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Multiscale Bioimaging Informatics


Research focuses:

The Multiscale Bioimaging Laboratory will focus on the following research topics. 1. Fundamental research on multiscale signal/image representation and analysis; 2. Multiscale bioimaging analysis from organ and tissue levels to molecular and cellular levels; and 3. Bioinformatics in human genomics and cytogenetics. Currently, we are working on information extraction and integration from multiscale and multimodality genomic imaging data. One of our goals is to bring the biomedical technique into commercial use. We are using a multidisciplinary approach and working closely with computational scientists, statisticians, medical geneticists and industrial engineers at Tulane Medical Center and all over the world.

 

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Personnel:

Yu-Ping Wang, Ph.D., PI

Hongbao Cao, Post-doc

Wenlong Tang, Post-doc

Junbo Duan, Post-doc

Dongdong Lin, Research assistant

Jinyao Li, Research assistant

 

Studies:

NIH: Accurate detection of chromosomal abnormalities with multi-color image processing (1R15GM088802-01)

NIH: A New Paradigm for Integrated Analysis of Multiscale Genomic Imaging Datasets (1R21LM010042-01)

NSF: Multiscale Genomic Imaging Informatics (ABI 0849932)

 




NIH: Accurate detection of chromosomal abnormalities with multi-color image processing (1R15GM088802-01)

The combination of high resolution assays in genomics with microscopic imaging has been used for the detection of complex chromosomal rearrangements, a significant but difficult problem in prenatal and postnatal diagnosis, birth defect detection and cancer research. As a recently developed molecular cytogenetic technique, multiplex fluorescence in situ hybridization (M-FISH) imaging has provided rapid and high resolution detection of chromosomal abnormalities associated with cancer and genetic disorders. However, the technique is currently limited to research use and only serves as an adjunct tool to the G-banding based monochromatic chromosomal karyotyping in a clinical laboratory. A primary barrier of the technique is the lower classification accuracy when classifying chromosomes from multi-color microscopic imaging data. Therefore, the goal of this R15 project is to develop innovative multi-spectral image processing and machine learning techniques for M-FISH image analysis so that chromosomal rearrangement detection can be made more reproducible, robust, and faster, thereby significantly increasing the ability and efficacy of this newly developed cellular imaging technique. Our proposed approaches such as multiscale feature extraction, nonlinear manifold analysis and adaptive fuzzy clustering are able to target specific features of multi-spectral imaging data, promising a significant improvement over the current techniques. In order to validate the technique and bring it into clinical use, we will partner with clinical geneticists, and cytogeneticists. In addition, we will collaborate with an industrial scientist, Dr. Kenneth Castleman, who is the pioneer in developing and commercializing cytogenetic imaging products. This research project will also enhance our research infrastructure in biomedical image informatics and provide undergraduate and graduate students opportunities to touch the frontier of molecular and cellular imaging by participating in the proposed research activities

 




NIH: A New Paradigm for Integrated Analysis of Multiscale Genomic Imaging Datasets (1R21LM010042-01)

A decade ago when microarray was first invented, it was hailed as "an array of hope" in Nature Genetics and has received a considerable amount of attention in biomedicine. Subsequently it has been called "an array of problems" in Nature Review. An inherent problem with microarray gene expression is that structural information is missing, which limits its ability in biological discovery. To overcome the poor reproducibility and accuracy of microarray imaging, there needs to be a shift in fundamental paradigms to those able to incorporate complementary and multiscale structural imaging information into microarray imaging. Fortunately, the latest progress in high resolution biomolecular imaging probe development coupled with advanced image analysis makes integrative and systematic studies of cellular systems possible. A cell can be labeled using multiscale and multimodality imaging, providing both structural and functional information. With multiscale imaging spreadsheets now available, there is an overwhelming need within the life sciences community to manage this information effectively, to analyze it comprehensively, and to apply the resulting knowledge in the understanding of the genetic system of a cell. However, the management and mining of this large-scale imaging information is limited by today's computational approaches and knowledge-sharing infrastructure. These problems represent a major impediment to progress in the emerging area of bio-molecular image informatics. Therefore, the goal of this project is to develop a unique genomic image management and mining system that can allow geneticists to search, correlate and integrate this multiscale and multi-modality imaging information in an easily operable fashion and further enable new biological discovery.

 




NSF: Multiscale Genomic Imaging Informatics (ABI 0849932)

We will build an imaging database management and analysis system that can integrate multiscale and multimodality structural genomic information with microarray gene expression for comprehensive and integrative analysis of a biological system. It is widely recognized that microarray imaging is limited by its poor reproducibility and accuracy. Recent progress in high resolution genomic probe development along with advanced image analysis techniques provides complimentary information to microarray gene expression. The project team will develop image processing and signal analysis algorithms to extract visual quantitative traits and structural genomic signatures from the results of high resolution genomic imaging techniques such as fluorescence in situ hybridization imaging and microarray based comparative genomic hybridization (aCGH). These quantitative structural signatures will then correlate with microarray gene expression patterns. Finally, this multiscale structural/functional information will be integrated for an improved characterization of biological systems. The imaging system developed through project will allow a biologist to answer the following ambitious and significant questions: "How chromosomal rearrangements (molecular karyotype) cause visual differences (phenotype); how they are correlated with gene expression patterns (genotype); and furthermore, how the integrated approach can lead to an improved characterization of a biological system?" The impact of this project will be further expanded by partnering with industries, outreach to minority and high school students, and by developing innovative bioimaging curricula.

 

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Department of Biostatistics, 1440 Canal Street, Suite 2001, New Orleans, LA 70112, 504-988-5164 kbranley@tulane.edu