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使用基于像素的分类方法在高通量条件下对共培养细胞表型进行定量分析。

Quantifying co-cultured cell phenotypes in high-throughput using pixel-based classification.

作者信息

Logan David J, Shan Jing, Bhatia Sangeeta N, Carpenter Anne E

机构信息

The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, United States.

Harvard-MIT Division of Health Sciences and Technology, MIT, E25-518, 77 Massachusetts Ave, Cambridge, MA 02139, United States.

出版信息

Methods. 2016 Mar 1;96:6-11. doi: 10.1016/j.ymeth.2015.12.002. Epub 2015 Dec 11.

Abstract

Biologists increasingly use co-culture systems in which two or more cell types are grown in cell culture together in order to better model cells' native microenvironments. Co-cultures are often required for cell survival or proliferation, or to maintain physiological functioning in vitro. Having two cell types co-exist in culture, however, poses several challenges, including difficulties distinguishing the two populations during analysis using automated image analysis algorithms. We previously analyzed co-cultured primary human hepatocytes and mouse fibroblasts in a high-throughput image-based chemical screen, using a combination of segmentation, measurement, and subsequent machine learning to score each cell as hepatocyte or fibroblast. While this approach was successful in counting hepatocytes for primary screening, segmentation of the fibroblast nuclei was less accurate. Here, we present an improved approach that more accurately identifies both cell types. Pixel-based machine learning (using the software ilastik) is used to seed segmentation of each cell type individually (using the software CellProfiler). This streamlined and accurate workflow can be carried out using freely available and open source software.

摘要

生物学家越来越多地使用共培养系统,在这种系统中,两种或更多种细胞类型在细胞培养中一起生长,以便更好地模拟细胞的天然微环境。共培养通常是细胞存活或增殖所必需的,或者是为了在体外维持生理功能。然而,让两种细胞类型在培养中共存会带来几个挑战,包括在使用自动图像分析算法进行分析时难以区分这两个群体。我们之前在基于图像的高通量化学筛选中分析了共培养的原代人肝细胞和小鼠成纤维细胞,使用分割、测量以及随后的机器学习相结合的方法,将每个细胞分类为肝细胞或成纤维细胞。虽然这种方法在初级筛选中成功地对肝细胞进行了计数,但成纤维细胞核的分割不太准确。在这里,我们提出了一种改进的方法,能更准确地识别这两种细胞类型。基于像素的机器学习(使用ilastik软件)用于分别对每种细胞类型进行种子分割(使用CellProfiler软件)。这种简化且准确的工作流程可以使用免费的开源软件来执行。

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