Non-destructive optical spectroscopic assay for high-throughput metabolic characterization of in vitro cell models and patient-derived organoids

用于体外细胞模型和患者来源类器官高通量代谢表征的无损光学光谱测定

• 批准号: 10666355
• 负责人: Caigang Zhu
• 金额: $ 18.74万
• 依托单位: UNIVERSITY OF KENTUCKY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10666355
• 关键词: 4T1; Algorithms; Biological Assay; Biological Markers; Biomedical Research; Breast Cancer Model; Breast Cancer Patient; Cancer Model; Cancer Patient; Cell Line; Cell model; Cells; Clinic; Clinical; Control Groups; Convulsions; Decision Making; Evaluation; Fatty Acids; Fiber; Fluorescence; Future; Genus Hippocampus; Goals; In Vitro; Incubated; Light; Machine Learning; Matrix Metalloproteinases; Measurement; Measures; Membrane Potentials; Metabolic; Metabolism; Modeling; Nature; Optics; Organoids; Outcome; Patients; Performance; Preparation; Radiation; Radiation Tolerance; Radiation therapy; Reader; Regimen; Role; Sampling; Siblings; Source; Specimen; Spectrum Analysis; Standardization; Stress; Survival Rate; System; Techniques; Technology; Testing; Therapeutic; Therapeutic Studies; Time; Tissues; Update; Work; anticancer research; cancer cell; cancer radiation therapy; cancer therapy; experience; experimental group; fluorophore; glucose uptake; high throughput screening; improved; in vivo; indexing; innovation; machine learning algorithm; malignant breast neoplasm; metabolomics; mitochondrial membrane; new technology; novel; novel strategies; pilot test; pre-clinical; preclinical study; predictive modeling; programs; radiation response; radioresistant; tool; tumor; tumor growth; tumor metabolism; uptake

Abstract To maximize cancer patients’ survival rate post-therapy, in vitro immortal cancer cell models and newly developed patient-derived organoids are widely used to study the role of tumor metabolism reprogramming in tumor growth and survival under therapeutics stresses. Although conducting longitudinal metabolic measurements on the same tumor sample during a course of therapy is critical for therapeutic studies, there are surprisingly few techniques that can provide a systems-level view of tumor metabolism on in vitro cancer models or organoids non-destructively. Several metabolic tools, such as Seahorse Assay and Metabolomics, provide standardized metabolic measurements but often require destructive sample preparation. Relying on the non-invasive nature of optical technique, this proposal seeks to fill the critical technical gap by developing an optical spectroscopic assay that will enable non-destructive high-throughput metabolism measurement on in vitro cancer models and organoids for cancer research. Specifically, we will develop a novel multi-channel fluorescence spectroscopic assay and a machine learning de-convolution algorithm to quantify the key metabolic parameters of in vitro cancer models (Aim 1). As there is a significant unmet clinical need for breast cancer (BC) radiotherapy (RT) sensitivity evaluation prior to treatment, we will demonstrate our non-destructive assay for early prediction of BC radiation responses within the decision-making window via longitudinal metabolic characterization of patient-derived organoids under radiation stresses (Aim 2). Our technology fills an important gap that exists between Seahorse Assay (in vitro cells) and Metabolomics (in vitro cells and ex vivo tissue) by providing a novel approach for non-destructive metabolism measurement on in vitro cancer models and patient-derived organoids. Our innovative RT sensitivity prediction model will directly impact BC patients by providing a novel paradigm for patients’ RT sensitivity prediction during the decision-making window. Once we demonstrate the proof-of-concept of our optical technique and the RT sensitivity prediction model, we will move our study to a large-scale trail in clinics with a goal of providing individualized RT for BC patients in our future R01 plan.

抽象的 为了最大化癌症患者的生存率，疗法后，体外不朽的癌细胞模型和新的 发达的患者衍生的类器官被广泛用于研究肿瘤代谢在重编程中的作用 治疗应力下的肿瘤生长和生存。虽然进行纵向代谢 在治疗过程中，对同一肿瘤样本的测量对于治疗研究至关重要 令人惊讶的是，很少有能够在体外癌症上提供肿瘤代谢的系统级别的视图 模型或类器官非破坏性。几种代谢工具，例如海马测定法和代谢组学， 提供标准化的代谢测量值，但通常需要破坏性样品制备。依靠 光学技术的非侵入性质，该提案旨在通过发展来填补关键的技术差距 光谱法测定将实现非破坏性高通量代谢测量 癌症研究的体外癌症模型和类器官。具体来说，我们将开发一种新颖的多通道 荧光光谱测定和机器学习De-Conconvolution算法，以量化密钥 体外癌症模型的代谢参数（AIM 1）。由于乳房有很大的未满足临床需求 癌症（BC）放疗（RT）敏感性评估在治疗前，我们将证明我们的无损 通过纵向进行决策窗口内BC辐射响应的早期预测的测定 在辐射应力下对患者衍生的类器官的代谢表征（AIM 2）。我们的技术填充 海马测定（体外细胞）和代谢组学（体外细胞和EX）之间存在的一个重要差距 体内组织）通过为体外癌症的非破坏性代谢测量提供新的方法 模型和患者衍生的类器官。我们创新的RT灵敏度预测模型将直接影响BC 通过在决策过程中为患者的RT灵敏度预测提供新的范式来提供新的范式 窗户。一旦我们证明了光学技术的概念和RT灵敏度预测 模型，我们将研究将我们的研究转移到诊所的一条大型步道，目的是为BC提供个性化的RT 我们将来的R01计划中的患者。