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

项目摘要： 相干拉曼散射提供分子指纹振动信息和高成像速度， 基于相干反斯托克斯拉曼散射（汽车）或受激拉曼散射的显微镜 (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，不能满足高光谱成像中的所有期望参数， 包括宽带宽、高信噪比（SNR）和高速。在推动这些物理极限时， 一个参数的优化往往是以牺牲其他优点为代价的。现时的建议 旨在通过协同整合，打破“优化没有免费的午餐”的传统思维， 先进的仪器和数据科学。一个多学科的团队，具有良好的合作记录 将继续进行拟议的研究。Ji-Xin Cheng（PI）是开发和应用 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马泰（co-I）是专门研究卵巢癌的癌症研究领域的领先专家。我们的目标是发展两个 互补的平台，将允许高速，高内容，高灵敏度的细胞映射 新陈代谢.第一个平台用于没有先验知识的样本。我们将建立一个多边形扫描仪， 在20微秒的时间尺度上两个啁啾脉冲之间的延迟。然后我们将部署深空光谱 学习去噪低信噪比的高光谱测量和提取显著信息， 增强SNR。这种综合方法有效地绕过了传统的收购之间的权衡 速度和SNR，并能够使用信息丰富的高速度，高通量，高光谱SRS成像 指纹拉曼谱带。第二个平台用于已知目标物种的样本。我们将开发一个 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， 保持相同的SNR。我们将开发一种新的“递归特征消除”方法来确定 最少的基本框架。在仪器方面，将部署快速调谐光纤激光器， 在一秒钟内获得稀疏采样的高光谱叠加，用于生命系统的研究。作为聚焦的 应用，我们将应用所提出的平台系统地研究代谢重编程， 对顺铂耐药的卵巢癌。我们专注的应用程序将揭示隐藏的签名， 这将为改善耐药性的治疗提供新的机会。 癌的

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.