Tools for automated cell identification and cell lineage tracking

自动细胞识别和细胞谱系追踪工具

• 批准号: EP/J00619X/1
• 负责人: Paul Rees
• 金额: $ 10.11万
• 依托单位: Swansea University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FJ00619X%2F1
• 关键词: Tools; automated; cell; identification; lineage

This project is a collaboration between the Broad Institute, Amnis, Cancer Research UK and the Centre for Nanohealth in Swansea. The sabbatical will allow Professor Paul Rees to visit both the Broad Institute and Amnis in order to develop new tools which will be used to measure cell lineages and to automate the identification of specific cell types in large cell populations. These tools will be tailored to the needs of clinicians by collaborating with Cancer Research UK, Broad Institute affiliated hospitals and a range of collaborators in the UK. However the aim is to foster long term collaboration between the two US partners, the group at Swansea and CRUK, London Research Institute. Therefore we have developed a long term research programme which will be instigated by Paul Rees's visit and sustained by future planned visits for the other members of the team in Swansea and provision for the US partners to visit the Swansea.The function of an organism is determined by the evolution of a cell population all descended from a single progenitor cell. The lineage (relationship tree) of a cell population evolving from one progenitor cell is often used as a measure of that cell population or organism's health. The most appropriate method of determining lineage is to take time lapse images using microscopy (bright field) of the cell population. The cell movement is tracked and mitosis events identified and the appropriate relationship between parent and daughter cells noted. This can be done manually or recently researchers are developing automated cell tracking algorithms. However this process is computationally intensive and fails if the cells move out of focus or if the cell boundary has a low contrast compared with the background. For this project our idea is to simply use the florescent endosomes as a surrogate marker for the cell so by simply tracking the endosomes we track the cell. Many clinical and research applications rely on the identification of a particular cell type with a large cell population. One of the best techniques for this type of application is flow cytometry where cells flow past a laser and the scattered light is detected. This allows the cell size and structure to be measured together with the fluorescence from markers which can label cell structure and function. At Swansea we use the recently developed imaging flow cytometer which is a hybrid system that enables each individual cell within a cell population to be imaged at very high speeds by flowing the cells in a fluid between an exciting laser and a camera. This is an ideal platform identifying specific cell types by image analysis rather than the intensity of a fluorescent marker or scatter signal which provides no spatial information and is a more ambiguous indirect measure of the cell property. However the very nature of imaging cytometry means any measurement requires the user to effectively process the vast number of images to detect the traits of cells required. This is usually done manually using the basic image processing tools supplied with the cytometer and each idividual image is inspected to check for target cells which is incredibly time consuming with cell populations often in excess of 10^6. As a second project we aim to develop a tool which uses both evolutionary algorithms (or genetic programming) and machine learning to determine which image processing algorithms (and combinations of algorithms) best distinguish between the target cells and non target cells. The algorithms used will be compatible with the current IDEAS imagestream software provided freely by Amnis (which currently only allows simple user driven masking filters to assess cells) to allow the inclusion of advance machine learning and evolutionary algorithms into the IDEAS platform.

该项目是布罗德研究所，Amnis，英国癌症研究中心和斯旺西纳米健康中心之间的合作。休假将允许Paul Rees教授访问布罗德研究所和Amnis，以开发新的工具，用于测量细胞谱系并自动识别大型细胞群中的特定细胞类型。这些工具将通过与英国癌症研究所、布罗德研究所附属医院和英国的一系列合作者合作，根据临床医生的需求量身定制。然而，其目的是促进两个美国合作伙伴之间的长期合作，该集团在斯旺西和CRUK，伦敦研究所。因此，我们制定了一项长期的研究计划，该计划将由Paul Rees的访问发起，并由斯旺西团队的其他成员的未来计划访问以及美国合作伙伴访问斯旺西的规定来维持。生物体的功能是由细胞群的进化决定的，这些细胞群都是由单个祖细胞进化而来的。从一个祖细胞进化而来的细胞群的谱系（关系树）通常被用作该细胞群或生物体健康的量度。确定谱系的最合适的方法是使用显微镜（亮场）拍摄细胞群的延时图像。跟踪细胞运动，鉴定有丝分裂事件，并记录母细胞和子细胞之间的适当关系。这可以手动完成，或者最近研究人员正在开发自动细胞跟踪算法。然而，该过程是计算密集型的，并且如果细胞移出焦点或者如果细胞边界与背景相比具有低对比度，则该过程失败。对于这个项目，我们的想法是简单地使用荧光素内体作为细胞的替代标记，因此通过简单地跟踪内体，我们跟踪细胞。许多临床和研究应用依赖于鉴定具有大细胞群体的特定细胞类型。这种类型的应用的最佳技术之一是流式细胞术，其中细胞流过激光并检测散射光。这允许测量细胞大小和结构以及来自标记细胞结构和功能的标记物的荧光。在斯旺西，我们使用最近开发的成像流式细胞仪，这是一个混合系统，使每个细胞内的细胞群体被成像在非常高的速度通过流动的细胞之间的流体激发激光和相机。这是通过图像分析而不是荧光标记物或散射信号的强度来识别特定细胞类型的理想平台，荧光标记物或散射信号不提供空间信息并且是细胞性质的更模糊的间接测量。然而，成像细胞术的本质意味着任何测量都需要用户有效地处理大量图像，以检测所需细胞的特性。这通常使用细胞仪提供的基本图像处理工具手动完成，并且检查每个单独的图像以检查靶细胞，这对于通常超过10^6的细胞群是非常耗时的。作为第二个项目，我们的目标是开发一种工具，它使用进化算法（或遗传编程）和机器学习来确定哪些图像处理算法（和算法组合）最好地区分目标细胞和非目标细胞。所使用的算法将与Amnis免费提供的当前IDEAS图像分析软件兼容（目前仅允许简单的用户驱动掩蔽过滤器评估细胞），以允许将先进的机器学习和进化算法纳入IDEAS平台。

项目成果

Reconstructing cell cycle and disease progression using deep learning.

• DOI: 10.1038/s41467-017-00623-3
• 发表时间: 2017-09-06
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Eulenberg P;Köhler N;Blasi T;Filby A;Carpenter AE;Rees P;Theis FJ;Wolf FA
• 通讯作者: Wolf FA

An Analysis of the Practicalities of Multi-Color Nanoparticle Cellular Bar-Coding.

多色纳米颗粒细胞条形码的实用性分析。

• DOI: 10.2174/1386207319666160408150649
• 发表时间: 2016
• 期刊: Combinatorial chemistry & high throughput screening
• 影响因子: 1.8
• 作者: Rees P

Nanoparticle vesicle encoding for imaging and tracking cell populations.

用于成像和跟踪细胞群的纳米颗粒囊泡编码。

• DOI: 10.1038/nmeth.3105
• 发表时间: 2014
• 期刊: Nature methods
• 影响因子: 48
• 作者: Rees P

Label-free cell cycle analysis for high-throughput imaging flow cytometry.

• DOI: 10.1038/ncomms10256
• 发表时间: 2016-01-07
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Blasi T;Hennig H;Summers HD;Theis FJ;Cerveira J;Patterson JO;Davies D;Filby A;Carpenter AE;Rees P
• 通讯作者: Rees P

A New Imaging Platform for Visualizing Biological Effects of Non-Invasive Radiofrequency Electric-Field Cancer Hyperthermia.

• DOI: 10.1371/journal.pone.0136382
• 发表时间: 2015
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Corr SJ;Shamsudeen S;Vergara LA;Ho JC;Ware MJ;Keshishian V;Yokoi K;Savage DJ;Meraz IM;Kaluarachchi W;Cisneros BT;Raoof M;Nguyen DT;Zhang Y;Wilson LJ;Summers H;Rees P;Curley SA;Serda RE
• 通讯作者: Serda RE