{"title":"Exploring heterogeneous cell population dynamics in different microenvironments by novel analytical strategy based on images.","authors":"Yihong Huang, Zidong Zhou, Tianqi Liu, Shengnan Tang, Xuegang Xin","doi":"10.1038/s41540-024-00459-w","DOIUrl":null,"url":null,"abstract":"<p><p>Understanding the dynamic states and transitions of heterogeneous cell populations is crucial for addressing fundamental biological questions. High-content imaging provides rich datasets, but it remains increasingly difficult to integrate and annotate high-dimensional and time-resolved datasets to profile heterogeneous cell population dynamics in different microenvironments. Using hepatic stellate cells (HSCs) LX-2 as model, we proposed a novel analytical strategy for image-based integration and annotation to profile dynamics of heterogeneous cell populations in 2D/3D microenvironments. High-dimensional features were extracted from extensive image datasets, and cellular states were identified based on feature profiles. Time-series clustering revealed distinct temporal patterns of cell shape and actin cytoskeleton reorganization. We found LX-2 showed more complex membrane dynamics and contractile systems with an M-shaped actin compactness trend in 3D culture, while they displayed rapid spreading in early 2D culture. This image-based integration and annotation strategy enhances our understanding of HSCs heterogeneity and dynamics in complex extracellular microenvironments.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":3.5000,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11542073/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"NPJ Systems Biology and Applications","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1038/s41540-024-00459-w","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Understanding the dynamic states and transitions of heterogeneous cell populations is crucial for addressing fundamental biological questions. High-content imaging provides rich datasets, but it remains increasingly difficult to integrate and annotate high-dimensional and time-resolved datasets to profile heterogeneous cell population dynamics in different microenvironments. Using hepatic stellate cells (HSCs) LX-2 as model, we proposed a novel analytical strategy for image-based integration and annotation to profile dynamics of heterogeneous cell populations in 2D/3D microenvironments. High-dimensional features were extracted from extensive image datasets, and cellular states were identified based on feature profiles. Time-series clustering revealed distinct temporal patterns of cell shape and actin cytoskeleton reorganization. We found LX-2 showed more complex membrane dynamics and contractile systems with an M-shaped actin compactness trend in 3D culture, while they displayed rapid spreading in early 2D culture. This image-based integration and annotation strategy enhances our understanding of HSCs heterogeneity and dynamics in complex extracellular microenvironments.
了解异质细胞群的动态状态和转变对于解决基本生物学问题至关重要。高含量成像技术提供了丰富的数据集,但要整合和注释高维和时间分辨数据集以剖析不同微环境中异质细胞群的动态变化却越来越难。我们以肝星状细胞(HSCs)LX-2为模型,提出了一种基于图像整合和注释的新型分析策略,以剖析2D/3D微环境中异质细胞群的动态。我们从大量图像数据集中提取了高维特征,并根据特征轮廓确定了细胞状态。时间序列聚类揭示了细胞形状和肌动蛋白细胞骨架重组的独特时间模式。我们发现,LX-2 在三维培养中表现出更复杂的膜动力学和收缩系统,其肌动蛋白紧密度呈 M 型趋势,而在早期二维培养中则表现出快速扩散。这种基于图像的整合和注释策略增强了我们对造血干细胞在复杂细胞外微环境中异质性和动态性的理解。
期刊介绍:
npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology.
We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.