Elco Bakker

Recognition of the importance of cell-to-cell variability in cellular decision-making and a growing interest in stochastic modeling of cellular processes has led to an increased demand for high density, reproducible, single-cell measurements in time-varying surroundings. We present ALCATRAS (A Long-term Culturing And TRApping System), a microfluidic device that can quantitatively monitor up to 1000 cells of budding yeast in a well-defined and controlled environment. Daughter cells are removed by fluid flow to avoid crowding allowing experiments to run for over 60 hours, and the extracellular media may be changed repeatedly and in seconds. We illustrate use of the device by measuring ageing through replicative life span curves, following the dynamics of the cell cycle, and examining history-dependent behaviour in the general stress response.

Motivation: Although high-content image cytometry is becoming increasingly routine, processing the large amount of data acquired during time-lapse experiments remains a challenge. The majority of approaches for automated single-cell segmentation focus on flat, uniform fields of view covered with a single layer of cells. In the increasingly popular microfluidic devices that trap individual cells for long term imaging, these conditions are not met. Consequently, most techniques for segmentation perform poorly. Although potentially constraining the generalizability of software, incorporating information about the microfluidic features, flow of media and the morphology of the cells can substantially improve performance. Results: Here we present DISCO (Data Informed Segmentation of Cell Objects), a framework for using the physical constraints imposed by microfluidic traps, the shape based morphological constraints of budding yeast and temporal information about cell growth and motion to allow tracking and segmentation of cells in microfluidic devices. Using manually curated datasets, we demonstrate substantial improvements in both tracking and segmentation when compared with existing software. Availability and implementation: The MATLAB code for the algorithm and for measuring performance is available at https://github.com/pswain/segmentation-software and the test images and the curated ground-truth results used for comparing the algorithms are available at http://datashare.is.ed.ac.uk/handle/10283/2002. Contact: mcrane2@uw.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

The impact of fluorescence microscopy has been limited by the difficulties of expressing measurements of fluorescent proteins in numbers of molecules. Absolute numbers enable the integration of results from different laboratories, empower mathematical modelling, and are the bedrock for a quantitative, predictive biology. Here we propose an estimator to infer numbers of molecules from fluctuations in the photobleaching of proteins tagged with Green Fluorescent Protein. Performing experiments in budding yeast, we show that our estimates of numbers agree, within an order of magnitude, with published biochemical measurements, for all six proteins tested. The experiments we require are straightforward and use only a wide-field fluorescence microscope. As such, our approach has the potential to become standard for those practising quantitative fluorescence microscopy.

Abstract Motivation Although high-content image cytometry is becoming increasingly routine, processing the large amount of data acquired during time-lapse experiments remains a challenge. The majority of approaches for automated single-cell segmentation focus on flat, uniform fields of view covered with a single layer of cells. In the increasingly popular microfluidic devices that trap individual cells for long term imaging, these conditions are not met. Consequently, most segmentation techniques perform poorly. Incorporating information about the microfluidic features, media flow and morphology of the cells can substantially improve performance, though it may constrain the generalizability of software. Results Here we present DISCO (Data Informed Segmentation of Cell Objects), a framework for using the physical constraints imposed by microfluidic traps, the shape based morphological constraints of budding yeast and temporal information about cell growth and motion, to allow tracking and segmentation of cells in micrflouidic devices. Using manually curated data sets, we demonstrate substantial improvements in both tracking and segmentation for this approach when compared with existing software. Availability The MATLAB ® code for the algorithm and for measuring performance is available at https://github.com/pswain/segmentation-software . The test images and the curated ground truth results used for comparing the algorithms are available at http://swainlab.bio.ed.ac.uk/ .

The advent and establishment of systems biology has cemented the idea that real understanding of biological systems requires quantitative models, that can be integrated to provide a complete description of the cell and its complexities. At the same time, synthetic biology attempts to leverage such quantitative models to efficiently engineer novel, predictable behaviour in biological systems. Together, these advances indicate that the future understanding and application of biology will require the ability to create, parameterise and discriminate between quantitative models of cellular processes in a rigorous and statistically sound manner. In this thesis we take the regulation of GAL1 expression in Saccharomyces cerevisiae as a test case and look at all aspects of this process: from reporter selection to data acquisition and statistical analysis. In chapter B we will discuss optimal fluorescent reporter selection and construction for investigating transcriptional dynamics, as well as procedures for quantifying and correcting the various sources of error in our microscope system. In chapter 3 we will describe software developed to analyse fluorescent microscopy images and convert them to gene expression data. A number of iterations of the software are tested against manually curated data sets, and the measurement error produced by its imperfections is quantified and discussed. In chapter 4 a method, based on fluctuations in photobleaching, is developed for quantifying both measurement error and the relationship between protein concentration and measured fluorescence. The method is refined and its efficacy discussed. In the last section I make a preliminary application of these methods to investigating the regulatory effect of the GAL10-lncRNA. Interesting phenomena are observed and further investigated using two new strains: genetic variants expressing a fluorescent reporter from the GAL1 promoter, one harbouring a wild type GAL1 promoter and one in which the binding site for the Gal10 noncoding RNA has been removed. The methods developed throughout the thesis are applied and the data analysed. A heterogeneous response, distinguishable between the two strains, is observed and related to cell-to-cell variations in growth rate.