Biology subjects

Goedhart, J.

Publications and source records attributed to Goedhart, J..

At least 37 records · Page 2Linked to original sources

bioRxiv2021

PlotXpress, a webtool for normalization and visualization of reporter expression data

In molecular cell biology, reporter assays are frequently used to investigate gene expression levels. Reporter assays employ a gene that encodes a light-emitting protein, of which the luminescence is quantified as a proxy of gene expression. Commercial parties provide reporter assay kits that include protocols and specialized detection machinery. However, downstream analysis of the output data and their presentation are not standardized. We have developed plotXpress to fill this gap, providing a free, open-source platform for the semi-automated analysis and standardized visualisation of experimental gene reporter data. Users can upload raw luminescence data acquired from a reporter gene assay with an internal control. In plotXpress, the data is corrected for sample variation with the internal control and the average for each condition is calculated. When a reference condition is selected the fold change is calculated for all other conditions, based on the selected reference. The results are shown as dot plots with a statistical summary, which can be adjusted to create publication-grade plots without requiring coding skills. Altogether, plotXpress is an open-source, low-threshold, web-based tool, that promotes a standardized and reproducible analysis while providing an appealing visualization of reporter data. The webtool can be accessed at: https://huygens.science.uva.nl/PlotXpress/ O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC="FIGDIR/small/451595v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1b499aborg.highwire.dtl.DTLVardef@1d4d375org.highwire.dtl.DTLVardef@186bac1org.highwire.dtl.DTLVardef@4ed7bc_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2021

A turquoise fluorescence lifetime-based biosensor for quantitative imaging of intracellular calcium

The most successful genetically encoded calcium indicators (GECIs) employ an intensity or intensiometric readout. Despite a large calcium-dependent change in fluorescence intensity, the quantification of calcium concentrations with GECIs is problematic, which is further complicated by the sensitivity of all GECIs to changes in the pH in the biological range. Here, we report on a novel sensing strategy in which a conformational change directly modifies the fluorescence quantum yield and fluorescence lifetime of a circular permutated turquoise fluorescent protein. The fluorescence lifetime is an absolute parameter that enables straightforward quantification, eliminating intensity-related artifacts. A new engineering strategy that optimizes lifetime contrast led to a biosensor that shows a 3-fold change in the calcium-dependent quantum yield and a fluorescence lifetime change of 1.3 ns. Additionally, the response of the calcium sensor is insensitive to pH between 6.2-9. As a result, the turquoise GECI enables robust measurements of intracellular calcium concentrations by fluorescence lifetime imaging. We demonstrate quantitative imaging of calcium concentration with the turquoise GECI in single endothelial cells and human-derived organoids. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=188 SRC="FIGDIR/small/449214v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@12beb07org.highwire.dtl.DTLVardef@5337f1org.highwire.dtl.DTLVardef@17db68forg.highwire.dtl.DTLVardef@5ca499_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2021

Visualizing and quantifying data from timelapse imaging experiments

One obvious feature of life is that it is highly dynamic. The dynamics can be captured by movies that are made by acquiring images at regular time intervals, a method that is also known as timelapse imaging. Looking at movies is a great way to learn more about the dynamics in cells, tissue and organisms. However, science is different from Netflix, in that it aims for a quantitative understanding of the dynamics. The quantification is important for the comparison of dynamics and to study effects of perturbations. Here, we provide detailed processing and analysis methods that we commonly use to analyze and visualize our timelapse imaging data. All methods use freely available open-source software and use example data that is available from an online data repository. The step-by-step guides together with example data allow for fully reproducible workflows that can be modified and adjusted to visualize and quantify other data from timelapse imaging experiments. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=194 SRC="FIGDIR/small/432684v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@d04086org.highwire.dtl.DTLVardef@3c304borg.highwire.dtl.DTLVardef@186bf56org.highwire.dtl.DTLVardef@17bbd6d_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2021

Visualizing endogenous RhoA activity with an improved localization-based, genetically encoded biosensor

Rho GTPases are regulatory proteins, which orchestrate cell features such as morphology, polarity and movement. Therefore, probing Rho GTPase activity is key to understanding processes such as development, cell migration and wound healing. Localization-based reporters for active Rho GTPases are attractive probes to study Rho GTPase-mediated processes, in real time with subcellular resolution in living cells and tissue. Until now, relocation RhoA biosensors seem to only be useful in certain organisms and have not been characterized well. In this paper, we systematically examined the contribution of the fluorescent protein and RhoA binding peptides, on the performance of localization-based sensors. To test the performance, we compared relocation efficiency and specificity in cell-based assays. We identified several improved localization-based, genetically encoded, fluorescent biosensors for detecting endogenous RhoA activity. This enables a broader application of RhoA relocation biosensors, which was demonstrated by using the improved biosensor to visualize RhoA activity, during cell division, during random migration, at the Golgi membrane and induced by G protein-coupled receptor signaling. Due to the improved avidity of the new biosensors for RhoA activity, cellular processes regulated by RhoA can be better understood. O_FIG O_LINKSMALLFIG WIDTH=134 HEIGHT=200 SRC="FIGDIR/small/430250v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@1847c24org.highwire.dtl.DTLVardef@f22c92org.highwire.dtl.DTLVardef@14c4aa8org.highwire.dtl.DTLVardef@1b9657d_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2021

Endothelial junctional membrane protrusions serve as hotspots for neutrophil transmigration

Upon inflammation, leukocytes rapidly transmigrate across the endothelium to enter the inflamed tissue. Evidence accumulates that leukocytes use preferred exit sites, though it is not yet clear how these hotspots in the endothelium are defined and how they are recognized by the leukocyte. Using lattice light sheet microscopy, we discovered that leukocytes prefer endothelial membrane protrusions at cell junctions for transmigration. Phenotypically, these junctional membrane protrusions are present in an asymmetric manner, meaning that one endothelial cell shows the protrusion and the adjacent one does not. Consequently, leukocytes cross the junction by migrating underneath the protruding endothelial cell. These protrusions depend on Rac1 activity and by using a photo-activatable Rac1 probe, we could artificially generate local exit-sites for leukocytes. Overall, we have discovered a new mechanism that uses local induced junctional membrane protrusions to facilitate/steer the leukocyte escape/exit from inflamed vessel walls.

cell biology↗

bioRxiv2020

SuperPlotsOfData - a web app for the transparent display and quantitative comparison of continuous data from discrete conditions

Plots and charts are graphical tools that make data intelligible and digestible by humans. But the oversimplification of data by only plotting the statistical summaries conflicts with the transparent communication of results. Therefore, plotting of all data is generally encouraged and this can be achieved by using a dotplot for discrete conditions. Dotplots, however, often fail to communicate whether the data are from different technical or biological replicates. The superplot has been proposed to improve the communication of experimental design and results. To simplify the plotting of data from discrete conditions as a superplot, the SuperPlotsOfData web app was generated. The tool offers easy and open access to state-of-the-art data visualization. In addition, it incorporates recent innovations in data visualization and analysis, including raindcloud plots and estimation statistics. The free, open-source webtool can be accessed at: https://huygens.science.uva.nl/SuperPlotsOfData/ O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/276881v2_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@1f7462aorg.highwire.dtl.DTLVardef@11fa09eorg.highwire.dtl.DTLVardef@16a17e9org.highwire.dtl.DTLVardef@47db57_HPS_FORMAT_FIGEXP M_FIG C_FIG

scientific communication and education↗

bioRxiv2020

VolcaNoseR - a web app for creating, exploring and sharing volcano plots

Comparative genome- and proteome-wide screens yield large amounts of data. To efficiently present such datasets and to simplify the identification of hits, the results are often presented in a type of scatterplot known as a volcano plot, which shows a measure of effect size versus a measure of significance. The data points with the largest effect size and a statistical significance beyond a user-defined threshold are considered as hits. Such hits are usually annotated in the plot by a label with their name. Volcano plots can represent ten thousands of data points, of which typically only a handful is annotated. The information of data that is not annotated is hardly or not accessible. To simplify access to the data and enable its re-use, we have developed an open source and online web tool with R/shiny. The web app is named VolcaNoseR and it can be used to create, explore, label and share volcano plots (https://huygens.science.uva.nl/VolcaNoseR). When the data is stored in an online data repository, the web app can retrieve that data together with user-defined settings to generate a customized, interactive volcano plot. Users can interact with the data, adjust the plot and share their modified plot together with the underlying data. Therefore, VolcaNoseR increases the transparency and re-use of large comparative genome- and proteome-wide datasets. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/082263v2_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@3e9b17org.highwire.dtl.DTLVardef@381077org.highwire.dtl.DTLVardef@c0f6caorg.highwire.dtl.DTLVardef@8a87c2_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2019

A new FRET biosensor enlightens cAMP signalling in budding yeast

The cAMP-PKA signalling cascade in budding yeast regulates adaptation to changing environments. We developed yEPAC, a FRET-based biosensor for cAMP measurements in yeast. We used this sensor with flow cytometry for high-throughput single cell-level quantification during dynamic changes in response to sudden nutrient transitions. We found that the characteristic cAMP peak differentiates between different carbon source transitions, and is rather homogenous among single-cells, especially for transitions to glucose. The peaks are mediated by a combination of extracellular sensing and intracellular metabolism. Moreover, the cAMP peak follows Webers law; its height scales with the relative, and not the absolute, change in glucose. Lastly, our results suggest that the cAMP peak height conveys information about prospective growth rates. In conclusion, our yEPAC-sensor makes possible new avenues for understanding yeast physiology, signalling and metabolic adaptation.

microbiology↗

bioRxiv2019

Seeing β-arrestin in action: The role of β-arrestins in Histamine 1 Receptor signaling

{beta}-arrestins regulate G protein-coupled receptor functions by influencing their signaling activity and intracellular location. Histamine is a major chemical mediator of allergic reactions, and its action is mainly mediated by the Gq/11- and Gi-coupled H1R. Contrary to accumulating insights into G protein-mediated signaling downstream of H1R, very little is known about the function of {beta}-arrestins in H1R signaling. Here, we describe dynamic, live cell measurements of {beta}-arrestin recruitment upon H1R activation in HEK293TN cells. Our observations classify H1R as a class A receptor, undergoing transient interactions with {beta}-arrestin. To investigate the relative contributions of G proteins and {beta}-arrestins to H1R signaling, we use specific G protein inhibitors, as well as {beta}-arrestin overexpression and depletion, and quantify various signaling outcomes in a panel of dynamic, live cell biosensor assays. Overall, we link {beta}-arrestins to desensitization of H1R-mediated signaling and show that ERK activation downstream of endogenous (HeLa, HUVEC) or transiently expressed (HEK293TN) H1R is largely Gq-mediated.

molecular biology↗

bioRxiv2019

Not so dry after all. DRY mutants of the AT1A receptor and H1 receptor can induce G protein-dependent signaling

GPCRs are seven transmembrane spanning receptors that regulate a wide array of intracellular signaling cascades in response to various stimuli. To do so, they couple to different heterotrimeric G proteins and adaptor proteins, including arrestins. Importantly, arrestins were shown to regulate GPCR signaling through G proteins, as well as promote G protein-independent signaling events. Several research groups have reported successful isolation of exclusively G protein-dependent and arrestin-dependent signaling downstream of GPCR activation using biased agonists or receptor mutants incapable of coupling to either arrestins or G proteins. In the latter category, the DRY mutant of the angiotensin II type 1 receptor was extensively used to characterize functional selectivity downstream of AT1AR. In an attempt to understand histamine 1 receptor signaling, we characterized the signaling capacity of the H1R DRY mutant in a panel of dynamic, live cell biosensor assays, including arrestin recruitment, heterotrimeric G-protein activation, Ca2+ signaling, protein kinase C activity, GTP binding of RhoA, and activation of ERK1/2. Here we show that both H1R DRY mutant and the AT1AR DRY mutant (used as a reference) are capable of efficient activation of G protein-mediated signaling. Therefore, contrary to common belief, they do not constitute suitable tools for dissection of arrestin-mediated, G protein-independent signaling downstream of these receptors.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=67 SRC=\"FIGDIR/small/773044v2_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (27K):\norg.highwire.dtl.DTLVardef@3b068eorg.highwire.dtl.DTLVardef@12b6a4borg.highwire.dtl.DTLVardef@14f66a1org.highwire.dtl.DTLVardef@1dad4b3_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2019

Seeing cells smell: Dynamic optical measurements of Ca2+ and cAMP signaling from Olfactory Receptors transiently expressed in HEK293TN cells

Olfactory receptors (ORs) constitute the largest family of G-protein coupled receptors. They are responsible for the perception of odor (olfaction) and also play important roles in other biological processes, including regulation of cell proliferation. Their increasing diagnostic and therapeutic potential, especially for cancer research, requests the ongoing development of methodologies that would allow their robust functional expression in non-olfactory cells, and dynamic analysis of their signaling pathways. To enable realtime detection of OR activity, we use single cell imaging with genetically encoded fluorescent biosensors, Yellow Cameleon or EPAC, which are routinely used for kinetic measurements of Ca2+ or cAMP signaling downstream of various G-protein coupled receptors. We demonstrate that the co-expression of Lucy-Rho tagged variants of ORs together with an accessory protein, RTP1s, in HEK293TN cells is sufficient to detect the activity of a panel of ORs. Using this methodology, we were able to detect both Ca2+ and cAMP signaling downstream of twelve ORs within 2 minutes from the application of odorant.

molecular biology↗

bioRxiv2019

PlotTwist - a web app for plotting and annotating time-series data

The results from time-dependent experiments are often used to generate plots that visualize how the data evolves over time. To simplify state-of-the-art data visualization and annotation of data from such experiments, an open source tool was created with R/shiny that does not require coding skills to operate. The freely available web app accepts wide (spreadsheet) and tidy data and offers a range of options to normalize the data. The data from individual objects can be shown in three different ways: (i) lines with unique colors, (ii) small multiples and (iii) heatmap-style display. Next to this, the mean can be displayed with a 95% confidence interval for the visual comparison of different conditions. Several color blind friendly palettes are available to label the data and/or statistics. The plots can be annotated with graphical features and/or text to indicate any perturbations that were applied during the time-lapse experiments. All user-defined settings can be stored for reproducibility of the data visualization. The app is dubbed PlotTwist and is available online: https://huygens.science.uva.nl/PlotTwist\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=166 SRC=\"FIGDIR/small/745612v2_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (34K):\norg.highwire.dtl.DTLVardef@1bad5e6org.highwire.dtl.DTLVardef@1312023org.highwire.dtl.DTLVardef@350cb5org.highwire.dtl.DTLVardef@d556c6_HPS_FORMAT_FIGEXP M_FIG C_FIG

scientific communication and education↗

bioRxiv2019

PlotsOfDifferences - a web app for the quantitative comparison of unpaired data

The quantitative comparison of data acquired under different conditions is an important aspect of experimental science. The most widely used statistic for quantitative comparisons is the p-value. However, p-values suffer from several shortcomings. The most prominent shortcoming that is relevant for quantitative comparisons is that p-values fail to convey the magnitude of differences. The differences between conditions are best quantified by the determination of effect size. To democratize the calculation of effect size, we have developed a web-based tool. The tool uses bootstrapping to resample mean or median values for each of the conditions and these values are used to calculate the effect size and their compatibility interval. The web tool generates a graphical output, showing the bootstrap distribution of the difference next to the actual data for optimal interpretation. A tabular output with statistics and effect sizes is also generated and the table can be supplemented with p-values that are calculated with a randomization test. The app that we report here is dubbed PlotsOfDifferences and is available at: https://huygens.science.uva.nl/PlotsOfDifferences\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=166 SRC=\"FIGDIR/small/578575_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (29K):\norg.highwire.dtl.DTLVardef@1b5b406org.highwire.dtl.DTLVardef@3e2807org.highwire.dtl.DTLVardef@b614eeorg.highwire.dtl.DTLVardef@181dd4_HPS_FORMAT_FIGEXP M_FIG C_FIG

scientific communication and education↗

bioRxiv2019

Selectivity of Guanine Nucleotide Exchange Factor-mediated Cdc42 activation in primary human endothelial cells

The Rho GTPase family is involved in actin dynamics and regulates the barrier function of the endothelium. One of the main barrier-promoting Rho GTPases is Cdc42, also known as cell division control protein 42 homolog. Currently, regulation of Cdc42-based signaling networks in endothelial cells (ECs) lack molecular details. To examine these, we focused on a subset of 15 Rho guanine nucleotide exchange factors (GEFs), which are expressed in the endothelium. By performing single cell FRET measurements with Rho GTPase biosensors in primary human ECs, we monitored GEF efficiency towards Cdc42 and Rac1. A new, single cell-based analysis was developed and used to enable the quantitative comparison of cellular activities of the full-length GEFs. Our data reveal a specific GEF dependent activation profile, with most efficient Cdc42 activation induced by PLEKHG2, FGD1, PLEKHG1 and pRex1 and the highest selectivity for FGD1. Additionally, we generated truncated GEF constructs that comprise only the catalytic dbl homology (DH) domain or together with the adjacent pleckstrin homology domain (DHPH). The DH domain by itself did not activate Cdc42, whereas the DHPH domain of ITSN1, ITSN2 and PLEKHG1 showed activity towards Cdc42. Together, our study characterized endothelial GEFs that may activate Cdc42, which will be of great value for the field of vascular biology. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=85 SRC="FIGDIR/small/541219v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@96ad9forg.highwire.dtl.DTLVardef@60aa3borg.highwire.dtl.DTLVardef@4811f0org.highwire.dtl.DTLVardef@191cfd1_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

cell biology↗

bioRxiv2018

In vivo characterisation of fluorescent proteins in budding yeast.

Fluorescent proteins (FPs) are widely used in many organisms, but are commonly characterised in vitro. However, the in vitro properties may poorly reflect in vivo performance. Therefore, we characterised 27 FPs in vivo using Saccharomyces cerevisiae as model organism. We linked the FPs via a T2A peptide to a control FP, producing equimolar expression of the 2 FPs from 1 plasmid. Using this strategy, we characterised the FPs for brightness, photostability, photochromicity and pH-sensitivity, achieving a comprehensive in vivo characterisation. Many FPs showed different in vivo properties compared to existing in vitro data. Additionally, various FPs were photochromic, which affects readouts due to complex bleaching kinetics. Finally, we codon optimized the best performing FPs for optimal expression in yeast, and found that codon-optimization alters FP characteristics. These FPs improve experimental signal readout, opening new experimental possibilities. Our results may guide future studies in yeast that employ fluorescent proteins.

molecular biology↗

bioRxiv2018

PlotsOfData - a web app for visualizing data together with its summaries

Reporting of the actual data in graphs and plots increases transparency and enables independent evaluation. On the other hand, data summaries are often used in graphs since they aid interpretation. State-of-the art data visualizations can be made with the ggplot2 package, which uses the ideas of a grammar of graphics to generate a graphic from multiple layers of data. However, ggplot2 requires coding skills and an understanding of the tidy data structure. To democratize state-of-the-art data visualization of raw data with a selection of statistical summaries, a web app was written using R/shiny that uses the ggplot2 package for generating plots. A multilayered approach together with adjustable transparency offers a unique flexibility, enabling users can to choose how to display the data and which of the data summaries to add. Four data summaries are provided, mean, median, boxplot, violinplot, to accommodate several types of data distributions. In addition, 95% confidence intervals can be added for visual inferences. By adjusting the transparency of the layers, the visualization of the raw data together with the summary can be tuned for optimal presentation and interpretation. The app is dubbed PlotsOfData and is available at: https://huygens.science.uva.nl/PlotsOfData/\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC=\"FIGDIR/small/426767v3_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (21K):\norg.highwire.dtl.DTLVardef@1f8a09eorg.highwire.dtl.DTLVardef@1f8feb3org.highwire.dtl.DTLVardef@19b781org.highwire.dtl.DTLVardef@5e7b55_HPS_FORMAT_FIGEXP M_FIG C_FIG

scientific communication and education↗

bioRxiv2018

Superfolder mTurquoise2ox optimized for the bacterial periplasm allows high efficiency in vivo FRET of cell division antibiotic targets.

Fluorescent proteins (FP)s are of vital importance to biomedical research. Many of the currently available fluorescent proteins do not fluoresce when expressed in non-native environments, such as the bacterial periplasm. This strongly limits the options for applications that employ multiple FPs, such as multiplex imaging or FRET. To address this issue, we have engineered a new cyan fluorescent protein based on mTurquoise2 (mTq2). The new variant is dubbed superfolder turquoise 2 ox (sfTq2ox) and is able to withstand challenging, oxidizing environments. sfTq2ox has improved folding capabilities and can be expressed in the periplasm at higher concentrations without toxicity. This was tied to the replacement of native cysteines that may otherwise form promiscuous disulfide-bonds. The improved sfTq2ox has the same spectroscopic properties as mTq2, i.e. high fluorescence lifetime and quantum yield. The sfTq2ox-mNeongreen FRET pair allows the detection of periplasmic protein-protein interactions with energy transfer rates exceeding 40 %. Employing the new FRET pair, we show the direct interaction of two essential periplasmic cell division proteins FtsL and FtsB and disrupt it by mutations, paving the way for in vivo antibiotic screening.

microbiology↗

bioRxiv2017

A FRET based biosensor for measuring Gα13 activation in single cells

Forster Resonance Energy Transfer (FRET) provides a way to directly observe the activation of heterotrimeric G-proteins by G-protein coupled receptors (GPCRs). To this end, FRET based biosensors are made, employing heterotrimeric G-protein subunits tagged with fluorescent proteins. These FRET based biosensors complement existing, indirect, ways to observe GPCR activation. Here we report on the insertion of mTurquoise2 at several sites in the human G13 subunit. Three variants were found to be functional based on i) plasma membrane localization and ii) ability to recruit p115-RhoGEF upon activation of the LPA2 receptor. The tagged G13 subunits were used as FRET donor and combined with cp173Venus fused to the G{gamma}2 subunit as the acceptor. We constructed G13 biosensors by generating a single plasmid that produces G13-mTurquoise2, G{beta}1 and cp173Venus-G{gamma}2. The G13 activation biosensors showed a rapid and robust response when used in primary human endothelial cells that were treated with thrombin, triggering endogenous protease activated receptors (PARs). This response was efficiently inhibited by the RGS domain of p115-RhoGEF and from the biosensor data we inferred that this is due to GAP activity. Finally, we demonstrated that the G13 sensor could be used to dissect heterotrimeric G-protein coupling efficiency in single living cells. We conclude that the G13 biosensor is a valuable tool for live-cell measurements that probe G13 activation.

cell biology↗