Katarina Mele

Identification of a distal GLUT4 trafficking event controlled by actin polymerization.

The insulin-stimulated trafficking of GLUT4 to the plasma membrane in muscle and fat tissue constitutes a central process in blood glucose homeostasis. The tethering, docking, and fusion of GLUT4 vesicles with the plasma membrane (PM) represent the most distal steps in this pathway and have been recently shown to be key targets of insulin action. However, it remains unclear how insulin influences these processes to promote the insertion of the glucose transporter into the PM. In this study we have identified a previously uncharacterized role for cortical actin in the distal trafficking of GLUT4. Using high-frequency total internal reflection fluorescence microscopy (TIRFM) imaging, we show that insulin increases actin polymerization near the PM and that disruption of this process inhibited GLUT4 exocytosis. Using TIRFM in combination with probes that could distinguish between vesicle transport and fusion, we found that defective actin remodeling was accompanied by normal insulin-regulated accumulation of GLUT4 vesicles close to the PM, but the final exocytotic fusion step was impaired. These data clearly resolve multiple steps of the final stages of GLUT4 trafficking, demonstrating a crucial role for actin in the final stage of this process.

Exocytotic vesicle behaviour assessed by total internal reflection fluorescence microscopy.

The regulated trafficking or exocytosis of cargo‐containing vesicles to the cell surface is fundamental to all cells. By coupling the technology of fluorescently tagged fusion proteins with total internal reflection fluorescence microscopy (TIRFM), it is possible to achieve the high spatio‐temporal resolution required to study the dynamics of sub‐plasma membrane vesicle trafficking and exocytosis. TIRFM has been used in a number of cell types to visualize and dissect the various steps of exocytosis revealing how molecules identified via genetic and/or biochemical approaches are involved in the regulation of this process. Here, we summarize the contribution of TIRFM to our understanding of the mechanism of exocytosis and discuss the novel methods of analysis that are required to exploit the large volumes of data that can be produced using this technique.

Multi-Target Tracking With Time-Varying Clutter Rate and Detection Profile: Application to Time-Lapse Cell Microscopy Sequences

Quantitative analysis of the dynamics of tiny cellular and sub-cellular structures, known as particles, in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking method capable of tracking numerous similar targets in the presence of high levels of noise, high target density, complex motion patterns and intricate interactions. In this paper, we propose a framework for tracking these structures based on the random finite set Bayesian filtering framework. We focus on challenging biological applications where image characteristics such as noise and background intensity change during the acquisition process. Under these conditions, detection methods usually fail to detect all particles and are often followed by missed detections and many spurious measurements with unknown and time-varying rates. To deal with this, we propose a bootstrap filter composed of an estimator and a tracker. The estimator adaptively estimates the required meta parameters for the tracker such as clutter rate and the detection probability of the targets, while the tracker estimates the state of the targets. Our results show that the proposed approach can outperform state-of-the-art particle trackers on both synthetic and real data in this regime.

Gab2 regulates cytoskeletal organization and migration of mammary epithelial cells by modulating RhoA activation

The oncogenic signal transducer Gab2 mediates altered cytoskeletal organization and enhanced cell migration of mammary epithelial cells via down-regulation of RhoA activity. This sheds new light on the role of Gab2 in cancer cell metastasis.

Novel Systems for Dynamically Assessing Insulin Action in Live Cells Reveals Heterogeneity in the Insulin Response

Regulated GLUT4 trafficking is a key action of insulin. Quantitative stepwise analysis of this process provides a powerful tool for pinpointing regulatory nodes that contribute to insulin regulation and insulin resistance. We describe a novel GLUT4 construct and workflow for the streamlined dissection of GLUT4 trafficking; from simple high throughput screens to high resolution analyses of individual vesicles. We reveal single cell heterogeneity in insulin action highlighting the utility of this approach – each cell displayed a unique and highly reproducible insulin response, implying that each cell is hard‐wired to produce a specific output in response to a given stimulus. These data highlight that the response of a cell population to insulin is underpinned by extensive heterogeneity at the single cell level. This heterogeneity is pre‐programmed within each cell and is not the result of intracellular stochastic events.

A multiple model probability hypothesis density tracker for time-lapse cell microscopy sequences

Quantitative analysis of the dynamics of tiny cellular and subcellular structures in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking method capable of tracking numerous similar targets in the presence of high levels of noise, high target density, maneuvering motion patterns and intricate interactions. The linear Gaussian jump Markov system probability hypothesis density (LGJMS-PHD) filter is a recent Bayesian tracking filter that is well-suited for this task. However, the existing recursion equations for this filter do not consider a state-dependent transition probability matrix. As required in many biological applications, we propose a new closed-form recursion that incorporates this assumption and introduce a general framework for particle tracking using the proposed filter. We apply our scheme to multi-target tracking in total internal reflection fluorescence microscopy (TIRFM) sequences and evaluate the performance of our filter against the existing LGJMS-PHD and IMM-JPDA filters.

A framework for generating realistic synthetic sequences of total internal reflection fluorescence microscopy images

Since generation of reliable ground truth annotation of fluorescence microscopy sequences is usually a laborious and expensive task, many proposed detection and tracking methods have been evaluated using synthetic data with known ground truth. However, differences between real and synthetic images may lead to inaccurate judgment about the performance of an algorithm. In this paper, we present a framework for generating realistic synthetic sequences of total internal reflection fluorescence microscope (TIRFM) through simulation of the image formation process and accurate measurement and dynamic models. The sequences generated using this framework appropriately reflect the complexities existing in real TIRFM sequences.

Automatic identification of fusion events in TIRF microscopy image sequences

This paper presents a novel computer vision system for automated identification of vesicle-plasma membrane fusion events in image sequences obtained from Total Internal Reflection Fluorescence (TIRF) microscopes. Identification of such events is important in order to better understand the process of exocytosis in cells. Manual analysis of thousands of images is painstakingly slow and subjective since the events are hard to recognize, even for experts. The proposed identification method assembles an image sequence in a 3D stack and extracts connected regions representing candidate events. Each candidate is then described by a set of novel domain specific descriptors. Similarity scores between genuine fusion events and fusion candidates are calculated in the PCA (Principal component analysis) eigenspace. The systems performance was evaluated on large TIRF movies as well as on simulated data. The results illustrate the ability of the proposed algorithm to find the majority of fusion events while maintaining a low false positive rate. To our knowledge this paper is the first to report a detailed analysis of fully automatic annotation of large TIRF image sequences.

Towards fully automated identification of vesicle-membrane fusion events in TIRF microscopy

Total internal reflection fluorescence microscopy (TIRF-M) is imposing itself as the tool of choice for studying biological activity in close proximity to the plasma membrane. For example, the exquisite selectivity of TIRF-M allows monitoring the movement of GFP-tagged vesicles and their recruitment to the plasma membrane of cells. We present a novel computer vision system for automatically identifying elusive vesicle-plasma membrane fusion events. Our method is based on robust object tracking and matched filtering. It should accelerate the quantification of TIRF-M data and allow the extraction of more biological information from image data to support research in diabetes and obesity.

Insect Soup Challenge: Segmentation, Counting, and Simple Classification

In the paper we present a method for segmentation of insects from the Insect Soup images. The method enables reliable segmentation of insects of variable size, shape and color. After segmentation, a set of properties are assigned to each segmented insect which enables classification into different categories. The approach was successfully applied on two different types of real life images: images from the Insect Soup Challenge and images acquired from traps in the field using low resolution cameras.