EAGER: Mobile-phone based single molecule imaging of DNA and length quantification to analyze copy-number variations in genome
EAGER:基于手机的 DNA 单分子成像和长度定量分析基因组中的拷贝数变异
基本信息
- 批准号:1444240
- 负责人:
- 金额:$ 30万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2014
- 资助国家:美国
- 起止时间:2014-10-01 至 2016-09-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
PI: Ozcan, AydoganInstitution: University of California-Los AngelesProposal number: 1444240Title: EAGER: Mobile-phone based single molecule imaging of DNA and length quantification to analyze copy-number variations in genomeThe aim of this proposal is to create a transformative fluorescent microscopy system that is integrated with next generation mobile-phones for imaging single DNA molecules. This field-portable imaging interface running on a smartphone will have the sensitivity and contrast to image single molecule DNA fragments over a large field of view. Demonstrating DNA imaging on a state-of-the-art mobile-phone would serve as a stepping stone to next-generation mobile micro-analysis, sensing and diagnostic tools and could lead to single molecule DNA sequencing on a smartphone.The proposed design will have the capability to be broadly used in various clinical applications including early detection of cancers (e.g. stomach and brain), nervous system disorders and drug resistance in infectious diseases. This cellphone based single molecule imaging, DNA platform could also assist health-care professionals, epidemiologists and policy makers to track emerging trends and shed more light on cause-effect relationships.Intellectual Description:Single molecule imaging and DNA length quantification, both of which are currently not feasible using mobile-phone based imaging systems; require extreme detection sensitivity, signal-to-noise ratio (SNR), spatial resolution and automated sample handling and processing interfaces. To provide a transformative solution to these important tasks, the PI will design a multifunctional portable imaging device installed on a smartphone which will allow sample preparation and single molecule imaging within the same opto-mechanical attachment. This fluorescence microscope on a smartphone will be designed by integrating a laser diode, a disposable nano-channel chip, an external lens and a thin-film based emission filter in a robust attachment created by 3D printing techniques. High SNR fluorescence signal detection will be achieved by implementing high-angle/oblique illumination so that the direct excitation beam will not enter the low NA collection lens. They will also develop a compressive sampling based DNA length-estimation method which will utilize (i) the measured point spread function of the fluorescent microscope on the mobile-phone; (ii) the spatial sparsity of the objects (fluorescently labeled DNA molecules); and (iii) the linearity of the stretched DNA molecules within the field of view as a-priori constraints to estimate the length of the DNA fragment of interest with an accuracy that is significantly better than the resolution of their initial imaging system.
PI:Ozcan,Aydoganinstitution:加利福尼亚大学洛杉矶分校编号:1444240TITLE:急切:基于移动电话的DNA和长度量化的基于移动电话的单分子成像,以分析该建议的基因组目标中的拷贝数变化,以创建与下一代群体相结合的Moles-Molecte Imparters Mobiles的基因组成。在智能手机上运行的这种现场可容纳的成像接口将具有与图像单分子DNA片段在大型视野上的敏感性和对比。在最先进的手机上演示DNA成像将成为下一代移动微型分析,感测和诊断工具的垫脚石,并可能导致在智能手机上进行单分子DNA测序。所提出的设计将具有在各种临床应用中广泛使用的能力,包括在各种临床应用中使用癌症,包括癌症和癌症的癌症(例如,癌症和脑袋)(例如,胃部和脑袋)。这款基于手机的单分子成像,DNA平台还可以协助医疗保健专业人员,流行病学家和决策者跟踪新兴趋势,并更加了解因果关系的关系。智能描述:单分子成像和DNA长度量化,目前使用基于移动电话的成像系统不可行的是目前不可行的;需要极端的检测灵敏度,信噪比(SNR),空间分辨率和自动样品处理和处理界面。为了为这些重要任务提供变革性的解决方案,PI将设计安装在智能手机上的多功能便携式成像设备,该设备将允许样品制备和同一光学机械附件中的单分子成像。智能手机上的荧光显微镜将通过集成激光二极管,一次性纳米通道芯片,外部镜头和基于薄膜的发射过滤器,以由3D打印技术创建的强大附件。高SNR荧光信号检测将通过实现高角度/倾斜照明来实现,以使直接激发光束不会进入低Na收集镜头。他们还将开发一种基于压缩抽样的DNA长度估计方法,该方法将利用(i)移动电话上荧光显微镜的测量点扩散功能; (ii)物体的空间稀疏性(荧光标记为DNA分子); (iii)视野中拉伸的DNA分子的线性性作为APRIORI的约束,以估计感兴趣的DNA片段的长度,其精度明显优于其初始成像系统的分辨率。
项目成果
期刊论文数量(0)
专著数量(0)
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会议论文数量(0)
专利数量(0)
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Aydogan Ozcan其他文献
All-Optical Computing of a Group of Linear Transformations Using a Polarization Multiplexed Diffractive Neural Network
使用偏振复用衍射神经网络对一组线性变换进行全光计算
- DOI:
10.1364/cleo_si.2023.sm3j.3 - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Jingxi Li;Yi;Onur Kulce;Deniz Mengu;Aydogan Ozcan - 通讯作者:
Aydogan Ozcan
An insertable glucose sensor using a compact and cost-effective phosphorescence lifetime imager and machine learning
一种插入式葡萄糖传感器,采用紧凑且经济高效的磷光寿命成像仪和机器学习
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Artem Goncharov;Z. Gorocs;Ridhi Pradhan;B. Ko;Ajmal Ajmal;Andres Rodriguez;David Baum;Marcell Veszpremi;Xilin Yang;Maxime Pindrys;Tianle Zheng;Oliver Wang;Jessica Ramella;Michael J. McShane;Aydogan Ozcan - 通讯作者:
Aydogan Ozcan
Time-Domain Terahertz Video Captured with a Plasmonic Photoconductive Focal-Plane Array
使用等离激元光电导焦平面阵列捕获的时域太赫兹视频
- DOI:
10.1364/cleo_at.2023.jth2a.113 - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Xurong Li;Deniz Mengu;Aydogan Ozcan;M. Jarrahi - 通讯作者:
M. Jarrahi
Deep Learning to Refocus 3D Images
深度学习重新聚焦 3D 图像
- DOI:
10.1364/opn.31.12.000057 - 发表时间:
2020 - 期刊:
- 影响因子:0
- 作者:
Yichen Wu;Y. Rivenson;Hongda Wang;Yilin Luo;Eyal Ben;L. Bentolila;C. Pritz;Aydogan Ozcan - 通讯作者:
Aydogan Ozcan
Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network
使用衍射光网络的多光谱定量相位成像
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:7.4
- 作者:
Che;Jingxi Li;Deniz Mengu;Aydogan Ozcan - 通讯作者:
Aydogan Ozcan
Aydogan Ozcan的其他文献
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{{ truncateString('Aydogan Ozcan', 18)}}的其他基金
PFI-TT: A Rapid Multiplexed Diagnostic Tool for Serology of Tick-Borne Diseases
PFI-TT:蜱传疾病血清学快速多重诊断工具
- 批准号:
2345816 - 财政年份:2024
- 资助金额:
$ 30万 - 项目类别:
Continuing Grant
Biopsy-free, label-free 3D virtual histology of intact skin
完整皮肤的免活检、免标记 3D 虚拟组织学
- 批准号:
2141157 - 财政年份:2022
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
Deep learning-based serological test for point-of-care analysis of COVID-19 immunity with a paper-based multiplexed sensor
基于深度学习的血清学测试,使用纸基多重传感器对 COVID-19 免疫力进行即时分析
- 批准号:
2149551 - 财政年份:2022
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
I-Corps: Multiplexed paper-based test for rapid diagnosis of early-stage Lyme Disease
I-Corps:用于快速诊断早期莱姆病的多重纸质测试
- 批准号:
2055749 - 财政年份:2021
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
EAGER: High-throughput early detection and analysis of COVID-19 plaque formation using time-lapse coherent imaging and deep learning
EAGER:使用延时相干成像和深度学习对 COVID-19 斑块形成进行高通量早期检测和分析
- 批准号:
2034234 - 财政年份:2020
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
EAGER: All-Optical Information Processing Device for Seeing Through Diffusers at the Speed of Light
EAGER:以光速透过漫射器的全光学信息处理装置
- 批准号:
2054102 - 财政年份:2020
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
NSF EAGER: DEEP LEARNING-BASED VIRTUAL HISTOLOGY STAINING OF TISSUE SAMPLES
NSF EAGER:基于深度学习的组织样本虚拟组织学染色
- 批准号:
1926371 - 财政年份:2019
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
PFI:BIC Human-Centered Smart-Integration of Mobile Imaging and Sensing Tools with Machine Learning for Ubiquitous Quantification of Waterborne and Airborne Nanoparticles
PFI:BIC 以人为中心的移动成像和传感工具与机器学习的智能集成,可实现水性和空气性纳米粒子的普遍定量
- 批准号:
1533983 - 财政年份:2015
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
EFRI-BioFlex: Cellphone-based Digital Immunoassay Platform for High-throughput Sensitive and Multiplexed Detection and Distributed Spatio-Temporal Analysis of Influenza
EFRI-BioFlex:基于手机的数字免疫分析平台,用于流感的高通量灵敏多重检测和分布式时空分析
- 批准号:
1332275 - 财政年份:2013
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
CAREER: A new Telemedicine Platform using Incoherent Lensfree Cell Holography and Microscopy On a Chip
事业:使用非相干无透镜细胞全息术和芯片显微镜的新型远程医疗平台
- 批准号:
0954482 - 财政年份:2010
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
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