Spectroscopy Assisted Laser Microdissection

光谱辅助激光显微切割

基本信息

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
项目类别：
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

项目摘要

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.

抽象的对肿瘤的分子理解极大地依赖于从上皮细胞中制备的适当样品组织。然而，上皮细胞通常被其他细胞类型所包围，并提取这些细胞对于正确的生物测量制备和分子测定的准确性至关重要。激光微分解（LM）由于提取方面的高空间特异性，在这项工作中贡献了很大的贡献定义的细胞群体和易用性。尽管LM提高了生化分析的精度，但仍然存在一些缺点。染色和人类监督的必要性限制了吞吐量，分子产量和样品的纯度。对提取的细胞种群的纯度几乎没有明确的控制或信心虽然很难从同一样品中提取多个细胞。结合形态特异性光谱，红外（IR）光谱成像的显微镜和分子敏感性被用于使用人工智能算法在复杂组织中自动化组织病理学识别光谱数据。该项目将通过与IR耦合LM来证明一种完全自动化的仪器显微镜。称为光谱辅助激光显微解剖（SLAM），开发的原型将是在准确性，速度和类型方面，使用艺术状态IR成像系统和商业LCM验证忠诚。最后，该方法将应用于从同一前列腺样本中提取不同类型的细胞到证明来自同一组织的多重LM（MUXLM）的能力。该项目直接解决需要减少LM的时间和劳动密集型性质。大满贯可以最大化通过自动化，高吞吐量和精确度用于下游分析的生物样品，同时进行无需用户疲劳或错误的细胞的全面获取，从而提供了更高完整性的样本和癌症分子分析的质量。