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Our research
The Loo lab is interested in understanding the principles governing complex cellular responses to small-molecule and hormone/cytokine perturbations. How do cells robustly integrate and process information in response to external stimulations? At the single-cell level, we systematically quantify changes in complex cellular phenotypes, such as cell morphology, protein expression, localization and post-translational modifications, under different external perturbations; and study properties of proteins or molecular interaction networks that can contribute to the observed phenotypes. These properties will help us to understand the mechanisms of the underlying biochemical pathways, and design new drug-discovery or therapeutic strategies. 
We have developed a phenotypic profiling platform for quantifying cellular phenotypes using high-throughput fluorescence microscopy. This platform uses automated image processing algorithms for cell segmentation and feature extraction, and is particularly useful for the analysis of image data sets that are too large, or of phenotypes that are too subtle, for reliable human scoring. The platform can process terabytes of image data and quantify millions of individual cells under different experimental conditions. BII is equiped with both wide-field and confocal fluorescence microscopes that allow us to acquire cellular images at the required throughput or resolution. |
Systems Biology |
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Journal of Cell Biology, Vol. 187, No. 3, 375-384 (2009) Heterogeneity in the physiological states and pharmacological responses of differentiating 3T3-L1 preadipocytes Increases in key components of adipogenesis and lipolysis pathways correlate at the population-averaged level during adipogenesis. However, differentiating preadipocytes are highly heterogeneous in cellular and lipid droplet (LD) morphologies, and the degree to which individual cells follow population-averaged trends is unclear. In this study, we analyze the molecular heterogeneity of differentiating 3T3-L1 preadipocytes using immunofluorescence microscopy. Unexpectedly, we only observe a small percentage of cells with high simultaneous expression of markers for adipogenesis (peroxisome proliferator-activated receptor γ [PPARγ], CCAAT/enhancer-binding protein α, and adiponectin) and lipid accumulation (hormone-sensitive lipase, perilipin A, and LDs). Instead, we identify subpopulations of cells with negatively correlated expressions of these readouts. Acute perturbation of adipocyte differentiation with PPARγ agonists, forskolin, and fatty acids induced subpopulation-specific effects, including redistribution of the percentage of cells in observed subpopulations and differential expression levels of PPARγ. Collectively, our results suggested that heterogeneity observed during 3T3-L1 adipogenesis reflects a dynamic mixture of subpopulations with distinct physiological states.
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Bioimage Informatics |
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Nature Methods, Vol. 6, 759-765 (2009) An approach for extensibly profiling the molecular states of cellular subpopulations Microscopy often reveals the existence of phenotypically distinct cellular subpopulations. However, additional characterization of observed subpopulations can be limited by the number of biomolecular markers that can be simultaneously monitored. Here we present a computational approach for extensibly profiling cellular subpopulations by freeing one or more imaging channels to monitor additional probes. In our approach, we trained classifiers to re-identify subpopulations accurately based on an enhanced collection of phenotypic features extracted from only a subset of the original markers. Then we constructed subpopulation profiles step-wise from replicate experiments, in which cells were labeled with different but overlapping marker sets. We applied our approach to identify molecular differences among subpopulations and to identify functional groupings of markers, in populations of differentiating mouse preadipocytes, polarizing human neutrophil-like cells and dividing human cancer cells.
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Nature Methods, Vol. 4, 445-453 (2007) Image-based multivariate profiling of drug responses from single cells Quantitative analytical approaches for discovering new compound mechanisms are required for summarizing high-throughput, image-based drug screening data. Here we present a multivariate method for classifying untreated and treated human cancer cells based on approx300 single-cell phenotypic measurements. This classification provides a score, measuring the magnitude of the drug effect, and a vector, indicating the simultaneous phenotypic changes induced by the drug. These two quantities were used to characterize compound activities and identify dose-dependent multiphasic responses. A systematic survey of profiles extracted from a 100-compound compendium of image data revealed that only 10–15% of the original features were required to detect a compound effect. We report the most informative image features for each compound and fluorescence marker set using a method that will be useful for determining minimal collections of readouts for drug screens. Our approach provides human-interpretable profiles and automatic determination of on- and off-target effects.
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Current Group Members
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LOO Lit Hsin, Ph.D.
Lit-Hsin Loo studied electrical and computer engineering
at Drexel University in Philadelphia, USA, and received his B.S. and M.S. in 2000, and
Ph.D. in 2004. To further pursue his interests in systems biology and pharmacology, he
became a postdoctoral fellow in the lab of Drs. Steven Altschuler and Lani Wu, which was
first located in the Bauer Center for Genomics Research (now called
FAS Center for Systems Biology) at
Harvard University,
and then in the Green Center for Systems Biology
and the Department of Pharmacology at the University of
Texas Southwestern Medical Center, USA. In 2010, he joined the
Bioinformatics Institute at Singapore as a Principal Investigator, and have been heading
the Complex Cellular Phenotype Analysis Group in the Imaging Informatics Division.
Awards:
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Danai LAKSAMEETHANASAN, Ph.D.
Danai Laksameethanasan received his D.Sc. in Technology from Helsinki
University of Technology, Finland, where he developed new
computational methods for three-dimensional reconstruction of cellular images
obtained from super-resolution microscopes.
He likes mathematics and is fascinated by biology. Currently, he studies
protein localizations using quantiative image analysis and mathematical modeling.
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TAN Rui Zhen, Ph.D.
Tan Rui Zhen received her Ph.D. in Biophysics
from Harvard University, USA. She studied rDNA transcription and development in
C. elegans using single-molecule FISH techniques. She is interested in studying complex
molecular interactions in large biological systems, and currently working on the phenotypic profiling
project.
Awards:
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TOH Wei Ling Geraldine, Ph.D.
Geraldine Toh obtained a Ph.D. from the National University of Ireland,
Galway, and subsequently carried out post-doctoral research at the
University of Dundee. She studied protein phosphorylation in DNA
damage signaling and repair processes in budding yeast, a "simple"
eukaryote. Having become increasingly interested in emergence and
complexity in biological systems, she is pursuing her second Ph.D. in
computational biology in our lab and working on the phenotypic profiling
project.
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TAN Wei Ling Cecilia
Cecilia Tan graduated from the University of Queensland with a Master Degree in Biotechnology in 2010,
where she studied the EAAT3 mRNA levels in different regions of human brain.
She has a strong interest in hands-on molecular-biology research. Cecilia is fascinated by how
"tiny" cells can play important roles in building life, and would like to
explore and understand how these cells carry out their functions. Currently, she is working on the
phenotypic profiling project.
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Available Positions
- Internship in computational analysis of microscopy images (2 May, 2012)
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Internship in Computational Analysis of Microscopy Images 2nd May, 2012 Description: An internship position in the area of quantitative image analysis is available in the Loo lab at the Bioinformatics Institute (BII) in Singapore. The group is interested in designing new computational algorithms for extracting biological information from microscopy images. The successful candidate will participate in the software development of image processing and machine learning tools. He/she will have the opportunity to work in a highly interdisciplinary and stimulating environment, and learn how computer science can help biologists to make biological discovery. The preferred duration of internship is three months, and the starting date is flexible. Qualifications: Candidates must have strong knowledge in C++ programming under the Linux environment, and basic knowledge of image processing or machine learning. Previous experience in bioinformatics or molecular and cell biology is desired, but not required. Candidates with interdisciplinary training in computer science and biology are especially encouraged to apply. Application Procedures: Applicants should contact Dr. Lit-Hsin Loo (loolh at bii dot a-star dot edu dot sg) for more information. |
Contact us
The Loo Lab is located at:
Bioinformatics Institute
30 Biopolis Street, #07-01 Matrix,
Singapore 138671
Email: loolh at bii dot a-star dot edu dot sg
Tel: (65) 6478 8298
Fax: (65) 6478 9048
About BII
The Bioinformatics Institute (BII) was set up by the Agency for Science and Technology Research
(A*STAR) in July 2001; it was re-launched with a strong scientific program in the autumn months of 2007.
Located in the Biopolis, BII is conceived as the computational biology research and postgraduate training
institute as well as a national resource centre in bioinformatics within the Biomedical Research Council
(BMRC) of A*STAR.
The BII focuses on theoretical approaches aimed at understanding biomolecular mechanisms that underlie
biological phenomena, the development of computational methods to support this discovery process, and
experimental verification of predicted molecular and cellular functions of genes and proteins with
biochemical methods. Together with the BMRC, A*STAR research institutes and multinational R&D organizations in the Biopolis,
the BII is situated in a conducive environment for exchange of scientific knowledge and friendly interaction
that will prompt greater collaborations, and position the Biopolis as a notable biomedical R&D hub in
Asia and in the world.
