实现骨组织空间转录组学的方法
May 3rd, 2024
在这里，我们描述了一种方法，该方法允许对新鲜获得的骨组织进行脱钙并保留高质量的RNA。还展示了一种方法，用于对非脱矿质骨骼的福尔马林固定石蜡包埋 （FFPE） 样品进行切片，以便在无法获得或无法收集新鲜组织的情况下获得高质量的结果。
我们的目标是找到更好的治疗方法来治疗称为骨肉瘤的人类骨癌。为了做到这一点，我们希望揭示导致这种肿瘤发病的生物学过程，尤其是导致其转移发展的生物学过程，这就是患者死亡的原因。通过对人类原发性骨肉瘤系转移的几个匹配标本进行空间转录组学分析，我们定位了位于肿瘤区域内的多个患者常见细胞群，并通过比较它们，我们确定了几个关键基因，这些基因可能代表预防肺转移的有价值的治疗靶点。
心脏组织（如骨骼）的特殊转录组学分析非常具有挑战性，因为无法保留高质量的 RNA 和组织形态，同时处理心脏组织进行切片。我们的协议填补了这一空白，使研究人员能够成功地将特殊的转录组学分析应用于心脏组织。
概览
本文介绍了一种在保留高质量RNA的同时对骨组织进行脱钙的方法，这对于空间转录组学分析至关重要。它还描述了一种对未脱钙骨头的FFPE样本进行切片的技术，确保在无法获得新鲜组织时仍能得到优质结果。
关键研究组成部分
科学领域
- 神经科学
- 肿瘤学
- 生物技术
背景
- 骨肉瘤是一种具有高转移潜力的人类骨癌。
- 转移是骨肉瘤患者死亡的主要原因。
- 空间转录组学分析因RNA保存问题而具有挑战性。
- 现有方法通常会影响组织形态。
研究目的
- 识别与骨肉瘤及其转移相关的生物过程。
- 揭示可作为治疗靶点的关键基因。
- 改进骨组织空间转录组学分析的RNA质量。
使用的方法
- 对骨组织进行脱钙以保存RNA。
- 对FFPE样本进行切片以供分析。
- 对匹配的样本进行空间转录组学分析。
- 比较原发肿瘤和转移瘤以识别共同的细胞群体。
主要结果
- 识别与骨肉瘤肺转移相关的关键基因。
- 成功保留脱钙骨组织中的RNA质量。
- 增强在具有挑战性的组织类型上进行空间转录组学分析的能力。
- 发现潜在的治疗靶点以预防转移。
结论
- 开发的方法能够更好地分析骨肉瘤及其转移。
- 改进的RNA保存技术可以帮助未来的研究。
- 研究结果可能有助于开发新的治疗策略。
常见问题
Executive Industry Relevance
Spatial transcriptomics of bone tissue addresses a critical gap in understanding the spatial organization of gene expression in hard tissues, directly impacting target validation and mechanistic de-risking in oncology research. This method enables high-quality RNA preservation and histological integrity, supporting predictive confidence in early discovery and translational studies of bone cancers such as osteosarcoma. Its integration enhances portfolio decision-making by enabling robust spatial analyses in previously inaccessible tissue types.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Enables spatially resolved transcriptomic profiling in bone, supporting functional target validation in oncology.
- Facilitates identification of cell populations and gene expression patterns relevant to tumor initiation and metastasis.
- Supports mechanistic de-risking by clarifying the spatial context of candidate targets in disease-relevant tissue.
Screening & Assay Development
- Provides validated protocols for preparing bone tissue sections with preserved RNA and morphology.
- Enables reproducible generation of quantitative spatial gene expression data for downstream assays.
- Supports assay standardization and scalability for high-throughput spatial transcriptomic workflows.
Translational & Preclinical Research
- Aligns spatial transcriptomic outputs with disease-relevant models of primary and metastatic osteosarcoma.
- Enables continuity from discovery through preclinical validation by supporting biomarker and target assessment in clinical tissue samples.
- Facilitates risk-adjusted advancement decisions by providing robust spatial molecular data from archived tissue banks.
Pipeline & Workflow Integration
This method bridges early discovery and translational research by enabling spatial transcriptomic analysis of both fresh and archived bone tissues, supporting workflows from target identification to preclinical validation.
- Discovery Biology: Supports hypothesis testing and pathway clarification by mapping gene expression in situ within bone tumors and metastases.
- Screening: Delivers reproducible, quantitative spatial data suitable for comparative analyses across conditions and timepoints.
- Analytics: Provides RNA integrity metrics (e.g., DV200) and spatial gene expression readouts for robust statistical analysis.
- Translational Research: Enables biomarker alignment and mechanistic studies in clinically relevant, banked tissue samples.
- Enterprise Reuse: Establishes a standardized, reusable protocol for spatial transcriptomics in hard tissues across oncology portfolios.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence and reduces mechanistic ambiguity in bone cancer research.
- Operational Value: Standardizes tissue processing and RNA preservation for reproducible spatial transcriptomic workflows.
- Strategic Value: Improves go/no-go decisions and capital efficiency by enabling robust spatial analyses in challenging tissue types.
- Portfolio Impact: Supports risk-adjusted prioritization and advancement of oncology assets targeting bone and metastatic disease.
Implementation Considerations
- Requires expertise in tissue processing, decalcification, and RNA quality assessment.
- Demands access to specialized instrumentation for sectioning and spatial transcriptomic analysis.
- Necessitates rigorous cross-team standardization to ensure reproducibility and data quality.
- Adaptation may be needed for different bone types or archived tissue conditions.
- Extended decalcification times can compromise RNA integrity and should be avoided.
