Development of plasmon-enhanced biosensing for multiplexed profiling of extracellular vesicles

用于细胞外囊泡多重分析的等离子体增强生物传感的发展

• 批准号: 10841218
• 负责人: Hyungsoon Im
• 金额: $ 33.4万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10841218
• 关键词: Administrative Supplement; Algorithm Design; Biosensing Techniques; Cells; Classification; Clinical; Computer software; Data; Data Set; Database Management Systems; Development; Feedback; Goals; Human; Image; Knowledge; Laboratories; Machine Learning; Modeling; Parents; Process; Reproducibility; Research Personnel; Sampling; System; Technology; Testing; Training; United States National Institutes of Health; Update; cellular imaging; clinical application; deep learning; deep learning model; extracellular vesicles; high dimensionality; insight; machine learning model; molecular imaging; nanoplasmonic; new technology; parent project; pre-clinical; sensor technology; tumor

The parent R01 project aims to advance nanoplasmonic sensing technology for robust multiplexed extracellular vesicle (EV) analysis and good reproducibility. The developed technology is validated using well- established preclinical and clinical samples to demonstrate the feasibility and potential of the new technology for clinical applications. In the course of the project, we have generated a large amount of imaging data from cells and EVs that are originating tumor and non-tumor models, as well as human clinical samples. This provides a new opportunity to develop a deep learning model to analyze high-dimensional and high-variate imaging data for machine-derived classification and uncover new insights. For robust deep-learning models, however, the quality of training data, besides the quantity of the data, is critical. Unbalanced data or embedded technical confounding factors often lead to the deep- or machine-learning models' decisions based on non- related or arbitrary parameters. Another problem is making false classifications when new input data is different from the data used for training, which is called out-of-distribution samples. These issues significantly hampered the deep-learning models' robustness with variable results and accuracies, resulting in disappointment and reduced enthusiasm for using AI models. The goal of this Administrative Supplement to the Parent R01 project is to develop a deep-learning-based data management software that digests massive cellular and molecular imaging data and produces balanced, confounder-free data sets ready for new deep- or machine-learning tasks. Specifically, we will design the algorithm for general users who do not necessarily have knowledge of the deep-learning framework. We will apply and test the software for multi-channel EV imaging data generated from the Parent R01 project and other cellular and EV imaging data from the past NIH project and other laboratories. The final software package and AI-ready data will be publicly shared with other researchers, and we will continuously provide feedback and updates through our IT core team. We envision that the software will offer a unique opportunity for researchers to create quality training data ready, reduce the technical barrier for researchers, and promote the use of their data for deep- or machine-learning models.

家长R01项目旨在推进纳米质感应技术，以促进稳健的多路复用 细胞外囊泡（EV）分析和良好的可重复性。使用良好的技术验证了开发的技术 建立的临床前和临床样品，以证明新技术的可行性和潜力 用于临床应用。在项目过程中，我们从 起源于肿瘤和非肿瘤模型以及人类临床样品的细胞和电动汽车。这 提供了一个新的机会来开发深度学习模型来分析高维和高变化 成像机器衍生分类和发现新见解的数据。对于强大的深度学习模型， 但是，除了数据的数量外，培训数据的质量至关重要。不平衡的数据或嵌入 技术混杂因素通常会导致基于非 - 相关或任意参数。另一个问题是当新输入数据不同时进行错误的分类 从用于培训的数据中，这被称为分布样本。这些问题严重阻碍了 深度学习模型的鲁棒性具有可变的结果和准确性，从而导致失望和 使用AI模型的热情降低了。对父母R01项目的行政补充的目标 是开发基于深度学习的数据管理软件，该软件可以消化大量的细胞和分子 成像数据并产生平衡的无混杂数据集，准备新的深入或机器学习 任务。具体而言，我们将为不一定了解的普通用户设计算法 深度学习框架。我们将应用并测试该软件生成的多通道电动汽车成像数据 来自Parent R01项目以及过去的NIH项目和其他其他蜂窝和电动汽车成像数据 实验室。最终的软件包和AI-Ready数据将与其他研究人员公开共享， 我们将通过IT核心团队不断提供反馈和更新。我们设想该软件将 为研究人员提供了一个独特的机会，可以准备好创建高质量的培训数据，减少技术障碍 研究人员，并促进其数据用于深层或机器学习模型。

项目成果

Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham.

• DOI: 10.1371/journal.pone.0277572
• 发表时间: 2023
• 期刊: PloS one
• 影响因子: 3.7

Recent Advances in Molecular Diagnosis of Pulmonary Fibrosis for Precision Medicine.

• DOI: 10.1021/acsptsci.2c00028
• 发表时间: 2022-07
• 期刊: ACS pharmacology & translational science
• 影响因子: 6
• 作者: M. Jeong;H. Han;D. Lagares;H. Im

Plasmon-Enhanced Characterization of Single Extracellular Vesicles.

• DOI: 10.1007/978-1-0716-3203-1_1
• 发表时间: 2023
• 期刊: Methods in molecular biology (Clifton, N.J.)

Dual-Enhanced Plasmonic Biosensing for Point-of-Care Sepsis Detection.

• DOI: 10.1021/acsnano.2c10371
• 发表时间: 2023-02-28
• 期刊: ACS NANO
• 影响因子: 17.1
• 作者: Chin, Lip Ket;Yang, Jun-Yeong;Chousterman, Benjamin;Jung, Sunghoon;Kim, Do-Geun;Kim, Dong-Ho;Lee, Seunghun;Castro, Cesar M.;Weissleder, Ralph;Park, Sung-Gyu;Im, Hyungsoon
• 通讯作者: Im, Hyungsoon

CRISPR/Cas13a-Based MicroRNA Detection in Tumor-Derived Extracellular Vesicles.

• DOI: 10.1002/advs.202301766
• 发表时间: 2023-08
• 期刊: ADVANCED SCIENCE
• 影响因子: 15.1
• 作者: Hong, Jae-Sang;Son, Taehwang;Castro, Cesar M. M.;Im, Hyungsoon
• 通讯作者: Im, Hyungsoon