SPATIAL, SINGLE-CELL ANALYSIS OF TUMOR TISSUES

Belgique
Posted 3 years ago

(ref. BAP-2020-129)

The Translational Cell and Tissue Research (TCTR) Unit is fully embedded in the pathology department at KULeuven and directly affiliated to the University Hospitals Leuven, one of the largest hospitals in Belgium and Europe. Decades of experience in pathological analysis is gathered for which morphological assessments of tissues are done on a daily basis by expert pathologists. A large biobank of human tissues is available in the archives of the unit. Through a close collaboration with the Oncology department of the University Hospitals Leuven, this department is ideal to do translational research on multiple cancer types. Pathology has undergone two major revolutions since it became a global and accepted clinical discipline in the first half of the 20th Century. First, the introduction of immunohistochemistry (IHC) in the 1970s followed by molecular techniques (MOL) in the early 2000s, have changed our understanding of tumor biology and redefined the field of precision medicine. IHC, after not having changed significantly for more than 40 years, is finally evolving from a one-marker-at-the-time technique to measuring a multitude (+50) of markers at the same time in the same tissue slide – a method that is known as multiplexing. Our team at KULeuven developed, in collaboration with the University of Milan-Bicocca, the Multiple Iterative Labeling by Antibody Neodeposition (MILAN) method for multiplexed immunohistochemistry (https://www.nature.com/protocolexchange/protocols/7017), which allows the simultaneous measurement of up to 100 proteins in a single tissue section at a single-cell resolution in hundreds of tumor samples. In contrast to many other single-cell methods in which the tissue is dissociated, MILAN preserves the spatial organization of the cells allowing us to study their behavior in their natural environment. By strategically selecting the right biomarkers, such approach therefore enables us not only to study the quantitative cellular composition of the tissue by phenotypic identification, but also to map their spatial distribution with a strong focus on cell-cell interactions via neighborhood analyses. Recent research is highlighting that such insights are becoming crucial in the field of precision medicine, whereby particular treatments (e.g. immunotherapies) need to be matched to the right patient population
Responsibilities

Several single-cell analysis methods have been developed to obtain multi-dimensional insights into the cellular composition of a tumor in its original spatial context. In our group, we are currently installing a technology platform for multiplex tissue analysis using immunohistochemistry, which is available to academic and industrial researchers. In this platform, we are at the moment putting major efforts to analyze hundreds of tissue samples from a wide variety of cancer types, including melanoma, brain tumors, lymphoma, kidney, lung and breast cancer. Because these efforts are generating enormous amounts of data that require more profound analysis, we are expanding our bioinformatics team to deal with a multitude of challenges that still require significant amounts of method development. This includes the development of novel algorithms for image (pre)processing, efficiently define and identify millions of single cells and their complex interactions in the spatial context of the tissue, and eventually link these features to clinical parameters, such as responses to therapy. To achieve this goal, state-of-the-art methods for machine/deep learning and/or artificial intelligence need to be installed. Clinical/pathological data interpretation will be done in close collaboration with the involved molecular biologists, clinicians and pathologists to ensure validity of all conclusions.

Profile
We are looking for a motivated and enthusiastic postdoctoral researcher with a strong interest in bioinformatics and experience in image-based analyses to strengthen our bio-informatics team of postdoctoral and PhD researchers. Experience with ML/DL and AI is obviously also a plus. We look for someone who wants to use his project to grow his/her skills in spatial single-cell data analysis and apply it to develop the clinical tools of the future. You will have the opportunity to work in a young, dynamic and interdisciplinary team that brings together bio-informaticians, clinicians, pathologists and molecular biologists.
Offer
We offer a full time 2-year postdoc position within our research team. During this time, you will also receive the chance to strategically shape the future of this challenging project. Being eligible to apply for additional fellowships (e.g. FWO) and international fellowships (e.g. Marie-Curie) is an added value.
Interested?
For more information please contact Prof. dr. Frederik De Smet, tel.: +32 16 37 25 75, mail: frederik.desmet@kuleuven.be or Prof. dr. Francesca Bosisio, tel.: +32 16 32 99 65, mail: francescamaria.bosisio@kuleuven.be.
You can apply for this job no later than March 08, 2020 via the online application tool
KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at diversiteit.HR@kuleuven.be.

Job Features

Job CategoryEnseignement et recherche scientifique

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