Cell segmentation by multi-resolution analysis and maximum likelihood estimation (MAMLE)

A Cell Segmentation/Tracking Tool Based on Machine Learning

The ability to gain quantifiable, single-cell data from time-lapse microscopy images is dependent upon cell segmentation and tracking. Here, we present a detailed protocol for obtaining quality time-lapse movies and introduce a method to identify (segment) and track cells based on machine learning techniques (Fiji's Trainable Weka Segmentation) and custom, open-source Python scripts. To provide a hands-on experience, we provide datasets obtained using the aforementioned protocol.

Segmentation of clustered cells in negative phase contrast images with integrated light intensity and cell shape information

Automated cell segmentation plays a key role in characterisations of cell behaviours for both biology research and clinical practices. Currently, the segmentation of clustered cells still remains as a challenge and is the main reason for false segmentation. In this study, the emphasis was put on the segmentation of clustered cells in negative phase contrast images. A new method was proposed to combine both light intensity and cell shape information through the construction of grey-weighted distance transform (GWDT) within preliminarily segmented areas. With the constructed GWDT, the clustered cells can be detected and then separated with a modified region skeleton-based method. Moreover, a contour expansion operation was applied to get optimised detection of cell boundaries. In this paper, the working principle and detailed procedure of the proposed method are described, followed by the evaluation of the method on clustered cell segmentation. Results show that the proposed method achieves an improved performance in clustered cell segmentation compared with other methods, with 85.8% and 97.16% accuracy rate for clustered cells and all cells, respectively.

Beyond the bulk: disclosing the life of single microbial cells

Microbial single cell analysis has led to discoveries that are beyond what can be resolved with population-based studies. It provides a pristine view of the mechanisms that organize cellular physiology, unbiased by population heterogeneity or uncontrollable environmental impacts. A holistic description of cellular functions at the single cell level requires analytical concepts beyond the miniaturization of existing technologies, defined but uncontrolled by the biological system itself. This review provides an overview of the latest advances in single cell technologies and demonstrates their potential. Opportunities and limitations of single cell microbiology are discussed using selected application-related examples.

Polar Localization of the Serine Chemoreceptor of Escherichia coli Is Nucleoid Exclusion-Dependent

We studied whether nucleoid exclusion contributes to the segregation and retention of Tsr chemoreceptor clusters at the cell poles. Using live time-lapse, single-cell microscopy measurements, we show that the single-cell spatial distributions of Tsr clusters have heterogeneities and asymmetries that are consistent with nucleoid exclusion and cannot be explained by the diffusion-and-capture mechanism supported by Tol-Pal complexes at the poles. Also, in cells subjected to ampicillin, which enhances relative nucleoid lengths, Tsr clusters locate relatively closer to the cell extremities, whereas in anucleated cells (deletion mutants for mukB), the Tsr clusters are closer to midcell. In addition, we find that the fraction of Tsr clusters at the poles is smaller in deletion mutants for Tol-Pal than in wild-type cells, although it is still larger than would be expected by chance. Also in deletion mutants, the distribution of Tsr clusters differs widely between cells with relatively small and large nucleoids, in a manner consistent with nucleoid exclusion from midcell. This comparison further showed that diffusion-and-capture by Tol-Pal complexes and nucleoid exclusion from the midcell have complementary effects. Subsequently, we subjected deletion mutants to suboptimal temperatures that are known to enhance cytoplasm viscosity, which hampers nucleoid exclusion effects. As the temperature was lowered, the fraction of clusters at the poles decreased linearly. Finally, a stochastic model including nucleoid exclusion at midcell and diffusion-and-capture due to Tol-Pal at the poles is shown to exhibit a cluster dynamics that is consistent with the empirical data. We conclude that nucleoid exclusion also contributes to the preference of Tsr clusters for polar localization.

Trajectory energy minimization for cell growth tracking and genealogy analysis

Cell growth experiments with a microfluidic device produce large-scale time-lapse image data, which contain important information on cell growth and patterns in their genealogy. To extract such information, we propose a scheme to segment and track bacterial cells automatically. In contrast with most published approaches, which often split segmentation and tracking into two independent procedures, we focus on designing an algorithm that describes cell properties evolving between consecutive frames by feeding segmentation and tracking results from one frame to the next one. The cell boundaries are extracted by minimizing the distance regularized level set evolution (DRLSE) model. Each individual cell was identified and tracked by identifying cell septum and membrane as well as developing a trajectory energy minimization function along time-lapse series. Experiments show that by applying this scheme, cell growth and division can be measured automatically. The results show the efficiency of the approach when testing on different datasets while comparing with other existing algorithms. The proposed approach demonstrates great potential for large-scale bacterial cell growth analysis.

Robust microbial cell segmentation by optical-phase thresholding with minimal processing requirements

High-throughput imaging with single-cell resolution has enabled remarkable discoveries in cell physiology and Systems Biology investigations. A common, and often the most challenging step in all such imaging implementations, is the ability to segment multiple images to regions that correspond to individual cells. Here, a robust segmentation strategy for microbial cells using Quantitative Phase Imaging is reported. The proposed method enables a greater than 99% yeast cell segmentation success rate, without any computationally-intensive, post-acquisition processing. We also detail how the method can be expanded to bacterial cell segmentation with 98% success rates with substantially reduced processing requirements in comparison to existing methods. We attribute this improved performance to the remarkably uniform background, elimination of cell-to-cell and intracellular optical artifacts, and enhanced signal-to-background ratio-all innate properties of imaging in the optical-phase domain. © 2017 International Society for Advancement of Cytometry.

Dissecting the stochastic transcription initiation process in live Escherichia coli

We investigate the hypothesis that, in Escherichia coli, while the concentration of RNA polymerases differs in different growth conditions, the fraction of RNA polymerases free for transcription remains approximately constant within a certain range of these conditions. After establishing this, we apply a standard model-fitting procedure to fully characterize the in vivo kinetics of the rate-limiting steps in transcription initiation of the Plac/ara-1 promoter from distributions of intervals between transcription events in cells with different RNA polymerase concentrations. We find that, under full induction, the closed complex lasts ∼788 s while subsequent steps last ∼193 s, on average. We then establish that the closed complex formation usually occurs multiple times prior to each successful initiation event. Furthermore, the promoter intermittently switches to an inactive state that, on average, lasts ∼87 s. This is shown to arise from the intermittent repression of the promoter by LacI. The methods employed here should be of use to resolve the rate-limiting steps governing the in vivo dynamics of initiation of prokaryotic promoters, similar to established steady-state assays to resolve the in vitro dynamics.

Robustness of the Process of Nucleoid Exclusion of Protein Aggregates in Escherichia coli

Escherichia coli segregates protein aggregates to the poles by nucleoid exclusion. Combined with cell divisions, this generates heterogeneous aggregate distributions in subsequent cell generations. We studied the robustness of this process with differing medium richness and antibiotics stress, which affect nucleoid size, using multimodal, time-lapse microscopy of live cells expressing both a fluorescently tagged chaperone (IbpA), which identifies in vivo the location of aggregates, and HupA-mCherry, a fluorescent variant of a nucleoid-associated protein. We find that the relative sizes of the nucleoid's major and minor axes change widely, in a positively correlated fashion, with medium richness and antibiotic stress. The aggregate's distribution along the major cell axis also changes between conditions and in agreement with the nucleoid exclusion phenomenon. Consequently, the fraction of aggregates at the midcell region prior to cell division differs between conditions, which will affect the degree of asymmetries in the partitioning of aggregates between cells of future generations. Finally, from the location of the peak of anisotropy in the aggregate displacement distribution, the nucleoid relative size, and the spatiotemporal aggregate distribution, we find that the exclusion of detectable aggregates from midcell is most pronounced in cells with mid-sized nucleoids, which are most common under optimal conditions. We conclude that the aggregate management mechanisms of E. coli are significantly robust but are not immune to stresses due to the tangible effect that these have on nucleoid size.

IMPORTANCE

Escherichia coli segregates protein aggregates to the poles by nucleoid exclusion. From live single-cell microscopy studies of the robustness of this process to various stresses known to affect nucleoid size, we find that nucleoid size and aggregate preferential locations change concordantly between conditions. Also, the degree of influence of the nucleoid on aggregate positioning differs between conditions, causing aggregate numbers at midcell to differ in cell division events, which will affect the degree of asymmetries in the partitioning of aggregates between cells of future generations. Finally, we find that aggregate segregation to the cell poles is most pronounced in cells with mid-sized nucleoids. We conclude that the energy-free process of the midcell exclusion of aggregates partially loses effectiveness under stressful conditions.

FogBank: a single cell segmentation across multiple cell lines and image modalities

Background

Many cell lines currently used in medical research, such as cancer cells or stem cells, grow in confluent sheets or colonies. The biology of individual cells provide valuable information, thus the separation of touching cells in these microscopy images is critical for counting, identification and measurement of individual cells. Over-segmentation of single cells continues to be a major problem for methods based on morphological watershed due to the high level of noise in microscopy cell images. There is a need for a new segmentation method that is robust over a wide variety of biological images and can accurately separate individual cells even in challenging datasets such as confluent sheets or colonies.

Results

We present a new automated segmentation method called FogBank that accurately separates cells when confluent and touching each other. This technique is successfully applied to phase contrast, bright field, fluorescence microscopy and binary images. The method is based on morphological watershed principles with two new features to improve accuracy and minimize over-segmentation.

First, FogBank uses histogram binning to quantize pixel intensities which minimizes the image noise that causes over-segmentation. Second, FogBank uses a geodesic distance mask derived from raw images to detect the shapes of individual cells, in contrast to the more linear cell edges that other watershed-like algorithms produce.

We evaluated the segmentation accuracy against manually segmented datasets using two metrics. FogBank achieved segmentation accuracy on the order of 0.75 (1 being a perfect match). We compared our method with other available segmentation techniques in term of achieved performance over the reference data sets. FogBank outperformed all related algorithms. The accuracy has also been visually verified on data sets with 14 cell lines across 3 imaging modalities leading to 876 segmentation evaluation images.

Conclusions

FogBank produces single cell segmentation from confluent cell sheets with high accuracy. It can be applied to microscopy images of multiple cell lines and a variety of imaging modalities. The code for the segmentation method is available as open-source and includes a Graphical User Interface for user friendly execution.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0431-x) contains supplementary material, which is available to authorized users.

In vivo kinetics of segregation and polar retention of MS2-GFP-RNA complexes in Escherichia coli

The cytoplasm of Escherichia coli is a crowded, heterogeneous environment. From single cell live imaging, we investigated the spatial kinetics and heterogeneities of synthetic RNA-protein complexes. First, although their known tendency to accumulate at the cell poles does not appear to introduce asymmetries between older and newer cell poles within a cell lifetime, these emerge with cell divisions. This suggests strong polar retention of the complexes, which we verified in their history of positions and mean escape time from the poles. Next, we show that the polar retention relies on anisotropies in the displacement distribution in the region between midcell and poles, whereas the speed is homogeneous along the major cell axis. Afterward, we establish that these regions are at the border of the nucleoid and shift outward with cell growth, due to the nucleoid's replication. Overall, the spatiotemporal kinetics of the complexes, which is robust to suboptimal temperatures, suggests that nucleoid occlusion is a source of dynamic heterogeneities of macromolecules in E. coli that ultimately generate phenotypic differences between sister cells.

In vivo transcription kinetics of a synthetic gene uninvolved in stress-response pathways in stressed Escherichia coli cells

The fast adaptation of Escherichia coli to stressful environments includes the regulation of gene expression rates, mainly of transcription, by specific and global stress-response mechanisms. To study the effects of mechanisms acting on a global level, we observed with single molecule sensitivity the effects of mild acidic shift and oxidative stress on the in vivo transcription dynamics of a probe gene encoding an RNA target for MS2d-GFP, under the control of a synthetic promoter. After showing that this promoter is uninvolved in fast stress-response pathways, we compared its kinetics of transcript production under stress and in optimal conditions. We find that, following the application of either stress, the mean rates of transcription activation and of subsequent RNA production of the probe gene are reduced, particularly under oxidative stress. Meanwhile, the noise in RNA production decreases under oxidative stress, but not under acidic shift. From distributions of intervals between consecutive RNA productions, we infer that the number and duration of the rate-limiting steps in transcription initiation change, following the application of stress. These changes differ in the two stress conditions and are consistent with the changes in noise in RNA production. Overall, our measurements of the transcription initiation kinetics of the probe gene indicate that, following sub-lethal stresses, there are stress-specific changes in the dynamics of transcription initiation of the probe gene that affect its mean rate and noise of transcript production. Given the non-involvement of the probe gene in stress-response pathways, we suggest that these changes are caused by global response mechanisms of E. coli to stress.