Alphan Altinok

Measuring single-cell gene expression dynamics in bacteria using fluorescence time-lapse microscopy

Quantitative single-cell time-lapse microscopy is a powerful method for analyzing gene circuit dynamics and heterogeneous cell behavior. We describe the application of this method to imaging bacteria by using an automated microscopy system. This protocol has been used to analyze sporulation and competence differentiation in Bacillus subtilis, and to quantify gene regulation and its fluctuations in individual Escherichia coli cells. The protocol involves seeding and growing bacteria on small agarose pads and imaging the resulting microcolonies. Images are then reviewed and analyzed using our laboratorys custom MATLAB analysis code, which segments and tracks cells in a frame-to-frame method. This process yields quantitative expression data on cell lineages, which can illustrate dynamic expression profiles and facilitate mathematical models of gene circuits. With fast-growing bacteria, such as E. coli or B. subtilis, image acquisition can be completed in 1 d, with an additional 1–2 d for progressing through the analysis procedure.

Dynamic Heterogeneity and DNA Methylation in Embryonic Stem Cells

Summary Cell populations can be strikingly heterogeneous, composed of multiple cellular states, each exhibiting stochastic noise in its gene expression. A major challenge is to disentangle these two types of variability and to understand the dynamic processes and mechanisms that control them. Embryonic stem cells (ESCs) provide an ideal model system to address this issue because they exhibit heterogeneous and dynamic expression of functionally important regulatory factors. We analyzed gene expression in individual ESCs using single-molecule RNA-FISH and quantitative time-lapse movies. These data discriminated stochastic switching between two coherent (correlated) gene expression states and burst-like transcriptional noise. We further showed that the “2i” signaling pathway inhibitors modulate both types of variation. Finally, we found that DNA methylation plays a key role in maintaining these metastable states. Together, these results show how ESC gene expression states and dynamics arise from a combination of intrinsic noise, coherent cellular states, and epigenetic regulation.

Automated tracking and modeling of microtubule dynamics

The method of microtubule tracking and dynamics analysis, presented here, improves upon the current means of manual and automated quantification of microtubule behavior. Key contributions are increasing accuracy and data volume, eliminating user bias and providing advanced analysis tools for the discovery of temporal patterns in cellular processes. By tracking the entire length of each resolvable microtubule, as opposed to only the tip, it is possible to boost dynamics studies with positional information that is virtually impossible to collect manually. We demonstrate the method on the analysis of a microtubule dataset, which was manually tracked and analyzed in the study of betaIII-tubulin isoform. Our results show that automated recognition of temporal patterns in cellular processes offers a highly promising potential

Activity Analysis in Microtubule Videos by Mixture of Hidden Markov Models

We present an automated method for the tracking and dynamics modeling of microtubules -a major component of the cytoskeleton- which provides researchers with a previously unattainable level of data analysis and quantification capabilities. The proposed method improves upon the manual tracking and analysis techniques by i) increasing accuracy and quantified sample size in data collection, ii) eliminating user bias and standardizing analysis, iii) making available new features that are impractical to capture manually, iv) enabling statistical extraction of dynamics patterns from cellular processes, and v) greatly reducing required time for entire studies. An automated procedure is proposed to track each resolvable microtubule, whose aggregate activity is then modeled by mixtures of Hidden Markov Models to uncover dynamics patterns of underlying cellular and experimental conditions. Our results support manually established findings on an actual microtubule dataset and illustrate how automated analysis of spatial and temporal patterns offers previously unattainable insights to cellular processes.

Combinatorial Tau Pseudophosphorylation MARKEDLY DIFFERENT REGULATORY EFFECTS ON MICROTUBULE ASSEMBLY AND DYNAMIC INSTABILITY THAN THE SUM OF THE INDIVIDUAL PARTS

Tau is a multiply phosphorylated protein that is essential for the development and maintenance of the nervous system. Errors in Tau action are associated with Alzheimer disease and related dementias. A huge literature has led to the widely held notion that aberrant Tau hyperphosphorylation is central to these disorders. Unfortunately, our mechanistic understanding of the functional effects of combinatorial Tau phosphorylation remains minimal. Here, we generated four singly pseudophosphorylated Tau proteins (at Thr231, Ser262, Ser396, and Ser404) and four doubly pseudophosphorylated Tau proteins using the same sites. Each Tau preparation was assayed for its abilities to promote microtubule assembly and to regulate microtubule dynamic instability in vitro. All four singly pseudophosphorylated Tau proteins exhibited loss-of-function effects. In marked contrast to the expectation that doubly pseudophosphorylated Tau would be less functional than either of its corresponding singly pseudophosphorylated forms, all of the doubly pseudophosphorylated Tau proteins possessed enhanced microtubule assembly activity and were more potent at regulating dynamic instability than their compromised singly pseudophosphorylated counterparts. Thus, the effects of multiple pseudophosphorylations were not simply the sum of the effects of the constituent single pseudophosphorylations; rather, they were generally opposite to the effects of singly pseudophosphorylated Tau. Further, despite being pseudophosphorylated at different sites, the four singly pseduophosphorylated Tau proteins often functioned similarly, as did the four doubly pseudophosphorylated proteins. These data lead us to reassess the conventional view of combinatorial phosphorylation in normal and pathological Tau action. They may also be relevant to the issue of combinatorial phosphorylation as a general regulatory mechanism.

Model based dynamics analysis in live cell microtubule images

BackgroundThe dynamic growing and shortening behaviors of microtubules are central to the fundamental roles played by microtubules in essentially all eukaryotic cells. Traditionally, microtubule behavior is quantified by manually tracking individual microtubules in time-lapse images under various experimental conditions. Manual analysis is laborious, approximate, and often offers limited analytical capability in extracting potentially valuable information from the data.ResultsIn this work, we present computer vision and machine-learning based methods for extracting novel dynamics information from time-lapse images. Using actual microtubule data, we estimate statistical models of microtubule behavior that are highly effective in identifying common and distinct characteristics of microtubule dynamic behavior.ConclusionComputational methods provide powerful analytical capabilities in addition to traditional analysis methods for studying microtubule dynamic behavior. Novel capabilities, such as building and querying microtubule image databases, are introduced to quantify and analyze microtubule dynamic behavior.

Variable Length Open Contour Tracking Using a Deformable Trellis

This paper focuses on contour tracking, an important problem in computer vision, and specifically on open contours that often directly represent a curvilinear object. Compelling applications are found in the field of bioimage analysis where blood vessels, dendrites, and various other biological structures are tracked over time. General open contour tracking, and biological images in particular, pose major challenges including scene clutter with similar structures (e.g., in the cell), and time varying contour length due to natural growth and shortening phenomena, which have not been adequately answered by earlier approaches based on closed and fixed end-point contours. We propose a model-based estimation algorithm to track open contours of time-varying length, which is robust to neighborhood clutter with similar structures. The method employs a deformable trellis in conjunction with a probabilistic (hidden Markov) model to estimate contour position, deformation, growth and shortening. It generates a maximum a posteriori estimate given observations in the current frame and prior contour information from previous frames. Experimental results on synthetic and real-world data demonstrate the effectiveness and performance gains of the proposed algorithm.

Deformable trellis: open contour tracking in bio-image sequences

This paper presents an open contour tracking method that employs an arc-emission hidden Markov model (HMM). The algorithm encodes the shape information of the structure in a spatially deformable trellis model that is iteratively modified to account for observations in subsequent frames. As the open contour is determined on the trellis of an HMM, a dynamic programming procedure reduces the computational complexity to linear in the length of the structure (or contour). The method was developed for tracking general curvilinear structures, and tested on subcellular image sequences, where microtubules grow, shrink and undergo lateral motion from frame to frame. Microtubule length changes are modeled by the addition of appropriate transient and absorbing states to the HMM. Our results provide experimental evidence for the proposed algorithms capability to track non-rigid curvilinear objects in challenging environments in terms of noise and clutter.

TRACING MICROTUBULES IN LIVE CELL IMAGES

Microtubule (MT) dynamics are traditionally analyzed from time lapse images by manual techniques that are laborious, approximate and often limited. Recently, computer vision techniques have been applied to the problem of automated tracking of MTs in live cell images. Aside of very low signal to noise ratios, live cell images of MTs exhibit severe clutter for accurate tracing of MT body. Moreover, intersecting and overlapping MT regions appear brighter due to additive fluorescence. In this paper, we present a MT body tracing algorithm that addresses the clutter without imposing directional constraints. We show that MT dynamics can be quantified with enhanced precision, and novel measurements that are beyond manual feasibility, can be obtained accurately. We demonstrate our results on actual images of MTs obtained by live cell fluorescence microscopy.

Image analysis and empirical modeling of gene and protein expression.

Protein gradients and gene expression patterns are major determinants in the differentiation and fate map of the developing embryo. Here we discuss computational methods to quantitatively measure the positions of gene expression domains and the gradients of protein expression along the dorsal-ventral axis in the Drosophila embryo. Our methodology involves three layers of data. The first layer, or the primary data, consists of z-stack confocal images of embryos processed by in situ hybridization and/or antibody stainings. The secondary data are relationships between location, usually an x-axis coordinate, and fluorescent intensity of gene or protein detection. Tertiary data comprise the optimal parameters that arise from fits of the secondary data to empirical models. The tertiary data are useful to distill large datasets of imaged embryos down to a tractable number of conceptually useful parameters. This analysis allows us to detect subtle phenotypes and is adaptable to any set of genes or proteins with a canonical pattern. For example, we show how insights into the Dorsal transcription factor protein gradient and its target gene ventral-neuroblasts defective (vnd) were obtained using such quantitative approaches.