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

该项目是英国斯旺西大学的研究人员与美国哈佛大学博德研究所和麻省理工学院的科学家合作完成的。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捉数千张细胞图像。理论上，可以对图像进行分析，以精确测量与细胞形态学相关的数百个特征；该项目将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。该软件将在几个演示实验中进行开发、改进和验证，包括细胞周期、原代血液成分细胞、免疫细胞激活和干细胞身份。目标将是使用尽可能少的荧光生物标记物，甚至不使用荧光生物标记物，从而消除对细胞的干扰。由此产生的开源软件将免费提供给全世界的科学家用于应用和临床研究，并将附有用户友好的培训材料和面对面的研讨会。该项目是合作和跨学科的，包括通过现有的无国界科学家计划培训计算生物学的早期职业科学家。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团队之前成功的生物数据挖掘跨学科合作的基础上。为了设计新的软件和方法来挖掘使用成像流式细胞术获得的大型数据集，该团队将开发算法来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，碎片和模糊），并量化每个细胞（通常是数千）的形态参数（通常是数百），包括各种尺寸，形状和纹理的测量。利用这些特征，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，以驱动项目合作伙伴在大量生物研究中使用成像流式细胞术的生物项目。目标将是使用尽可能少的荧光生物标记物，甚至不使用荧光生物标记物，从而消除对细胞的干扰。该项目将为科学界提供一个经过验证的开源软件工具箱，其中包含图像处理和机器学习算法，生物学家可以随时使用。

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.