Bakker, E. | Search bioRxiv

bioRxiv2018

Estimating numbers of fluorescent molecules in single cells by analysing fluctuations in photobleaching

The impact of fluorescence microscopy has been limited by the difficulties of express-ing 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 develop a general algorithm to infer numbers of molecules from fluctuations in the photobleaching of proteins tagged with Green Fluorescent Protein. To untangle measurement noise from stochastic fluctuations, we use the linear noise approximation and Kalman filtering within a framework of Bayesian inference. Not only do our results agree with biochemical measurements for multiple proteins in budding yeast, but we also provide a statistically verified model of measurement noise for fluorescence microscopes. 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.

systems biology↗

bioRxiv2017

Morphologically Constrained and Data Informed Cell Segmentation of Budding Yeast

MotivationAlthough 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.\n\nResultsHere 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.\n\nAvailabilityThe MATLAB(R) 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/.

bioinformatics↗