Background:Lumbar disc degeneration(LDD)displays considerable heterogeneity in terms of clinical features and pathological changes.However,researchers have not clearly determined whether the transcriptome variations i...Background:Lumbar disc degeneration(LDD)displays considerable heterogeneity in terms of clinical features and pathological changes.However,researchers have not clearly determined whether the transcriptome variations in LDD could be used to identify or interpret the causes of heterogeneity in clinical features.This study aimed to identify the transcriptomic classification of degenerated discs in LDD patients and whether the molecular subtypes of LDD could be accurately predicted using clinical features.Methods:One hundred and twenty-two nucleus pulposus(NP)tissues from 108 patients were consecutively collected for bulk RNA sequencing(RNA-seq).An unsupervised clustering method was employed to analyze the bulk RNA matrix.Differential analysis was performed to characterize the transcriptional signatures and subtype-specific extracellular matrix(ECM)dysregulation.The cell subpopulation states of each subtype were inferred by integrating bulk and single-cell sequencing datasets.Transwell and dual-luciferase reporter gene assays were employed to investigate possible molecular mechanisms involved.Machine learning algorithm diagnostic prediction models were developed to correlate molecular classification with clinical features.Results:LDD was classified into 4 subtypes with distinct molecular signatures and ECM remodeling:C1 with collagenesis,C2 with ossification,C3 with low chondrogenesis,and C4 with fibrogenesis.Chond1-3 in C1 dominated disc collagenesis via the activation of the mechanosensors TRPV4 and PIEZO1;NP progenitor cells in C2 exhibited chondrogenic and osteogenic phenotypes;Chond1 in C3 was linked to a disrupted hypoxic microenvironment leading to reduced chondrogenesis;Macrophages in C4 played a crucial role in disc fibrogenesis via the secretion of tumor necrosis factor-α(TNF-α).Furthermore,the random forest diagnostic prediction model was proven to have a robust performance[area under the receiver operating characteristic(ROC)curve:0.9312;accuracy:0.84]in stratifying the molecular subtypes of LDD based on 12 clinical features.Conclusions:Our study delineates 4 distinct molecular subtypes of LDD that can be accurately stratified on the basis of clinical features.The identification of these subtypes would facilitate precise diagnostics and guide the development of personalized treatment strategies for LDD.展开更多
Orthopedic conditions have emerged as global health concerns,impacting approximately 1.7 billion individuals worldwide.However,the limited understanding of the underlying pathological processes at the cellular and mol...Orthopedic conditions have emerged as global health concerns,impacting approximately 1.7 billion individuals worldwide.However,the limited understanding of the underlying pathological processes at the cellular and molecular level has hindered the development of comprehensive treatment options for these disorders.The advent of single-cell RNA sequencing(scRNA-seq)technology has revolutionized biomedical research by enabling detailed examination of cellular and molecular diversity.Nevertheless,investigating mechanisms at the single-cell level in highly mineralized skeletal tissue poses technical challenges.In this comprehensive review,we present a streamlined approach to obtaining high-quality single cells from skeletal tissue and provide an overview of existing scRNA-seq technologies employed in skeletal studies along with practical bioinformatic analysis pipelines.By utilizing these methodologies,crucial insights into the developmental dynamics,maintenance of homeostasis,and pathological processes involved in spine,joint,bone,muscle,and tendon disorders have been uncovered.Specifically focusing on the joint diseases of degenerative disc disease,osteoarthritis,and rheumatoid arthritis using scRNA-seq has provided novel insights and a more nuanced comprehension.These findings have paved the way for discovering novel therapeutic targets that offer potential benefits to patients suffering from diverse skeletal disorders.展开更多
基金supported by the National Natural Science Foundation of China(32270887,82272507,32200654,82430079,and 82472519)the National Key Research and Development Program of China(2022YFA1103202)+7 种基金the Chongqing High-End Medical Talents for Middle-aged and Young(YXGD202408)the Army Scientific and Technological Innovation Talents Prioritized Suppor t Program(2023-124)the Natural Science Foundation of Chongqing(CSTB2023NSCQ-ZDJO008)the Postdoctoral Innovative Talent Support Program(BX20220397)the Open Project of State Key Laboratory of TraumaBurns and Combined Injury(SFLKF202201)the Project for Enhancing Innovation of Army Medical University(2023XJS39)the Talent Innovation Training Program at the Army Medical Center(ZXZYTSYS09)。
文摘Background:Lumbar disc degeneration(LDD)displays considerable heterogeneity in terms of clinical features and pathological changes.However,researchers have not clearly determined whether the transcriptome variations in LDD could be used to identify or interpret the causes of heterogeneity in clinical features.This study aimed to identify the transcriptomic classification of degenerated discs in LDD patients and whether the molecular subtypes of LDD could be accurately predicted using clinical features.Methods:One hundred and twenty-two nucleus pulposus(NP)tissues from 108 patients were consecutively collected for bulk RNA sequencing(RNA-seq).An unsupervised clustering method was employed to analyze the bulk RNA matrix.Differential analysis was performed to characterize the transcriptional signatures and subtype-specific extracellular matrix(ECM)dysregulation.The cell subpopulation states of each subtype were inferred by integrating bulk and single-cell sequencing datasets.Transwell and dual-luciferase reporter gene assays were employed to investigate possible molecular mechanisms involved.Machine learning algorithm diagnostic prediction models were developed to correlate molecular classification with clinical features.Results:LDD was classified into 4 subtypes with distinct molecular signatures and ECM remodeling:C1 with collagenesis,C2 with ossification,C3 with low chondrogenesis,and C4 with fibrogenesis.Chond1-3 in C1 dominated disc collagenesis via the activation of the mechanosensors TRPV4 and PIEZO1;NP progenitor cells in C2 exhibited chondrogenic and osteogenic phenotypes;Chond1 in C3 was linked to a disrupted hypoxic microenvironment leading to reduced chondrogenesis;Macrophages in C4 played a crucial role in disc fibrogenesis via the secretion of tumor necrosis factor-α(TNF-α).Furthermore,the random forest diagnostic prediction model was proven to have a robust performance[area under the receiver operating characteristic(ROC)curve:0.9312;accuracy:0.84]in stratifying the molecular subtypes of LDD based on 12 clinical features.Conclusions:Our study delineates 4 distinct molecular subtypes of LDD that can be accurately stratified on the basis of clinical features.The identification of these subtypes would facilitate precise diagnostics and guide the development of personalized treatment strategies for LDD.
基金National Key Research and Development Program of China(2022YFA1103202)National Natural Science Foundation of China(82272507,32270887,and 32200654)+6 种基金Natural Science Foundation of Chongqing(CSTB2023NSCQ-ZDJO008)Postdoctoral Innovative Talent Support Program(BX20220397)Independent Research Project of State Key Laboratory of Trauma and Chemical Poisoning(SFLKF202201)Project for Enhancing Innovation of Army Medical University(2023X1839)Talent Innovation Training Program at the Army Medical Center(ZXZYTSYS09)General Hospital of Western Theater Command Research Project(2021-XZYG-B10)University Grants Committee,Research Grants Council of Hong Kong,China(14113723,N_CUHK472/22,C7030-18G,T13-402/17-N,and AoE/M-402/20)。
文摘Orthopedic conditions have emerged as global health concerns,impacting approximately 1.7 billion individuals worldwide.However,the limited understanding of the underlying pathological processes at the cellular and molecular level has hindered the development of comprehensive treatment options for these disorders.The advent of single-cell RNA sequencing(scRNA-seq)technology has revolutionized biomedical research by enabling detailed examination of cellular and molecular diversity.Nevertheless,investigating mechanisms at the single-cell level in highly mineralized skeletal tissue poses technical challenges.In this comprehensive review,we present a streamlined approach to obtaining high-quality single cells from skeletal tissue and provide an overview of existing scRNA-seq technologies employed in skeletal studies along with practical bioinformatic analysis pipelines.By utilizing these methodologies,crucial insights into the developmental dynamics,maintenance of homeostasis,and pathological processes involved in spine,joint,bone,muscle,and tendon disorders have been uncovered.Specifically focusing on the joint diseases of degenerative disc disease,osteoarthritis,and rheumatoid arthritis using scRNA-seq has provided novel insights and a more nuanced comprehension.These findings have paved the way for discovering novel therapeutic targets that offer potential benefits to patients suffering from diverse skeletal disorders.
