14 NSFBIO: Mining of imaging flow cytometry data for label free, single cell analysis

14 NSFBIO：挖掘成像流式细胞术数据以进行无标记单细胞分析

• 批准号: BB/N005163/1
• 负责人: Paul Rees
• 金额: $ 18.85万
• 依托单位: Swansea University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FN005163%2F1
• 关键词: 14; NSFBIO; Mining; imaging; flow

The project is a collaboration between researchers at Swansea University, UK and scientists at the Broad Institute of Harvard and MIT, Cambridge, US. The project will develop and demonstrate software to mine data from imaging flow cytometers. These instruments can capture thousands of images of cells per second. The images can in theory be analyzed to precisely measure hundreds of features related to cellular morphology; this project is to develop advanced machine-learning software to accomplish this, unlocking the otherwise hidden information within the images. The software will be developed, improved, and validated in several demonstration experiments involving the cell cycle, the component cells of primary blood, immune cell activation, and stem cell identity. The goal will be to use as few or indeed no fluorescent biomarkers, eliminating the need to perturb cells. The resulting open-source software will be freely available to scientists worldwide for both applied and clinical research, and will be accompanied by user-friendly training materials and in-person workshops. The project is collaborative and interdisciplinary and includes training early career-stage scientists in computational biology, via the existing Scientists without Borders program. The project involves close collaboration with a host of researchers from both the UK and US who use imaging flow cytometers and builds on a previous successful interdisciplinary collaboration in biological data mining by the teams at the Broad Institute and Swansea University.In order to devise the novel software and methodology to mine the large datasets acquired using imaging flow cytometry, the team will develop algorithms to seamlessly import data from an imaging cytometer, robustly segment cells, quality-filter them (e.g., for debris and blur), and quantify morphological parameters (usually hundreds) for each cell (usually thousands), including various measures of size, shape, and texture. Using these features, trained machine-learning algorithms will identify cell phenotypes of interest or otherwise characterize the state of cell in driving biological projects from project partners who use imaging flow cytometry in a host of biological research studies. The goal will be to use as few or indeed no fluorescent biomarkers, eliminating the need to perturb cells. The project will give the scientific community a validated, open-source software toolbox of image processing and machine learning algorithms readily usable by biologists.

该项目是英国斯旺西大学的研究人员与美国剑桥哈佛和麻省理工学院布罗德研究所的科学家合作开展的。该项目将开发和演示从成像流式细胞仪中挖掘数据的软件。这些仪器每秒可以捕获数千张细胞图像。理论上可以分析图像，以精确测量与细胞形态相关的数百个特征;该项目旨在开发先进的机器学习软件来实现这一目标，解锁图像中隐藏的信息。该软件将在涉及细胞周期，原代血液的组成细胞，免疫细胞活化和干细胞身份的几个演示实验中进行开发，改进和验证。我们的目标是尽可能少地使用荧光生物标志物，甚至不使用荧光生物标志物，从而消除干扰细胞的需要。由此产生的开放源码软件将免费提供给世界各地的科学家，用于应用和临床研究，并将附带方便用户的培训材料和面对面的讲习班。该项目是合作和跨学科的，包括通过现有的科学家无国界计划培训计算生物学的早期职业阶段科学家。该项目涉及与来自英国和美国的许多研究人员的密切合作，他们使用成像流式细胞仪，并建立在布罗德研究所和斯旺西大学的团队在生物数据挖掘方面的成功跨学科合作的基础上。为了设计新颖的软件和方法来挖掘使用成像流式细胞仪获得的大型数据集，该团队将开发算法以无缝地从成像细胞仪导入数据，稳健地分割细胞，对它们进行质量过滤（例如，对于碎片和模糊），并量化每个细胞（通常数千个）的形态参数（通常数百个），包括大小、形状和纹理的各种测量。使用这些功能，经过训练的机器学习算法将识别感兴趣的细胞表型或以其他方式表征细胞状态，以推动项目合作伙伴的生物项目，这些项目合作伙伴在许多生物研究中使用成像流式细胞术。我们的目标是尽可能少地使用荧光生物标志物，甚至不使用荧光生物标志物，从而消除干扰细胞的需要。该项目将为科学界提供一个经过验证的开源图像处理和机器学习算法软件工具箱，供生物学家使用。

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

Reconstructing cell cycle and disease progression using deep learning

使用深度学习重建细胞周期和疾病进展

• DOI: 10.1101/081364
• 发表时间: 2016
• 作者: Eulenberg P

An open-source solution for advanced imaging flow cytometry data analysis using machine learning.

• DOI: 10.1016/j.ymeth.2016.08.018
• 发表时间: 2017-01-01
• 期刊: Methods (San Diego, Calif.)
• 作者: Hennig H;Rees P;Blasi T;Kamentsky L;Hung J;Dao D;Carpenter AE;Filby A
• 通讯作者: Filby A

CDK control pathways integrate cell size and ploidy information to control cell division.

• DOI: 10.7554/elife.64592
• 发表时间: 2021-06-11
• 期刊: eLife
• 影响因子: 7.7
• 作者: Patterson JO;Basu S;Rees P;Nurse P
• 通讯作者: Nurse P

Reduced Cationic Nanoparticle Cytotoxicity Based on Serum Masking of Surface Potential.

• DOI: 10.1166/jbn.2016.2134
• 发表时间: 2016-01
• 期刊: Journal of biomedical nanotechnology
• 影响因子: 2.9
• 作者: McConnell KI;Shamsudeen S;Meraz IM;Mahadevan TS;Ziemys A;Rees P;Summers HD;Serda RE
• 通讯作者: Serda RE