Squamous cell carcinoma of the bronchus is the second most common and fatal subtype of lung cancer. In the process of squamous carcinogenesis, the normal bronchial epithelium undergoes a series of histologic transformations known as the metaplasia-dysplasia-carcinoma sequence. These intermediate histologic patterns are called premalignant lesions, and occur prior to the development of cancer. Compared to early stage cancer, survival following resection of premalignant lesions approaches 100%, highlighting the promise of lung cancer interception. However, because of our lack of understanding of the molecular events during squamous carcinogenesis, we are currently unable to predict which lesions will progress to cancer, and we do not have molecular targets for noninvasive treatment. The work in this thesis seeks to improve our understanding of the changes associated with grades of premalignant histology and progression at the level of single cells. I analyzed single cell RNA sequencing data from a cohort of 41 lesions from 26 patients, encompassing the normal-appearing bronchus, premalignant lesions, and early stage carcinoma. I described histology-associated changes in basal cells. Basal cells from low grade lesions expressed genes related to the maintenance of the normal epithelium, while basal cells from high grade lesions expressed genes related to the cell cycle and detoxification of the airway from smoking toxicants. Secondly, I identified a high grade lesion undergoing the epithelial-to-mesenchymal transition. These cells transitioned from a high grade basal cell state, lost their expression of basal cell markers, and expressed canonical EMT genes, including SPARC, FN1, and MMP2. Finally, I identified shifts in T cell subtypes and widespread expression of exhaustion markers PD-1, CTLA4, LAG3, and TIGIT co-occurring with high grade basal cells. Secondly, I leveraged our single cell data to identify gene modules associated with histology and progression in bulk RNA sequencing data. I identified a module of genes expressed in B and dendritic cells involved in antigen presentation through the MHC II pathway whose expression was decreased in progressive lesions. I also identified a module of stromal-expressed genes that were less expressed in progressive lesions, which had previously been unidentified. Associations between module expression and progression were validated in a second data set. This work improves our understanding of the signaling and interactions between cell types associated with histology and progression of premalignant lesions. These findings may be used to improve our prognostication and treatment of premalignant lesions.

During the last years, important progress has been made in modeling early brain development using 3-dimensional in vitro systems, so-called cerebral organoids. These can be grown from pluripotent stem cells of different species such as our closest living relatives, the chimpanzees and from patients carrying disease mutations that affect brain development. This offers the possibility to study uniquely human features of brain development as well as to identify gene networks altered in neurological diseases. Profiling the transcriptional landscape of cells provides insights into how gene expression programs have changed during evolution and are affected by disease. Previously, studies of this kind were realized using bulk RNA-sequencing, essentially measuring ensemble signals of genes across potentially heterogeneous populations and thus obscured subtle changes with respect to transient cell states or cellular subtypes. However, remarkable advances during the last years have enabled researchers to profile the transcriptomes of single cells in high throughput. This thesis demonstrates how single-cell transcriptomics can be used to dissect human-specific features of the developing and adult brain as well as cellular subpopulations dysregulated in a malformation of the cortex.

Populationen kanzeröser Zellen können aus verschiedenen Subpopulationen mit distinkten phänotypischen Profilen bestehen. Diese Dissertation verwendet mathematische Modellierung sowie die Analyse von Einzelzell-Genexpressionsdaten zur Beantwortung von Fragen über die Entstehung, das Wachstum und die Behandlung von Tumoren im Kontext einer solchen intratumoralen Heterogenität. / Tumoural cell populations may consist of different subpopulations with distinct phenotypic profiles. This thesis applies mathematical modelling as well as the analysis of single-cell gene expression data to questions related to the emergence, growth and treatment of tumours in the context of such an intratumoural heterogeneity.

I present novel solutions to two different classes of computational problems that arise in the study of complex cellular processes. The first problem arises in the context of planning large-scale genetic cross experiments that can be used to validate predictions of multigenic perturbations made by mathematical models. (i) I present CrossPlan, a novel methodology for systematically planning genetic crosses to make a set of target mutants from a set of source mutants. CrossPlan is based on a generic experimental workflow used in performing genetic crosses in budding yeast. CrossPlan uses an integer-linear-program (ILP) to maximize the number of target mutants that we can make under certain experimental constraints. I apply it to a comprehensive mathematical model of the protein regulatory network controlling cell division in budding yeast. (ii) I formulate several natural problems related to efficient synthesis of a target mutant from source mutants. These formulations capture experimentally-useful notions of verifiability (e.g., the need to confirm that a mutant contains mutations in the desired genes) and permissibility (e.g., the requirement that no intermediate mutants in the synthesis be inviable). I present several polynomial time or fixed-parameter tractable algorithms for optimal synthesis of a target mutant for special cases of the problem that arise in practice. The second problem I address is inferring gene regulatory networks (GRNs) from single cell transcriptomic (scRNA-seq) data. These GRNs can serve as starting points to build mathematical models. (iii) I present BEELINE, a comprehensive evaluation of state-of-the-art algorithms for inferring gene regulatory networks (GRNs) from single-cell gene expression data. The evaluations from BEELINE suggest that the area under the precision-recall curve and early precision of these algorithms are moderate. Techniques that do not require pseudotime-ordered cells are generally more accurate. Based on these results, I present recommendations to end users of GRN inference methods. BEELINE will aid the development of gene regulatory network inference algorithms. (iv) Based on the insights gained from BEELINE, I propose a novel graph convolutional neural network (GCN) based supervised algorithm for GRN inference form single-cell gene expression data. This GCN-based model has a considerably better accuracy than existing supervised learning algorithms for GRN inference from scRNA-seq data and can infer cell-type specific regulatory networks. / Doctor of Philosophy / A small number of key molecules can completely change the cell's state, for example, a stem cell differentiating into distinct types of blood cells or a healthy cell turning cancerous. How can we uncover the important cellular events that govern complex biological behavior? One approach to answering the question has been to elucidate the mechanisms by which genes and proteins control each other in a cell. These mechanisms are typically represented in the form of a gene or protein regulatory network. The resulting networks can be modeled as a system of mathematical equations, also known as a mathematical model. The advantage of such a model is that we can computationally simulate the time courses of various molecules. Moreover, we can use the model simulations to predict the effect of perturbations such as deleting one or more genes. A biologist can perform experiments to test these predictions. Subsequently, the model can be iteratively refined by reconciling any differences between the prediction and the experiment. In this thesis I present two novel solutions aimed at dramatically reducing the time and effort required for this build-simulate-test cycle. The first solution I propose is in prioritizing and planning large-scale gene perturbation experiments that can be used for validating existing models. I then focus on taking advantage of the recent advances in experimental techniques that enable us to measure gene activity at a single-cell resolution, known as scRNA-seq. This scRNA-seq data can be used to infer the interactions in gene regulatory networks. I perform a systematic evaluation of existing computational methods for building gene regulatory networks from scRNA-seq data. Based on the insights gained from this comprehensive evaluation, I propose novel algorithms that can take advantage of prior knowledge in building these regulatory networks. The results underscore the promise of my approach in identifying cell-type specific interactions. These context-specific interactions play a key role in building mathematical models to study complex cellular processes such as a developmental process that drives transitions from one cell type to another

Anti-CD19 CAR-T cell immunotherapy is a hopeful treatment option for patients with B cell lymphomas, however it copes with partly severe adverse effects like neurotoxicity. Single-cell resolved molecular data sets in combination with clinical parametrization allow for comprehensive characterization of cellular subpopulations, their transcriptomic states, and their relation to the adverse effects. We here present a re-analysis of single-cell RNA sequencing data of 24 patients comprising more than 130,000 cells with focus on cellular states and their association to immune cell related neurotoxicity. For this, we developed a single-cell data portraying workflow to disentangle the transcriptional state space with single-cell resolution and its analysis in terms of modularly-composed cellular programs. We demonstrated capabilities of single-cell data portraying to disentangle transcriptional states using intuitive visualization, functional mining, molecular cell stratification, and variability analyses. Our analysis revealed that the T cell composition of the patient’s infusion product as well as the spectrum of their transcriptional states of cells derived from patients with low ICANS grade do not markedly differ from those of cells from high ICANS patients, while the relative abundancies, particularly that of cycling cells, of LAG3-mediated exhaustion and of CAR positive cells, vary. Our study provides molecular details of the transcriptomic landscape with possible impact to overcome neurotoxicity.

Tissue-resident stem cells are present in many adult organs, where they are important for organ homeostasis and repair in response to injury. However, the signals that activate these cells and the mechanisms governing how these cells self-renew or differentiate are highly context dependent and incompletely understood, particularly in non-hematopoietic tissues. In the skin, melanocyte stem cells (McSCs) are responsible for replenishing mature pigmented melanocytes. In mammals, these cells reside in the hair follicle bulge and bulb niches where they are activated during homeostatic hair follicle turnover and following melanocyte destruction, as occurs in vitiligo and other skin hypopigmentation disorders. Recently, we identified adult McSCs in the zebrafish. To elucidate mechanisms governing McSC self-renewal and differentiation fates we analyzed individual transcriptomes from thousands of melanocyte lineage cells during the regeneration process. We identified transcriptional signatures for McSCs, deciphered transcriptional changes and intermediate cell states during regeneration, and analyzed cell-cell signaling changes to discover mechanisms governing melanocyte regeneration. We identified KIT signaling via the RAS/MAPK pathway as a regulator of McSC direct differentiation. Analysis of the scRNAseq dataset also revealed a population of mitfa/aox5 co-expressing cells that divides following melanocyte destruction, likely corresponding to cells that undergo self-renewal. Our findings show how different subpopulations of mitfa-positive cells underlie regeneration and differentiation of at least one subpopulation requires reactivation of developmental KIT signaling to properly reconstitute the melanocyte stripe.

Die EGFR-Signalübertragung steuert viele verschiedene zelluläre Prozesse in allen Arten von Epithelzellen, einschließlich des Darmepithels. Diese Prozesse reichen von Proliferation und Wachstum über Differenzierung bis hin zu Autophagie und Apoptose. Die vorliegende Studie zielt darauf ab, die Signalveränderungen zu charakterisieren, die im Darmepithel als Reaktion auf EGF-induzierten Hungerstress stattfinden. Kontraintuitiv führte eine 24-stündige EGF-Starre zu einer deutlichen Phosphorylierung von EGFR, MEK1/2 und ERK1/2, was auf eine Aktivierung dieser Signalachse in Darmzellen hindeutet. Diese Veränderungen waren am signifikantesten in den undifferenzierten CD44-reichen Krypta-Basiszellen. Interessanterweise war die EGF-Starvation-induzierte ERK1/2-Phosphorylierung mit der Hochregulierung einer Untergruppe von ERK-Zielgenen verbunden, bei denen es sich zumeist um primäre Zielgene handelt. Die Überexpression des EGFR-Liganden HBEGF und des FGFR-Liganden FGF1 in ausgehungerten Zellen könnte für die hungerbedingte Zunahme der MAPK-Aktivität verantwortlich sein, obwohl eine erhöhte Sekretion dieser Liganden durch ausgehungerte Organoide nicht bestätigt werden konnte. Dennoch wird die kompensatorische Ligandensekretion durch die Beobachtung gestützt, dass die erneute Zugabe von EGF zu ausgehungerten Organoiden die pERK1/2-Spiegel auf den Ausgangswert zurücksetzt, was bedeutet, dass EGF mit einem anderen von ausgehungerten Zellen sezernierten Liganden um den EGFR konkurriert. Zusätzlich zu HBEGF wurde festgestellt, dass andere Gene, die für den Schutz, das Überleben und die Regeneration des Darmepithels bekannt sind, in ausgehungerten Organoiden überexprimiert werden, wie z. B. Reg3b. Insgesamt können die in dieser Studie berichteten EGF-induzierten Veränderungen der MAPK-Signalübertragung und der globalen Genexpression als ein überlebensförderndes Programm interpretiert werden, das bevorzugt in Darmstammzellen und frühen Vorläuferzellen aktiviert wird. / EGFR signaling drives many different cellular processes in all kinds of epithelial cells including the intestinal epithelium. Such processes range from proliferation and growth to differentiation to autophagy and apoptosis. The present study aims to characterize signaling changes that take place in the intestinal epithelium in response to EGF starvation-induced stress using epithelial organoids derived from the mouse duodenum and human colorectal tumor tissue. Counterintuitively, 24 h EGF starvation induced a prominent phosphorylation of EGFR, MEK1/2 and ERK1/2 indicating an activation of this signaling axis in intestinal cells. These changes were most significant in the undifferentiated CD44-high crypt base cells. Interestingly, EGF starvation-induced ERK1/2 phosphorylation was associated with upregulation of a subset of ERK target genes that were mostly primary-response targets. Overexpression of the EGFR ligand HBEGF and the FGFR ligand FGF1 in starved cells may account for starvation-driven increase in MAPK activity, although an increased secretion of these ligands by starved organoids was not confirmed. Nevertheless, compensatory ligand secretion is still supported by the observation that EGF re-addition to starved organoids restores pERK1/2 levels to baseline which implies that EGF competes for EGFR with some other ligand secreted by starved cells. In addition to HBEGF, other genes known to promote protection, survival and regeneration of the intestinal epithelium were found to be overexpressed in starved organoids such as Reg3b. Collectively, EGF starvation-induced changes in MAPK signaling and global gene expression reported in this study can be interpreted as a pro-survival program that gets activated preferentially in intestinal stem cells and early progenitors.

No description available.

Les voies aériennes humaines sont bordées d'un épithélium pseudostratifié composé principalement de cellules basales et de cellules pyramidales parmi lesquelles figurent les cellules sécrétrices de mucus et les cellules multiciliées. Toutes ces cellules contribuent à la clairance mucociliaire des voies respiratoires. Cet épithélium se régénère lentement dans des conditions homéostatiques, mais il est capable de se régénérer rapidement après agression grâce à des processus de prolifération, de migration, de polarisation et de différenciation. Chez les patients atteints de maladies respiratoires chroniques telles que la broncho-pneumopathie chronique obstructive, l'asthme ou la mucoviscidose, la réparation tissulaire est souvent défectueuse, caractérisée par une perte de cellules multiciliées et une hyperplasie des cellules sécrétrices, ayant pour conséquence une clairance mucociliaire affectée. La séquence des événements cellulaires conduisant à un tissu fonctionnel ou remodelé est encore mal décrite. Notre principal objectif a été d’identifier les types cellulaires successifs mis en jeu lors de la régénération tissulaire et les événements moléculaires responsables d'une régénération saine ou pathologique. Nous avons analysé la composition cellulaire de l’épithélium des voies respiratoires à plusieurs stades de différenciation en utilisant un modèle de culture 3D in vitro qui reproduit la composition cellulaire in vivo. En appliquant une méthode de transcriptomique sur cellule unique couplée à des méthodes bioinformatiques, nous avons établi les hiérarchies cellulaires permettant de reconstruire les différentes trajectoires cellulaires mises en jeu lors de la régénération de l’épithélium des voies respiratoires humaines. Après avoir confirmé les lignages cellulaires qui ont été précédemment décrits, nous avons découvert une nouvelle trajectoire reliant les cellules sécrétrices de mucus aux cellules multiciliées. Nous avons également caractérisé de nouvelles populations cellulaires et de nouveaux acteurs moléculaires impliqués dans le processus de régénération de l'épithélium des voies respiratoires humaines. Enfin, grâce à ces approches, nous avons mis en évidence des réponses spécifiques de chaque type cellulaire survenant dans des situations pathologiques d’hyperplasie sécrétoire. Ainsi, nos données, en apportant d'importantes contributions à la compréhension de la dynamique de différenciation de l’épithélium des voies respiratoires humaines, ouvrent de nouvelles voies vers l’identification de cibles thérapeutiques. / Human airways are lined by a pseudostratified epithelium mainly composed of basal and columnar cells, among these cells we can find multiciliated, secretory cells and goblet cells. All these cells work together in the mucociliary clearance of the airways. This epithelium regenerates slowly under homeostatic conditions but is able to recover quickly after aggressions through proliferation, migration, polarization and differentiation processes. However, in patients with chronic pulmonary diseases such as chronic obstructive pulmonary disease, asthma or cystic fibrosis, epithelial repair is defective, tissue remodeling occurs, leading to loss of multiciliated cells and goblet cell hyperplasia, impairing correct mucociliary clearance. The sequence of cellular events leading to a functional or remodelled tissue are still poorly described. Hence, we aim at identifying the successive cell types appearing during tissue regeneration and the molecular events that are responsible for healthy or pathological regeneration. We have analysed airway epithelial cell composition at several stages of differentiation using an in vitro 3D culture model which reproduces in vivo epithelial cell composition. Applying single cell transcriptomics and computational methods, we have identified cell lineage hierarchies and thus constructed a comprehensive cell trajectory roadmap in human airways. We have confirmed the cell lineages that have been previously described and have discovered a novel trajectory linking goblet cells to multiciliated cells. We have also discovered novel cell populations and molecular interactors involved in the process of healthy human airway epithelium regeneration. Using these approaches, we have finally shed light on cell-type specific responses involved in pathological goblet cell hyperplasia. Our data, by bringing significant contributions to the understanding of differentiation’s dynamics in the context of healthy and pathological human airway epithelium, may lead to the identification of novel therapeutic targets.

RNA ist ein zentrales Molekül in der Zelle und essentiell für ihre Lebensfunktionen. Die durchschnittliche Halbwertszeit von RNA-Molekülen limitiert jedoch die zeitliche Auflösung herkömmlicher RNA-Sequenzierung, da geringe Änderungen im Transkriptom kaum zu erkennen sind, bis eine gewisse Anzahl an Molekülen akkumuliert. Durch metabolische Markierung von RNA (SLAMseq) kann die Auflösung deutlich erhöht werden. Hierfür werden der Probe markierte Nucleotide (4sU/4sUTP) zugesetzt, die dann zufällig in neu transkribierte RNA inkorporiert werden und eine Unterscheidung zwischen ‚neuer‘ und ‚alter‘ RNA erlauben. In dieser Arbeit werden eine der ersten Einzelzell-SLAMseq-Methoden, die dazugehörige Datenanalyse-Software sowie drei Anwendungen der entwickelten Methoden vorgestellt. Die erste Anwendung verwendet Einzelzell-SLAMseq, um zwischen maternaler (alter) und zygotischer (neuer) RNA in sich entwickelnden Zebrafischembryos bis zur Gastrulation zu unterscheiden. Im Rahmen des Projekts entstand der erste Einzelzell-SLAMseq-Datensatz in einem vollständigen Wirbeltier, der es außerdem erlaubt, im Vorfeld identifizierten lokalisierten maternalen Transkripten zeitlich zu folgen. Diese – vorher uncharakterisierten –Transkripte wurden während der Gastrulation in den Keimzellen angereichert gefunden, was Rückschlüsse auf ihre mögliche Funktion erlaubt. Die zweite Anwendung konzentriert sich auf die neu transkribierte RNA und verwendet (Einzelzell-)SLAMseq, um Transkripte, die in Reaktion auf Stress während der Probenaufbereitung hergestellt wurden, zu identifizieren und rechnerisch zu entfernen. Die Vorteile der Methode werden in mehreren Systemen und Geweben (Mausherz, Zebrafischlarve, Maus-Microglia) demonstriert. In der dritten Anwendung wird eine Machbarkeitsstudie für in vivo SLAMseq zur Identifikation der initialen Immunantwort nach Makrophagenstimulation präsentiert, die auf einen deutlichen Gewinn an zeitlicher Auflösung durch SLAMseq hindeutet. / RNA is a central molecule in the cell and essential to its life functions. With the average RNA half life being multiple hours, regular RNA sequencing has an intrinsic limit on temporal resolution, where small changes in the transcriptome are not picked up until a certain amount of transcripts has build up. This resolution can be greatly improved using RNA metabolic labelling (SLAMseq), where labelled nucleotides (4sU/4sUTP) are added to the samples. These nucleotides are randomly incorporated into nascent transcripts and allow distinction between RNA produced before and after introduction of the labelling agent. This thesis presents one of the first high throughput single cell SLAMseq protocols, an accompanying computational pipeline for data analysis as well as three applications for the developed methods. The first application uses single cell SLAMseq to distinguish between maternal (unlabelled) and zygotic (labelled) transcripts in early zebrafish development (up to mid-gastrulation). This project generated the first single cell SLAMseq dataset in a whole vertebrate. Additionally the data allows to follow a previously discovered set of vegetally localised maternal transcripts in time and determine that these specific transcripts are mainly enriched in the primordial germ cells at gastrulation, therefore ascribing a potential function to a set of so far uncharacterised genes. The second application focuses on newly transcribed RNA and uses (single cell) SLAMseq as a technique to identify and remove transcripts generated in response to sample preparation stress. The method’s benefits are demonstrated in multiple systems and tissues, among them mouse cardiomyocytes, zebrafish larvae and mouse microglia. Finally as the third application an in vivo proof of concept study of SLAMseq to identify first response genes in macrophage stimulation is presented, where the introduction of 4sU shows clear advantages in temporal resolution compared to unlabelled data.