Protein molecules and their interactions via protein-protein interactions (PPIs) are at the core of cellular functions. While such global PPI networks have been useful for analyzing gene function and effects of genetic variants, they do not resolve tissue and cell-typespecific interactions. Here we leverage recent advances in single-cell RNA sequencing (scRNA-seq) to reconstruct cell-type-specific PPI networks across different tissues to enable a context-sensitive analysis of immune cells’ gene-protein pathways. Targeting B cells, T cells, and macrophage cells as a proof-of-principle, we used scRNA-seq data across different tissues from the Tabula Muris mouse consortium. We mapped the protein-coding DEGs to a protein-protein interaction network database (STRING v.11). Topological and global similarity analysis of the networks revealed distinct properties between tissues highlighting tissue-specific behaviors for each cell type. For example, we found that degree and clustering coefficients distributions were tissue-specific. Different cell types and tissues displayed specific characteristics, and in particular, the splenic PPI networks were different compared to other analyzed tissues for all the immune cell types examined. For example, the pairwise comparison of the Jaccard index for node similarity and the mantel test correlation analysis showed that the spleen’ node and PPI networks are more different than any other tissues for each cell type examined. The physiological and anatomical properties that distinguish the spleen from other examined tissues might explain why the splenic PPI networks tend to be less similar compared to other tissues. The cell-type-specific network analyses using the different distance measures between the adjacency matrices on the hub nodes such as Euclidean, Manhattan, Jaccard, and Hamming distances showed a macrophage-specific behavior not observed in B cells and T cells, confirming their lineage differences. Finally, we explored the rewiring of selected hub nodes and transcription factors in the PPI networks along with their biological enrichments to validate our observations. The suggested biological validity of our results confirms the relevance of data-driven reconstruction of these context-sensitive networks using more advanced network inference algorithms. In conclusion, scRNA-seq enables the reconstruction of global unspecific PPI networks into cell and tissue-specific networks, thereby providing an increased resolution of the biological context.

Large-scale transcriptomics data studies revolutionised the fields of systems biology and medicine, allowing to generate deeper mechanistic insights into biological pathways and molecular functions. However, conventional bulk RNA-sequencing results in the analysis of an averaged signal of many input cells, which are homogenised during the experimental procedure. Hence, those insights represent only a coarse-grained picture, potentially missing information from rare or unidentified cell types. Allowing for an unprecedented level of resolution, single-cell transcriptomics may help to identify and characterise new cell types, unravel developmental trajectories, and facilitate inference of cell type-specific networks. Besides all these tempting promises, there is one main limitation that currently hampers many downstream tasks: single-cell RNA-sequencing data is characterised by a high degree of sparsity. Due to this limitation, no reliable network inference tools allowed to disentangle the hidden information in the single-cell data. Single-cell correlation networks likely hold previously masked information and could allow inferring new insights into cell type-specific networks. To harness the potential of single-cell transcriptomics data, this dissertation sought to evaluate the influence of data dropout on network inference and how this might be alleviated. However, two premisses must be met to fulfil the promise of cell type-specific networks: (I) cell type annotation and (II) reliable network inference. Since any experimentally generated scRNA-seq data is associated with an unknown degree of dropout, a benchmarking framework was set up using a synthetic gold data set, which was subsequently affected with different defined degrees of dropout. Aiming to desparsify the dropout-afflicted data, the influence of various imputations tools on the network structure was further evaluated. The results highlighted that for moderate dropout levels, a deep count autoencoder (DCA) was able to outperform the other tools and the unimputed data. To fulfil the premiss of cell type annotation, the impact of data imputation on cell-cell correlations was investigated using a human retina organoid data set. The results highlighted that no imputation tool intervened with cell cluster annotation. Based on the encouraging results of the benchmarking analysis, a window of opportunity was identified, which allowed for meaningful network inference from imputed single-cell RNA-seq data. Therefore, the inference of cell type-specific networks subsequent to DCA-imputation was evaluated in a human retina organoid data set. To understand the differences and commonalities of cell type-specific networks, those were analysed for cones and rods, two closely related photoreceptor cell types of the retina. Comparing the importance of marker genes for rods and cones between their respective cell type-specific networks exhibited that these genes were of high importance, i.e. had hub-gene-like properties in one module of the corresponding network but were of less importance in the opposing network. Furthermore, it was analysed how many hub genes in general preserved their status across cell type-specific networks and whether they associate with similar or diverging sub-networks. While a set of preserved hub genes was identified, a few were linked to completely different network structures. One candidate was EIF4EBP1, a eukaryotic translation initiation factor binding protein, which is associated with a retinal pathology called age-related macular degeneration (AMD). These results suggest that given very defined prerequisites, data imputation via DCA can indeed facilitate cell type-specific network inference, delivering promising biological insights. Referring back to AMD, a major cause for the loss of central vision in patients older than 65, neither the defined mechanisms of pathogenesis nor treatment options are at hand. However, light can be shed on this disease through the employment of organoid model systems since they resemble the in vivo organ composition while reducing its complexity and ethical concerns. Therefore, a recently developed human retina organoid system (HRO) was investigated using the single-cell toolbox to evaluate whether it provides a useful base to study the defined effects on the onset and progression of AMD in the future. In particular, different workflows for a robust and in-depth annotation of cell types were used, including literature-based and transfer learning approaches. These allowed to state that the organoid system may reproduce hallmarks of a more central retina, which is an important determinant of AMD pathogenesis. Also, using trajectory analysis, it could be detected that the organoids in part reproduce major developmental hallmarks of the retina, but that different HRO samples exhibited developmental differences that point at different degrees of maturation. Altogether, this analysis allowed to deeply characterise a human retinal organoid system, which revealed in vivo-like outcomes and features as pinpointing discrepancies. These results could be used to refine culture conditions during the organoid differentiation to optimise its utility as a disease model. In summary, this dissertation describes a workflow that, in contrast to the current state of the art in the literature enables the inference of cell type-specific gene regulatory networks. The thesis illustrated that such networks indeed differ even between closely related cells. Thus, single-cell transcriptomics can yield unprecedented insights into so far not understood cell regulatory principles, particularly rare cell types that are so far hardly reflected in bulk-derived RNA-seq data.

Endemic Burkitt lymphoma (eBL) is the leading pediatric cancer in sub-Saharan Africa and is associated with Epstein-Barr virus (EBV) and Plasmodium falciparum malaria co-infections. Current treatment options in Africa are combination chemotherapy with a survival rate hovering around 50%. Relapsed or refractory eBL patients have failed to receive any targeted treatments in the clinic. Our focus was to delineate immune responses in eBL, interrogate the tumor variation in responses to targeted treatments and develop mouse models that can be used to target essential mediators of tumor pathogenesis. Immune-based treatments including immune checkpoint inhibition have recently become an effective therapeutic modality in oncology. However, some B cell lymphomas such as Hodgkin Lymphoma (HL), are more receptive to checkpoint inhibition than others suggesting a need to understand the efficacy of checkpoint inhibition on different lymphoma subtypes. Checkpoint inhibitors act by blocking inhibitory receptors on T cells and improving anti-tumor responses. One of the goals of this thesis was to characterize checkpoint inhibitors on Tumor-infiltrating lymphocytes (TILs) in eBL tumors and to identify T cell subsets that exhibit increased expression of inhibitory receptors, poor cytokine production, poor proliferation and express transcription factors associated with exhaustion. Using scRNA seq, we identified T cell clusters that co-expressed inhibitory receptors, poor proliferative markers but also sustained costimulatory signals, as well as cytokine expression suggesting a pre dysfunctional state and not terminally exhausted state. Furthermore, we quantified the dominant co-inhibitory receptors PD1 and TIGIT that are upregulated in the tumor microenvironment via immunohistochemistry (IHC) and in peripheral blood of eBL patients via flow cytometry. We compared eBL patients with healthy pediatric cohorts with a history of persistent malaria exposure to those who had little to no malaria infections, to understand uniquely T cell mediated responses in BL children. Tumors had high co-expression of PD1 and TIGIT but fewer PD1 only populations, suggesting that both ligands may play a role in restraining immune activation via IHC. Next, we investigated if PD1 ligands or TIGIT ligands were overexpressed in eBL tumors. Nectin-2, TIGIT ligand was highly expressed in eBL tumors but was not highly correlated with TIGIT expression. These studies provide insights for PD1/ TIGIT blockade in Burkitt lymphoma patients. Additionally, we established new patient-derived cell lines from eBL tumors to study tumor variation and to study targeted treatments. We established five new patient-derived eBL lines BL717, BL 719, BL720, BL725, and BL740 that were interrogated for their inter-patient variation by studying their gene expression profiles. Further, we developed a patient cell-line derived xenograft (CDX) mouse model by injecting newly patient-derived BL cell lines in immunodeficient mice (NSG BL) and studying BL tumorigenesis. Having successfully established NSG BL tumors, we observed differences in tumor growth sensitivity and survival. We tested rituximab efficacy, one of the most established treatments for B cell lymphomas in our mouse model. We also identified pathways associated with unfolded protein response (UPR) and the mammalian target of rapamycin (mTOR) signaling, as well as apoptosis in one of the cell line xenografts, BL740, in response to rituximab. BL717, BL720 cell line xenograft failed to control tumor growth and was enriched in IFN-ɑ signature genes. This mouse model will prove to be useful to study combination therapy against eBL tumors as well as mechanisms of resistance to drug targets. Collectively, these studies provide insights into intratumoral variation including subtypes during tumor progression and expression profiles of TILs in eBL tumors. This will be important in designing new therapeutic strategies as well as help pose novel therapeutic targets.

No description available.

Les technologies récentes de séquençage de l’ARN de cellules uniques (scRNA-seq, pour single cell RNA-seq) ont permis de quantifier le niveau d’expression des gènes au niveau de la cellules, alors que les technologies standards de séquençage de l’ARN (RNA-seq, ou bulk RNA-seq) ne permettaient de quantifier que l’expression moyenne des gènes dans un échantillon de cellules. Cette résolution supérieure a permis des avancées majeures dans le domaine de la recherche biomédicale, mais a également posé de nouveaux défis, notamment computationnels. Les données qui découlent des technologies de scRNA-seq sont en effet complexes et plus bruitées que les données de bulk RNA-seq. En outre, les technologies sont nombreuses et leur nombre explose, nécessitant chacune un prétraitement plus ou moins différent. De plus en plus de méthodes sont ainsi développées, mais il n’existe pas encore de norme établie (gold standard) pour le prétraitement et l’analyse de ces données. Le laboratoire du Dr. Mader a récemment fait l’acquisition de la technologie Drop-Seq (une technologie haut débit de scRNA-seq), nécessitant une expertise nouvelle pour le traitement des données qui en découlent. Dans ce mémoire, différentes étapes du prétraitement des données issues de la technologie Drop-Seq sont donc passées en revue et le fonctionnement de certains outils dédiés à cet effet est étudié, permettant d’établir des lignes directrices pour de futures expériences au sein du laboratoire du Dr. Mader. Cette étude est menée sur les premiers jeux de données générés avec la technologie Drop-Seq du laboratoire, issus de lignées cellulaires du cancer du sein. Les méthodes d’analyses, moins spécifiques à la technologie, ne sont pas étudiées dans ce mémoire, mais une analyse exploratoire des jeux de données du laboratoire pose les bases pour une analyse plus poussée. / Recent single cell RNA sequencing technologies (scRNA-seq) have enabled the quantification of gene expression levels at the cellular level, while standard RNA sequencing technologies (RNA-seq, or bulk RNA-seq) have only been able to quantify the average gene expression in a sample of cells. This higher resolution has allowed major advances in biomedical research, but has also raised new challenges, in particular computational ones. The data derived from scRNA-seq technologies are indeed complex and noisier than bulk RNA-seq data. In addition, the number of scRNA-seq technologies is exploding, each of them requiring a rather different pre-processing. More and more methods are thus being developed, but there is still no gold standard for the preprocessing and analysis of these data. Dr. Mader’s laboratory has recently invested in the Drop-Seq technology (a high-throughput scRNAseq technology), requiring new expertise for the processing of the resulting data. In this thesis, different steps for the pre-processing of Drop-Seq data are reviewed and the behavior of some of the dedicated tools are studied, allowing to establish guidelines for future experiments in Dr. Mader’s laboratory. This study is conducted on the first data sets generated with the Drop-Seq technology of the laboratory, derived from breast cancer cell lines. Analytical methods, less specific to the technology, are not investigated in this thesis, but an exploratory analysis of the lab’s datasets lays the foundation for further analysis.

High-grade serous ovarian cancer (HGSOC) is the most aggressive subtype of ovarian cancer, and its heterogeneity poses a challenge for the discovery of reliable diagnostic biomarkers, therapeutic targets, and predicting treatment response, particularly to immunotherapy. The current standard diagnostic and treatment options are inadequate, resulting in late diagnosis and poor prognosis. To improve our understanding of the immunophenotype of tumours, potentially enhancing diagnostic and treatment capabilities, the aim of the present study was to develop a stringent workflow for studying the immune microenvironment of HGSOC tumours. We utilized publicly available single-cell RNA sequencing data and literature to identify genes enriched in certain cell types of HGSOC tumours, followed by validation using immunofluorescent-based multiplex protein profiling. A 9-plex immunofluorescence workflow was developed using the Opal™ system, and quantitative image analysis was performed to evaluate the expression of PD-L1, CD8A, FoxP3, CD163, KRT7, PDGFRB, and CD79A in large tissue sections of ovarian cancer. Each of these markers are specific to different cell types, and by staining the multiplex marker panel together with new markers with little or no literature linked to HGSOC we can gain novel insights on the immune microenvironment of HGSOC. In this project, for a proof of concept, we focused on two proteins; GZMK and SLAMF7. The optimized multiplex panel developed as part of this project will be used to identify cell-type-specific markers that may play a crucial role in the immune microenvironment of HGSOC, which could lead to better immunophenotype stratification of patients and a more optimal immunotherapy response. Moreover, the panel could also be used to study markers of less well-known immune cell types, further improving our understanding of HGSOC. Overall, this project has the potential to significantly contribute to the development of reliable diagnostic biomarkers and therapeutic targets for HGSOC, ultimately improving patient outcomes.

Adult hippocampal neurogenesis is orchestrated by neural stem cell (NSC) activity. Some associations exist between autophagy and neurogenesis, yet much remains unknown about autophagic regulation of adult neurogenesis. This thesis interrogates the requirement and role of Beclin1 and Atg5, two regulators of autophagy, in the formation of adult hippocampal neurons. To examine adult brain NSCs, the experiments presented in the first objective of this thesis test the ability to isolate adult NSCs using flow cytometry and a DNA-binding dye, DyeCycleViolet. While adult NSCs could not be isolated from the adult neurogenic niches using this methodology, it was effective in isolating endothelial cells. This provided valuable insight on the use of DNA-binding dyes and a new method for isolation of brain endothelial cells. The next objective determines the role of Beclin1 in adult NSCs and their progeny using an inducible model. Beclin1 loss in Nestin-expressing hippocampal NSCs resulted in reduced proliferation, autophagy, and adult neurogenesis within one month. Single-cell RNA sequencing and other methods illuminated that loss of Beclin1 resulted in mitosis reduction, disrupted mitotic regulation of chromatin maintenance, and induction of DNA damage. The final objective first tests whether Beclin1 loss results in similar deficits within GLAST-expressing NSCs and progeny. This model mirrored neurogenesis deficits and requirement of Beclin1 in mitosis and DNA maintenance. Next, to test whether this phenotype occurs with other autophagy proteins, Atg5 was removed from GLAST NSCs. This resulted in reduced autophagy and a transient decrease in neurons in the absence of any effect on NSC proliferation. Thus, proliferation deficits are unique to Beclin1 loss and do not underlie reduced adult hippocampal neurogenesis after Atg5 removal. This work demonstrates a novel discovery of mitosis regulation in adult NSCs by Beclin1, and individual roles of Beclin1 and Atg5 in neurogenesis.

Durant le développement préimplantatoire, les défauts de ségrégation des chromosomes conduisent à l'héritage d'un nombre incorrect de chromosomes, connu sous le nom d'aneuploïdie, qui provoque l'infertilité. L’imagerie à intervalle du développement préimplantatoire est introduite pour sélectionner le meilleur embryon et des efforts sont en cours pour utiliser l'imagerie non invasive pour identifier les ovocytes euploïdes en métaphase-II comme prédicteur de la viabilité future de l'embryon. Il est déjà bien établi que les ovocytes de mammifères en métaphase-II subissent des mouvements cytoplasmiques stéréotypés qui peuvent être visualisés par imagerie non invasive à fond clair à intervalle, appelée « flux cytoplasmique ». Ici, nous avons émis l'hypothèse que le flux cytoplasmique pourrait être affecté par le statut de ploïdie de l'ovule et donc être un outil de sélection utile pour sélectionner les ovules euploïdes de manière non invasive. Nous avons développé des conditions pour générer des ovules euploïdes et aneuploïdes à partir du même bassin d'ovocytes sains. Nous avons ensuite utilisé la microscopie d'imagerie en temps réel DIC, permettant de visualiser et de mesurer le flux cytoplasmique sans manipulation de l'ovule. Les mouvements cytoplasmiques ont été liés au statut de ploïdie pour chaque ovule individuel par immunofluorescence. Nos résultats montrent qu'il n'y a pas de différence de flux cytoplasmique entre les ovules euploïdes et aneuploïdes. Nos données démontrent que l'état de la ploïdie n'a pas d'impact sur les mouvements cytoplasmiques, suggérant que l'utilisation d'une imagerie non invasive pour essayer de distinguer l'état de la ploïdie entre des ovocytes autrement sains sera difficile. / Chromosome segregation errors during early development lead to inheritance of incorrect number of chromosomes, known as aneuploidy, which causes infertility and birth defects. Time-lapse microscopy of preimplantation development is being widely introduced with the aim of selecting the best embryo and efforts to use non-invasive brightfield imaging to identify euploid oocytes at metaphase-II as a predictor of future embryo viability are underway. It is already well established that mammalian metaphase-II oocytes undergo stereotyped cytoplasmic movements that can be visualised by non-invasive brightfield timelapse imaging, termed “cytoplasmic flow”. Here, we hypothesised that this cytoplasmic flow might be affected by ploidy status of the egg and therefore be a useful selection tool to select euploid eggs non-invasively. To address this, we developed conditions to generate euploid and aneuploid eggs from the same pool of otherwise healthy oocytes. We then used DIC live-imaging microscopy, which allowed us to visualise and measure flow without any manipulation to the egg. Importantly, individual eggs were scored for their ploidy status by immunofluorescence, so that cytoplasmic movements could be related to ploidy on an egg-by-egg basis. Our results show that there is no difference in cytoplasmic flow between euploid and aneuploid eggs. Therefore, our data demonstrates that ploidy status does not impact biologically relevant stereotyped cytoplasmic movements, suggesting that using non-invasive imaging to try to distinguish ploidy status between otherwise healthy oocytes will be challenging.

Bronchopulmonary dysplasia (BPD) is a chronic lung disease and one of the most severe complications that develop in premature infants following mechanical ventilation, exposure to supplemental oxygen, and inflammation. The hallmarks of the lung pathology are arrested lung development, including fewer and larger alveoli with less septation, thickening of alveolar septa, and impaired development of the capillary network. BPD is associated with increased mortality, respiratory morbidity, neurodevelopmental impairment, and increased healthcare costs. Significant advancements in neonatology in the last several decades, including antenatal steroids and exogenous surfactant replacement therapy, more gentle ventilation methods, and judicious oxygen use, have allowed for the survival of more preterm infants. However, the incidence of BPD still remains high and currently, there is no cure for the disease. Novel effective interventions at this stage of life are of exceptional value. Considering their great potential in promoting tissue regeneration and modulating inflammation, mesenchymal stromal cells (MSCs) represent a promising avenue for treating several disorders, including BPD. Umbilical cord-derived MSCs (UC-MSCs) offer biological advantages over other MSC sources (easily available, high proliferative capacity, and better repair efficacy). Pioneering work in our lab showed that MSCs prevent injury to the developing lung in a rat model mimicking BPD. However, there are still considerable challenges that must be overcome before MSCs can be effectively implemented in clinical trials. As such, UC-MSC heterogeneity is poorly understood, with concerns regarding variations from donors and batches. Thus, to improve the reproducibility of basic research and clinical applications, and to identify the optimal therapeutic cell product, better molecular characterization of UC-MSCs and the development of standardized BPD models will be essential in the clinical translation of MSC therapy for BPD. Moreover, considering that BPD is a disease of prematurity, the therapeutic potential of UC-MSCs isolated from preterm birth is of major interest. In the study presented here, using single-cell RNA sequencing (scRNA-seq), we characterized MSCs isolated from the UC of term and preterm pregnancies at delivery (term and preterm donors), as well as non-progenitor control cell line, human neonatal dermal fibroblasts (HNDFs). Moreover, we associated UC-MSC transcriptomic profiles with their therapeutic potential in hyperoxia-induced lung injury in neonatal rats. Finally, we developed and characterized a novel two-hit (2HIT) BPD model in neonatal mice, assessed UC-MSCs' optimal route of injection, timing, and dose, and evaluated their therapeutic effects in that model. We showed that UC-MSCs isolated from the majority of term and preterm donors, including preterm donors with pregnancy-related complications, have limited heterogeneity and possessed a transcriptome enriched in genes related to cell cycle and cell proliferation activity (termed "progenitor-like" cells). In contrast, UC-MSCs isolated from one term and two preterm donors with preeclampsia displayed a unique transcriptome comprised of many genes related to fibroblast activity, including extracellular matrix (ECM) organization (termed "fibroblast-like" cells). In addition, treatment with progenitor-like UC-MSCs, but not with fibroblast-like cells nor HNDFs, significantly improved lung structure, function, and pulmonary hypertension (PH) in hyperoxia-induced lung injury in neonatal rats. We identified marker genes for the therapeutic UC-MSCs (progenitor-like cells) and non-therapeutic cells (fibroblast-like cells and HNDFs). Among them, the high expression of major histocompatibility complex class I (MHCI) is associated with a reduced therapeutic effect. Furthermore, we developed a novel 2HIT BPD mice model with in-depth characterization of the innate immune response and lung injury. 2HIT injury caused a transient type 1 proinflammatory cytokine response and a significant decrease in type 2 anti-inflammatory cytokine lung expression and number of anti-inflammatory M2 type alveolar macrophages. Moreover, 2HIT mice showed impaired lung compliance and growth. Repeated intravenous (i.v.) injections of UC-MSCs at a dose of 20×10⁶ cells/kg body weight (BW) on postnatal day (PD) one and two improved survival, BW, lung compliance, and growth of 2HIT animals. In conclusion, scRNA-seq experimentation provided evidence that UC-MSCs isolated from different donors harbor different transcriptomes with progenitor-like or fibroblast-like characteristics. Only progenitor-like cells provided a therapeutic effect in hyperoxia-induced lung injury in neonatal rats. The development of a novel murine 2HIT BPD model allowed us to characterize the innate immune response and lung pathology and confirm the optimal dose of UCMSCs to provide therapeutic potential in that model. These results will enable better therapeutic selection of UC-MSCs and help improve treatment regimen prior to ultimate clinical translation.

Treating cancer is difficult as the disease is complex and drug responses often depend on the patient's characteristics. Precision medicine aims to solve this by selecting individualized treatments. Since this involves the analysis of large datasets, machine learning can be used to make the drug selection process more efficient. Traditionally, such models utilize bulk gene expression data. However, this potentially masks information from small cell populations and fails to address tumor heterogeneity. Therefore, this thesis applies data deconvolution methods to bulk gene expression data and estimates the corresponding cell type-specific gene expression profiles. This "increases" the resolution of the input data for the drug response prediction. A hold-out dataset, LODOCV and LOCOCV were used for the evaluation of this approach. Furthermore, all results are compared against a baseline model, which was trained on bulk data. Overall, the accuracy of the cell type-specific model did not show an improvement compared to the bulk model. It also prioritizes information from bulk samples, which makes the additional data unnecessary. The robustness of the cell type-specific model is slightly lower than that of the bulk model. Note, that these outcomes are not necessarily due to a flaw in the underlying concept, but may be connected to poor deconvolution results as the same reference matrix was used for the deconvolution of all bulk samples regardless of the cancer type or disease.