A two-scale map of global gene expression for characterising in vitro engineered cells

In recent years the possibility to engineer cells in vitro has encountered signi cant progress. This engineering of cellular states, which is tightly coupled to the eld of stem cell research, is considered to be a very useful technology for the generation of specialised cells for drug development, disease modelling, or regenerative medicine. One important part of this process is the quality control, i.e. the detailed characterisation of the end products, to ensure that the transformed cells are similar to their in vivo counterparts. Many markers and functional assays exist that can be used for quality control of these cells. However, most of them focus on speci c, relatively narrow properties of the cells, neglecting a global overall comparison to the desired cell type. Here, we present a genome-wide gene expression microarray based approach to cell characterisation, providing complementary information to the commonly used single gene or morphological markers. We use a dimension reduction approach to localise newly generated microarray data in the high dimensional expression space. Using a combination of unsupervised and supervised dimension reduction methods, we establish a two-scale map of global gene expression with phenotypic interpretation of the coordinates. This two-scale map is used to characterise several di erent samples. It is rst validated on a dataset of 24 di erent tissues and cell lines as well as on two datasets of arti cially mixed tissues. Using these datasets, it is shown that the developed method outperforms three existing methods for RNA based global cell characterisation and that it provides increased information compared to the purely unsupervised or purely supervised dimension reduction methods. Application of the two-scale map to characterise in vitro transformed cells prooves to be useful in providing complementary information to the typical marker based or morphological criteria. In this respect, we could identify two examples of in vitro transformed cells where the transformation process is incomplete on a global expression level. Furthermore, we can show that in vitro di erentiation of pluripotent stem cells results in immature cells that are similar to embryonic of fetal tissues of the respective type. Using microarray data from arti cial mixtures of di erent tissues, we can observe clear non-linear e ects in the data that t well to the current understanding of the relationship between the RNA content of cells and the measurement signal of microarrays. Such non-linear e ects are currently not captured by the proposed linear dimension reduction approach and give important hints for further improvements of the method. In addition to quality control of in vitro transformed cells, the two-scale decomposition approach developed in this thesis may also be useful for a number of other applications, such as the analysis of drug response pro les or disease progression. Zusammenfassung Die gezielte Modi zierung von Zellen in der Kulturschale hat in den letzten Jahren signi kante Fortschritte gemacht. Diese eng mit der Stammzellfoschung zusammenhängende Technologie hat groÿes Potenzial zur Herstellung von spezialisierten Zellen für die Medikamentenentwicklung, die Modellierung von Krankheiten, sowie die regenerative Medizin. Ein wichtiger Schritt im Herstellungsprozess der Zellen ist die Qualitätskontrolle, das heiÿt die detaillierte Charakterisierung der zellulären Endprodukte, die sicherstellen soll, dass die generierten Zellen den entsprechenden körpereigenen Zellen möglichst stark ähneln. Diese Qualitätskontrolle der Zellen kann mit vielen verschiedene Markern und funktionalen Untersuchungsmethoden bewerkstelligt werden. Die meisten dieser Methoden analysieren jedoch nur einen relativ beschränkten Bereich der zellulären Eigenschaften und vernachlässigen dabei den globalen Vergleich zum angestrebten Zelltyp. In der vorliegenden Arbeit präsentieren wir einen Ansatz der auf der Analyse der genomweiten Genexpression mittels Microarrays basiert und somit komplementäre Informationen zu den üblicherweise genutzten Einzelgen oder Morphologie-basierten Markern liefert. Dazu nutzen wir einen Dimensionsreduktionsansatz um neu generierte Microarray Daten im eigentlich hochdimensionalen Genexpressionsraum zu lokalisieren. Wir kombinieren dabei unsupervidierte und supervidierte Dimensionsreduktionsmethoden und etablieren damit eine zwei-Skalen Landkarte der globalen Genexpression mit der Möglichkeit der direkten phenotypischen Interpretation der Koordinaten. Durch die Anwendung dieser zwei-Skalen Landkarte auf in vitro transformierte Zellen können wir zeigen, wie nützlich dieser Ansatz zur Generierung komplementärer Informationen zu den typischen Markern oder morphologischen Informationen ist. In diesem Zusammenhang zeigen wir zwei Beispiele in vitro transformierter Zellen, bei denen der Transformationsprozess auf globaler Genexpressionsbasis nicht vollständig abgeschlossen ist. Desweiteren können wir zeigen, dass durch die in vitro Di erenzierung pluripotenter Stammzellen meist nicht vollständig ausgereiften Zellen entstehen, die eine hohe Ähnlichkeit zu embryonalen oder fetalen Zellen des entsprechenden Typs aufweisen. Basierend auf Microarray Daten von künstlichen Mischungen verschiedener Gewebe, beobachten wir deutliche nicht-lineare E ekte in den Daten, die gut zum momentanen Verständnis der Beziehung von RNS Menge und Messsignal passen. Diese nicht-linearen E ekte werden aktuell von dem hier vorgestellten linearen Dimensionsreduktionsmodell nicht berücksichtigt, geben aber wertvolle Hinweise für mögliche zukünftige Verbesserungen des Modells. Zusätzlich zur Qualitätskontrolle in vitro transformierter Zellen, könnte der hier entwickelte zwei-Skalen Ansatz auch für einige andere Anwendungen, wie die Analyse der Medikamentenwirkung auf Zellen oder den Vergleich verschiedener Stadien der Krankheitsentwicklung, nützlich sein. Acknowledgements First of all I would like to thank my supervisor Prof. Andreas Schuppert for the proposition of this interesting topic, his very valuable advises and the fruitful discussions about the mysteries of biological data analysis. Andreas, I enjoyed (and still enjoy) the time in your group very much and I thank you for your important comments on my thesis and the high exibility and independence you gave me for the conduction of my research. The plenty of anecdotes you told us during lunch breaks were always a very welcome escape from work and I could learn about some common pitfalls in management and daily life from them. I also want to thank my second advisor Prof. Martin Zenke for his continuous support of my work, the helpful comments on my thesis and the very valuable discussions about the results from the microarray data analyses. Martin, I could learn a lot about stem cell biology from you and it was and is a pleasure to work together with you in the StemCellFactory project. I thank all my collaborators from the various research projects for the very interesting and fruitful joint research that we conducted. I want to name especially Wolfgang Wanger, Bernhard Schuldt, Franz-Josef Müller, and Ben MacArthur with whom I had several attractive collaborations. Many thanks go also to Daniela Malan, Philip Sasse, Carolin Haubenreich, Oliver Brüstle, Paul Wanek, Hatim Hemeda, and all the other partners from the StemCellFactory project for the joint work in the quality control and the overall very inspiring teamwork in building up this fancy machine. Thanks to Joana Frobel, Arne Schenk, and Roman Goetzke for the joint analysis of the methylation data and to Qiong Lin for the helpful conversations about bioinformatics and data analysis. I want to thank especially my colleagues Ste en Schaper, Nina Kusch, Maryam Montazeri, Ali Hadizadeh, Max Eck, Jayesh Bhat as well as my former colleagues Sonja Vivas, Bernhard Schuldt, and Arne Schenk for the interesting and inspiring time with them. I enjoyed the private as well as work-related discussions and felt always very comfortable in our group. The same holds true for the plenty of colleagues from AICES with whom I had some nice retreats in Monschau and Vaals, funny conversations during lunch break, and overall a very good time. Special thanks go to my room mates in the Rogowski building (Callum, Thang, Marcus, Atanas, Bernhard, Sonia, Raheel, Arianna) for the great working environment during my rst two years. I am also very thankful to Hülya Ulu-Esser, Nicole Faber, Nadine Bachem, and all the other members of the service team for their administrative support. Many thanks go to Ste en, Arne and my brother Christian for proof-reading of this thesis. Your comments and corrections were highly appreciated and helped me a lot in nalising iv this thesis. Last but not least I want to thank my family for their continuous support during my time as a doctoral candidate, my studies, and basically throughout my whole life. Special thanks go to my wife Katrin who pushed me forward when I was in a crisis and who helped me to relax from my work when I needed a time out. Thank you for your patience and your belief in me and my work! Thanks to all of you and to all other people that supported me and that are not explicitly named here!