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: 10344774
• 负责人: Ji-Xin Cheng
• 金额: $ 52.04万
• 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/10344774
• 关键词: Amino Acids; Amplifiers; Bypass; Carboplatin; 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; base; cancer cell; denoising; human disease; imaging platform; improved; instrumentation; 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.

项目概要： 提供分子指纹振动信息和高成像速度、相干拉曼散射 显微镜，基于相干反斯托克斯拉曼散射 (CARS) 或受激拉曼散射 (SRS)，允许以亚微米空间分辨率对活细胞和/或组织进行实时振动成像 然而，基于仪器的进步并不能满足高光谱成像中所有所需的参数， 包括宽带宽、高信噪比 (SNR) 和高速度。在突破这些物理极限的过程中， 以牺牲其他优势为代价来优化一个参数是很常见的。目前的提案 旨在通过协同整合来打破“优化没有免费午餐”的传统思维 先进的仪器和数据科学。具有良好合作记录的多学科团队 将继续进行拟议的研究。 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领域的领先专家。 Daniela Matei（co-I）是卵巢癌领域的领先癌症研究专家。我们的目标是开发两个 互补平台将实现高速、高内涵和高灵敏度的细胞图谱 代谢。第一个平台适用于没有先验知识的样本。我们将构建一个多边形扫描仪来调整 20 微秒时间尺度上两个线性调频脉冲之间的延迟。然后我们将部署深度空间光谱 学习对低信噪比高光谱测量进行去噪并提取显着信息 增强的信噪比。这种综合方法有效地绕过了收购之间的传统权衡 速度和信噪比，并利用信息量实现高速、高通量、高光谱 SRS 成像 指纹拉曼带。第二个平台适用于具有已知目标物种的样品。我们将开发一个 稀疏采样高光谱成像策略将整体速度提高一个数量级，同时 保持相同的信噪比。我们将开发一种新颖的“递归特征消除”方法来确定 基本帧的最小数量。在仪器方面，将部署快速调谐光纤激光器 一秒钟内获取稀疏采样的高光谱堆栈，用于生命系统的研究。作为一个专注 应用程序，我们将应用所提出的平台来系统地研究代谢重编程 对顺铂耐药的卵巢癌。我们的重点应用程序将揭示隐藏的签名 与耐药性相关，这将为改善耐药性治疗开辟新的机会 癌症。