Single-cell quantification of molecules and rates using open-source microscope-based cytometry

Gordon, Andrew H.; Colman‐Lerner, Alejandro; Chin, Tina; Benjamin, Kirsten R.; Yu, Richard; Brent, Roger

doi:10.1038/nmeth1008

Cited by 206 publications

(270 citation statements)

References 32 publications

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“…For this purpose, we first screened transcription factor genes for feedback on protein abundance. We used fluorescence microscopy (25) to measure protein expression from a single genomic copy of each factor tagged with GFP in a diploid strain (26) while varying levels of an untagged copy of the factor (Fig. 1A).…”

Section: Resultsmentioning

confidence: 99%

Negative feedback confers mutational robustness in yeast transcription factor regulation

Denby

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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Organismal fitness depends on the ability of gene networks to function robustly in the face of environmental and genetic perturbations. Understanding the mechanisms of this stability is one of the key aims of modern systems biology. Dissecting the basis of robustness to mutation has proven a particular challenge, with most experimental models relying on artificial DNA sequence variants engineered in the laboratory. In this work, we hypothesized that negative regulatory feedback could stabilize gene expression against the disruptions that arise from natural genetic variation. We screened yeast transcription factors for feedback and used the results to establish ROX1 (Repressor of hypOXia) as a model system for the study of feedback in circuit behaviors and its impact across genetically heterogeneous populations. Mutagenesis experiments revealed the mechanism of Rox1 as a direct transcriptional repressor at its own gene, enabling a regulatory program of rapid induction during environmental change that reached a plateau of moderate steady-state expression. Additionally, in a given environmental condition, Rox1 levels varied widely across genetically distinct strains; the ROX1 feedback loop regulated this variation, in that the range of expression levels across genetic backgrounds showed greater spread in ROX1 feedback mutants than among strains with the ROX1 feedback loop intact. Our findings indicate that the ROX1 feedback circuit is tuned to respond to perturbations arising from natural genetic variation in addition to its role in induction behavior. We suggest that regulatory feedback may be an important element of the network architectures that confer mutational robustness across biology.R obustness of organismal function in the face of perturbations is critical for fitness. Since the seminal work of Waddington (1), biologists have remarked on the stability of phenotypes against environmental and genetic variation, and understanding how organisms achieve robustness remains one of the major challenges in systems biology (2-4). Much of the search for molecular mechanisms of robustness has focused on gene regulation. Characteristics of regulatory networks that confer robustness include pathway redundancy and master regulatory organization (5), phenotypic capacitors (6-8), paired activating and inhibiting inputs (9), and cooperative and feed-forward regulation (10). Additionally, negative regulatory feedback, in which a biomolecule represses its own abundance, can buffer variation in gene expression (11,12), and negative feedback loops have been shown to underlie robustness to variable environmental conditions and stochastic intracellular change (13-15). Negative feedback may also confer network stability against the effects of mutations (3, 16), but evidence for negative feedback as a driver of mutational robustness in vivo has been at a premium (17); the relevance of this principle to natural genetic variation remains largely unknown.In this work, we focused on negative feedback in yeast hypoxia regulation motiva...

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Section: Resultsmentioning

confidence: 99%

Negative feedback confers mutational robustness in yeast transcription factor regulation

Denby

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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“…Notably, higher abundance proteins did not exhibit higher cell to cell variation as previously reported for yeast proteins in general (24,25). We did not calculate single cell abundance for these proteins, because we had not measured their degradation rates, with the exception of Ste5 (23). However, fluorescence of cells expressing Fus3-YFP was only approximately twofold higher than fluorescence of cells expressing YFP-Ste5 (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…We developed an improved immunoblotting protocol and took advantage of previous work that enabled accurate single cell quantification of fluorescent fusion proteins (23). We used these methods to quantify abundances of key components of the pheromone response system.…”

Section: Discussionmentioning

confidence: 99%

Scaffold number in yeast signaling system sets tradeoff between system output and dynamic range

Thomson

Benjamin

Bush

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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Although the proteins comprising many signaling systems are known, less is known about their numbers per cell. Existing measurements often vary by more than 10-fold. Here, we devised improved quantification methods to measure protein abundances in the Saccharomyces cerevisiae pheromone response pathway, an archetypical signaling system. These methods limited variation between independent measurements of protein abundance to a factor of two. We used these measurements together with quantitative models to identify and investigate behaviors of the pheromone response system sensitive to precise abundances. The difference between the maximum and basal signaling output (dynamic range) of the pheromone response MAPK cascade was strongly sensitive to the abundance of Ste5, the MAPK scaffold protein, and absolute system output depended on the amount of Fus3, the MAPK. Additional analysis and experiment suggest that scaffold abundance sets a tradeoff between maximum system output and system dynamic range, a prediction supported by recent experiments.quantitative immunoblotting | single-cell fluorescence quantification | genetic algorithmic model optimization T he Saccharomyces cerevisiae pheromone response system has been useful for understanding eukaryotic cell signaling (1-3). In haploid cells, the pheromone response system detects mating pheromone in the extracellular environment secreted by yeast of the opposite mating type. The system triggers a number of responses, including induction of gene expression, arrest in cell cycle progression, changes in morphology, and eventually, mating. The molecules and interactions by which the system operates are relatively well-understood (Fig. 1).Changes in the number of molecules per cell (hereafter called abundances) of signaling proteins can alter quantitative signaling behaviors. For example, in the pheromone response system, overexpression of Fus3 increases pheromone-induced sensitivity to cell cycle arrest (4), and overexpression of Gpa1 decreases pheromone-induced transcription (5). Similarly, changes in the abundances of MAPK cascade scaffold proteins (Ste5 in the yeast pheromone response system) ( Fig. 1) can alter system behavior. In models, when scaffold is limiting, increasing scaffold abundance first increases system output and then, eventually sequesters the associated protein kinases onto separate scaffolds, diminishing the number of fully assembled complexes and system output (6). This peak and decline behavior has been experimentally shown for Ste5 in this yeast system (7) and the Kinase Suppressor of Ras (KSR) scaffold protein in Ras signaling in Xenopus oocytes (8).Previous investigators reported focused and genome-wide inventories of pheromone system protein abundances (9-13). These measurements differed by up to 12-fold (Fig. 2) (14, 15). We thought some discrepancies might be because of differences and systematic biases in quantification methods (Discussion). We developed improved measurement methods and used these methods to quantify system proteins. We used...

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“…We obtained cell dimensions from bright-field images (see ref. 60 for a discussion on the use of fluorescence or brightfield images for identifying cell boundaries). Specific procedures in Visual Basic using object libraries supplied by AxioVision (Zeiss) software were written to detect cell boundaries as pixels markedly darker compared with both the surrounding background and the cell interior.…”

Section: Methodsmentioning

confidence: 99%

The critical size is set at a single-cell level by growth rate to attain homeostasis and adaptation

et al. 2012

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Budding yeast cells are assumed to trigger start and enter the cell cycle only after they attain a critical size set by external conditions. However, arguing against deterministic models of cell size control, cell volume at start displays great individual variability even under constant conditions. Here we show that cell size at start is robustly set at a single-cell level by the volume growth rate in G1, which explains the observed variability. We find that this growth-rate-dependent sizer is intimately hardwired into the start network and the Ydj1 chaperone is key for setting cell size as a function of the individual growth rate. mathematical modelling and experimental data indicate that a growth-rate-dependent sizer is sufficient to ensure size homeostasis and, as a remarkable advantage over a rigid sizer mechanism, it reduces noise in G1 length and provides an immediate solution for size adaptation to external conditions at a population level.

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Single-cell quantification of molecules and rates using open-source microscope-based cytometry

Cited by 206 publications

References 32 publications

Negative feedback confers mutational robustness in yeast transcription factor regulation

Negative feedback confers mutational robustness in yeast transcription factor regulation

Scaffold number in yeast signaling system sets tradeoff between system output and dynamic range

The critical size is set at a single-cell level by growth rate to attain homeostasis and adaptation

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