基于线扫描振动和频生成显微镜的多模态非线性高光谱化学成像
2023年12月1日
开发了一种多模态快速高光谱成像框架，用于获取宽带振动和频生成（VSFG）图像以及明场二次谐波生成（SHG）成像模式。由于红外频率与分子振动共振，揭示了对称性允许样品的微观结构和介观形态学知识。
我们的目标是研究介观异质自组装样品，例如生物组织，以揭示其化学成分和分子排列。我们正在探索这些自材料的微观排列和介观形态如何与它们的微观特性相关。随着高数值孔径和反射式显微镜物镜的进步，我们和其他人已经展示了分辨率为1平方微米的振动和频生成显微镜，它可以同时记录软材料的图像，还可以在空间上分辨光谱，形成所谓的高光谱图像。
高光谱成像在多个维度上记录实验数据，两个在空间中，一个在频率上，可能在一个时间上。然而，快速收集、存储和分析此类大数据以最大限度地利用信息仍然是一个挑战。仅对图像进行采样以收集有用的数据点也具有挑战性。
通过开发更快的线扫描技术，我们可以将数据采集时间缩短 100 倍。此外，该显微镜还兼容和频产生（SFG）、二次谐波产生（SHG）以及宽视场成像。多模态成像允许使用光学成像和关联各种图像模态对样品进行快速检测。
SHG成像辅助光学技术，但缺乏光谱分辨能力，在生物物理学中应用广泛。我们的SFG显微镜现在将能够提供目前在软组织生物物理研究中缺失的强大的分子水平见解。
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概述
本研究提出了一个多模态高光谱成像框架，旨在捕捉宽带振动和频率生成（VSFG）图像，同时结合明视野和二次谐波生成（SHG）成像模式。该框架旨在增强对自组装生物组织中微观和中观结构的理解。
关键研究组成部分
科学领域
- 神经科学
- 生物物理学
- 成像技术
背景
- 自组装样品表现出中观异质性。
- 了解化学成分和分子排列至关重要。
- 显微镜技术的进步提高了分辨率和成像能力。
- 高光谱成像在多个维度捕捉数据。
研究目的
- 探讨微观排列与中观形态之间的关系。
- 提高软材料的高分辨率成像。
- 解决高光谱成像中数据收集和分析的挑战。
所用方法
- 开发多模态成像框架。
- 利用振动和频率生成显微镜。
- 同时记录多种成像模式。
- 高光谱图像的数据收集和分析技术。
主要结果
- 成功获取了宽带VSFG图像。
- 提高了自组装生物组织成像的分辨率。
- 对样品的结构和形态特征有了深入了解。
- 识别了数据管理和分析中的挑战。
结论
- 开发的框架显著提高了成像能力。
- 它提供了关于生物组织特性的宝贵见解。
- 未来的工作将集中在改进数据处理技术。
常见问题
Executive Industry Relevance
Multimodal nonlinear hyperspectral chemical imaging using line-scanning VSFG microscopy enables high-resolution, label-free visualization of chemical and structural heterogeneity in biological tissues and self-assembled materials. This capability addresses a critical gap in resolving mesoscopic morphology and molecular arrangement, supporting predictive confidence in early discovery and target validation. The approach enhances portfolio decision-making by providing molecular-level insights that are not accessible with conventional imaging modalities.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Enables direct visualization of non-centrosymmetric and chiral structures relevant to biological targets.
- Supports functional target validation by correlating chemical composition with mesoscopic morphology.
- Improves predictive confidence in mechanistic studies of soft tissue and self-assembled systems.
Screening & Assay Development
- Facilitates preparation of validated biological systems for downstream screening workflows.
- Provides quantitative, spatially resolved chemical information for assay standardization.
- Enables reproducible imaging of heterogeneous samples, supporting reliable compound evaluation.
Translational & Preclinical Research
- Aligns imaging outputs with disease-relevant tissue structures for translational biomarker studies.
- Supports continuity from discovery through preclinical validation by resolving molecular arrangements in situ.
- Reduces biological risk by clarifying structure-function relationships in complex samples.
Pipeline & Workflow Integration
This imaging platform integrates from early discovery through preclinical research, bridging hypothesis testing, target validation, and translational studies.
- Discovery Biology: Provides high-content, label-free chemical imaging for hypothesis testing and pathway clarification.
- Screening: Delivers reproducible, quantitative outputs for assay readiness and compound triage.
- Analytics: Generates hyperspectral datasets enabling robust comparison of sample conditions and morphologies.
- Translational Research: Connects molecular imaging to disease-relevant tissue analysis, supporting biomarker alignment.
- Enterprise Reuse: Offers a scalable, multimodal imaging capability adaptable across diverse biological and material systems.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence and reduces mechanistic ambiguity in target validation.
- Operational Value: Accelerates data acquisition and standardizes imaging workflows for heterogeneous samples.
- Strategic Value: Informs go/no-go decisions and enhances capital efficiency by providing molecular-level insights early in the pipeline.
- Portfolio Impact: Supports risk-adjusted prioritization and advancement of discovery and preclinical programs.
Implementation Considerations
- Requires expertise in nonlinear optical imaging and hyperspectral data analysis.
- Demands advanced instrumentation, including high numerical aperture objectives and line-scanning technology.
- Necessitates cross-team standardization for data acquisition and interpretation.
- Adaptable to a range of biological and material model systems with appropriate optimization.
- Data management and analysis infrastructure must address the challenges of large hyperspectral datasets.
