soueid-Baumgarten, S.; De Vlaminck, I.; Elemento, O.; Cosgrove, B.D. Single-Cell Analysis of the Muscle Stem Cell Hierarchy Identifies Heterotypic Communication Signals Involved in Skeletal Muscle Regeneration. Cell Rep. 2020, 30, 3583–3595.e5.
  112. Dell’Orso, S.; Juan, A.H.; Ko, K.-D.; Naz, F.; Perovanovic, J.; Gutierrez-Cruz, G.; Feng, X.; Sartorelli, V. Single cell analysis of adult mouse skeletal muscle stem cells in homeostatic and regenerative conditions. Development 2019, 146, dev174177.
  113. Oprescu, S.N.; Yue, F.; Qiu, J.; Brito, L.F.; Kuang, S. Temporal Dynamics and Heterogeneity of Cell Populations during Skeletal Muscle Regeneration. iScience 2020, 23, 100993.
  114. Rubenstein, A.B.; Smith, G.R.; Raue, U.; Begue, G.; Minchev, K.; Ruf-Zamojski, F.; Nair, V.D.; Wang, X.; Zhou, L.; Zaslavsky, E.; et al. Single-cell transcriptional profiles in human skeletal muscle. Sci. Rep. 2020, 10, 229.
  115. Bentzinger, C.F.; Wang, Y.X.; Dumont, N.A.; Rudnicki, M.A. Cellular dynamics in the muscle satellite cell niche. EMBO Rep. 2013, 14, 1062–1072.
  116. Duan, D.; Goemans, N.; Takeda, S.; Mercuri, E.; Aartsma-Rus, A. Duchenne muscular dystrophy. Nat. Rev. Dis. Primers 2021, 7, 13.
  117. Mercuri, E.; Bönnemann, C.G.; Muntoni, F. Muscular dystrophies. Lancet 2019, 394, 2025–2038.
  118. Malecova, B.; Gatto, S.; Etxaniz, U.; Passafaro, M.; Cortez, A.; Nicoletti, C.; Giordani, L.; Torcinaro, A.; De Bardi, M.; Bicciato, S.; et al. Dynamics of cellular states of fibro-adipogenic progenitors during myogenesis and muscular dystrophy. Nat. Commun. 2018, 9, 3670.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Orthopedics
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Site Feng
,
Jiahao Li
,
Jingjing Tian
,
Sheng Lu
,
Yu Zhao
View Times: 515
Revisions: 2 times (View History)
Update Date: 09 Feb 2023
Table of Contents
    1000/1000
    Hot Most Recent
    ScholarVision Creations
    Feedback