York Biomedical Research Institute, University of York, York, UK.
Department of Biology, University of York, York, UK.
Nat Commun. 2023 Apr 3;14(1):1854. doi: 10.1038/s41467-023-37447-3.
With phenotypic heterogeneity in whole cell populations widely recognised, the demand for quantitative and temporal analysis approaches to characterise single cell morphology and dynamics has increased. We present CellPhe, a pattern recognition toolkit for the unbiased characterisation of cellular phenotypes within time-lapse videos. CellPhe imports tracking information from multiple segmentation and tracking algorithms to provide automated cell phenotyping from different imaging modalities, including fluorescence. To maximise data quality for downstream analysis, our toolkit includes automated recognition and removal of erroneous cell boundaries induced by inaccurate tracking and segmentation. We provide an extensive list of features extracted from individual cell time series, with custom feature selection to identify variables that provide greatest discrimination for the analysis in question. Using ensemble classification for accurate prediction of cellular phenotype and clustering algorithms for the characterisation of heterogeneous subsets, we validate and prove adaptability using different cell types and experimental conditions.
鉴于整个细胞群体的表型异质性已得到广泛认可，因此需要采用定量和时变分析方法来描述单细胞形态和动力学。我们提出了 CellPhe，这是一种用于无偏细胞表型特征描述的模式识别工具包，适用于延时视频。CellPhe 可从多个分割和跟踪算法导入跟踪信息，从而实现来自不同成像模式（包括荧光）的自动细胞表型分析。为了最大限度地提高下游分析的数据质量，我们的工具包包括自动识别和消除由不准确的跟踪和分割引起的错误细胞边界。我们提供了从单个细胞时间序列中提取的大量特征，可进行自定义特征选择，以确定为相关分析提供最大区分度的变量。我们使用集成分类进行准确的细胞表型预测，以及聚类算法进行异质子集的特征描述，使用不同的细胞类型和实验条件进行验证和证明适应性。
