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

 描述（由申请人提供）：流式细胞术是高通量单细胞分析的最重要工具之一。荧光标记作为流式细胞术中细胞分析的主要方法。然而，荧光标签并不适用于所有情况，特别是小分子（例如代谢物），标记可能会显着扰乱其特性。由固有分子振动产生的拉曼光谱信号提供了检测细胞内特定分子和区分细胞状态的关键方法。已经报道了基于拉曼的微流体装置。然而，自发拉曼散射的非常小的横截面导致低的拉曼信号水平和因此长的数据采集时间，这与高速流动条件不兼容。该项目的长期目标是建立一个以分子指纹振动作为对比的高通量高含量单细胞分析平台。本申请的具体目标是开发基于受激拉曼散射（SRS）过程的振动光谱细胞仪。Ji-Xin Cheng（PI）实验室最近的几项进展，包括高灵敏度的飞秒SRS成像，无锁定SRS信号检测和用于多路SRS成像的调谐放大器阵列，为计划的仪器奠定了基础。PI已为拟议研究组建了一个跨学科团队。J. Paul罗宾逊博士（合作PI）是基于荧光的流式细胞仪开发和应用的领导者，他将为射流和多通道检测系统的设计带来专业知识。Bartek Rajwa博士（联合PI）将为光谱细胞术数据分析和机器学习提供专业知识。该团队将通过多通道检测分散的SRS信号来设计和构建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），开发光谱解混和机器学习分析工具，该工具能够将从SRS光谱和标记的生物标志物获得的信息联合收割机用于细胞的功能分类（目标3），并验证SRS流式细胞仪用于单细胞代谢的无标记检测的能力（目的4）。SRS流式细胞仪具有每秒分析数千个细胞的速度，可以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.