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

项目总结： 提供分子指纹振动信息和高成像速度的相干拉曼散射 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的所有期望参数， 包括宽带、高信噪比和高速率。在推动这些物理限制的过程中，它 以牺牲其他优点为代价来优化一个参数是很常见的。目前的提案 旨在通过协同整合的方式，打破这一传统思维中的“优化没有免费午餐” 先进的仪器和数据科学。具有良好协作记录的多学科团队 将继续进行拟议的研究。郑继新(Pi)是一位领先的软件开发和应用专家 SRS化学成像。雷田(co-i)是计算显微镜和机器学习领域的领先专家。 Daniela Matei(co-i)是专门研究卵巢癌的领先癌症研究专家。我们的目标是开发两个 互为补充的平台，可实现高速、高内容和高灵敏度的细胞映射 新陈代谢。第一个平台是在没有先验知识的情况下提供样品。我们将构建一个多边形扫描仪来调整 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 学会对低信噪比的高光谱测量进行去噪，并提取大量的显著信息 增强的信噪比。这种集成的方法有效地绕过了传统的收购之间的权衡 速度和信噪比，实现高速、高通量、高光谱SRS成像 指纹拉曼谱带。第二个平台是已知目标物种的样本。我们将开发一种 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，将部署一个快速调谐光纤激光器来 在一秒内获取稀疏采样的高光谱叠加，用于生命系统的研究。作为一个专注的 应用，我们将应用建议的平台来系统地研究代谢重新编程 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，这些签名 与抗药性有关，这将为改善抗药性的治疗打开新的机会 癌症。

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.