Dataset Information

Automated analysis of time-lapse imaging of nuclear translocation by retrospective strategy and its application to STAT1 in HeLa cells.

ABSTRACT: Cell-based image analysis of time-lapse imaging is mainly challenged by faint fluorescence and dim boundaries of cellular structures of interest. To resolve these bottlenecks, a novel method was developed based on "retrospective" analysis for cells undergoing minor morphological changes during time-lapse imaging. We fixed and stained the cells with a nuclear dye at the end of the experiment, and processed the time-lapse images using the binary masks obtained by segmenting the nuclear-stained image. This automated method also identifies cells that move during the time-lapse imaging, which is a factor that could influence the kinetics measured for target proteins that are present mostly in the cytoplasm. We then validated the method by measuring interferon gamma (IFN?) induced signal transducers and activators of transcription 1 (STAT1) nuclear translocation in living HeLa cells. For the first time, automated large-scale analysis of nuclear translocation in living cells was achieved by our novel method. The responses of the cells to IFN? exhibited a significant drift across the population, but common features of the responses led us to propose a three-stage model of STAT1 import. The simplicity and automation of this method should enable its application in a broad spectrum of time-lapse studies of nuclear-cytoplasmic translocation.

SUBMITTER: Han F

PROVIDER: S-EPMC3220678 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Automated analysis of time-lapse imaging of nuclear translocation by retrospective strategy and its application to STAT1 in HeLa cells.

Han Fujun F Liang Peizhou P Wang Feifei F Zeng Lingyun L Zhang Biliang B

PloS one 20111118 11

Cell-based image analysis of time-lapse imaging is mainly challenged by faint fluorescence and dim boundaries of cellular structures of interest. To resolve these bottlenecks, a novel method was developed based on "retrospective" analysis for cells undergoing minor morphological changes during time-lapse imaging. We fixed and stained the cells with a nuclear dye at the end of the experiment, and processed the time-lapse images using the binary masks obtained by segmenting the nuclear-stained ima ...[more]

PMID: 22125613

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Project description:Rationale: Basal-like breast cancer (BLBC) is associated with high grade, distant metastasis, and poor prognosis; however, the mechanism underlying aggressiveness of BLBC is still unclear. Emerging evidence has suggested that phospholipid scramblase 1 (PLSCR1) is involved in tumor progression. Here, we aimed to study the possible involvement and molecular mechanisms of PLSCR1 contributing to the aggressive behavior of BLBC. Methods: The potential functions of PLSCR1 in breast cancer cells were assessed by Western blotting, colony formation, migration and invasion, Cell Counting Kit-8 assay, mammosphere formation and flow cytometry. The relationship between nuclear translocation of PLSCR1 and transactivation of STAT1 was examined by immunostaining, co-IP, ChIP, and quantitative reverse transcription PCR. The effect of PLSCR1 expression on BLBC cells was determined by in vitro and in vivo tumorigenesis and a lung metastasis mouse model. Results: Compared to other subtypes, PLSCR1 was considerably increased in BLBC. Phosphorylation of PLSCR1 at Tyr 69/74 contributed to the nuclear translocation of this protein. PLSCR1 was enriched in the promoter region of STAT1 and enhanced STAT3 binding to the STAT1 promoter, resulting in transactivation of STAT1; STAT1 then enhanced cancer stem cell (CSC)-like properties that promoted BLBC progression. The knockdown of PLSCR1 led to significant inhibitory effects on proliferation, migration, invasion, tumor growth and lung metastasis of BLBC cells. Clinically, high PLSCR1 expression was strongly correlated with large tumor size, high grade, metastasis, chemotherapy resistance, and poor survival, indicating poor prognosis in breast cancer patients. Conclusions: Our data show that overexpression and nuclear translocation of PLSCR1 provide tumorigenic and metastatic advantages by activating STAT1 signaling in BLBC. This study not only reveals a critical mechanism of how PLSCR1 contributes to BLBC progression, but also suggests potential prognostic indicators and therapeutic targets for this challenging disease.

Project description:Time-lapse imaging of cell colonies in microfluidic chambers provides time series of bioimages, i.e., biomovies. They show the behavior of cells over time under controlled conditions. One of the main remaining bottlenecks in this area of research is the analysis of experimental data and the extraction of cell growth characteristics, such as lineage information. The extraction of the cell line by human observers is time-consuming and error-prone. Previously proposed methods often fail because of their reliance on the accurate detection of a single cell, which is not possible for high density, high diversity of cell shapes and numbers, and high-resolution images with high noise. Our task is to characterize subpopulations in biomovies. In order to shift the analysis of the data from individual cell level to cellular groups with similar fluorescence or even subpopulations, we propose to represent the cells by two new abstractions: the particle and the patch. We use a three-step framework: preprocessing, particle tracking, and construction of the patch lineage. First, preprocessing improves the signal-to-noise ratio and spatially aligns the biomovie frames. Second, cell sampling is performed by assuming particles, which represent a part of a cell, cell or group of contiguous cells in space. Particle analysis includes the following: particle tracking, trajectory linking, filtering, and color information, respectively. Particle tracking consists of following the spatiotemporal position of a particle and gives rise to coherent particle trajectories over time. Typical tracking problems may occur (e.g., appearance or disappearance of cells, spurious artifacts). They are effectively processed using trajectory linking and filtering. Third, the construction of the patch lineage consists in joining particle trajectories that share common attributes (i.e., proximity and fluorescence intensity) and feature common ancestry. This step is based on patch finding, patching trajectory propagation, patch splitting, and patch merging. The main idea is to group together the trajectories of particles in order to gain spatial coherence. The final result of CYCASP is the complete graph of the patch lineage. Finally, the graph encodes the temporal and spatial coherence of the development of cellular colonies. We present results showing a computation time of less than 5?min for biomovies and simulated films. The method, presented here, allowed for the separation of colonies into subpopulations and allowed us to interpret the growth of colonies in a timely manner.

Dataset Information

Automated analysis of time-lapse imaging of nuclear translocation by retrospective strategy and its application to STAT1 in HeLa cells.

Publications

Automated analysis of time-lapse imaging of nuclear translocation by retrospective strategy and its application to STAT1 in HeLa cells.

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