High-content High-speed Chemical Imaging of Metabolic Reprogramming by Integration of Advanced Instrumentation and Data Science

通过先进仪器和数据科学的集成进行代谢重编程的高内涵高速化学成像

• 批准号: 10543185
• 负责人: Ji-Xin Cheng
• 金额: $ 45.62万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10543185
• 关键词: Amino Acids; Amplifiers; Bypass; Carboplatin; Cell Separation; Cell physiology; Cells; Cellular Metabolic Process; Chemicals; Cholesterol; Cisplatin; Collaborations; Computers and Advanced Instrumentation; Data Science; Development; Drug resistance; Fatty Acids; Fiber; Fingerprint; Glucose; Homeostasis; Image; Individual; Knowledge; Lasers; Learning; Legal patent; Lipids; Machine Learning; Malignant Neoplasms; Malignant neoplasm of ovary; Maps; Measurement; Measures; Metabolic; Metabolism; Microscope; Microscopy; Molecular Profiling; Noise; Organism; Physiologic pulse; Price; Research; Resistance; Resolution; Sampling; Scanning; Side; Signal Transduction; Speed; Stress; Thinking; Time; Tissues; anticancer research; cancer cell; denoising; human disease; imaging platform; improved; instrumentation; invention; metabolic imaging; millisecond; multidisciplinary; novel; refractory cancer; spectroscopic imaging; submicron; tool; tumor metabolism; vibration

Project Summary: Providing molecular fingerprint vibration information and high imaging speed, coherent Raman scattering microscopy, based on either coherent anti-Stokes Raman scattering (CARS) or stimulated Raman scattering (SRS), allows real-time vibrational imaging of living cells and/or tissues with sub-micron spatial resolution These instrumentation-based advances, however, do not fulfill all the desired parameters in hyperspectral imaging, including broad bandwidth, high signal to noise ratio (SNR) and high speed. In pushing these physical limits, it is common that one parameter is optimized at the price of sacrificing other advantages. The current proposal aims to break this conventional thinking of "no free lunch in optimization" through a synergistic integration of advanced instrumentation and data science. A multidisciplinary team with a strong track record of collaborations will pursue the proposed studies. Ji-Xin Cheng (PI) is a leading expert in the development and applications of SRS chemical imaging. Lei Tian (co-I) is a leading expert in computational microscopy and machine learning. Daniela Matei (co-I) is a leading expert in cancer research specialized in ovarian cancer. We aim to develop two complementary platforms that will allow high-speed, high-content, and high-sensitivity mapping of cell metabolism. The first platform is for samples without prior knowledge. We will build a polygon scanner to tune the delay between two chirped pulses on a 20-microsecond time scale. We will then deploy deep spatial-spectral learning to denoise the low-SNR hyperspectral measurements and extract salient information with much enhanced SNR. This integrated approach effectively bypasses the conventional tradeoff between acquisition speed and SNR and enables high-speed, high-throughput, hyperspectral SRS imaging using informative fingerprint Raman bands. The second platform is for samples with known target species. We will develop a sparsely sampled hyperspectral imaging strategy to increase the overall speed by one order of magnitude while maintaining the same SNR. We will develop a novel "recursive feature elimination" approach to determine the minimum number of essential frames. On the instrumentation side, a fast-tuning fiber laser will be deployed to acquire a sparsely sampled hyperspectral stack within one second for the study of living systems. As a focused application, we will apply the proposed platforms to systematically investigate metabolic reprogramming in ovarian cancers that are cisplatin resistant. Our focused application will unveil hidden signatures that are associated with drug resistance, which will open new opportunities for improved treatment of drug-resistant cancers.

项目摘要： 提供分子指纹振动信息和高成像速度，连贯的拉曼散射 显微镜，基于连贯的反stokes拉曼散射（CAR）或刺激的拉曼散射 （SRS），允许使用亚微米空间分辨率的活细胞和/或组织实时振动成像 但是，基于仪器的进步并不能满足高光谱成像中的所有所需参数， 包括宽带宽，高信号与噪声比（SNR）和高速。在推动这些物理极限时， 很常见的是，一个参数以牺牲其他优势的价格进行了优化。当前的建议 旨在通过协同融合的共同融合来打破这种传统的思想 高级仪器和数据科学。具有良好合作记录的多学科团队 将进行拟议的研究。 Ji-Xin Cheng（PI）是开发和应用领域的领先专家 SRS化学成像。 Lei Tian（Co-​​I）是计算显微镜和机器学习方面的领先专家。 Daniela Matei（Co-I）是专门研究卵巢癌的癌症研究的领先专家。我们的目标是发展两个 互补平台将允许细胞的高速，高含量和高敏性映射 代谢。第一个平台是无知识的样本。我们将建造一个多边形扫描仪来调整 在20英寸的时间尺度上，两个chir脉脉冲之间的延迟。然后，我们将部署深空光谱 学会降级低SNR高光谱测量并提取显着信息 增强的SNR。这种综合方法有效地绕过了收购之间的常规权衡 速度和SNR并启用高速，高通量，高光谱SRS成像 指纹拉曼乐队。第二个平台用于具有已知目标物种的样品。我们将发展一个 稀疏采样的高光谱成像策略以将整体速度提高一个数量级，而 保持相同的SNR。我们将开发一种新颖的“递归功能消除”方法来确定 必需框架数量最少。在仪器方面，将部署快速调整的光纤激光器 在一秒钟内，以获取稀疏采样的高光谱堆栈，以进行生活系统研究。作为专注的 应用，我们将应用提出的平台系统地研究代谢重编程 抗顺铂的卵巢癌。我们集中的应用将揭示隐藏的签名 与耐药性有关，这将为改善耐药性治疗的新机会开放 癌症。