David Nam

Retracing in Correlative Light Electron Microscopy: Where is My Object of Interest?

Correlative light electron microscopy (CLEM) combines the strengths of light and electron microscopy in a single experiment. There are many ways to perform a CLEM experiment and a variety of microscopy modalities can be combined either on separate instruments or as completely integrated solutions. In general, however, a CLEM experiment can be divided into three parts: probes, processing, and analysis. Most of the existing technologies are focussed around the development and use of probes or describe processing methodologies that explain or circumvent some of the compromises that need to be made when performing both light and electron microscopy on the same sample. So far, relatively little attention has been paid to the analysis part of CLEM experiments. Although it is an essential part of each CLEM experiment, it is usually a cumbersome manual process. Here, we briefly discuss each of the three above-mentioned steps, with a focus on the analysis part. We will also introduce an automated registration algorithm that can be applied to the analysis stage to enable the accurate registration of LM and EM images. This facilitates tracing back the right cell/object seen in the light microscope in the EM.

Feature-based registration for correlative light and electron microscopy images

In this paper we present a feature-based registration algorithm for largely misaligned bright-field light microscopy images and transmission electron microscopy images. We first detect cell centroids, using a gradient-based single-pass voting algorithm. Images are then aligned by finding the flip, translation and rotation parameters, which maximizes the overlap between pseudo-cell-centers. We demonstrate the effectiveness of our method, by comparing it to manually aligned images. Combining registered light and electron microscopy images together can reveal details about cellular structure with spatial and high-resolution information.

A Novel Framework for Segmentation of Secretory Granules in Electron Micrographs

It is still a standard practice for biologists to manually analyze transmission electron microscopy images. This is not only time consuming but also not reproducible and prone to induce subjective bias. For large-scale studies of insulin granules inside beta cells of the islet of Langerhans, an automated method for analysis is essential. Due to the complex structure of the images, standard microscopy segmentation techniques cannot be applied. We present a new approach to segment and measure transmission electron microscopy images of insulin granule cores and membranes from beta cells of rat islets of Langerhans. The algorithm is separated into two broad components, core segmentation and membrane segmentation. Core segmentation proceeds through three steps: pre-segmentation using a novel level-set active contour, morphological cleaning and a refining segmentation on each granule using a novel dual level-set active contour. Membrane segmentation is achieved in four steps: morphological cleaning, membrane sampling and scaling, vector field convolution for gap filling and membrane verification using a novel convergence filter. We show results from our algorithm alongside popular microscopy segmentation methods; the advantages of our method are demonstrated. Our algorithm is validated by comparing automated results to a manually defined ground truth. When the number of granules detected is compared to the number of granules in the ground truth a precision of 91% and recall of 87% is observed. The average granule areas differ by 13.35% and 6.08% for core and membranes respectively, when compared to the average areas of the ground truth. These results compare favorably to previously published data.

Active contour based segmentation for insulin granule cores in electron micrographs of beta islet cells

Transmission electron microscopy images of beta islet cells contain many complex structures, making it difficult to accurately segment insulin granule cores. Quantification of sub cellular structures will allow biologists to better understand cellular mechanics. Two novel, level set active contour models are presented in this paper. The first utilizes a shape regularizer to reduce oversegmentation. The second contribution is a dual active contour, which achieves accurate core segmentations. The segmentation algorithm proceeds through three stages: an initial rough segmentation using the first contribution, cleaning using morphological techniques and a refining step using the proposed dual active contour. Our method is validated on a set of manually defined ground truths.

A novel approach to identifying merging/splitting events in time-lapse microscopy

This paper investigates the complex motion of particles in the endocytic pathway. We propose a novel tracking method, which identifies merging and splitting events of vesicles, in dual channel fluorescence confocal microscopy. Large amounts of quantitative data are needed for biologists to make sound conclusions about cellular dynamics. Having an automated method also allows biologists to identify rare events, which would otherwise be very time consuming. A co-localisation state is introduced to identify when vesicles are merged, across two channels. The approach is based on a probabilistic association between estimated vesicle states in each channel. We incorporate this into a reversible jump Markov chain Monte Carlo scheme. The approach has been successfully applied to synthetic videos as well as real data.

RJMCMC-based tracking of vesicles in fluorescence time-lapse microscopy

Vesicles are a key component for the transport of materials throughout the cell. To manually analyze the behaviors of vesicles in fluorescence time-lapse microscopy images would be almost impossible. This is also true for the identification of key events, such as merging and splitting. In order to automate and increase the reliability of this processes we introduce a Reversible Jump Markov chain Monte Carlo method for tracking vesicles and identifying merging/splitting events, based on object interactions. We evaluate our method on a series of synthetic videos with varying degrees of noise. We show that our method compares well with other state-of-the-art techniques and well-known microscopy tracking tools. The robustness of our method is also demonstrated on real microscopy videos.

Insulin Granule Segmentation in 3-D TEM Beta Cell Tomograms.

David Nam1 D.Nam@bristol.ac.uk Judith Mantell2,3 J.Mantell@bristol.ac.uk David Bull1 Dave.Bull@bristol.ac.uk Paul Verkade2,3,4/shared last author P.Verkade@bristol.ac.uk Alin Achim1 Alin.Achim@bristol.ac.uk 1 Visual Information Laboratory University of Bristol, UK 2 Wolfson Bioimaging Facility University of Bristol, UK 3 School of Biochemistry University of Bristol, UK 4 School of Physiology and Pharmacology University of Bristol, UK

A novel 2D and 3D method for automated insulin granule measurement and its application in assessing accepted preparation methods for electron microscopy

Transmission electron microscopy images of insulin-producing beta cells in the islets of Langerhans contain many complex structures, making it difficult to accurately segment insulin granules. Furthermore the appearance of the granules and surrounding halo and limiting membrane can vary enormously depending on the methods used for sample preparation. An automated method has been developed using active contours to segment the insulin core initially and then expand to segment the halos [1]. The method has been validated against manual measurements and also yields higher accuracy than other automated methods [2]. It has then been extended to three dimensions to analyse a tomographic reconstruction from a thick section of the same material. The final step has been to produce a GUI and use the automated process to compare a number of different electron microscopy preparation protocols including chemical fixation (where many of halos are often distended) and to explore the many subtleties of high pressure freezing (where the halos are often minimal, [3]).