表型
人口
聚类分析
系列(地层学)
细胞
生物
细胞命运测定
计算机科学
计算生物学
人工智能
遗传学
转录因子
古生物学
基因
人口学
社会学
作者
Maciej Dobrzyński,Marc‐Antoine Jacques,Olivier Pertz
出处
期刊:Methods in molecular biology
日期:2022-01-01
卷期号:: 183-206
标识
DOI:10.1007/978-1-0716-2277-3_13
摘要
Fluorescent live cell time-lapse microscopy is steadily contributing to our better understanding of the relationship between cell signaling and fate. However, large volumes of time-series data generated in these experiments and the heterogenous nature of signaling responses due to cell-cell variability hinder the exploration of such datasets. The population averages insufficiently describe the dynamics, yet finding prototypic dynamic patterns that relate to different cell fates is difficult when mining thousands of time-series. Here we demonstrate a protocol where we identify such dynamic phenotypes in a population of PC-12 cells that respond to a range of sustained growth factor perturbations. We use Time-Course Inspector, a free R/Shiny web application to explore and cluster single-cell time-series.
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