Complex Cellular Phenotype Analysis Group

Drs. Lit-Hsin Loo and Daniele Zink received the Lush Prize 2016 (Science Award)
for their high-throughput in vitro nephrotoxicity models.

Our research is focused on the development of in vitro and computational models for predicting the toxicity and/or targets of chemical compounds. We develop novel cellular phenotypic profiling methods and tools that can automatically classify the effects of compounds with diverse or even unknown chemical structures. These technologies allow us to build highly accurate and scalable in vitro cell-based models for predicting organ-specific toxicities. Our models could be used as high-throughput alternatives to animal testing.




Our current projects include:


Toxicity prediction based on phenotypic profiles: We have developed an approach to predict xenobiotic toxicity using high-throughput imaging of cultured human cells, quantitative phenotypic profiling, and machine learning models. We have applied this approach to human proximal tubular cells (PTCs) in collaboration with Dr. Daniele Zink from the Institute of Bioengineering and Nanotechnology (IBN, A*STAR), and built the first high-throughput in vitro model for nephrotoxicity prediction.


Toxicity prediction based on inflammation and signaling markers: We have also developed predictive nephrotoxicity models based on the gene expression levels of two pro-inflammatory cytokines, namely interleukin (IL)-6 and -8. These models can accurately predict the toxicity of xenobiotics in both primary human PTCs and induced pluripotent stem cells (iPSC)-derived PTC-like cells.


Single-cell phenotypic profiling: We develop image and data analysis tools to extract biological information from microscopy images generated from high-throughput, cell-based screening experiments. These tools include the Protein Localization Analysis and Search Tools (PLAST) and the "cellXpress" software platform.

 
News & Announcements
  • (24 Oct 2017) We are recruiting a postdoctoral fellow in high-throughput and systems toxicology. We have many exciting projects and collaborations awaiting the candidate.
  • (24 Oct 2017) Our paper on High-throughput Prediction of Nephrotoxicity in Humans has been accepted by ATLA. The paper gives a nice review of the journey that we have taken to develop the first predictive high-throughput nephrotoxicity model.
  • (19 Oct 2017) The software engineer vacancy in our lab has been filled. We are happy to welcome Paul Cain, who will join our lab starting November 2017. Paul will be leading the development of "Project X".
  • (10 May 2017) We are recruiting a software engineer in big data analytics.
  • (15 Feb 2017) Our paper on predicting TNFa sensitivity based on early spatiotemporal-specific changes in intermediate signals has has been accepted by Scientific Reports.
  • (12 Nov 2016) Daniele and Lit-Hsin have won the Lush Prize 2016, Science Award for their work in developing animal-free Methods to predict the toxic effects of chemicals on the human kidney. This competitive, international prize aims to recognize research work that produces effective non-animal safety tests. The Straits Times published an article about our award. The award ceremony was held in London [online video].
  • (30 June 2016) Our review paper on image-based phenotypic profiling has been accepted by Cytometry A. Congratulations to Nic!
  • (28 May 2016) A postdoctoral research fellow position in computational toxicology is available in our lab. Please visit the "Careers" page.
  • (5 Feb 2016) Our work in nephrotoxicity prediction was featured in the Science section of today's Straits Times [Main article, Side article].

Our research


The research in Loo's Lab is focused on the development of in vitro and computational models for predicting the toxicity and/or targets of chemical compounds. We develop novel cellular phenotypic profiling methods and tools that can automatically classify the effects of compounds with diverse or even unknown chemical structures. These technologies allow us to build highly accurate and scalable in vitro cell-based models for predicting organ-specific toxicities. Our models can be used as high-throughput alternatives to animal testing. Our current projects include:



Toxicity prediction based on phenotypic profiles

Xenobiotics are chemicals foreign to the body, such as drugs, food ingredients, industrial chemicals, toxins, or environmental pollutants. Major organs in the body that are targeted by xenobiotics include the liver, kidneys, lungs, and heart. Animal testing is a standard approach for predicting xenobiotic toxicity in human, but suffers from the problems of long turnaround time, low throughput, and sometimes poor prediction of human toxicity. To overcome these problems, our group has developed an approach to predict xenobiotic toxicity using high-throughput imaging of cultured human cells, quantitative phenotypic profiling, and machine learning models. Instead of using standard cell death indicators as toxicity endpoints, we unbiasedly measure large numbers of quantitative phenotypic descriptors (or “features”) under in vitro conditions, and automatically search for feature combinations that can accurately predict in vivo organ-specific toxicity. Our models based on the identified phenotypic profiles can accurately classify xenobiotic compounds according to their modes of action without any prior knowledge of their chemical structures or mechanisms. Therefore, our approach is especially suitable for testing large numbers of chemicals with diverse or unknown structures or injury mechanisms.

We have applied this approach to human proximal tubular cells (PTCs) in collaboration with Dr. Daniele Zink from the Institute of Bioengineering and Nanotechnology (IBN, A*STAR), and built the first high-throughput in vitro model for nephrotoxicity prediction. Using phenotypic profiling, we identified a small set of chromatin and cytoskeletal features that can predict the human in vivo PTC toxicity of 44 reference compounds with ~82 % (primary PTCs) or 89 % (immortalized PTCs) balanced accuracies. Surprisingly, our results also revealed that a DNA damage response is commonly induced by different PTC toxicants that have diverse chemical structures and injury mechanisms. We also found that PTC-toxic compounds induced highly variable cell death responses, and therefore in vitro cell death indicators cannot be used to replace our phenotypic features. Together, our results show that human nephrotoxicity can be predicted with high efficiency and accuracy by combining cell-based and computational methods that are suitable for automation. Our nephrotoxicity models are ready for large-scale and industrial applications. Currently, we are also developing models for other major organs targeted by xenobiotics.


In the news:

  • (June 2016) A*STAR Research Highlight [Link].
  • (Feb 2016) Science section of Straits Times [Main article, and side article].
  • (Jab 2016) A*STAR's press release [Link].


Related publications:

  • (2015) High-throughput imaging-based nephrotoxicity prediction for xenobiotics with diverse chemical structures, Ran Su, et al., Archives of Toxicology, in press. [Link]



Toxicity prediction based on inflammation and signaling markers

Besides cell injury, toxic xenobiotics may also induce signaling or inflammatory responses in their targeted organs. We have also developed predictive nephrotoxicity models based on the gene expression levels of two pro-inflammatory cytokines, namely interleukin (IL)-6 and -8. Although the models only have two variables, we found that non-linear decision thresholds automatically determined using a machine learning algorithm called “random forest” gives higher prediction accuracy that linear thresholds determined either manually or using other algorithms. Our IL-6 and -8 models can accurately predict the toxicity of xenobiotics in both primary human PTCs and induced pluripotent stem cells (iPSC)-derived PTC-like cells. The results suggest that inflammation is a general response of PTCs to PTC-toxic compounds, and our computational methods are applicable to different sources of PTCs. The use of iPS cells will allow us to develop personalized or disease-specific cellular response models. We are also developing other predictive models based on the activations and subcellular localization patterns of intracellular signaling pathways.


Related publications:

  • (2015) Prediction of drug-induced nephrotoxicity and injury mechanisms with human induced pluripotent stem cell-derived cells and machine learning methods, Karthikeyan Kandasamy, et al., Scientific Reports, 5:12337. [Link]
  • (2014) Supervised prediction of drug-induced nephrotoxicity based on interleukin-6 and -8 expression levels, Ran Su, et al., BMC Bioinformatics, 15(Suppl 16):S16. [Link]



High-throughput phenotypic profiling methods and tools

Cellular phenotypes are observable characteristics of cells resulting from the interactions of internal and external chemical or biochemical factors. Imaging-based phenotypic screens under large numbers of experimental conditions can be used to study the influences of these factors to cellular phenotypes. Phenotypic profiling is a computational procedure to construct quantitative and compact representations of cellular phenotypes based on the cellular images collected in these screens. We have developed several computational methods for phenotypic profiling, which include the Drug-Profiling (“D-profiling”) algorithm, the Protein-localization Profiling (“P-profiling”) algorithm, and a random-forest-based recursive feature elimination algorithm. We have used the phenotypic profiles constructed using these methods to classify the effects of small molecules, annotate protein localization patterns, compare spatial and functional divergence of proteins, or predict toxicity effects of xenobiotic compounds.


Our group also develops the cellXpress platform, which is a software environment for performing phenotypic profiling and constructing computational models based on the generated profiles. The platform includes a user-friendly and highly-scalable software application called “cellXpress” for extracting phenotypic features from microscopy images of cells (http://www.cellXpress.org). The platform also has a web interface called “Protein Localization Analysis and Search Tools” (PLAST) for searching and comparing phenotypic profiles (http://plast.bii.a-star.edu.sg). The platform is designed to handle terabytes of image data and quantify millions of individual cells under different experimental conditions. It is used by most of our past and on-going projects.


Related publications:

  • (2014) Quantitative protein localization signatures reveal an association between spatial and functional divergences of proteins, Lit-Hsin Loo, et al., PLOS Computational Biology, 10(3):e1003504. [Link]
  • (2013) cellXpress: a fast and user-friendly software platform for profiling cellular phenotypes, Danai Laksameethanasan, et al., BMC Bioinformatics, 14(Suppl 16):S4. [Link]
  • (2007) Image-based multivariate profiling of drug responses from single cells, Lit-Hsin Loo, et al., Nature Methods, Vol. 4, 445-453. [Link]


Publications


12. (2017) High-throughput prediction of nephrotoxicity in humans. Lit-Hsin Loo, Daniele Zink. Alternatives to Laboratory Animals, 45:241-252. [PDF]
11. (2017) Early spatiotemporal-specific changes in intermediate signals are predictive of cytotoxic sensitivity to TNFa and co-treatments. Lit-Hsin Loo, Nicola Bougen-Zhukov, Wei-Ling Cecilia Tan. Scientific Reports, 7:43541. [Link]
10. (2017) Large-scale image-based screening and profiling of cellular phenotypes. Nicola Bougen-Zhukov, Sheng Yang Loh, Hwee Kuan Lee, Lit-Hsin Loo. Cytometry Part A, 91A:115-125. [Link | PDF]
9. (2016) High-throughput imaging-based nephrotoxicity prediction for xenobiotics with diverse chemical structures. Ran Su, Sijing Xiong, Daniele Zink, and Lit-Hsin Loo. Archives of Toxicology, 90:2793-2808. [Link]
8. (2015) Prediction of drug-induced nephrotoxicity and injury mechanisms with human induced pluripotent stem cell-derived cells and machine learning methods. Karthikeyan Kandasamy, Jacqueline Chuah, Ran Su, Peng Huang, Kim Guan Eng, Sijing Xiong, Yao Li, Chun Siang Chia, Lit-Hsin Loo, and Daniele Zink. Scientific Reports, 5:12337. [Link]
7. (2014) Supervised prediction of drug-induced nephrotoxicity based on interleukin-6 and -8 expression levels. Ran Su, Yao Li, Daniele Zink, and Lit-Hsin Loo. BMC Bioinformatics, 15(Suppl 16):S16. [Link]
6. (2014) Quantitative protein localization signatures reveal an association between spatial and functional divergences of proteins. Lit-Hsin Loo, Danai Laksameethanasan, Yi-Ling Tung. PLOS Computational Biology, 10(3):e1003504. [Link]
5. (2013) cellXpress: a fast and user-friendly software platform for profiling cellular phenotypes. Danai Laksameethanasan, Rui Zhen Tan, Geraldine Wei-Ling Toh and Lit-Hsin Loo. BMC Bioinformatics, 14(Suppl 16):S4. [Link]
4. (2009) Heterogeneity in the physiological states and pharmacological responses of differentiating 3T3-L1 preadipocytes. Lit-Hsin Loo, Hai-Jui Lin, Dinesh K. Singh, Kathleen M. Lyons, Steven J. Altschuler and Lani F. Wu. Journal of Cell Biology, Vol. 187, No. 3, 375-384. [Link | JCB In-Focus article]
3. (2009) An approach for extensibly profiling the molecular states of cellular subpopulations. Lit-Hsin Loo, Hai-Jui Lin, Robert J. Steininger III, Yanqin Wang, Lani F. Wu and Steven J. Altschuler. Nature Methods, Vol. 6, 759-765. [Link]
2. (2007) Image-based multivariate profiling of drug responses from single cells. Lit-Hsin Loo, Lani F. Wu and Steven J. Altschuler. Nature Methods, Vol. 4, 445-453. [Link]
1. (2007) New criteria for selecting differentially expressed genes. Lit-Hsin Loo, Samuel Roberts, Leonid Hrebien, and Moshe Kam. IEEE Engineering in Medicine and Biology Magazine, Vol. 26, 17-26.

Current Team Members




NameTitle
LOO Lit HsinPrincipal investigator
James A. MILLERPostdoctoral fellow
Sreetama BASUPostdoctoral fellow
LEE Jia Ying JoeyResearch officer
Janice Goh Jia NiResearch officer (A*STAR NSS Scholar)
Paul CainSoftware engineer


Loo Lit Hsin

LOO Lit Hsin, Ph.D.


Principal Investigator
Complex Cellular Phenotype Analysis Group
Bioinformatics Institute, A*STAR

Adjunct Assistant Professor
Department of Pharmacology
Yong Loo Lin School of Medicine
National University of Singapore
Email: lhloo at bii dot a-star dot edu dot sg

Lit-Hsin Loo is leading a small interdisciplinary team of scientists at BII developing in vitro and computational models for predicting the toxicity and/or targets of chemical compounds with diverse or unknown structures. His team has developed novel image-based phenotypic profiling methods and tools that led to the first high-throughput and predictive in vitro platform for nephrotoxicity prediction. Before joining BII, Dr. Loo was a postdoctoral fellow in the Bauer Center for Genomics Research at Harvard University (2005), and then in the Department of Pharmacology at the UTSW Medical Center, USA (2005-2010).

Awards:
  • (2016) Lush Prize 2016, Science Award
  • (2010) University of Texas Southwestern Medical Center Postdoctoral Award
  • (2009) Alfred Gilman Postdoctoral Award
  • (2005) Drexel Doctoral Award in Mathematical Sciences and Engineering



James

James A. MILLER, Ph.D.


Postdoctoral Research Fellow
Email: jamesam at bii dot a-star dot edu dot sg

James is an alumnus of Brasenose College, Oxford, graduating in 2009 from the Department of Chemistry. His Masters project was a theoretical study of phase spaces for confined matter inside carbon nanotubes. In 2015, he defended his doctoral thesis in computational nuclear physics at the University of Sheffield, and went on to postdoctoral work in the British National Nuclear Laboratory developing predictive algorithms for the treatment of highly active fission products. Now investigating biological as opposed to radiological toxins, James' work in the group focuses on image-based machine learning and cheminformatics. Through automated identification and classification of the microscopic changes in cells exposed to different stimuli, the intention is to develop novel methods of organ-specific toxicity prediction.


Sreetama

Sreetama BASU, Ph.D.


Postdoctoral Research Fellow
Email: sreetamab at bii dot a-star dot edu dot sg

Sreetama obtained her doctorate degree in 2015 from School of Computing, National University of Singapore. Her research interests include signal processing and machine learning and her PhD thesis was on reconstruction of neurnal morphology from 3D microscopy image volumes using marked point process models. Her work in the group focuses on adapting state-of-the-art computer vision and machine learning techniques for computational toxicity analyses on high-throughput cellular images. Before joining BII, Sreetama was a postdoctoral fellow in the Computational Biology group of IBENS (Institute of Biologie, Ecole Normale Superior), Paris.


Joey

LEE Jia Ying Joey, MSc.


Research Officer
Email: leejy at bii dot a-star dot edu dot sg

Joey graduated with a MSc degree in the field of Bioorganometallic Chemistry from the Department of Chemistry and Biological Chemistry of Nanyang Technological University (NTU), Singapore. In her Masters project, she investigated the interaction of triosmium carbonyl clusters with different of amino acids and oligopeptides with regards to its selectivity toward different residues. Her current work in the group focuses on the development and validation of high-throughput image-based experiments for organ-specific toxicity prediction.


Joey

GOH Jia Ni Janice


Research Officer
Email: gohjn at bii dot a-star dot edu dot sg

Janice obtained her undergraduate degree in 2016 from the Department of Pharmacy, National University of Singapore. Janice is also a licensed pharmacist under the Singapore Pharmacy Council. Her research interests include metabolonomics, pharmaco- and toxicokinetic modeling. Her current work focuses on developing screening assays for toxicity to help prioritize chemicals for further safety testing. In the future, Janice hopes to win the Ig Nobel Prize which aims to "honor achievements that first make people laugh, and then make them think." Laughter after all, is the best medicine.


Loo Lab Alumni


Postdoctoral Fellows:
  1. Nicola Bougen (2013-17) - now at Otago University, New Zealand
  2. Ran Su (2013-16) - now Associate Professor at Tianjin University, China
  3. Danai Laskameethanasan (2010-13)

Research Officers:
  1. Michelle Su Yu Fah (2014-17)
  2. Mark NEO Wei Xuan (2014-16)
  3. Cecilia TAN Wei Ling (2010-13)

Interns:
  1. Thaddea Chua Yun Fang (University of Toronto, 2017)
  2. Au Hui Her (University of Sheffield, 2016)
  3. Ang Yoong Kwei (University of Melbourne, 2016)
  4. Vanessa KEE Ting Zhen (Monash University, 2015)
  5. LEE Yin Yeng (Nanyang Technological University, 2014)
  6. Yi-Ling Irene TUNG (University of Michigan, 2013)
  7. TAN Wey Chyn (Multimedia University, 2011)




Life in the Loo Lab



Aug 2017. Joey, James, and Lit-Hsin attended the WC10 at Seattle.


Aug 2017. The Loo Lab Band, Seattle.

Careers @ the Loo Lab

We are always interested in postdoctoral-fellow candidates with backgrounds relevant to our work. Every year, we also take in one to two undergraduate interns to provide them with hand-on experiences in scientific research projects. Some of our interns also contributed to our published work. If you are interested to join our group, please send your CV to Dr. Lit-Hsin Loo (loolh at bii dot a-star dot edu dot sg). Specific openings in the lab are listed below:


  1. Postdoctoral Research Fellow in High-throughput and Systems Toxicology (24 October 2017)
  2. Internship in cell biology
  3. Internship in computational analysis of microscopy images


Postdoctoral Fellow in High-throughput and Systems Toxicology

Description:

A postdoctoral research fellow position in the area of high-throughput and systems toxicology is available in the Loo lab at the Bioinformatics Institute (BII), A*STAR, Singapore. The group is interested in studying chemical-induced toxicity using quantitative data modeling and cellular imaging methods. The group has recently developed the first high-throughput cellular imaging and computational platform for predicting kidney toxicity.

The successful candidate will be part of an interdisciplinary team that develops novel in vitro and computational models for predicting the toxicity of food ingredients, industrial chemicals, environmental pollutants, and drugs. He/she will develop new in vitro human cell-based assays to study the modes-of-action of toxic chemicals using high-throughput imaging-based phenotypic profiling, RNA sequencing, mass spectrometry, and other molecular biology techniques. The candidate will have the opportunity to work in a highly stimulating environment, and participate in a global effort to develop next-generation and animal-free technologies for chemical safety assessment.

Qualifications:

  • Candidates must have a Ph.D. in either pharmacology/pharmacy, toxicology, molecular and cell biology, systems biology, bioengineering, chemical engineering, chemistry, or other related fields.
  • Previous experience in working with human cells, studying drug/chemical responses, molecular biology experiments, and immunofluorescence microscopy are required.
  • Candidates with previous experience in high-throughput screening, gene expression profiling, or developing in vitro cellular models/assays are especially encouraged to apply.
  • Candidates must also possess good communication skills, be fluent in both spoken and written English, and able to work independently


Application Procedures:

Applicants should send

  • a one-page cover letter briefly describing prior research experience and accomplishments,
  • a one-page statement of research interests,
  • CV,
  • contact information of two references, and
  • reprints of one to two publications
to Dr. Loo Lit Hsin (loolh at bii dot a-star dot edu dot sg). Only shortlisted candidates will be notified.


Internship in Cell Biology

Description:

An internship position in the area of cell biology is available in the Loo lab at the Bioinformatics Institute (BII) in Singapore. The successful candidate will study the responses of lung epithelial cells to drugs and other chemical compounds. He/she will have the opportunity to work in a highly interdisciplinary and stimulating environment, and engage in innovative cell biology research. The preferred duration of internship is three months, and the starting date is flexible.

Qualifications:

Candidates must have completed university course work in the areas of biological and/or chemical sciences. Previous experience in performing cell culture work and/or molecular biology experiments is desired.

Application Procedures:

Applicants should contact Dr. Lit-Hsin Loo (loolh at bii dot a-star dot edu dot sg) for more information.


Internship in Computational Software Development

Description:

An internship position in the area of computational software development is available in the Loo lab at the Bioinformatics Institute (BII) in Singapore. 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 machine learning and web programming. 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


[Direction to BII]


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.



© 2011-17, Complex Cellular Phenotype Analysis Group.