Single-Cell Imaging of ERK Signaling Using Fluorescent Biosensors

Pargett, Michael; Gillies, Taryn E.; Teragawa, Carolyn K.; Sparta, Breanne; Albeck, John G.

doi:10.1007/978-1-4939-7154-1_3

Cited by 33 publications

(42 citation statements)

References 23 publications

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“…Traditionally, cells are segmented by using a fixed intensity threshold [16][17][18][19] . Although this has been sufficient to segment yeast cells, mammalian cells exhibit large variation in their nuclear DNA resulting in variable DAPI intensities (Figure 2A).…”

Section: Nuclear Segmentation In Three Dimensionsmentioning

confidence: 99%

Automated cell boundary and 3D nuclear segmentation of cells in suspension

Kesler

Thiemicke

et al. 2019

Preprint

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To characterize cell types, cellular functions and intracellular processes, an understanding of the differences between individual cells is required. Although microscopy approaches have made tremendous progress in imaging cells in different contexts, the analysis of these imaging data sets is a long-standing, unsolved problem. The few robust cell segmentation approaches that exist often rely on multiple cellular markers and complex time-consuming image analysis. Recently developed deep learning approaches can address some of these challenges, but they require tremendous amounts of data and well-curated reference data sets for algorithm training. We propose an alternative experimental and computational approach, called CellDissect, in which we first optimize specimen 2 preparation and data acquisition prior to image processing to generate high quality images that are easier to analyze computationally. By focusing on fixed suspension and dissociated adherent cells, CellDissect relies only on widefield images to identify cell boundaries and nuclear staining to automatically segment cells in two dimensions and nuclei in three dimensions. This segmentation can be performed on a desktop computer or a computing cluster for higher throughput. We compare and evaluate the accuracy of different nuclear segmentation approaches against manual expert cell segmentation for different cell lines acquired with different imaging modalities.

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Section: Nuclear Segmentation In Three Dimensionsmentioning

confidence: 99%

Automated cell boundary and 3D nuclear segmentation of cells in suspension

Kesler

Thiemicke

et al. 2019

Preprint

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“…Time-lapse wide-field microscopy was performed as described previously (Hung et al, 2017b;Pargett et al, 2017). Briefly, 25000 cells were seeded one day prior to imaging in glass-bottom 96-well plates (Cellvis P96-1.5H-N, Mountain View, CA) pretreated with type I collagen (Gibco A10483-01) to promote cell adherence.…”

Section: Live-cell Fluorescence Microscopymentioning

confidence: 99%

Cell-to-cell variability in AMPK activation reveals autonomous cycles in cellular energy balance

Kosaisawe¹,

Sparta²,

Pargett³

et al. 2019

Preprint

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Cellular metabolism can be reconfigured to balance nutrient processing (catabolism) and cellular demands (anabolism). However, the kinetics of reconfiguration within individual mammalian cells, and heterogeneity between cells, have remained unexplored. Using live-cell imaging, we investigate the kinetics of bioenergetic adaptation in individual cells. In response to acute inhibition of oxidative phosphorylation, AMPK substrates are phosphorylated bimodally, identifying cells in different underlying states of anabolism and catabolism.Manipulation of glycolysis, insulin/Akt signaling, or protein synthesis shifts the distribution of these states.Long-term lineage analysis confirms that this cellular energy balance cycles over time within individual cells, independently of the cell division cycle. We further demonstrate that AMPK inhibits the ERK and mTOR cell growth signaling pathways specifically when anabolism is in excess. Our results reveal dynamic fluctuations of energetic balance, establish distinct time scales of cellular energetic control, and open opportunities for more precise prediction and control of cellular metabolic functions.

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“…The process with multiple reporters is nearly identical to that with a single reporter. See (Pargett et al, 2017) for a specific implementation focused on a single reporter in MCF-10A epithelial cells. The goal is to keep the cells viable in an environment accessible to the microscope with high throughput and the ability to add treatments.…”

Section: Basic Protocol 2: Time-lapse Imaging Of Living Cellsmentioning

confidence: 99%

“…Image processing procedures are complex with multiple layers of subroutines, so this protocol should be reviewed carefully to understand the full process prior to developing or implementing software. A detailed implementation for MATLAB is available in (Pargett et al, 2017). Alternative software solutions that perform the same functions are equally appropriate; for example, CellProfiler provides a publicly available solution with many of the features described here in a graphical user interface (Carpenter et al, 2006).…”

Section: Basic Protocol 3: Processing For Multi-channel Time-series Imentioning

confidence: 99%

See 1 more Smart Citation

Live‐Cell Imaging and Analysis with Multiple Genetically Encoded Reporters

Pargett

Albeck

2018

CP Cell Biology

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Genetically encoded live-cell reporters measure signaling pathway activity at the cellular level with high temporal resolution, often revealing a high degree of cell-to-cell heterogeneity. By using multiple spectrally distinct reporters within the same cell, signal transmission from one node to another within a signaling pathway can be analyzed to quantify factors such as signaling efficiency and delay. With other reporter configurations, correlation between different signaling pathways can be quantified. Such analyses can be used to establish the mechanisms and consequences of cell-to-cell heterogeneity and can inform new models of the functional properties of signaling pathways. In this unit, we describe an approach for designing and executing live-cell multiplexed reporter experiments. We also describe approaches for analyzing the resulting time-course data to quantify correlations and trends between the measured parameters at the single-cell level. © 2018 by John Wiley & Sons, Inc.

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Single-Cell Imaging of ERK Signaling Using Fluorescent Biosensors

Cited by 33 publications

References 23 publications

Automated cell boundary and 3D nuclear segmentation of cells in suspension

Automated cell boundary and 3D nuclear segmentation of cells in suspension

Cell-to-cell variability in AMPK activation reveals autonomous cycles in cellular energy balance

Live‐Cell Imaging and Analysis with Multiple Genetically Encoded Reporters

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