Bilateral BBSRC-NSF/BIO: Mining of imaging flow cytometry data for label-free, single cell analysis

该项目的目标是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以千计的细胞图像。从理论上讲，这些图像可以被分析来精确测量与细胞外观相关的数百个特征(形态)；这个项目是为了开发先进的机器学习软件来实现这一点，解锁图像中原本隐藏的信息。该软件将在几个演示实验中进行开发、改进和验证，这些实验涉及细胞周期、原始血液的组成细胞、免疫细胞激活和干细胞识别。其目标将是使用尽可能少的甚至不使用荧光生物标记物，消除对细胞的干扰。由此产生的开源软件将免费提供给世界各地的科学家进行应用和临床研究，并将伴随着用户友好的培训材料和面对面的研讨会。该项目是协作性和跨学科的，包括通过现有的无国界科学家计划培训职业生涯早期的计算生物学科学家。该项目涉及与使用成像流式细胞仪的研究人员密切合作，并建立在生物数据挖掘领域成功的跨学科工作的基础上。为了设计新的软件和方法来挖掘使用成像流式细胞仪获取的大数据集，该团队将开发算法来无缝地从成像细胞仪导入数据，对细胞进行强有力的分割，对它们进行质量过滤(例如，碎片和模糊)，并对每个细胞(通常是数千个)的形态参数(通常是数百个)进行量化，包括大小、形状和质地的各种测量。利用这些功能，训练有素的机器学习算法将识别感兴趣的细胞表型，或者以其他方式表征细胞在推动来自项目合作伙伴的生物项目中的状态，这些合作伙伴在一系列生物研究中使用成像流式细胞术。其目标将是使用尽可能少的甚至不使用荧光生物标记物，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、开放源代码的图像处理和机器学习算法软件工具箱，供生物学家随时使用。有关该项目的更多信息，请访问：http://www.broadinstitute.org/~anne/Carpenter_NSF_ImagingFlowCytometry.html

The goal of this project is to 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 a cell's appearance ("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 researchers using imaging flow cytometers and builds on successful interdisciplinary work in biological data mining. 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. More information about the project can be found at: http://www.broadinstitute.org/~anne/Carpenter_NSF_ImagingFlowCytometry.html