Bright Field Microscopy as an Alternative to Whole Cell Fluorescence in Automated Analysis of Macrophage Images

Fluorescence microscopy is the standard tool for detection and analysis of cellular phenomena. This technique, however, has a number of drawbacks such as the limited number of available fluorescent channels in microscopes, overlapping excitation and emission spectra of the stains, and phototoxicity.We here present and validate a method to automatically detect cell population outlines directly from bright field images. By imaging samples with several focus levels forming a bright field -stack, and by measuring the intensity variations of this stack over the -dimension, we construct a new two dimensional projection image of increased contrast. With additional information for locations of each cell, such as stained nuclei, this bright field projection image can be used instead of whole cell fluorescence to locate borders of individual cells, separating touching cells, and enabling single cell analysis. Using the popular CellProfiler freeware cell image analysis software mainly targeted for fluorescence microscopy, we validate our method by automatically segmenting low contrast and rather complex shaped murine macrophage cells.The proposed approach frees up a fluorescence channel, which can be used for subcellular studies. It also facilitates cell shape measurement in experiments where whole cell fluorescent staining is either not available, or is dependent on a particular experimental condition. We show that whole cell area detection results using our projected bright field images match closely to the standard approach where cell areas are localized using fluorescence, and conclude that the high contrast bright field projection image can directly replace one fluorescent channel in whole cell quantification. Matlab code for calculating the projections can be downloaded from the supplementary site: http://sites.google.com/site/brightfieldorstaining

In vivo kinetics of transcription initiation of the lar promoter in Escherichia coli. Evidence for a sequential mechanism with two rate-limiting steps

<p>Abstract</p> <p>Background</p> <p>In <it>Escherichia coli </it>the mean and cell-to-cell diversity in RNA numbers of different genes vary widely. This is likely due to different kinetics of transcription initiation, a complex process with multiple rate-limiting steps that affect RNA production.</p> <p>Results</p> <p>We measured the <it>in vivo </it>kinetics of production of individual RNA molecules under the control of the lar promoter in <it>E. coli</it>. From the analysis of the distributions of intervals between transcription events in the regimes of weak and medium induction, we find that the process of transcription initiation of this promoter involves a sequential mechanism with two main rate-limiting steps, each lasting hundreds of seconds. Both steps become faster with increasing induction by IPTG and Arabinose.</p> <p>Conclusions</p> <p>The two rate-limiting steps in initiation are found to be important regulators of the dynamics of RNA production under the control of the lar promoter in the regimes of weak and medium induction. Variability in the intervals between consecutive RNA productions is much lower than if there was only one rate-limiting step with a duration following an exponential distribution. The methodology proposed here to analyze the <it>in vivo </it>dynamics of transcription may be applicable at a genome-wide scale and provide valuable insight into the dynamics of prokaryotic genetic networks.</p

Cell-to-cell diversity in protein levels of a gene driven by a tetracycline inducible promoter

<p>Abstract</p> <p>Background</p> <p>Gene expression in <it>Escherichia coli </it>is regulated by several mechanisms. We measured in single cells the expression level of a single copy gene coding for green fluorescent protein (GFP), integrated into the genome and driven by a tetracycline inducible promoter, for varying induction strengths. Also, we measured the transcriptional activity of a tetracycline inducible promoter controlling the transcription of a RNA with 96 binding sites for MS2-GFP.</p> <p>Results</p> <p>The distribution of GFP levels in single cells is found to change significantly as induction reaches high levels, causing the Fano factor of the cells' protein levels to increase with mean level, beyond what would be expected from a Poisson-like process of RNA transcription. In agreement, the Fano factor of the cells' number of RNA molecules target for MS2-GFP follows a similar trend. The results provide evidence that the dynamics of the promoter complex formation, namely, the variability in its duration from one transcription event to the next, explains the change in the distribution of expression levels in the cell population with induction strength.</p> <p>Conclusions</p> <p>The results suggest that the open complex formation of the tetracycline inducible promoter, in the regime of strong induction, affects significantly the dynamics of RNA production due to the variability of its duration from one event to the next.</p

Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images

<p>Abstract</p> <p>Background</p> <p>Several algorithms have been proposed for detecting fluorescently labeled subcellular objects in microscope images. Many of these algorithms have been designed for specific tasks and validated with limited image data. But despite the potential of using extensive comparisons between algorithms to provide useful information to guide method selection and thus more accurate results, relatively few studies have been performed.</p> <p>Results</p> <p>To better understand algorithm performance under different conditions, we have carried out a comparative study including eleven spot detection or segmentation algorithms from various application fields. We used microscope images from well plate experiments with a human osteosarcoma cell line and frames from image stacks of yeast cells in different focal planes. These experimentally derived images permit a comparison of method performance in realistic situations where the number of objects varies within image set. We also used simulated microscope images in order to compare the methods and validate them against a ground truth reference result. Our study finds major differences in the performance of different algorithms, in terms of both object counts and segmentation accuracies.</p> <p>Conclusions</p> <p>These results suggest that the selection of detection algorithms for image based screens should be done carefully and take into account different conditions, such as the possibility of acquiring empty images or images with very few spots. Our inclusion of methods that have not been used before in this context broadens the set of available detection methods and compares them against the current state-of-the-art methods for subcellular particle detection.</p

Image Analysis Algorithms for Single-Cell Study in Systems Biology

With the contiguous shift of biology from a qualitative toward a quantitative field of research, digital microscopy and image-based measurements are drawing increased interest. Several methods have been developed for acquiring images of cells and intracellular organelles. Traditionally, acquired images are analyzed manually through visual inspection. The increasing volume of data is challenging the scope of manual analysis, and there is a need to develop methods for automated analysis. This thesis examines the development and application of computational methods for acquisition and analysis of images from single-cell assays. The thesis proceeds with three different aspects.First, a study evaluates several methods for focusing microscopes and proposes a novel strategy to perform focusing in time-lapse imaging. The method relies on the nature of the focus-drift and its predictability. The study shows that focus-drift is a dynamical system with a small randomness. Therefore, a prediction-based method is employed to track the focus-drift overtime. A prototype implementation of the proposed method is created by extending the Nikon EZ-C1 Version 3.30 (Tokyo, Japan) imaging platform for acquiring images with a Nikon Eclipse (TE2000-U, Nikon, Japan) microscope.Second, a novel method is formulated to segment individual cells from a dense cluster. The method incorporates multi-resolution analysis with maximum-likelihood estimation (MAMLE) for cell detection. The MAMLE performs cell segmentation in two phases. The initial phase relies on a cutting-edge filter, edge detection in multi-resolution with a morphological operator, and threshold decomposition for adaptive thresholding. It estimates morphological features from the initial results. In the next phase, the final segmentation is constructed by boosting the initial results with the estimated parameters. The MAMLE method is evaluated with de novo data sets as well as with benchmark data from public databases. An empirical evaluation of the MAMLE method confirms its accuracy.Third, a comparative study is carried out on performance evaluation of state-ofthe-art methods for the detection of subcellular organelles. This study includes eleven algorithms developed in different fields for segmentation. The evaluation procedure encompasses a broad set of samples, ranging from benchmark data to synthetic images. The result from this study suggests that there is no particular method which performs superior to others in the test samples. Next, the effect of tetracycline on transcription dynamics of tetA promoter in Escherichia coli (E. coli ) cells is studied. This study measures expressions of RNA by tagging the MS2d-GFP vector with a target gene. The RNAs are observed as intracellular spots in confocal images. The kernel density estimation (KDE) method for detecting the intracellular spots is employed to quantify the individual RNA molecules.The thesis summarizes the results from five publications. Most of the publications are associated with different methods for imaging and analysis of microscopy. Confocal images with E. coli cells are targeted as the primary area of application. However, potential applications beyond the primary target are also made evident. The findings of the research are confirmed empirically

Dissection of the Rate Constants of a Transcription Repression Mechanism from Live Single Cell, Single Molecule Microscopy Data

Transcription is a critical process in cells, as it allows to transform the information stored in the DNA and shaped by evolution, into RNA molecules that, once translated into proteins, are capable of performing a multitude of tasks that are necessary for maintain-ing the cell alive. Aside from identifying the main molecules involved in transcription, to fully compre-hend this process, we need to characterize its dynamics. This will allow a better under-standing of the mechanisms regulating gene expression. The regulatory mechanisms of gene expression are the means by which cells activate or repress, fully or to some extent, a gene’s transcriptional activity. It is this regulation that makes possible the response to environmental changes, as well as the establishment of critical internal cycles, such as the cycle responsible for cell replication. Here, we investigated, at the single cell, single gene level, the dynamics of the process of transcriptional regulation the promoter LacO3O1 by gene-specific regulatory mole-cules, namely, inducers. Our goal was to, from live, single cell, single molecule data, obtain the values of the rate constants associated with the repression mechanism of tran-scription of this promoter. Based on direct measurements of RNA production kinetics at different induction levels, and by estimating the RNA production rate at infinite induction we inferred that, under full induction, the LacO3O1 promoter, on average, spends 12% of the time between consecutive RNA productions in the OFF state

Extrinsic Noise Effects Regulation at the Single Gene and Small Gene Network Levels

Recent studies of gene expression in Escherichia coli using novel in vivo measurement techniques revealed that protein and RNA numbers from a gene differ between genetically identical cells. To unravel the causes for this, measurements were conducted and models were developed. These studies revealed that this diversity arises from extrinsic and intrinsic noise. The former is due to cell-to-cell variability in numbers of molecules involved, such as RNA polymerase (RNAp), transcription factors, etc. The latter is due to the stochastic nature of the chemical reactions combined with the fact that the molecules and genes involved exist in small numbers. One aspect that has not been given much attention so far, is the unique nature of the dynamics of transcription of each promoter of the gene regulatory network (GRN). This process has multiple rate-limiting steps whose duration differs between promoters. How this may diversify the variability in RNA and protein numbers between genes is unknown. To address this, we use single-cell empirical data and stochastic models with empirically validated parameter values and study how the kinetics of transcription of a gene affects the influence of extrinsic noise on the kinetics. Interestingly, we find that promoters whose open complex formation is longer lasting tend to suppress the propagation of extrinsic noise that affects only the steps prior to initiation of the open complex formation. In particular, our studies indicate that the cell-to-cell variability in RNA numbers depends on the transcription kinetics. As such, it is sequence-dependent. Further, in a 2-gene toggle switch, we find that its mean switching frequency depends on the transcription kinetics of the promoters but not on the cell-to-cell RNAp variability. On the other hand, the cell-to-cell variability in switching frequency is affected by these two variables. Meanwhile, in a Repressilator network (3 genes where each gene represses the next), we measured the mean and standard deviation of the period of oscillation. From these measurements in silico, we found that both parameters are independent of the RNAP cell-to-cell variability, but are strongly controlled by the transcription kinetics of each of its genes. We conclude that the transcription kinetics of the component genes is a key regulator of small genetic circuits, as it can be used as a tunable filter of extrinsic noise. Overall, the kinetics of the rate-limiting steps in transcription of individual genes act as ‘master regulators’ of the expression of individual genes and the behavior of genetic cir-cuits’, such as switching dynamics, period of oscillation, etc

Stochastic Processes as a Source of Cell to Cell Diversity and Cellular Ageing

Even populations of monoclonal cells exhibit phenotypic diversity. There are several sources generating such diversity, including stochasticity in the dynamics of gene expression, and the stochastic partitioning of molecules during division. This thesis focuses on the construction and simulation of a realistic model of gene expression and on the stochastic partitioning of cellular components during cell division. First, we present and make use of statistical methods to extract information on the kinetics of gene expression from live-cell measurements at the single RNA molecule level. This information allows us to characterize the kinetics of the multi-stepped process of transcription initiation, including the degree of noise in transcript production, as well as the kinetics of partitioning of protein aggregates by the cell’s poles. A model of single gene expression in a growing population of cells and a model of ageing in bacteria are then constructed based upon these measurements. Next, we present a new simulator which uses the Stochastic Simulation Algorithm to simulate the dynamics of intracellular processes in populations of cells, each of which able to grow and divide with random partitioning of molecules. Cells are represented in the simulator by compartments that can be created and destroyed at runtime. Logarithmic simulation algorithms and efficient data structures were designed and are here presented, which minimize the computational cost of simulating the dynamics of large cell populations that involve a large number of chemical reactions