多重免疫荧光结合空间图像分析，用于肿瘤微环境的临床和生物学评估
2023年6月2日
10.6K 次观看
本文描述了一种手动酪胺信号放大（TSA）多重免疫荧光（mIF）结合图像分析和空间分析的方案。该方案可与福尔马林固定石蜡包埋（FFPE）切片一起使用，根据实验室中可用的玻片扫描仪，每张载玻片可染色两到六种抗原。
在我们的实验室，我们正在研究人类结直肠癌。其进展为转移性疾病及其对治疗（包括免疫疗法）的反应。我们正试图通过研究原发性和转移性疾病中的肿瘤免疫微环境和肿瘤内微生物组来寻找新的预测和预后生物标志物。
在我们的项目中，我们目前正在实施多种技术，例如RNA测序，全外显子组测序，TCR测序循环游离DNA分析，免疫荧光和荧光研究所杂交。目前，我们的主要挑战是将荧光研究所杂交染色与酪胺信号放大多重免疫荧光相结合，以研究微生物组与免疫细胞之间的相互作用。我们的方法稳健、可重现、易于使用且具有成本效益。
它可以在任何拥有荧光扫描仪的实验室中进行设置，并且该方法可以与任何市售IHC抗体一起使用，而不是通常针对受限抗原组进行优化的市售试剂盒。
概述
本文描述了一种用于手动酪氨酸胺信号放大（TSA）多重免疫荧光（mIF）结合图像和空间分析的协议。该方法适用于甲醛固定石蜡包埋（FFPE）切片，可在每张切片上染色多个抗原。
关键研究组成部分
科学领域
- 神经科学
- 免疫学
- 病理学
背景
- 调查人类结直肠癌及其进展。
- 关注肿瘤免疫微环境和肿瘤内微生物组。
- 使用RNA测序和免疫荧光等多种技术。
- 综合分析中结合不同染色方法的挑战。
研究目的
- 寻找新的预测和预后生物标志物。
- 研究微生物组与免疫细胞之间的相互作用。
- 开发一种稳定和可重复的染色方法。
使用的方法
- 手动酪氨酸胺信号放大多重免疫荧光。
- 使用高复合荧光算法进行图像分析。
- 荧光原位杂交结合免疫荧光。
- FFPE组织切片的详细染色协议。
主要结果
- 在单张切片上成功染色多个抗原。
- 优化抗体特异性和信噪比。
- 有效的图像分析产生可量化的数据。
- 展示该方法在各种实验室的适用性。
结论
- TSA mIF方法灵活且具有成本效益。
- 可以适用于各种抗体和抗原。
- 这种方法增强了对肿瘤生物学的理解。
常见问题
什么是酪氨酸胺信号放大？
酪氨酸胺信号放大是一种用于增强免疫荧光染色信号的方法，可以检测低丰度蛋白质。
这种方法可以染色多少种抗原？
该方法根据可用的荧光切片扫描仪，每张切片可以染色两到六种抗原。
这种方法适用于所有类型的抗体吗？
是的，该方法可以与任何商业可用的免疫组化抗体一起使用，不同于针对特定面板优化的试剂盒。
该协议的主要应用是什么？
该协议主要用于癌症研究，以研究肿瘤微环境和免疫相互作用。
这种方法可以在任何实验室中实施吗？
可以，只要实验室有荧光切片扫描仪，该方法就可以轻松设置。
染色过程中的关键步骤是什么？
关键步骤包括组织固定、脱蜡、阻断、抗体孵育和酪氨酸胺试剂的应用。
Executive Industry Relevance
Multiplex immunofluorescence (mIF) with spatial image analysis enables high-content, quantitative mapping of the tumor microenvironment (TME), supporting biomarker discovery and mechanistic de-risking in oncology pipelines. This workflow provides actionable insights into immune and stromal cell interactions, informing predictive biomarker strategies and translational research. Its reproducibility and compatibility with diverse antibodies position it as a scalable capability for enterprise-level oncology R&D.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Enables spatially resolved interrogation of immune and tumor cell interactions for target validation.
- Supports mechanistic de-risking by quantifying cell-type densities and proximities within tumor compartments.
- Facilitates identification of novel predictive and prognostic biomarkers for portfolio triage.
Screening & Assay Development
- Prepares validated tissue systems for downstream multiplexed biomarker screening.
- Delivers standardized, reproducible, and quantitative spatial data for assay development.
- Enables robust evaluation of antibody specificity and signal-to-noise optimization for screening readiness.
Translational & Preclinical Research
- Aligns spatial biomarker analysis with disease-relevant models for translational continuity.
- Supports risk-adjusted advancement decisions by linking TME features to therapeutic response hypotheses.
- Provides quantitative outputs for cross-study and cross-cohort comparisons in preclinical research.
Pipeline & Workflow Integration
This mIF and spatial analysis workflow bridges early discovery, biomarker validation, and translational research in oncology portfolios.
- Discovery Biology: Quantifies immune and stromal cell distributions to clarify TME-driven mechanisms.
- Screening: Standardizes multiplexed tissue assays for reproducible, quantitative readouts.
- Analytics: Exports spatial and density data for statistical comparison of tumor compartments and cell interactions.
- Translational Research: Links spatial biomarker patterns to therapeutic response, supporting preclinical-to-clinical continuity.
- Enterprise Reuse: Adaptable to any antibody panel and tumor type, enabling broad portfolio application.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence in biomarker and target selection by resolving spatial cell interactions.
- Operational Value: Delivers reproducible, scalable, and cost-effective multiplexed tissue analysis.
- Strategic Value: Informs go/no-go decisions and reduces late-stage biological risk through robust TME characterization.
- Portfolio Impact: Enables risk-adjusted prioritization of oncology assets based on spatial biomarker evidence.
Implementation Considerations
- Requires expertise in immunofluorescence, image analysis, and statistical data processing.
- Needs access to a fluorescent slide scanner and compatible image analysis software.
- Demands cross-team standardization of antibody validation and staining protocols.
- Adaptable across tumor types and antibody panels with appropriate optimization.
- Dependent on rigorous antibody specificity and signal-to-noise optimization for reliable outputs.
Biopharma FAQs
Why does null hypothesis testing matter for spatial biomarker validation?
Null hypothesis testing enables objective assessment of whether observed spatial patterns in cell distributions are statistically significant, supporting robust target validation and reducing false discovery risk in biomarker development.
How does independent variable isolation fit multiplex immunofluorescence analysis?
Isolating independent variables, such as specific cell types or tumor compartments, allows for precise quantification of their spatial relationships and effects, strengthening mechanistic insights and discovery-stage decision making.
What do quantitative dependent variable measurements enable in TME analysis?
Quantitative measurements of cell density and spatial proximity enable direct comparison across samples and conditions, facilitating biomarker discovery and supporting translational research continuity.
Why are replication requirements critical for cross-functional biomarker studies?
Replication ensures that spatial and quantitative findings are reproducible across cohorts and platforms, enabling reliable cross-functional collaboration and enterprise-wide data integration.
Which statistical analysis capabilities are required before implementing spatial image analysis?
Robust statistical tools are needed to analyze exported spatial and density data, assess significance, and control for multiple comparisons, ensuring that findings inform actionable R&D decisions.
