SYNERGISTIC DRUG TARGET DISCOVERY VIA INTEGRATION OF SPATIALLY RESOLVED TRANSCRIPTOMCIS AND OSTEOBLAST LINEAGE-TRACING STUDIES = 조골세포 계열 추적 연구와 공간전사체학의 결합을 통한 상승적 약물 타겟 발굴

다국어 초록 (Multilingual Abstract)

Osteoporosis is a prevalent skeletal disease characterized by an imbalance between bone formation and resorption, compounded by limited efficacy and safety concerns of existing therapeutics. A deeper understanding of osteoblast dynamics—particularly the reversible transition of bone lining cells (BLCs) into active osteoblasts—is essential for identifying new anabolic targets. However, technical challenges in isolating marker-poor and spatially restricted BLCs have hindered progress in defining the molecular pathways governing their reactivation.
In this dissertation, I introduce the Spatially Resolved Laser-Activated Cell Sorter (SLACS), a high-resolution ROI-based spatial isolation technology that overcomes critical limitations of existing spatial omics platforms. By integrating SLACS with inducible lineage-tracing, I develop a spatial transcriptomic framework that enables precise molecular profiling of osteoblast states—active, inactive, and reactivated—in their native anatomical context.
Using this system, I identify transcriptional signatures that distinguish BLCs from active osteoblasts and discover TGF-β signaling as the most strongly downregulated pathway associated with osteoblast reactivation. Through differential expression analysis, pathway enrichment, and correlation analyses, I demonstrate that TGF-β suppression is a central molecular event enabling the transition of BLCs into bone-forming osteoblasts.
To functionally validate this discovery, I conduct protein-level assays, in vitro bone-mimetic studies, lineage-traced in vivo experiments, and hindlimb unloading models. These results demonstrate that TGF-β inhibition not only enhances BLC activation but also synergizes with sclerostin blockade (Scl-Ab) to augment bone formation and suppress bone resorption. Quantitative synergy scoring further confirms that dual inhibition produces effects exceeding the sum of monotherapies, particularly in trabecular bone.
Collectively, this dissertation establishes SLACS as a powerful spatial omics technology for resolving previously inaccessible cell states and identifies TGF-β signaling as a mechanistically grounded and therapeutically actionable target for osteoporosis. The combined inhibition of sclerostin and TGF-β represents a promising dual-action strategy for increasing bone mass, offering translational potential for next-generation osteoporosis therapies.

국문 초록 (Abstract)

골다공증은 골형성과 골흡수 간의 불균형으로 인해 발생하는 대표적 근골격계 질환으로, 현재 사용되는 치료제들은 효능의 한계와 안전성 문제를 동시에 지니고 있다. 이러한 임상적 한계를 해결하기 위해서는 특히 휴지 상태의 bone lining cell(BLC)이 재활성화되어 조골세포로 전환되는 가역적 과정에 대한 이해가 필수적이다. 그러나 BLC는 표지 인자가 거의 없고 골 표면에 극도로 얇게 분포하기 때문에 기존 기술로는 이들을 분리·분석하는데 기술적 제약이 존재해 왔다.
본 학위논문에서는 이러한 한계를 극복하기 위해 고해상도 ROI 기반 공간 분리 기술인 SLACS(Spatially Resolved Laser-Activated Cell Sorter)를 도입하였다. SLACS를 inducible lineage-tracing 시스템과 결합함으로써, 활성·비활성·재활성 조골세포 상태를 원 조직 환경(spatial context)을 유지한 채 정밀하게 분자적 분석이 가능한 공간 전사체 분석 플랫폼을 구축하였다.
본 플랫폼을 통해 BLC와 활성 조골세포를 구분하는 전사체적 특징을 규명하였고, 특히 BLC의 재활성화와 가장 밀접하게 연관된 경로로 TGF-β 신호전달의 강력한 하향조절을 발견하였다. 차등 유전자 발현 분석, 경로 풍부도 분석, 상관분석을 종합한 결과, TGF-β 신호의 억제가 BLC가 골형성 조골세포로 전환되는 핵심 분자 이벤트임을 확인하였다.
이러한 발견을 기능적으로 검증하기 위해 단백질 수준 분석, 탈광물질화 골지지체 기반의 in vitro 모사 실험, lineage tracing을 적용한 in vivo 분석, 그리고 하중부하 제거(hindlimb unloading) 모델 실험을 수행하였다. 그 결과, TGF-β 억제가 BLC 재활성화를 촉진할 뿐 아니라, sclerostin 억제제(Scl-Ab)와 병용 시 강력한 상승작용(synergy)을 발휘함을 확인하였다. 특히 경골 해면골에서 두 약물의 병용은 단독 처리 대비 월등한 골형성 증가 및 골흡수 억제를 유도하였으며, 정량적 synergy score 분석 역시 이러한 상승효과를 정량적으로 입증하였다.
종합적으로, 본 연구는 SLACS가 기존 기술로는 접근 불가능했던 세포 상태를 해결할 수 있는 강력한 공간 오믹스 플랫폼임을 입증하였으며, BLC 재활성화를 유도하는 기전으로서 TGF-β 신호전달을 규명하였다. 나아가, sclerostin과 TGF-β의 병행 억제는 골량 증가를 위한 새로운 이중 작용 기반 치료 전략으로서 높은 잠재적 임상적 가치를 지닌다. 본 연구는 차세대 골다공증 치료제 개발을 위한 가능성을 제공한다는 점에서 중요한 의의를 갖는다.

목차 (Table of Contents)

• Abstract i
• Contents iv
• List of Figures viii
• List of Tables xxi
• Chapter 1 Introduction 1
• 1.1 Spatial Omics 4
• 1.1.1 Array-based Approaches 5
• 1.1.2 Image-based approaches 7
• 1.1.3 ROI-based Approaches 9
• 1.1.4 Spatially Resolved Laser-Activated Cell Sorter (SLACS) 11
• 1.2 Overview of SLACS-based Target Discovery and Therapeutic Development 14
• 1.2.1 Clinical Unmet Needs of Osteoporosis (Chapter 2) 15
• 1.2.2 Integrating Lineage-Tracing with SLACS-based Spatial Omics (Chapter 3) 15
• 1.2.3 Target Discovery for Reactivating Bone Lining Cells (Chapter 4) 16
• 1.2.4 Target Validation of Bone Mass Enhancement (Chapter 5) 16
• Chapter 2 Application in Osteoporosis 17
• 2.1 Introduction 18
• 2.2 Clinical Unmet Needs of Osteoporosis 20
• 2.2.1 Bone Homeostasis 20
• 2.2.2 Anti-resoptive Agents 22
• 2.2.3 Anabolic Agents 24
• 2.3 Characterization of Bone Lining Cells 28
• 2.3.1 BLCs as a Target for Anabolic Agents 29
• 2.3.2 Challenges to Characterize BLCs 30
• 2.4 Conclusion 34
• Chapter 3 Integration of Osteoblast-specific Lineage-Tracing Study and SLACS-based Transcriptomics 35
• 3.1 Introduction 36
• 3.2 Osteoblast-specific Lineage-Tracing Study 37
• 3.2.1 Lineage-Tracing Mouse Model 37
• 3.2.2 Dmp1 Promoter 39
• 3.2.3 Experimental Design 42
• 3.3 SLACS-based Enrichment and Transcriptomics 44
• 3.3.1 SLACS-based Enrichment 44
• 3.3.2 Transcriptomic Data Acquisition 46
• 3.3.3 Transcriptomic Feature Validation 47
• 3.4 Conclusion 51
• Chapter 4 Target Discovery for Reactivating BLCs into Active Obs 52
• 4.1 Introduction 53
• 4.2 Osteoblast State Validation 54
• 4.2.1 Differentially Expressed (DE) Genes 55
• 4.2.2 Characterization of Wnt Signaling 57
• 4.2.3 Osteoblast Marker Activity 58
• 4.3 Rationale of Target Selection 59
• 4.3.1 Gene Set Enrichment Analysis (GSEA) 59
• 4.3.2 Pearson Correlation Analysis 61
• 4.3.3 TGF-β signaling 63
• 4.4 Conclusion 66
• Chapter 5 Target Validation 67
• 5.1 Introduction 68
• 5.2 Single-cell RNA Sequencing 70
• 5.2.1 Enzymatic Dissociation 70
• 5.2.2 Integration Analysis 73
• 5.2.3 Cross-Species Label Transfer to Human Patient Datasets 78
• 5.3 Signal-level Validation of TGF-β Pathway Activity 81
• 5.3.1 pSmad3 Immunohistochemistry (IHC) 82
• 5.3.2 Age-dependent experiments 84
• 5.4 In vitro Functional Study of TGF-β-mediated Osteoblast Regulation 86
• 5.4.1 Demineralized Bone Paper System 86
• 5.4.2 Osteoblast Culture System-based TGF-β perturbation 88
• 5.5 In vivo Functional Study Using the Lineage-Tracing Model 92
• 5.5.1 Dmp1-Cre mouse model 92
• 5.5.2 GFP+ Cell Activity 93
• 5.6 In vivo Functional Study with Hindlimb Unloading Model 96
• 5.6.1 Homdlimb Unloading Mouse Model 96
• 5.6.2 Micro-computed Tomography (uCT) 97
• 5.6.3 Dynamic Histomorphometric Analysis 99
• 5.6.4 TRAP Staining 101
• 5.6.5 Serum Bone Turnover Markers 102
• 5.6.6 Local Pathway Modulation 102
• 5.6.7 Synergy Score 105
• 5.6.8 Dual Action and Mechanistic Basis of Synergy Between TGF-β and Sclerostin Inhibition 108
• 5.7 Conclusion 110
• Chapter 6 Conclusion 111
• Bibliography 114
• Abstract in Korean 126