Quantitative fluorescence microscopy methods for studying transcription with application to the yeast GAL1 promoter

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

Human postprandial responses to food and potential for precision nutrition

Metabolic responses to food influence risk of cardiometabolic disease, but large-scale high-resolution studies are lacking. We recruited n = 1,002 twins and unrelated healthy adults in the United Kingdom to the PREDICT 1 study and assessed postprandial metabolic responses in a clinical setting and at home. We observed large inter-individual variability (as measured by the population coefficient of variation (s.d./mean, %)) in postprandial responses of blood triglyceride (103%), glucose (68%) and insulin (59%) following identical meals. Person-specific factors, such as gut microbiome, had a greater influence (7.1% of variance) than did meal macronutrients (3.6%) for postprandial lipemia, but not for postprandial glycemia (6.0% and 15.4%, respectively); genetic variants had a modest impact on predictions (9.5% for glucose, 0.8% for triglyceride, 0.2% for C-peptide). Findings were independently validated in a US cohort (n = 100 people). We developed a machine-learning model that predicted both triglyceride (r = 0.47) and glycemic (r = 0.77) responses to food intake. These findings may be informative for developing personalized diet strategies. The ClinicalTrials.gov registration identifier is NCT03479866

A microfluidic system for studying ageing and dynamic single-cell responses in budding yeast

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

Estimating numbers of intracellular molecules through analysing fluctuations in photobleaching

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.Bakker, Elco; Swain, P. (2019). Estimating numbers of intracellular molecules through analysing fluctuations in photobleaching, [dataset]. University of Edinburgh. School of Biological Sciences. https://doi.org/10.7488/ds/2594

Morphologically constrained and data informed cell segmentation of budding yeast

This is all the data associated with the paper "Morphologically Constrained and Data Informed Cell Segmentation of Budding Yeast": a description of the cell segmentation software developed in the Swain Laboratory for segmenting microscopy images of yeast (Saccharomyces cerevisiae) cells in the ALCATRAS microfluidic device. Included are the raw images, ground truth segmentation and the automated segmentations of all the software tested.Bakker, Elco; Bandiera, Lucia; Crane, Matt. (2017). Morphologically Constrained and Data Informed Cell Segmentation of Budding Yeast, [dataset]. University of Edinburgh

Cell cycle dynamics in ALCATRAS.

<p>A) Frames from a time-lapse movie showing a cell expressing Whi5p-GFP across one cell cycle. Fluorescence is localized in the nucleus during late M and early G1 phases. B) Plot of the nuclear localization of Whi5p over time for the cell shown in A. Frames in A are from the shaded period. Over many hours the cell undergoes a large number of cell cycles, resulting in a strongly periodic signal. The inset shows the power spectrum derived from applying Welch’s windowing algorithm and the Fourier transform to this data with a single peak at the frequency of the cell cycle. C) Histogram showing the distribution of cell cycle times of mother cells undergoing their first three divisions in ALCATRAS. D) Kymograph illustrating the change in replicative age of individual cells during the experiment. To aid visualization of the cell cycle, each alternate division is marked by a different colour. The total number of divisions undergone in the device when odd is shown by the colour map; even numbers of division are depicted in dark blue. Cells have been ordered by their number of divisions, and only cells that remain alive and in their original traps throughout are illustrated (299 cells). Flow rates were 2 µl/min from each input syringe pump (4 µl/min total).</p

Operation of ALCATRAS.

<p>A, B) Schematics showing the removal of a daughter cell by the media flow when the mother buds at the top of the trap (A) or at the bottom (B). In both cases the flow is from top to bottom (red arrow). The newly formed daughter cells follow the streamlines shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100042#pone.0100042.s001" target="_blank">Fig. S1A</a>. C) Microscopy images of removal of daughter cells in the device. The cells are expressing Doa1p-GFP, and the fluorescence image has been overlaid with the DIC image for clarity. Scale bar indicates 5 µm. D) Success rates for four ALCATRAS experiments. The number of cells retained in their original traps over the time course is plotted for two independent experiments using each of ALCATRAS 1 (red – more dense spacing of traps) or ALCATRAS 2 (blue – less dense spacing of traps). Only cells that were present in the traps at the first time point are included. Results were scored manually from a random subset of the fields imaged. Numbers include cells that have visibly died during the experiment. For a more detailed breakdown of cell loss and replacement, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100042#pone.0100042.s003" target="_blank">Fig. S3</a>. Flow rates were 2 µl/min from each input syringe pump (4 µl/min total). E) Cell viability plotted as a function of the number of replications (n = 422). Cells were observed for 62 hours, and replicative lifespans were scored manually. The mean lifespan is 22.4. Flow rates were 2 µl/min from each input syringe pump (4 µl/min total). F) Kymograph showing Hsp104-GFP expression over time with imaging every 10 mins. The median fluorescence intensity within the area of each cell (n = 1003) at each time point is shown by the colour map. Only cells that are present during the first hour of the experiment and that remain in their original traps for at least 10 hours are shown.</p