Spectroscopy Assisted Laser Microdissection

光谱辅助激光显微切割

• 批准号: 10284780
• 负责人: Rohit Bhargava
• 金额: $ 18.82万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10284780
• 关键词: Address; Animal Model; Area; Artificial Intelligence; Attention; Automation; Biochemical; Biological; Biological Assay; Biomedical Research; Carcinoma; Cell Extracts; Cells; Chemicals; Clinical; Clinical Protocols; Collecting Cell; Collection; Complex; Computer software; Coupling; Data; Development; Disease; Dissection; Dyes; Epithelial; Epithelial Cells; Fatigue; Fibroblasts; Film; Foundations; Goals; Hand; Heterogeneity; Histology; Human; Hybrids; Image; Imaging technology; Immune; Intervention; Label; Lasers; Learning; Light; Location; Malignant Neoplasms; Malignant neoplasm of prostate; Measures; Microdissection; Microscopic; Microscopy; Molecular; Molecular Analysis; Morphology; Nature; Optics; Pathologist; Performance; Polymers; Population; Positioning Attribute; Preparation; Process; Prostate; RNA; Reaction; Research; Sampling; Slice; Slide; Solid; Specificity; Specimen; Spectrum Analysis; Speed; Spottings; Stains; Stromal Cells; Structural Chemistry; Supervision; System; Technology; Testing; Thinness; Time; Tissue Model; Tissue Stains; Tissues; Tube; Twin Multiple Birth; Validation; Variant; anticancer research; base; cell type; clinical application; clinical care; clinical diagnostics; design; design and construction; high throughput analysis; imaging system; improved; infrared microscopy; instrument; instrumentation; intelligent algorithm; interest; laser capture microdissection; mixed cell culture; molecular pathology; next generation sequencing; preservation; prototype; spectroscopic imaging; tumor; tumor microenvironment; user-friendly

Abstract Molecular understanding of tumors relies greatly on appropriate samples to be prepared from epithelial cells in tissues. Epithelial cells, however, are often surrounded by other cell types and extracting pure populations of these cells is crucial for correct biospecimen preparation and resulting accuracy of molecular assays. Laser microdissection (LM) has contributed immensely in this effort due to its high spatial specificity in the extraction of defined cell populations and ease of use. While LM has enhanced the precision of biochemical analysis, several drawbacks remain. The necessity of staining and human supervision limits throughput, molecular yield and purity of samples. There is little explicit control or confidence in the purity of extracted cell populations while it is difficult to extract multiple cells from the same sample. Combining the morphologic specificity of microscopy and molecular sensitivity of spectroscopy, infrared (IR) spectroscopic imaging been employed to automate histopathologic recognition in complex tissues using artificial intelligence algorithms applied of spectral data. This project will demonstrate a completely automated instrument by coupling LM with IR microscopy. Termed spectroscopy-assisted laser microdissection (SLaM), the developed prototype will be validated using state of the art IR imaging systems and commercial LCM in terms of accuracy, speed and type fidelity. Last, the approach will be applied to extract cells of different types from the same prostate sample to demonstrate the capability to multiplex LM (muxLM) from the same tissue. The project directly addresses the need to reduce the time- and labor-intensive nature of LM. SLaM can maximize the quality and utility of biological samples used for downstream analyses by automation, high throughput and precision while enabling a comprehensive acquisition of cells without user fatigue or error, thereby providing a sample of higher integrity and quality for cancer molecular analysis.

摘要 对肿瘤的分子理解在很大程度上依赖于从上皮细胞制备适当的样本 纸巾。然而，上皮细胞通常被其他类型的细胞包围，并提取出纯净的 这些细胞对于正确的生物样品制备和由此产生的分子分析的准确性至关重要。激光 由于在提取过程中具有高度的空间特异性，显微解剖(LM)在这方面做出了巨大的贡献 确定的细胞群体和易用性。虽然LM提高了生化分析的精确度， 几个缺点依然存在。染色和人工监督的必要性限制了产量、分子产量 以及样品的纯度。对提取的细胞群体的纯度几乎没有明确的控制或置信度 而从同一样本中提取多个细胞是困难的。结合了细菌的形态特异性 利用光学显微镜和分子光谱的灵敏度、红外光谱成像 利用人工智能算法实现复杂组织的自动组织病理识别 光谱数据。这个项目将演示一种完全自动化的仪器，通过将LM与IR相结合来实现 显微镜。被称为光谱辅助激光显微解剖(SLAM)，开发的原型将是 在精度、速度和类型方面，使用最先进的红外成像系统和商用LCM进行了验证 富达。最后，该方法将用于从同一前列腺样本中提取不同类型的细胞，以 展示从相同组织中多路传输LM(MuxLM)的能力。该项目直接解决了 需要减少物流的时间和劳动力密集型的性质。SLAM可以最大限度地提高质量和效用 用于下游分析的生物样品通过自动化、高通量和精确度实现 在没有用户疲劳或错误的情况下全面获取电池，从而提供更高完整性的样本 和癌症分子分析的质量。