Using label-free Raman microscopy to predict therapeutic resistance of TNBC cells

本申请是对特别利益通知(NOSI)的回应，该通知被确认为 不是-CA-23-045。 细胞的异质性已经变得至关重要，限制了治疗的有效性 癌症治疗。细胞异质性的表现可以在生化上观察到。 (分子组成和结构)、形态和机械水平，最终影响细胞 功能。在考虑非侵入性单细胞方法时，评估的主要方法 群体生化异质性是基于细胞的荧光激活细胞分选(FACS) 曲面标记。虽然速度非常快，但这种方法只量化了由 用户；但如果标记不可靠怎么办？例如，研究表明，浓缩细胞基于 CD44表面标志物的流式细胞术仅提供一组细胞，其中5%呈干细胞样细胞 行为。那么所有其他潜在的脂类或核酸化学的分子变化呢 在FACS实验中是看不见的，这可能是细胞中区分特征的信息？ 在本补充请求中，我们建议开发一种无标记的分子成像方法来 表征细胞拉曼指纹并训练卷积神经网络(CNN)进行预测 异种人群中的治疗耐药性。我们将使用高速、无标签的非线性 拉曼散射，通过量化细胞的丰度来捕捉细胞的整体分子指纹 代谢物、脂类、蛋白质和核酸，并将这些数据与CNN培训和功能 药物筛查。在我们初步发现的基础上，将通过培养来开发和培训CNN 不同乳腺癌亚群与阿霉素浓度升高的关系及检测 活细胞和死细胞拉曼指纹(目标1)。在AIM 1开发的CNN将进行测试 预测两个亚群共培养的亚群治疗敏感性的能力(目的 2)。该项目不仅提供了一种对异种癌细胞进行表型鉴定的新方法，还将 与下游功能或分子测试完全兼容，如肿瘤启动或RNAseq 测量。

This application is being submitted in response to the Notice of Special Interest (NOSI) identified as NOT-CA-23-045. Cellular heterogeneity has become critically important, limiting the effectiveness of therapeutics in cancer treatment. The manifestations of cellular heterogeneity can be observed at the biochemical (molecular composition and structure), morphological, and mechanical level, ultimately affecting cell function. When considering non-invasive single-cell methods, the primary methods to assess population biochemical heterogeneity are based on fluorescence activated cell sorting (FACS) of cell surface markers. While extremely rapid, this method quantifies only specific molecules chosen by the user; but what if the marker is unreliable? For example, studies have shown that enriching cells based on FACS of CD44 surface markers only provides a population of cells in which 5% show stem-like behavior. What about all the other potential molecular changes in lipids or nucleic acid chemistry that were invisible in the FACS experiment, which might be informative as differentiating features in cells? In this supplement request, we propose to develop a label-free, molecular imaging approach to characterize cellular Raman fingerprints and train a convolutional neural network (CNN) to predict therapeutic resistance in heterogenous populations. We will use high-speed, label-free nonlinear Raman scattering, which captures a holistic molecular fingerprint of a cell by quantifying abundance of metabolites, lipids, proteins, and nucleic acids and couple this data with CNN training and functional drug screening. Building off our preliminary findings, a CNN will be developed and trained by culturing different breast cancer subpopulations with increasing concentrations of doxorubicin and measuring both live and dead cell Raman fingerprints (Aim 1). The CNN developed in Aim 1 will be tested for its ability to predict subpopulation therapeutic susceptibility in co-cultures of the two subpopulations (Aim 2). This project offers not only a new take on phenotyping heterogeneous cancer cells, but it will also be fully compatible with downstream functional or molecular testing such as tumor initiation or RNAseq measurements.