AbstractMusculoskeletal traumatic injuries (MTI) involve soft tissue lesions adjacent to a bone fracture leading to fibrous nonunion. The impact of MTI on the inflammatory response to fracture and on the immunomodulation of skeletal stem/progenitor cells (SSPCs) remains unknown. Here, we used single-nucleus transcriptomic analyses to describe the immune cell dynamics after bone fracture and identified distinct macrophage subsets with successive pro-inflammatory, pro-repair and anti-inflammatory profiles.

摘要肌肉骨骼创伤（MTI）涉及骨折附近的软组织病变，导致纤维不愈合。MTI对骨折炎症反应和骨骼干/祖细胞（SSPCs）免疫调节的影响尚不清楚。在这里，我们使用单核转录组学分析来描述骨折后的免疫细胞动力学，并鉴定出具有连续促炎，促修复和抗炎特征的不同巨噬细胞亚群。

Concurrently, SSPCs transition via a pro- and anti-inflammatory fibrogenic phase of differentiation prior to osteochondrogenic differentiation. In a preclinical MTI mouse model, the injury response of immune cells and SSPCs is disrupted leading to a prolonged pro-inflammatory phase and delayed resolution of inflammation.

同时，在骨软骨形成分化之前，SSPC通过促炎和抗炎纤维化阶段的分化转变。在临床前MTI小鼠模型中，免疫细胞和SSPC的损伤反应被破坏，导致促炎期延长和炎症消退延迟。

Macrophage depletion improves bone regeneration in MTI demonstrating macrophage involvement in fibrous nonunion. Finally, pharmacological inhibition of macrophages using the CSF1R inhibitor Pexidartinib ameliorates healing. These findings reveal the coordinated immune response of macrophages and skeletal stem/progenitor cells as a driver of bone healing and as a primary target for the treatment of trauma-associated fibrosis..

巨噬细胞耗竭改善了MTI中的骨再生，表明巨噬细胞参与了纤维不连。最后，使用CSF1R抑制剂Pexidartinib对巨噬细胞的药理学抑制可改善愈合。这些发现揭示了巨噬细胞和骨骼干/祖细胞的协调免疫反应是骨愈合的驱动因素，也是治疗创伤相关纤维化的主要靶点。。

IntroductionMusculoskeletal conditions affect approximately 1.71 billion people and are the principal contributor to disability worldwide.1 Among them, musculoskeletal traumatic injuries (MTI), marked by extensive soft tissue damage associated with a bone fracture, are a leading cause of fracture nonunion.

引言肌肉骨骼疾病影响约17.1亿人，是全球残疾的主要原因。其中，以骨折相关的广泛软组织损伤为特征的肌肉骨骼创伤性损伤（MTI）是骨折不愈合的主要原因。

Clinical management of MTI is challenging as current treatments are mostly surgical with variable healing outcomes.2 The consequences of MTI on bone regeneration are poorly understood, limiting our ability to address these debilitating conditions of fracture nonunion.As observed in many tissues, bone repair following injury is initiated by an inflammatory response and a transient fibrotic response of skeletal stem/progenitor cells (SSPCs), followed by a reparative phase to reestablish tissue integrity and function.3,4,5,6 However, how immune cell dynamics influence the fate of SSPCs after bone injury is still poorly understood.

MTI的临床管理具有挑战性，因为目前的治疗大多是手术治疗，愈合结果可变[2]。MTI对骨再生的影响知之甚少，限制了我们解决这些骨折不愈合的衰弱状况的能力。正如在许多组织中观察到的那样，损伤后的骨修复是由骨骼干/祖细胞（SSPC）的炎症反应和瞬时纤维化反应引发的，然后是修复阶段以重建组织完整性和功能[3,4,5,6]。然而，免疫细胞动力学如何影响骨损伤后SSPC的命运仍然知之甚少。

SSPCs reside mostly in the periosteum, as well as bone marrow and skeletal muscles neighboring the fracture site.3,7,8,9,10,11 Upon fracture, SSPC differentiation occurs in a complex injury environment, where inflammatory cells migrate and play essential roles in the initiation and progression of the repair process.

SSPC主要存在于骨膜以及骨折部位附近的骨髓和骨骼肌中[3,7,8,9,10,11]。骨折后，SSPC分化发生在复杂的损伤环境中，炎症细胞迁移并在修复过程的开始和进展中发挥重要作用。

Among inflammatory cell types, macrophages are suspected to be crucial regulators of the early stages of bone healing.12,13,14 Inflammatory and resident macrophages remove necrotic cells and debris and secrete chemotactic mediators to recruit SSPCs at the fracture site.12,15,16,17,18,19 Conversely, prolonged inflammation and delay in the clearance of macrophages may delay healing.20,21,22,23,24,25 Specific pro-fibrotic macrophage subtypes have been incriminated in fibrotic diseases and impaired tissue regeneration.19,26,27,28 In MTI.

在炎症细胞类型中，巨噬细胞被认为是骨愈合早期的关键调节因子[12,13,14]。炎症和常驻巨噬细胞去除坏死细胞和碎片，分泌趋化介质，在骨折部位募集SSPC[12,15,16,17,18,19]。相反，长时间的炎症和巨噬细胞清除的延迟可能会延迟愈合[20,21,22,23,24,25]。特定的促纤维化巨噬细胞亚型已被纳入纤维化疾病和组织再生受损[19,26,27,28]。

Periosteum and callus/hematoma

骨膜和骨痂/血肿

Aligned snRNAseq datasets were filtered to retain only nuclei expressing between 200 and 5 000 genes and expressing less than 0.5% of mitochondrial genes and 2.5% of ribosomal genes. Contamination from myogenic cells was removed from the analysis. After filtering, the datasets of murine samples were composed of 1 203 nuclei for uninjured dataset, 634 nuclei for day 1 post-fracture dataset, 1 370 nuclei for day 3 post-fracture dataset, 1 971 nuclei for day 5 post-fracture dataset and 997 nuclei for day 7 post-fracture dataset.

过滤比对的snRNAseq数据集，以仅保留表达200至5000个基因且表达少于0.5%的线粒体基因和2.5%的核糖体基因的细胞核。从分析中去除了肌原细胞的污染。过滤后，小鼠样本的数据集由未受伤数据集的1 203个核，骨折后第1天数据集的634个核，骨折后第3天数据集的1 370个核，骨折后第5天数据集的1 971个核和第7天骨折后数据集的997个核组成。

The datasets were merged and scaled on mitochondrial and ribosomal content and cell cycle. Clustering was performed using the first 20 principal components and a resolution of 1.7. Immune cell clusters from the integration were isolated to perform subset analysis and were reclustered using the first 10 principal components and a resolution of 1.7.

数据集被合并并根据线粒体和核糖体含量以及细胞周期进行缩放。使用前20个主成分和1.7的分辨率进行聚类。分离来自整合的免疫细胞簇以进行子集分析，并使用前10个主成分和1.7的分辨率重新聚类。

SSPC, fibrogenic, chondrogenic and osteogenic clusters from the integration were isolated to perform subset analysis. The subset was reclustered using the first 20 principal components and a resolution of 0.5..

分离来自整合的SSPC，纤维化，软骨形成和成骨簇以进行子集分析。使用前20个主成分和0.5的分辨率对子集进行了重新聚类。。

For the day 5 post-MTI dataset, we filtered nuclei expressing between 200 and 5 000 genes and expressing less than 5% of mitochondrial genes and 2% of ribosomal genes. After filtering and removal of myogenic cells, we obtained 995 nuclei. We integrated this dataset with the day 5 post-fracture dataset, scaled on mitochondrial and ribosomal content and clustered using the first 30 principal components and a resolution of 1.0 For subset analysis, we used the 20 first components and a resolution of 1.5 for the immune subset and the 15 first components and a resolution of 0.5 for the IIFC subset..

对于MTI数据集后的第5天，我们过滤了表达200至5000个基因且表达少于5%的线粒体基因和2%的核糖体基因的细胞核。过滤并去除肌原细胞后，我们获得了995个核。我们将该数据集与骨折后第5天的数据集集成，根据线粒体和核糖体含量进行缩放，并使用前30个主成分和1.0的分辨率进行聚类。对于子集分析，我们使用了20个第一成分和1.5的分辨率免疫子集和15个第一成分，IIFC子集的分辨率为0.5。。

Skeletal muscle

骨骼肌

Datasets from8 were used for these analyses (GSE195940). These datasets correspond to sorted GFP+ cells isolated from the skeletal muscle surrounding the tibia of Prx1Cre; R26mTmG mice without injury and at days 3 and 5 post-fracture and MTI. Cells expressing between 1 000 and 8 000 genes and <20% mitochondrial genes were retained for analysis.

来自8的数据集用于这些分析（GSE195940）。这些数据集对应于从Prx1Cre胫骨周围骨骼肌分离的分选的GFP+细胞；。保留表达1000至8000个基因和20%以下线粒体基因的细胞进行分析。

Pericytes and tenocytes were removed from the analysis. The integrated dataset of days 0, 3 and 5 post-fracture was regressed using mitochondrial content, the number of genes detected within each cell, and performed using top 2 000 features and the 30 first components with a resolution set at 1.3. The integrated dataset of days 0, 3 and 5 post-fracture and MTI was regressed using mitochondrial and ribosomal content and then performed using top 2 000 features and the 25 first components with a resolution set at 1.1..

从分析中除去周细胞和肌腱细胞。骨折后第0,3和5天的综合数据集使用线粒体含量，每个细胞内检测到的基因数量进行回归，并使用前2000个特征和分辨率设置为1.3的30个第一组分进行。使用线粒体和核糖体含量对骨折后第0天，第3天和第5天的综合数据集和MTI进行回归，然后使用前2000个特征和分辨率设置为1.1的25个第一组分进行。。

Pseudotime analysisMonocle3 v1.0.0 was used for pseudotime analysis.67 Single-cell trajectories were determined using monocle3 default parameters. The starting point of the pseudotime trajectory was determined as the cells from the uninjured dataset with the highest expression of stem/progenitor marker genes (Ly6a, Cd34, Dpp4, Pi16).Lineage scoreLineage score was calculated by the mean of the expression of specific markers from the literature listed in Table S2.Single cell regulatory network inference using SCENICSingle cell regulatory network inference and clustering (SCENIC)38 was used to infer transcription factor (TF) networks active in the subsets of IIFCs.

伪时间分析monocle3 v1.0.0用于伪时间分析.67使用monocle3默认参数确定单细胞轨迹。假时间轨迹的起点被确定为来自未受伤数据集的细胞，其具有最高的干/祖细胞标记基因（Ly6a，Cd34，Dpp4，Pi16）表达。谱系得分谱系得分是通过表S2中列出的文献中特定标记物的表达平均值计算的。使用SCENICSingle cell regulatory network inference and clustering（SCENIC）38的单细胞调节网络推断用于推断活跃在IIFC子集中的转录因子（TF）网络。

Analysis was performed using recommended parameters using the packages SCENIC v1.3.1, AUCell v1.16.0, and RcisTarget v1.14 and the motif databases RcisTarget and GRNboost.Cell-cell interaction using CellChat and ConnectomeCell communication analysis was performed using the R package CellChat37 and Connectome68 with default parameters on the complete fracture combined dataset and on the subset of immune cells.Statistical analysisData are presented as mean ± s.d.

使用推荐的参数，使用软件包Scientive v1.3.1，AUCell v1.16.0和RcisTarget v1.14以及motif数据库RcisTarget和GRNboost进行分析。使用CellChat和ConnectomeCell的细胞-细胞相互作用使用R软件包CellChat37和Connectome68进行细胞通讯分析，并在完整的骨折组合数据集和免疫细胞子集上使用默认参数。统计分析数据以平均值±s.d表示。

and were obtained from at least two independent experiments. Statistical significance was determined with a two-sided Mann–Whitney test and reported from GraphPad Prism v9.0.2. Differences were considered to be significant when P < 0.05, and reported as *P < 0.05, **P <  0.01 and ***P < 0.001..

并且是从至少两个独立的实验中获得的。统计显着性通过双侧Mann-Whitney检验确定，并从GraphPad Prism v9.0.2报告。当P<0.05时，差异被认为是显着的，并报告为*P<0.05，**P<0.01和***P<0.001。。

Data availability

数据可用性

The single-nucleus RNAseq dataset generated for this study are deposited in GEO (GSE268276). Single-nucleus RNAseq datasets from Perrin et al. are deposited in GEO (GSE234451). This paper does not report original code.

为这项研究生成的单核RNAseq数据集保存在GEO（GSE268276）中。Perrin等人的单核RNAseq数据集保存在GEO（GSE234451）中。本文不报告原始代码。

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Download referencesAcknowledgementsWe thank A. Guigan, O. Ruckebusch and A. Henry from the Flow Cytometry platforms of IMRB, L. Slimani and K. Henri from Life Imaging Facility of Paris Cité University (Plateforme Imagerie du Vivant “Micro-CT platform”), the staff from the IMRB genomic and bioinformatic platforms for advice and technical assistance.

下载参考文献致谢我们感谢来自巴黎城市大学生命成像设施（Plateforme Imagerie du Vivant“Micro-CT平台”）的IMRB流式细胞仪平台的A.Guigan，O.Ruckebusch和A.Henry，IMRB基因组和生物信息学平台的工作人员提供建议和技术援助。

We thank C. Goachet, V. Bretegnier, G. Chemin, E. Rohart and S. Lakhlifi for technical assistance or advice.FundingThis work was supported by National Institute of Arthritis and Musculoskeletal and Skin Diseases R01 AR072707 (C.C. and Ted Miclau) and R01 AR081671 (C.C. and Ralph Marcucio), Agence Nationale de la Recherche ANR-18-CE14-0033 and ANR-21-CE18-007-01 (C.C.)Author informationAuthor notesThese authors contributed equally: Yasmine Hachemi, Simon PerrinAuthors and AffiliationsUniv Paris Est Creteil, INSERM, IMRB, Creteil, FranceYasmine Hachemi, Simon Perrin, Maria Ethel, Julia Vettese, Blandine Geisler & Céline ColnotDepartment of Cell and Molecular Biology, Karolinska Institutet, Stockholm, SwedenAnais Julien & Christian GöritzCenter for Neuromusculoskeletal Restorative Medicine, Hong Kong Science Park, Shatin, Hong KongChristian GöritzAuthorsYasmine HachemiView author publicationsYou can also search for this author in.

。资助这项工作得到了美国国家关节炎、肌肉骨骼和皮肤病研究所R01 AR072707（C.C.和Ted Miclau）和R01 AR081671（C.C.和拉尔夫·马库西奥）、国家研究所ANR-18-CE14-0033和ANR-21-CE18-007-01（C.C.）的支持。作者信息作者注意到，这些作者做出了同样的贡献：Yasmine Hachemi，Simon Perrinathors和附属机构IV Paris Est Creteil，INSERM，IMRB，Creteil，FranceYasmine Hachemi，Simon Perrin，Maria Ethel，Julia Vettel Tese，Blandine Geisler＆Céline Colnot斯德哥尔摩卡罗林斯卡研究所细胞与分子生物学系，瑞典朱利安和克里斯蒂安·格里茨神经肌肉骨骼修复医学中心，香港沙田香港科技园克里斯汀·格里茨作者Yasmine HachemiView作者出版物你也可以在中搜索这位作者。

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PubMed Google ScholarContributionsConceptualization: Y.H., S.P., A.J., C.C. Methodology: Y.H., S.P., C.C. Formal Analysis: Y.H., S.P. Investigation: Y.H., S.P., A.J., M.E., J.V., B.G., Resources: C.G. Writing – Original Draft: Y.H., S.P., C.C. Writing – Review & Editing: A.J., M.E.

PubMed谷歌学术贡献概念化：Y.H.，S.P.，A.J.，C.C.方法论：Y.H.，S.P.，C.C.正式分析：Y.H.，S.P.调查：Y.H.，S.P.，A.J.，M.E.，J.V.，B.G.，资源：C.G.写作-原稿：Y.H.，S.P.，C.C.写作-评论和编辑：A.J.，M.E。

Visualization: Y.H., S.P. Supervision: C.C. Funding Acquisition: C.C.Corresponding authorCorrespondence to.

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Reprints and permissionsAbout this articleCite this articleHachemi, Y., Perrin, S., Ethel, M. et al. Multimodal analyses of immune cells during bone repair identify macrophages as a therapeutic target in musculoskeletal trauma.

转载和许可本文引用本文Hachemi，Y.，Perrin，S.，Ethel，M。等人。骨修复过程中免疫细胞的多模式分析确定巨噬细胞是肌肉骨骼创伤的治疗靶点。

Bone Res 12, 56 (2024). https://doi.org/10.1038/s41413-024-00347-3Download citationReceived: 14 December 2023Revised: 04 April 2024Accepted: 23 May 2024Published: 29 September 2024DOI: https://doi.org/10.1038/s41413-024-00347-3Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

骨研究12,56（2024）。https://doi.org/10.1038/s41413-024-00347-3Download引文接收日期：2023年12月14日修订日期：2024年4月4日接受日期：2024年5月23日发布日期：2024年9月29日OI：https://doi.org/10.1038/s41413-024-00347-3Share本文与您共享以下链接的任何人都可以阅读此内容：获取可共享链接对不起，本文目前没有可共享的链接。。

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