Recruiting research assistant in statistical optics

Fast electron detectors are rapidly changing electron microscopy. These detectors allow probabilistic imaging of very many noisy, incomplete, chance observations that are then statistically classified and interpreted. This mode of imaging has enabled unprecedented high resolution imaging of dynamical and heterogeneous systems.

The Centre for BioImaging Sciences at the National University of Singapore is one of the few places in the world that develops new algorithmic approaches in such probabilistic electron microscopy. We are looking for motivated, aspiring scientists who would like to join us in developing this emerging field of statistical electron microscopy.

CBIS PI Shee Mei Lok awarded National Research Foundation Investigatorship

Assistant Professor Lok is one of the recipients of the prestigious National Research Foundation Investigatorship.

Dengue Virus (DENV) infects approximately 100 million people each year. Increased travel, together with global climate change will result in further geographical expansion of the territory of the dengue mosquito vector, Aedes aegypti. There is an urgent need to develop safe and effective dengue therapeutics and vaccine.

CBIS-PI-portraits-sheemeiLok
CBIS PI Shee Mei Lok at the Titan Krios at the National University of Singapore Centre for BioImaging Research

In vitro experiments have shown that non-neutralizing antibodies can enhance DENV infection of Fc receptor bearing macrophages, one of the natural host cells for the virus. This suggested that the presence of non-neutralizing epitopes in a vaccine could potentially increase the chances that a person who had received the vaccine would develop the severe form of the disease, dengue hemorrhagic fever. For this reason, a more promising approach for engineering an effective DENV vaccine is to focus on including neutralizing epitopes. Thus, mapping of neutralizing epitopes is a necessary component of DENV vaccine research. Furthermore, understanding the neutralization mechanism of antibodies and the entry of DENV into the host cells also could aid in the design of targeted therapeutics.

The research in her laboratory therefore, focuses on the understanding of the pathology of dengue virus infection and the mechanism of neutralization by antibodies and other molecules so as to facilitate the development of suitable vaccines and therapeutics. A combination of molecular, immunological, biochemical and structural techniques (x ray crystallography and cryoEM image reconstruction techniques) will be used to achieve these aims.

Read more about Associate Professor Lok’s research.

Bayesian Model Selection Applied to the Analysis of FCS Data of Fluorescent Proteins in vitro and in vivo

by Guangyu Sun,†$ Syuan-Ming Guo, Cathleen Teh,§ Vladimir Korzh,§# Mark Bathe, and Thorsten Wohland*,†$#

Department of Chemistry, National University of Singapore, 117543 Singapore, $Centre for Bioimaging Sciences, National University of Singapore, 117557 Singapore, Laboratory for Computational Biology and Biophysics, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States, §Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, 138673 Singapore, #Department of Biological Sciences, National University of Singapore, 117543 Singapore

Sun et al. – Anal. Chem. 87 (2015) 4326−4333

Fluorescence Correlation Spectroscopy (FCS) is a powerful technique to investigate molecular dynamics with single molecule sensitivity. In particular, in the life sciences it has found widespread application using fluorescent proteins as molecularly specific labels. However, FCS data analysis and interpretation using fluorescent proteins remains challenging due to typically low signal-to-noise ratio of FCS data and correlated noise in autocorrelated datasets. As a result, naive fitting procedures that ignore these important issues typically provide similarly good fits for multiple competing models without clear distinction of which model is preferred given the signal-to-noise ratio present in the data. Recently, we introduced a Bayesian model selection procedure to overcome this issue with FCS data analysis. The method accounts for the highly correlated noise that is present in FCS datasets and additionally penalizes model complexity to prevent over interpretation of FCS data. Here, we apply this procedure to evaluate FCS data from fluorescent proteins assayed in vitro and in vivo. Consistent with previous work, we demonstrate that model selection is strongly dependent on the signal-to-noise ratio of the measurement, namely excitation intensity and measurement time, and is sensitive to saturation artifacts. Under fixed, low intensity excitation conditions physical transport models can unambiguously be identified. However, at excitation intensities that are considered moderate in many studies, unwanted artifacts are introduced that result in non-physical models to be preferred. We also determined the appropriate fitting models of a GFP tagged secreted signaling protein, Wnt3, in live zebrafish embryos, which is necessary for the investigation of Wnt3 expression and secretion in development. Bayes model selection therefore provides a robust procedure to determine appropriate transport and photophysical models for fluorescent proteins when appropriate models are provided, to help detect and eliminate experimental artifacts in solution, cells and in living organisms.

Read the entire article.

Learn more about Thorsten Wohland’s research.