461 research outputs found

    A microfluidic setup for quantifying single-cell transcription regulatory dynamics

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    Bacteria are exposed to fluctuations in their environment and can respond to such changes by regulating gene expression, often at the level of transcription. Since gene expression is an inherently stochastic process, identical cells within a single environment display heterogeneous expression levels. To understand how the stochastic processes in gene expression affect the dynamics of single-cell gene regulation it is necessary to observe gene expression in single cells in changing environments. Recently developed microfluidic devices combined with quantitative fluorescence time-lapse microscopy allow lineages of single cells to be followed over long timescales and to measure their growth and gene expression phenotypes simultaneously. However these devices are missing the environmental control needed to study gene regulation. Therefore we set out to find a way to combine the longterm observation of single cells with precise environmental control in a single microfluidic chip. As a basis we chose a device called the Mother Machine in which single files of cells are growing in small dead end growth channels. These growth channels are connected to a main channel with a constant flow of medium for nutrient diffusion into the growth channels. The cells at the dead end of the growth channels are trapped and when dividing push their progeny into the main channel where they are removed by the flow. Therefore the trapped cell can be monitored essentially for its whole lifetime, while its progeny can only be observed for a short timeframe before they leave the growth channel. By combining the Mother Machine design with a specialized dual input junction and mixing serpentines for environmental control we developed a device that offers new prospects in studying gene regulation. Together with the device we developed an easy to use software solution to analyze data from Mother Machine like devices together with our collaboration partners. This integrated experimental and computational setup will be an important tool to understand the genetic basis for differences in single-cell expression distributions, and to understand how natural selection has shaped single-cell gene regulation. As a first example we show how single cells differ in the regulation of the expression of the lac operon when exposed to alternating changes in the available carbon source switching between glucose and lactose every 4h

    Structured learning of assignment models for neuron reconstruction to minimize topological errors

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    © 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Structured learning provides a powerful framework for empirical risk minimization on the predictions of structured models. It allows end-to-end learning of model parameters to minimize an application specific loss function. This framework is particularly well suited for discrete optimization models that are used for neuron reconstruction from anisotropic electron microscopy (EM) volumes. However, current methods are still learning unary potentials by training a classifier that is agnostic about the model it is used in. We believe the reason for that lies in the difficulties of (1) finding a representative training sample, and (2) designing an application specific loss function that captures the quality of a proposed solution. In this paper, we show how to find a representative training sample from human generated ground truth, and propose a loss function that is suitable to minimize topological errors in the reconstruction. We compare different training methods on two challenging EM-datasets. Our structured learning approach shows consistently higher reconstruction accuracy than other current learning methods.Peer ReviewedPostprint (author's final draft

    Efficient Algorithms for Moral Lineage Tracing

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    Lineage tracing, the joint segmentation and tracking of living cells as they move and divide in a sequence of light microscopy images, is a challenging task. Jug et al. have proposed a mathematical abstraction of this task, the moral lineage tracing problem (MLTP), whose feasible solutions define both a segmentation of every image and a lineage forest of cells. Their branch-and-cut algorithm, however, is prone to many cuts and slow convergence for large instances. To address this problem, we make three contributions: (i) we devise the first efficient primal feasible local search algorithms for the MLTP, (ii) we improve the branch-and-cut algorithm by separating tighter cutting planes and by incorporating our primal algorithms, (iii) we show in experiments that our algorithms find accurate solutions on the problem instances of Jug et al. and scale to larger instances, leveraging moral lineage tracing to practical significance.Comment: Accepted at ICCV 201

    Image Processing and Simulation Toolboxes of Microscopy Images of Bacterial Cells

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    Recent advances in microscopy imaging technology have allowed the characterization of the dynamics of cellular processes at the single-cell and single-molecule level. Particularly in bacterial cell studies, and using the E. coli as a case study, these techniques have been used to detect and track internal cell structures such as the Nucleoid and the Cell Wall and fluorescently tagged molecular aggregates such as FtsZ proteins, Min system proteins, inclusion bodies and all the different types of RNA molecules. These studies have been performed with using multi-modal, multi-process, time-lapse microscopy, producing both morphological and functional images. To facilitate the finding of relationships between cellular processes, from small-scale, such as gene expression, to large-scale, such as cell division, an image processing toolbox was implemented with several automatic and/or manual features such as, cell segmentation and tracking, intra-modal and intra-modal image registration, as well as the detection, counting and characterization of several cellular components. Two segmentation algorithms of cellular component were implemented, the first one based on the Gaussian Distribution and the second based on Thresholding and morphological structuring functions. These algorithms were used to perform the segmentation of Nucleoids and to identify the different stages of FtsZ Ring formation (allied with the use of machine learning algorithms), which allowed to understand how the temperature influences the physical properties of the Nucleoid and correlated those properties with the exclusion of protein aggregates from the center of the cell. Another study used the segmentation algorithms to study how the temperature affects the formation of the FtsZ Ring. The validation of the developed image processing methods and techniques has been based on benchmark databases manually produced and curated by experts. When dealing with thousands of cells and hundreds of images, these manually generated datasets can become the biggest cost in a research project. To expedite these studies in terms of time and lower the cost of the manual labour, an image simulation was implemented to generate realistic artificial images. The proposed image simulation toolbox can generate biologically inspired objects that mimic the spatial and temporal organization of bacterial cells and their processes, such as cell growth and division and cell motility, and cell morphology (shape, size and cluster organization). The image simulation toolbox was shown to be useful in the validation of three cell tracking algorithms: Simple Nearest-Neighbour, Nearest-Neighbour with Morphology and DBSCAN cluster identification algorithm. It was shown that the Simple Nearest-Neighbour still performed with great reliability when simulating objects with small velocities, while the other algorithms performed better for higher velocities and when there were larger clusters present

    Shaping bacterial population behavior through computer-interfaced control of individual cells

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    This is the final version. Available from Springer Nature via the DOI in this record.Strains and data are available from the authors upon request. Custom scripts for the described setup are available as Supplementary Software.Bacteria in groups vary individually, and interact with other bacteria and the environment to produce population-level patterns of gene expression. Investigating such behavior in detail requires measuring and controlling populations at the single-cell level alongside precisely specified interactions and environmental characteristics. Here we present an automated, programmable platform that combines image-based gene expression and growth measurements with on-line optogenetic expression control for hundreds of individual Escherichia coli cells over days, in a dynamically adjustable environment. This integrated platform broadly enables experiments that bridge individual and population behaviors. We demonstrate: (i) population structuring by independent closed-loop control of gene expression in many individual cells, (ii) cell-cell variation control during antibiotic perturbation, (iii) hybrid bio-digital circuits in single cells, and freely specifiable digital communication between individual bacteria. These examples showcase the potential for real-time integration of theoretical models with measurement and control of many individual cells to investigate and engineer microbial population behavior.European Union's Seventh Frame ProgrammeAustrian Science FundAgence Nationale de la RechercheAgence Nationale de la RechercheAgence Nationale de la Recherch

    Novel technologies to study single-cell response to environmental stimuli

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    Antibiotic tolerant phenotypes, such as persister and viable but non culturable cells (VBNC), are known to be present in isogenic bacterial populations. These phenotypes are now recognised as an important factor in the recalcitrance of infections and the development of antibiotic resistance; which itself is currently a major global health crisis. However, despite their clinical importance, we still know little about the mechanisms behind their formation and the relationship between the two phenotypes. Due to the relatively low abundance of the two phenotypes within the population and, in the case of VBNC cells, their ability to remain dormant for extended periods of time, high throughput single cell approaches currently provide the best opportunities for investigating them; in particular microfluidics has emerged as an exciting platform for investigating phenotypic heterogeneity at the single cell level due to the control it allows of the extracellular environment. Using antibiotic persistence as a proxy, we identify temporal windows in which a growing E. coli population exhibits significant changes in phenotypic heterogeneity and determine highly regulated genes and pathways at the population level. We then develop a high throughput microfluidic protocol, based on the pre-existing Mother Machine device, to investigate persister and VBNC cells before, during and after antibiotic exposure at the single cell level. We then developed the first fully automated image analysis pipeline that is capable of analysing Mother Machine images acquired in both bright field and phase contrast imaging modalities. The combination of our protocol and image analysis software allowed us to investigate the role of the previously identified genes in the formation of antibiotic persister and VBNC cells, where we identify potential biomarkers for these phenotypes before exposure to antibiotic. We then used the microfluidic set up to investigate the relationship between protein aggregation and antibiotic persister and VBNC cells. We find that protein aggregation can be correlated to the expression of exogenous proteins and that cells containing visible protein aggregates are, in turn, more likely to be persister or VBNC cells; providing further evidence that these phenotypes are not distinct and are instead part of one physiological continuum

    A Primal-Dual Solver for Large-Scale Tracking-by-Assignment

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    We propose a fast approximate solver for the combinatorial problem known as tracking-by-assignment, which we apply to cell tracking. The latter plays a key role in discovery in many life sciences, especially in cell and developmental biology. So far, in the most general setting this problem was addressed by off-the-shelf solvers like Gurobi, whose run time and memory requirements rapidly grow with the size of the input. In contrast, for our method this growth is nearly linear. Our contribution consists of a new (1) decomposable compact representation of the problem; (2) dual block-coordinate ascent method for optimizing the decomposition-based dual; and (3) primal heuristics that reconstructs a feasible integer solution based on the dual information. Compared to solving the problem with Gurobi, we observe an up to~60~times speed-up, while reducing the memory footprint significantly. We demonstrate the efficacy of our method on real-world tracking problems.Comment: 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 202
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