Multi-Modal Data-Driven Platform for Multiplexed Cellular Antigen Classification using Nano-electronic Barcoded Particles for Whole Blood Applications

使用纳米电子条形码颗粒进行全血应用的多重细胞抗原分类的多模态数据驱动平台

• 批准号: 2002511
• 负责人: Umer Hassan
• 金额: $ 50万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2002511&HistoricalAwards=false
• 关键词: Multi; Modal; Data; Driven; Platform

This project is to develop a platform that can classify human leukocytes, which play a critical role in the body’s defense against a plethora of diseases like sepsis, cancer, and other chronic and acute diseases. State-of-the-art healthcare facilities rely on bulky and costly instruments which require manual sample processing and highly trained technical staff to perform blood analysis. In this work, an artificial intelligence enabled data-driven biosensor platform will be developed for hematology analysis. It will be based on multi-modal sensing, integrated microfluidics, and the ability to perform automated sample processing from whole blood samples. Furthermore, the proposed sensing platform will be equipped with real-time measurement capability and machine learning models to train the sensors data and provide reconfigurability and resource optimization as required. The proposed dynamic reconfigurable data driven biosensor will advance biomedical research and will have great potential to benefit human health and welfare. This cross disciplinary project will train undergraduate and graduate students in areas of sensors, systems, and bionanotechnology. The project will enable the integration of research into educational efforts directed towards engineering students. The PIs outreach activities will include engaging K-12 students, the local health-care industry, and the general public through educational lectures and making them available online for broad dissemination of knowledge. The proposal will enable the development of a next generation in-vitro diagnostic platform equipped with multi-model sensing and nano-barcoded particles to perform reconfigurable biomarker selection in whole blood samples. Human blood cells play a critical role in immune system activation in response to infections. The concentration of these immune cells in whole blood and their membrane receptor densities may change in different diseases and their respective pathogenesis. The heterogeneity of the cellular classification needs to be quantified to provide a personalized diagnostics and monitoring system for patients in a hospital setting. The biosensing platform will be integrated with multi-modal sensing including electrical and optical detectors which will allow to correct for inherent device-to-device variation to improve sensor performance. Immune cells conjugated with functionalized nano-barcoded particles will be quantified simultaneously with an impedance detector and smartphone image sensor. Further, the proposed biosensor will be equipped with real-time data analysis using machine learning to enable a reconfigurable system for resource optimization and biomarker selection. An integrated biochip will be used to perform reconfigurable multiplexing and quantify multiple inflammatory biomarkers from patient blood samples. The proposed sensor will enable multiplexed cellular antigen classification from a drop of whole blood with time to result (TOR) for less than 30 minutes. Sensors will be benchmarked with patient clinical samples. Furthermore, it is envisioned that the biosensor platform to be generic and reconfigurable with pre-functionalized cartridges that can be swapped out for different infectious diseases.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在开发一个可以对人类白细胞进行分类的平台，人类白细胞在人体防御败血症、癌症以及其他慢性和急性疾病等多种疾病方面发挥着关键作用。最先进的医疗机构依赖于笨重且昂贵的仪器，这些仪器需要手动样本处理和训练有素的技术人员来进行血液分析。在这项工作中，将开发一个支持人工智能的数据驱动生物传感器平台，用于血液学分析。它将基于多模式传感、集成微流体以及对全血样本进行自动样本处理的能力。此外，所提出的传感平台将配备实时测量能力和机器学习模型，以训练传感器数据并根据需要提供可重构性和资源优化。所提出的动态可重构数据驱动生物传感器将推进生物医学研究，并将具有造福人类健康和福祉的巨大潜力。这个跨学科项目将培训传感器、系统和生物纳米技术领域的本科生和研究生。该项目将使研究融入针对工程专业学生的教育工作中。 PI 的外展活动将包括通过教育讲座吸引 K-12 学生、当地医疗保健行业和公众参与，并在网上提供这些讲座以广泛传播知识。该提案将有助于开发配备多模型传感和纳米条形码颗粒的下一代体外诊断平台，以在全血样本中执行可重构的生物标志物选择。人类血细胞在免疫系统响应感染的激活中发挥着关键作用。全血中这些免疫细胞的浓度及其膜受体密度可能在不同的疾病及其各自的发病机制中发生变化。需要量化细胞分类的异质性，以便为医院环境中的患者提供个性化的诊断和监测系统。该生物传感平台将与包括电气和光学探测器在内的多模态传感集成，这将允许纠正固有的设备间差异，以提高传感器性能。与功能化纳米条形码颗粒结合的免疫细胞将通过阻抗检测器和智能手机图像传感器同时进行定量。此外，所提出的生物传感器将配备使用机器学习的实时数据分析功能，以实现用于资源优化和生物标志物选择的可重构系统。集成生物芯片将用于执行可重新配置的多重分析并量化患者血液样本中的多种炎症生物标志物。所提出的传感器将能够在不到 30 分钟的时间内对一滴全血进行多重细胞抗原分类，并得到结果 (TOR)。传感器将以患者临床样本为基准。此外，预计生物传感器平台将是通用的，并且可以通过预功能化的盒进行重新配置，这些盒可以针对不同的传染病进行更换。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Time-domain signal averaging to improve microparticles detection and enumeration accuracy in a microfluidic impedance cytometer.

• DOI: 10.1002/bit.27910
• 发表时间: 2021-11
• 期刊: Biotechnology and bioengineering
• 影响因子: 3.8
• 作者: Ashley BK;Hassan U
• 通讯作者: Hassan U

Functionalization of hybrid surface microparticles for in vitro cellular antigen classification.

用于体外细胞抗原分类的杂交表面微粒的功能化。

• DOI: 10.1007/s00216-020-03026-4
• 发表时间: 2021-01
• 期刊: Analytical and bioanalytical chemistry
• 影响因子: 4.3
• 作者: Ashley BK;Sui J;Javanmard M;Hassan U
• 通讯作者: Hassan U

Point-of-critical-care diagnostics for sepsis enabled by multiplexed micro and nanosensing technologies.

• DOI: 10.1002/wnan.1701
• 发表时间: 2021-09
• 期刊: Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology
• 作者: Ashley BK;Hassan U
• 通讯作者: Hassan U