Dataset Information

Quantifying E2F1 protein dynamics in single cells.

ABSTRACT: Background:E2F1 protein, a major effector of the Rb/E2F pathway plays a central role in regulating cell-fate decisions involved in proliferation, apoptosis, and differentiation. Its expression is highly dynamic and tightly modulated through a combination of transcriptional, translational and posttranslational controls. However, the mechanisms by which its expression and activity can promote different cellular outcomes remain to be fully elucidated. To better document E2F1 expression in live cells, we have engineered a series of fluorescent E2F1 protein reporters that quantitatively capture E2F1 protein dynamics. Methods:Reporter constructs, under the control of the mouse or human E2F1 proximal promoter, were designed to express an E2F1-Venus fusion protein incapable of binding DNA. In addition, constructs either included or excluded the 3' untranslated region (3'UTR) of the E2F1 gene. These constructs were introduced into fibroblasts and epithelial cells, and expression of the fusion reporter protein was validated and quantified in single cells using live imaging. Results:In all cases, expression of the reporter protein effectively recapitulated the behavior of E2F1 under various conditions, including cell cycle progression and genotoxic stress. No or little fluorescent signal of the reporter was detected in G0, but as the cycle progressed, expression of the reporter protein steadily increased in the nucleus, peaking a few hours before cell division, but declining to baseline 2-3 h prior to the onset of mitosis. The absence of the E2F1 3'UTR in the constructs led to considerably higher steady-state levels of the fusion protein, which although normally regulated, exhibited a slightly less complex dynamic profile during the cell cycle or genotoxic stress. Lastly, the presence or absence of Rb failed to impact the overall detection and levels of the reporter proteins. Conclusions:Our validated E2F1 protein reporters complement nicely other reporters of the Rb/E2F pathway and provide a unique tool to follow the complex dynamics of E2F1 expression in real time in single cells.

SUBMITTER: Mathey-Prevot B

PROVIDER: S-EPMC7295171 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Quantifying E2F1 protein dynamics in single cells.

Mathey-Prevot Bernard B Parker Bao-Tran BT Im Carolyn C Hong Cierra C Dong Peng P Yao Guang G You Lingchong L

Quantitative biology (Beijing, China) 20200306 1

<h4>Background</h4>E2F1 protein, a major effector of the Rb/E2F pathway plays a central role in regulating cell-fate decisions involved in proliferation, apoptosis, and differentiation. Its expression is highly dynamic and tightly modulated through a combination of transcriptional, translational and posttranslational controls. However, the mechanisms by which its expression and activity can promote different cellular outcomes remain to be fully elucidated. To better document E2F1 expression in l ...[more]

PMID: 32542116

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Project description:Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within time-lapse images exist, most require human input, are specialized to the experimental set up, or lack accuracy. Here, we introduce DeLTA 2.0, a purely Python workflow that can rapidly and accurately analyze images of single cells on two-dimensional surfaces to quantify gene expression and cell growth. The algorithm uses deep convolutional neural networks to extract single-cell information from time-lapse images, requiring no human input after training. DeLTA 2.0 retains all the functionality of the original version, which was optimized for bacteria growing in the mother machine microfluidic device, but extends results to two-dimensional growth environments. Two-dimensional environments represent an important class of data because they are more straightforward to implement experimentally, they offer the potential for studies using co-cultures of cells, and they can be used to quantify spatial effects and multi-generational phenomena. However, segmentation and tracking are significantly more challenging tasks in two-dimensions due to exponential increases in the number of cells. To showcase this new functionality, we analyze mixed populations of antibiotic resistant and susceptible cells, and also track pole age and growth rate across generations. In addition to the two-dimensional capabilities, we also introduce several major improvements to the code that increase accessibility, including the ability to accept many standard microscopy file formats as inputs and the introduction of a Google Colab notebook so users can try the software without installing the code on their local machine. DeLTA 2.0 is rapid, with run times of less than 10 minutes for complete movies with hundreds of cells, and is highly accurate, with error rates around 1%, making it a powerful tool for analyzing time-lapse microscopy data.

Dataset Information

Quantifying E2F1 protein dynamics in single cells.

Publications

Quantifying E2F1 protein dynamics in single cells.

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