Compartmentalization of a Bistable Switch Enables Memory to Cross a Feedback-Driven Transition

SummaryCells make accurate decisions in the face of molecular noise and environmental fluctuations by relying not only on present pathway activity, but also on their memory of past signaling dynamics. Once a decision is made, cellular transitions are often rapid and switch-like due to positive feedback loops in the regulatory network. While positive feedback loops are good at promoting switch-like transitions, they are not expected to retain information to inform subsequent decisions. However, this expectation is based on our current understanding of network motifs that accounts for temporal, but not spatial, dynamics. Here, we show how spatial organization of the feedback-driven yeast G1/S switch enables the transmission of memory of past pheromone exposure across this transition. We expect this to be one of many examples where the exquisite spatial organization of the eukaryotic cell enables previously well-characterized network motifs to perform new and unexpected signal processing functions

Reverse Engineering of the Spindle Assembly Checkpoint

The Spindle Assembly Checkpoint (SAC) is an intracellular mechanism that ensures proper chromosome segregation. By inhibiting Cdc20, a co-factor of the Anaphase Promoting Complex (APC), the checkpoint arrests the cell cycle until all chromosomes are properly attached to the mitotic spindle. Inhibition of Cdc20 is mediated by a conserved network of interacting proteins. The individual functions of these proteins are well characterized, but understanding of their integrated function is still rudimentary. We here describe our attempts to reverse-engineer the SAC network based on gene deletion phenotypes. We begun by formulating a general model of the SAC which enables us to predict the rate of chromosomal missegregation for any putative set of interactions between the SAC proteins. Next the missegregation rates of seven yeast strains are measured in response to the deletion of one or two checkpoint proteins. Finally, we searched for the set of interactions that correctly predicted the observed missegregation rates of all deletion mutants. Remarkably, although based on only seven phenotypes, the consistent network we obtained successfully reproduces many of the known properties of the SAC. Further insights provided by our analysis are discussed

A fully-automated, robust, and versatile algorithm for long-term budding yeast segmentation and tracking.

Live cell time-lapse microscopy, a widely-used technique to study gene expression and protein dynamics in single cells, relies on segmentation and tracking of individual cells for data generation. The potential of the data that can be extracted from this technique is limited by the inability to accurately segment a large number of cells from such microscopy images and track them over long periods of time. Existing segmentation and tracking algorithms either require additional dyes or markers specific to segmentation or they are highly specific to one imaging condition and cell morphology and/or necessitate manual correction. Here we introduce a fully automated, fast and robust segmentation and tracking algorithm for budding yeast that overcomes these limitations. Full automatization is achieved through a novel automated seeding method, which first generates coarse seeds, then automatically fine-tunes cell boundaries using these seeds and automatically corrects segmentation mistakes. Our algorithm can accurately segment and track individual yeast cells without any specific dye or biomarker. Moreover, we show how existing channels devoted to a biological process of interest can be used to improve the segmentation. The algorithm is versatile in that it accurately segments not only cycling cells with smooth elliptical shapes, but also cells with arbitrary morphologies (e.g. sporulating and pheromone treated cells). In addition, the algorithm is independent of the specific imaging method (bright-field/phase) and objective used (40X/63X/100X). We validate our algorithm's performance on 9 cases each entailing a different imaging condition, objective magnification and/or cell morphology. Taken together, our algorithm presents a powerful segmentation and tracking tool that can be adapted to numerous budding yeast single-cell studies

Segmentation.

<p>(<b>A</b>) A phase image region around the previously selected seed is opened. (<b>B</b>) The phase image is transformed into a series of binary images by applying thresholds such that any pixel that is lower than the threshold is assigned as ‘putative cell’ (red regions) or ‘non-cell’ (blue regions). In total 256 ( = 2⁸) thresholds are applied corresponsing to all possible values the phase image can take (example thresholds 2, 50 and 130 are shown here). (<b>C</b>) The watershed algorithm is applied to all thresholded images from (B). The result is a set of distinct regions. Each region is compared with the seed (inset) and the phase image and classified as ‘cell’ or ‘non-cell’. (<b>D</b>). All ‘cell’ areas, from all thresholds, are summed together (<b>E</b>). The composite image allows for more accurate segmentation than an optimized single threshold.</p

An Algorithm to Automate Yeast Segmentation and Tracking

<div><p>Our understanding of dynamic cellular processes has been greatly enhanced by rapid advances in quantitative fluorescence microscopy. Imaging single cells has emphasized the prevalence of phenomena that can be difficult to infer from population measurements, such as all-or-none cellular decisions, cell-to-cell variability, and oscillations. Examination of these phenomena requires segmenting and tracking individual cells over long periods of time. However, accurate segmentation and tracking of cells is difficult and is often the rate-limiting step in an experimental pipeline. Here, we present an algorithm that accomplishes fully automated segmentation and tracking of budding yeast cells within growing colonies. The algorithm incorporates prior information of yeast-specific traits, such as immobility and growth rate, to segment an image using a set of threshold values rather than one specific optimized threshold. Results from the entire set of thresholds are then used to perform a robust final segmentation.</p> </div

Example of fluorescence and morphological signal extraction.

<p>(<b>A–C</b>) Morphological parameters, here major/minor axis and cell area are calculated using our algorithm (the same colors represent the same cells in A–C). (<b>D</b>) Composite phase and fluorescence images of cells containing Whi5-GFP and Htb2-mCherry. (<b>E</b>) Example cell trace of nuclear Whi5-GFP. The nuclear Whi5-GFP is calcualted by fitting a 2D Gaussian to the brightest point for each cell after applying an averageing filter and assigning the brightest 25% as the nucleus. (<b>F</b>) Example of a cell whose nucleus is initially in focus at the 240^th minute, but subsequently goes out of focus. (<b>G</b>) Example trace of observed and corrected nuclear Whi5-GFP for the cell shown in (F). Pheromone was added at the 153^rd minute, and the cell shown is shmooing at the 390^th minute. All Whi5-GFP is expected to be nuclear during pheromone arrest, supporting our use of the corrected (red) trace over the uncorrrected (green) trace, which drops significantly.</p

Flowchart of the image analysis algorithm (see text).

<p>Flowchart of the image analysis algorithm (see text).</p

List of strains.

<p>All strains used are congenic with W303.</p