High-throughput high-content single cell analysis by multichannel stimulatedRaman flow cytometry

 描述(申请人提供)：流式细胞术是高通量单细胞分析最重要的工具之一。在流式细胞术中，荧光标记是细胞分析的主要方法。然而，荧光标记并不适用于所有情况，特别是小分子(例如代谢物)，对其进行标记可能会显著干扰其性质。由固有分子振动产生的拉曼光谱信号为检测细胞内的特定分子和区分细胞状态提供了一种关键方法。已有基于拉曼的微流控器件的报道。然而，自发拉曼散射截面很小，导致拉曼信号电平低，数据采集时间长，这与高速流动条件不相容。该项目的长期目标是建立一个以分子指纹振动为对比的高通量、高含量的单细胞分析平台。目前应用的具体目标是开发一种基于受激拉曼散射(SRS)过程的振动光谱细胞仪。吉新城(PI)实验室的几项最新进展，包括高灵敏度的飞秒SRS成像、免锁定SRS信号检测和用于多路SRS成像的调谐放大器阵列，为计划中的仪器奠定了基础。国际和平研究所已经为拟议的研究组建了一个跨学科小组。J.Paul Robinson博士(co-Pi)是基于荧光的流式细胞仪开发和应用的领导者，他将为流体和多通道检测系统的设计带来专业知识。Bartek Rajwa博士(合伙)将提供光谱细胞术数据分析和机器学习方面的专业知识。该小组将设计和建造通过多通道检测分散的SRS信号的SRS流式细胞仪(目标1)，构建能够收集SRS和荧光数据的串联系统(目标2)，开发能够结合从SRS光谱获得的信息和用于细胞功能分类的标记生物标记物的光谱分解和机器学习分析工具(目标3)，并验证SRS流式细胞仪无标记检测单细胞代谢的能力(目标4)。凭借每秒分析数千个细胞的速度，SRS流式细胞仪将能够高通量分析单细胞化学成分，这是基于荧光的流式细胞仪无法达到的。

 DESCRIPTION (provided by applicant): Flow cytometry is one of the most important tools for high-throughput single cell analysis. Fluorescent labeling acts as the primary approach for cellular analysis in flow cytometry. Nevertheless, fluorescent tags are not applicable to all cases especially small molecules (e.g. metabolites) for which labeling may significantly perturb their properties. Raman spectroscopic signals arising from inherent molecular vibrations provide a key approach to detect specific molecules inside cells and to differentiate cellular state. Raman-based microfluidic devices have been reported. However, the very small cross section of spontaneous Raman scattering results in low Raman signal level and consequently long data acquisition time, which is not compatible with the high- speed flow condition. The long-term goal of the proposed project is to establish a high-throughput high-content single cell analysis platform using molecular fingerprint vibrations as contrast. The specific objective of current application is to develop a vibrational spectroscopic cytometer based on the stimulated Raman scattering (SRS) process. Several recent advances in the Ji-Xin Cheng (PI) lab, including the highly sensitive femtosecond SRS imaging, lock-in free SRS signal detection and a tuned amplifier array for multiplex SRS imaging, pave the foundation for the planned instrumentation. The PI has assembled an interdisciplinary team for the proposed study. Dr. J. Paul Robinson (co-PI) is a leader in development and applications of fluorescence-based flow cytometer and he will bring expertise to the design of fluidics and multichannel detection systems. Dr. Bartek Rajwa (co-PI) will provide expertise for spectroscopic cytometry data analysis and machine learning. The team will design and construct a SRS flow cytometer by multichannel detection of dispersed SRS signal (Aim 1), construct a tandem system able to collect SRS and fluorescence data (Aim 2), develop spectral un-mixing and machine-learning analysis tools able to combine the information obtained from SRS spectra and labeled biomarkers for functional classification of cells (Aim 3), and validate the capability of SRS flow cytometer for label-free detection of single-cell metabolism (Aim 4). With a speed of analyzing thousands of cells per second, SRS flow cytometer will enable high-throughput analysis of single-cell chemical content which is beyond the reach by fluorescence-based flow cytometer.