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ThomasKislingerPhD
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Professional Statement
The human genome has been sequenced and now sophisticated technologies are used to characterize the proteome, the set of proteins generated by that genome, as a function of developmental stage, environment, tissue-type, and pathology. The goal of our research is to understand, on a systems level, the complex biological processes that are fundamental to physiology and disease. A particular focus of our laboratory is cancer research (ovarian and breast cancer) and vascular/cardiovascular biology.
Our laboratory develops and applies powerful tools of proteomics and bioinformatics to analyze the protein complement in protein complexes, cells, tissues and organisms. Our goal is not only to determine the level of proteins but also their localization, dynamics, and interaction partners. In recent years we have developed sensitive methodologies to isolate specific subcellular fractions from mammalian cell culture or tissues.
Currently our focus is on optimizing technologies that allow for the sensitive and selective isolation and characterization of surface, plasma membrane proteins from cell culture (in vitro) or directly from the surface of microvascular endothelial cells (in vivo). The ultimate goal of these studies is to investigate the proteome dynamics of surface membrane proteins in the vasculature of disease models or in cell culture in response to specific treatments. Additionally, we are interested in dynamics of proteinprotein interactions. To accomplish this goal we are applying tandem affinity purifications (TAP-tagging) in combination with mass spectrometry. Briefly, the protein of interest (bait) is expressed as a fusion protein with a dual affinity tag. This allows for mild and efficient purification of this protein and most of its interacting partners.
Our laboratory develops and applies powerful tools of proteomics and bioinformatics to analyze the protein complement in protein complexes, cells, tissues and organisms. Our goal is not only to determine the level of proteins but also their localization, dynamics, and interaction partners. In recent years we have developed sensitive methodologies to isolate specific subcellular fractions from mammalian cell culture or tissues.
Currently our focus is on optimizing technologies that allow for the sensitive and selective isolation and characterization of surface, plasma membrane proteins from cell culture (in vitro) or directly from the surface of microvascular endothelial cells (in vivo). The ultimate goal of these studies is to investigate the proteome dynamics of surface membrane proteins in the vasculature of disease models or in cell culture in response to specific treatments. Additionally, we are interested in dynamics of proteinprotein interactions. To accomplish this goal we are applying tandem affinity purifications (TAP-tagging) in combination with mass spectrometry. Briefly, the protein of interest (bait) is expressed as a fusion protein with a dual affinity tag. This allows for mild and efficient purification of this protein and most of its interacting partners.
Professional Info
Credential:
PhD
Research interests:
Application of expression proteomics and allied computational tools to address specific biological questions.
Developing and applying modern bioinformatics tools to the analysis and annotation of the generated datasets.
Developing and applying modern bioinformatics tools to the analysis and annotation of the generated datasets.
Awards and publications:
http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=PureSearch&db=pubmed&term=kislinger%20t%5Bau%5D
Hospital or other affiliation:
Ontario Cancer Institute
Personal Bio (My story)
Thomas Kislinger is a Scientist at the Division of Cancer Genomics and Proteomic at the Ontario Cancer Institute, and the Assistant Professor at the Department of Medical Biophysics. As the Canada Research Chair in Proteomics and Cancer Research at the University of Toronto, Dr. Kislinger is currently focusing on optimizing technologies that allow for sensitive and selective isolation and characterization of surface proteins from cell culture (in vitro) or directly from the surface of microvascular endothelial cells (in vivo).
ThomasKislingerPhD Activities
Proteomics is a modern version of protein biochemistry. It combines methods and approaches from classic protein biochemistry with analytical chemistry and computer science to study the “proteome”. The proteome is defined as the entirety of proteins expressed by an organism, tissue, cell or organelle at any given time. In contrast to the genome, the proteome is highly dynamic, therefore reflecting changes throughout development, external influences (i.e. environment) and/or disease. Proteomics is a specific scientific discipline to study the proteome. This new type of science termed “systems biology” has the goal to study an organism at the systems level (i.e. obtaining information on all proteins present in a cell, rather than studying a single protein and/or pathway). In terms of cancer research, proteomics has several main applications. First, model organisms such as cell culture models or animal models are used to study basic mechanisms of cancer research. These could be identification of changes in the cellular proteome in cancer cell lines in response to a treatment, identification of protein targets of a given drug, etc. Similar approaches are also applied using banked human tissues from biopsies or surgery. A second application of proteomics in cancer research is for the discovery of biomarkers. A biomarker is a biological substance (i.e., protein, lipid, post-translational modification, etc.) that is used to evaluate the presence, progression, or treatment-response of a disease. To be clinically useful, a biomarker should be readily accessible (i.e. from body fluids) and provide sufficient sensitivity and specificity by minimizing false negatives and false positives. Since proteomics is a powerful tool to identify and quantify hundreds to thousands of proteins in a single sample intense research has been focused on proteomics-based biomarker discovery. The main analytical platform that is used to study these questions is mass spectrometry, a powerful tool for the identification and quantification of proteins in complex systems.
Proteomics is a modern version of protein biochemistry. It combines methods and approaches from classic protein biochemistry with analytical chemistry and computer science to study the “proteome”. The proteome is defined as the entirety of proteins expressed by an organism, tissue, cell or organelle at any given time. In contrast to the genome, the proteome is highly dynamic, therefore reflecting changes throughout development, external influences (i.e. environment) and/or disease. Proteomics is a specific scientific discipline to study the proteome. This new type of science termed “systems biology” has the goal to study an organism at the systems level (i.e. obtaining information on all proteins present in a cell, rather than studying a single protein and/or pathway). In terms of cancer research, proteomics has several main applications. First, model organisms such as cell culture models or animal models are used to study basic mechanisms of cancer research. These could be identification of changes in the cellular proteome in cancer cell lines in response to a treatment, identification of protein targets of a given drug, etc. Similar approaches are also applied using banked human tissues from biopsies or surgery. A second application of proteomics in cancer research is for the discovery of biomarkers. A biomarker is a biological substance (i.e., protein, lipid, post-translational modification, etc.) that is used to evaluate the presence, progression, or treatment-response of a disease. To be clinically useful, a biomarker should be readily accessible (i.e. from body fluids) and provide sufficient sensitivity and specificity by minimizing false negatives and false positives. Since proteomics is a powerful tool to identify and quantify hundreds to thousands of proteins in a single sample intense research has been focused on proteomics-based biomarker discovery. The main analytical platform that is used to study these questions is mass spectrometry, a powerful tool for the identification and quantification of proteins in complex systems.
New answer by ThomasKislingerPhD (Researcher (Verified)) in topic(s) Biochemistry, Cancer Research, Proteomics, Cancer
1
Answer |
5 months ago
Proteomics is currently not used in a clinical setting to treat cancer patients. Rather proteomics is at the interface of basic science and clinical research. This type of research is termed “translational research”. For example biomarkers discovered by proteomics will need to go through several rounds of independent and rigorous validations. Markers that are validated and shown to provide clinical applicability (i.e. improved diagnosis, prognosis or treatment stratification) could then be integrated into a clinical setting. Based on results obtain by a proteomics experiment sensitive yet simple assays need to be developed to be applicable in a routine clinical setting.
Proteomics is currently not used in a clinical setting to treat cancer patients. Rather proteomics is at the interface of basic science and clinical research. This type of research is termed “translational research”. For example biomarkers discovered by proteomics will need to go through several rounds of independent and rigorous validations. Markers that are validated and shown to provide clinical applicability (i.e. improved diagnosis, prognosis or treatment stratification) could then be integrated into a clinical setting. Based on results obtain by a proteomics experiment sensitive yet simple assays need to be developed to be applicable in a routine clinical setting.
New answer by ThomasKislingerPhD (Researcher (Verified)) in topic(s) Biochemistry, Cancer Research, Proteomics, Cancer
2
Answers |
5 months ago
Bioinformatics is computer science applied to biological questions. Since all genomics-type sciences such as next-gen sequencing, transcriptomics and proteomics generate large amount of data, computational and statistical approaches are required to evaluate and analyze the generated data to guarantee high data quality. Additionally, since more and more proteomics and genomics data are generated and deposited into the public domain data mining and integration strategies, based on bioinformatics, are becoming more common. In the context of cancer research integration of multiple datasets can be used to validate individual results and prioritize biological pathways or targets for validation. Bioinformatics is therefore highly integrated with proteomics for the interpretation of results. Ultimately these types of “systems” strategies will shed new light on cancer biology and ultimately provide better understanding human cancer biology. This in turn will lead to more rationally designed drugs, more individualized treatments and better biomarkers.
Bioinformatics is computer science applied to biological questions. Since all genomics-type sciences such as next-gen sequencing, transcriptomics and proteomics generate large amount of data, computational and statistical approaches are required to evaluate and analyze the generated data to guarantee high data quality. Additionally, since more and more proteomics and genomics data are generated and deposited into the public domain data mining and integration strategies, based on bioinformatics, are becoming more common. In the context of cancer research integration of multiple datasets can be used to validate individual results and prioritize biological pathways or targets for validation. Bioinformatics is therefore highly integrated with proteomics for the interpretation of results. Ultimately these types of “systems” strategies will shed new light on cancer biology and ultimately provide better understanding human cancer biology. This in turn will lead to more rationally designed drugs, more individualized treatments and better biomarkers.
Bioinformatics is computer science applied to biological questions. Since all genomics-type sciences such as next-gen sequencing, transcriptomics and proteomics generate large amount of data, computational and statistical approaches are required to evaluate and analyze the generated data to guarantee high data quality. Additionally, since more and more proteomics and genomics data are generated and deposited into the public domain data mining and integration strategies, based on bioinformatics, are becoming more common. In the context of cancer research integration of multiple datasets can be used to validate individual results and prioritize biological pathways or targets for validation. Bioinformatics is therefore highly integrated with proteomics for the interpretation of results. Ultimately these types of “systems” strategies will shed new light on cancer biology and ultimately provide better understanding human cancer biology. This in turn will lead to more rationally designed drugs, more individualized treatments and better biomarkers.
New answer by ThomasKislingerPhD (Researcher (Verified)) in topic(s) Biology, Bioinformatics, Cancer Research, Cancer
1
Answer |
5 months ago
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