The 15q13.3 microdeletion is a common genetic disorder associated with multiple neurodevelopmental disorders including autism spectrum disorder, epilepsy, and schizophrenia. Patients have diverse clinical presentations, often prompting genetic assays that identify the CNV in the clinic. This late-stage screening leaves a considerable gap in our understanding of the prenatal and prediagnostic developmental impairments in these individuals, providing a barrier to understanding the disease pathobiology. We provide the first investigation into embryonic brain development of individuals with the 15q13.3 microdeletion by generating multiple 3D neural organoid models from the largest clinical cohort in reported literature. We incorporated unguided and guided forebrain organoid models into our multi-transcriptomic phenotyping pipeline to uncover changes in cell type emergence and disruptions to circuit development, all of which had underlying changes to cell adhesion pathways. Specifically, we identified accelerated growth trajectories in 15q13.3del unguided neural organoids and used single cell RNA sequencing to identify changes in radial glia dynamics that affect neurogenesis. We measured changes in the pseudotemporal trajectory of matured unguided neural organoids, and later identified disruptions in synaptic signaling modules amongst the primary constituents to neural circuitry, excitatory and inhibitory neurons. We leveraged dorsal and ventral forebrain organoid models to better assess circuit dynamics, as they faithfully produce the excitatory and inhibitory neurons in the pallium and subpallium, respectively. We then used the entire 15q13.3del cohort and performed bulk RNA sequencing on each tissue type at two timepoints and discovered convergence on transcriptional dysregulation and disruptions to human-specific zinc finger proteins localized to chromosome 19. We also identified cell type-specific vulnerabilities to DNA damage and cell migration amongst the dorsal and ventral organoids, respectively, which was consistent with the excitatory and inhibitory neural subpopulations amongst the unguided neural organoids scRNA Seq, respectively. We then examined neuron migration in a 3D assembloid model by sparsely labeling dorsal-ventral forebrain organoids from multiple genotype-lineage combinations. Light sheet microscopy identified deficits in inhibitory neuron migration and morphology, but not migration distance, suggesting a complex disruption to cortical circuitry. This novel combination of cell type characterization, pathway identification, and circuitry phenotyping provides a novel perspective of how the 15q13.3 deletions impair prenatal development and can be applied to other NDD models to leverage understanding of early disease pathogenesis. / Dissertation / Doctor of Science (PhD) / The development of the human brain is a highly complex and tightly regulated process that requires the participation of multiple cell types throughout development. Disturbances to the emergence, differentiation, or placement of these cell types can cause disruptions and local miswiring of neural circuits, which is often associated with neurodevelopmental disorders (NDDs). The 15q13.3 microdeletion syndrome is a highly complex condition associated with multiple NDDs and has seldom been studied in a human context. To address this, we used stem cells derived from a 15q13.3 microdeletion syndrome cohort and their typically developing familial controls to generate unguided (“whole brain”) and region-specific organoids to investigate early fetal development across time. We used the largest 15q13.3 microdeletion cohort in reported literature to identify shared disruptions in early developmental milestones such as neurogenesis, neural migration, and neural patterning. We identified expansion of specific cell populations, including progenitors that later give rise to mature neurons. Abnormalities persisted in more mature cell populations, including the inhibitory neurons responsible for establishing critical microcircuitry in the human cortex. By generating guided organoids that enrich for excitatory and inhibitory neural populations, we were able to merge the models to form assembloids, where we captured early migratory and morphological deficits in inhibitory neuron populations, which is supported by the multi-transcriptomics experiments performed in both organoid models. This study provides a framework for examining fetal development in a neurodevelopmental disorder context. By using the 15q13.3 microdeletion background, we found novel disruptions in cell type emergence and circuit formation previously unreported in mouse or 2D neuron models, highlighting the utility of the phenotyping platform for disease modeling.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第25491号 / 医博第5091号 / 新制||医||1073(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 浅野 雅秀, 教授 篠原 隆司, 教授 近藤 玄 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Vascular smooth muscle cell (VSMC) accumulation is a hallmark of atherosclerosis and vascular injury. However, fundamental aspects of proliferation and the phenotypic changes within individual VSMCs, which underlie vascular disease remain unresolved. In particular, it is not known if all VSMCs proliferate and display plasticity, or whether individual cells can switch to multiple phenotypes. To assess whether proliferation and plasticity in disease is a general characteristic of VSMCs or a feature of a subset of cells, multi-colour lineage labelling is used to demonstrate that VSMCs in injury-induced neointimal lesions and in atherosclerotic plaques are oligo-clonal, derived from few expanding cells, within mice. Lineage tracing also revealed that the progeny of individual VSMCs contribute to both alpha Smooth muscle actin (aSma)-positive fibrous cap and Mac-3-expressing macrophage-like plaque core cells. Co-staining for phenotypic markers further identified a double-positive aSma+ Mac3+ cell population, which is specific to VSMC-derived plaque cells. In contrast, VSMC-derived cells generating the neointima after vascular injury generally retained expression of VSMC markers and upregulation of Mac3 was less pronounced. Monochromatic regions in atherosclerotic plaques and injury-induced neointima did not contain VSMC-derived cells expressing a different fluorescent reporter protein, suggesting that proliferation-independent VSMC migration does not make a major contribution to VSMC accumulation in vascular disease. Similarly, VSMC proliferation was examined in an Angiotensin II perfusion model of aortic aneurysm in mice, oligo-clonal proliferation was observed in remodelling regions of the vasculature, however phenotypic changes were observed in a large proportion of VSMCs, suggesting that the majority of VSMCs have some potential to modulate their phenotype. To understand the mechanisms behind the inherent VSMC heterogeneity and observed functionality, the single cell transcriptomic techniques Smart-seq2 and the Chromium 10X system were optimized for use on VSMCs. The work within this thesis suggests that extensive proliferation of a low proportion of highly plastic VSMCs results in the observed VSMC accumulation after injury, and the atherosclerotic and aortic aneurysm models of cardiovascular disease.

Malgré les progrès réalisés dans le séquençage de l’ADN, nous n’avons pas encore compris comment le phénotype d’un organisme se rapporte au contenu de son génome. Cependant, il est devenu clair que l'impact des gènes dépend du contexte. La simple présence d'un gène dans un génome ne nous informe pas du moment où il est exprimé et des autres gènes qui y sont exprimés. Comprendre comment l'expression des gènes est régulée est un élément nécessaire pour comprendre comment les phénotypes émergent d'un génotype donné. Les facteurs de transcription, qui peuvent activer ou réprimer l'expression d'un gène, forment un réseau complexe d'interactions entre eux et leurs gènes ciblés. Ce réseau consiste en une hiérarchie de groupes de facteurs de transcription fortement liés, chacun lié à des processus cellulaires distincts. La structure de ce réseau de régulation transcriptionnelle est-elle significative pour la réponse transcriptionnelle d'une cellule? Ici, nous utilisons une protéine de liaison à l'ADN programmable appelée CRISPR (répétitions courtes palindromiques groupées régulièrement) pour perturber l'expression génique des régulateurs globaux au sein du réseau de régulation transcriptionnelle. Ces régulateurs mondiaux régulent de nombreux processus cellulaires distincts et ont de nombreuses cibles génétiques. Le système CRISPR nous permet de perturber ces régulateurs dans toutes les combinaisons possibles, y compris les perturbations d'ordre supérieur avec tous les régulateurs mondiaux potentiellement ciblés perturbés en même temps. Nous enregistrons ensuite à la fois le modèle d'expression du transciptome en utilisant le séquençage de l'ARN et l'adéquation de chaque souche. Nous trouvons que la structure du réseau de régulation augmente la dimensionnalité de la réponse transcriptionnelle plutôt que de la réduire. Cela se traduit par une épistasie importante au-delà des interactions par paires. Cela a des implications sur la façon dont ces réseaux évoluent. L'épistasie par paires que nous trouvons entre les facteurs de transcription globaux repose sur la présence ou l'absence d'autres perturbations. Cela implique que d'autres perturbations pourraient agir comme des mutations de potentialisation. Le nombre de voies d'évolution potentielles augmente avec les épistasies d'ordre élevé, même si cela ne nous dit rien sur la qualité de ces voies. Fait important, les répliques de cette thèse sont toujours en cours et les données présentées ici n’ont pas encore exclu les artefacts expérimentaux. / Despite advances in DNA sequencing, we have yet to understand how an organism’s phenotype relates to the contents of their genome. However it has become clear that the impact of genes are context dependant. The mere presence of a gene within a genome does not inform us of when it is expressed, and which other genes are expressed along with it. Understanding how gene expression is regulated is a necessary piece of understanding how phenotypes emerge from a given genotype. Transcription factors, which can activate or repress the expression of a gene, form a complex network of interactions between themselves and their targeted genes. This network consists of a hierarchy of groups of strongly connected transcription factors, each relating to distinct cellular processes. Is the structure of this transcriptional regulatory network significant to the transcriptional response of a cell? Here we use a programmable DNA binding protein called CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) to perturb gene expression of global regulators within the transcriptional regulatory network. These global regulators are regulating many distinct cellular processes and have many genetic targets. The CRISPR system allows us to perturb these regulators in all possible combinations, including higher order perturbations with potentially all targeted global regulators perturbed at the same time. We then record both the expression pattern of the transciptome using RNA sequencing, and the fitness of each strain. We find that the structure of the regulatory network increases the dimensionality of the transcriptional response rather than reducing it. This results in significant high order epistasis beyond pair-wise interactions. This has implications for how these networks evolve. The pair-wise epistasis we find between global transcription factors rely on the presence or absence of other perturbations. This implies that other perturbations could act as potentiating mutations. The number of potential evolutionary paths increases with high order epistasis, although this alone tells us nothing about the quality of those paths. Importantly, the replicates for this thesis are still on-going and the data presented here has not yet excluded experimental artefacts.

Seasonal allergic rhinitis (SAR) is a disease caused by allergens from both environmental and genetic factors. Some researchers have studied the SAR based on traditional genetic methodologies. As technology develops, a new technique called single-cell RNA sequencing (scRNA-seq) is developed, which can generate high-dimension data. We apply two machine learning (ML) algorithms, random forest (RF) and partial least squares discriminant analysis (PLS-DA), for cell source classification and gene selection based on the SAR scRNA-seq time-series data from three allergic patients and four healthy controls denoised by single-cell variational inference (scVI). We additionally propose a new fitting method consisting of bootstrap and cubic smoothing splines to fit the averaged gene expressions per cell from different populations. To sum up, we find that both RF and PLS-DA could provide high classification accuracy, and RF is more preferable, considering its stable performance and strong gene-selection ability. Based on our analysis, there are 10 genes having discriminatory power to classify cells of allergic patients and healthy controls at any timepoints. Although there is no literature founded to show the direct connections between such 10 genes and SAR, the potential associations are indirectly confirmed by some studies. It shows a possibility that we can alarm allergic patients before a disease outbreak based on their genetic information. Meanwhile, our experiment results indicate that ML algorithms may discover something between genes and SAR compared with traditional techniques, which needs to be analyzed in genetics in the future.

Vitiligo is an autoimmune disease in which CD8+ T cells selectively destroy melanocytes, leading to a patchy, disfiguring depigmentation of the skin. Our group and others have highlighted the central role of IFN-γ-dependent chemokines in the progression of disease; however, IFN-γ is also reported to have pleiotropic effects on melanocyte biology. We examined whether IFN-γ has a direct role in melanocyte killing. We tested the T-cell effector functions IFN-γ, Fas ligand and perforin by deleting them from autoreactive T cells used to induce vitiligo in mice. We found that disease incidence, disease severity and T cell accumulation in the skin was reduced in mice receiving adoptive transfer of either IFN-γ deficient or Fas ligand deficient gp100-specific T cells; however, perforin was dispensable and led to increased disease scores and T cell accumulation. To determine how melanocytes are affected by IFN-γ signaling during vitiligo, we performed single-cell RNA-sequencing on suction blister biopsies obtained from vitiligo and healthy subjects. We discovered that integrin expression and TGFb2 signaling was decreased only in lesional melanocyte transcriptomes. Moreover, melanocytes appear to participate in their own demise by increasing HLA expression and recruiting effector cells through the chemotactic ligand CCL18. The loss of melanocyte retention factors may explain their clean disappearance from the skin during keratinocyte turnover. Taken together, we believe IFN-γ production by autoreactive T cells in the skin leads to clean loss of melanocytes by downregulation of melanocyte retention factors and by increasing their potential to be detected by effector cells during vitiligo.

Panarthropoda is a monophyletic group comprised of arthropods and lobopods, molting animals with a segmented body, paired appendages, dorsal brain, and ventral nerve cords. Evolutionary Developmental Biology (EvoDevo) is an interdisciplinary field that seeks to understand how changes in development form the basis for variations in morphology and phenotypic evolution, including the genetic network underlying these processes. To study the evolution of panarthropods from such an EvoDevo perspective, one typically uses standard molecular techniques. A first step here is to investigate the expression of a gene of interest in order to find out where and when it is transcribed during development. A hallmark of EvoDevo studies is its comparative character, often with respect to model organisms such as the fruit fly Drosophila melanogaster. Recently developed single-cell RNA sequencing technologies allow the profiling of a plethora of gene expression on the level of individual cells, and thus provide a much more detailed insight into gene expression. In Paper I, I applied standard molecular techniques used in EvoDevo research such as PCR, gene cloning, probe synthesis and whole mount in situ hybridization, to investigate the embryonic expression patterns of the tiptop/teashirt (tio/tsh) and spalt (sal) genes in a range of arthropods representing all main groups of this phylum, and an onychophoran. In the arthropod model Drosophila, these genes act as trunk-specifiers, and the objective of my work was to find out if this is conserved in Arthropoda or even Panarthropoda as a whole. I provide comprehensive data on arthropod tio/tsh and sal expression, including the first data from an onychophoran. The results support the idea that tio/tsh genes are involved in the development of ‘trunk’ segments by regulating limb development. In addition, my data suggest that the function of Sal is unlikely to be conserved in trunk vs head development. Early expression of sal, however, is in line with a potential homeotic function of this gene, at least in Arthropoda. In Paper II, I provide an embryonic tissue dissociation protocol for embryos of the common house spider Parasteatoda tepidariorum that I developed and that I successfully applied for single-cell RNA sequencing. In addition, I report on the progress of this experiment, and provide and discuss preliminary results.

Globally, cancer is one of the leading causes of mortality, with an estimated 18.1 million cancer cases, 10 million deaths, and 1.9 million new cases diagnosed in 2020 (Sung et al., 2021). However, during the past several decades, cancer survival has improved such that 82% of children and >2/3 of adults diagnosed with cancer will survive beyond five years (World Health Organization (WHO) - Childhood Cancer, 2021). Skeletal muscle atrophy and fibrosis are long-term adverse effects experienced by 80% of cancer survivors for which there is no available therapy (Paulino, 2004). These long-term consequences are related to the toxicity from the cancer treatment, leading to alterations in skeletal muscle function which can lead to comorbidities and increased mortality among cancer survivors (Paulino, 2004; Williams et al., 2016). Thus, novel approaches to address the long-term effects of cancer therapy on skeletal muscle are critically needed. Exercise training is a potential non-pharmacological strategy that improves common cancer- and treatment-related side effects (Mustian et al., 2012). Specifically, exercise programs that combine resistance and endurance training (RET) have been shown to improve muscle strength and cardiovascular fitness in cancer survivors (Tong et al., 2020). The mechanisms responsible for these effects remain unknown. The remarkable plasticity of skeletal muscle relies primarily on muscle stem (satellite) cells (MuSCs) (Lepper et al., 2011) that are regulated, in part, by muscle-resident stromal cells (Bentzinger et al., 2013). These different stromal cell types, including: vascular endothelial cells (ECs), immune cells, and mesenchymal progenitors, also known as fibro-adipogenic progenitors (FAPs), create the muscle stem cell niche (Yin et al., 2013). FAPs possess a dual role as they are involved in skeletal muscle maintenance and regeneration by secreting pro-myogenic trophic factors (Biferali et al., 2019; Joe et al., 2010; Uezumi et al., 2010; Wosczyna et al., 2019), but also contribute to fibrotic and fatty tissue accumulation in chronic degenerative conditions (Uezumi et al., 2010). The divergent features of FAPs highly depend on signals they receive from their microenvironment (Giuliani et al., 2021); however, FAP's contribution to cancer treatment-induced muscle pathology in cancer survivors remains unknown. The overall objective of this thesis is to begin to develop an understanding of the role of FAPs in cancer treatment-induced muscle pathology and to determine if RET represents an effective therapy to prevent the long-term muscle defects of juvenile cancer plus therapy.

Single-cell RNA sequencing has become a standard technique to characterize tissue development. Hereby, cross-sectional snapshots of the diversity of cell transcriptomes were transformed into (pseudo-) longitudinal trajectories of cell differentiation using computational methods, which are based on similarity measures distinguishing cell phenotypes. Cell development is driven by alterations of transcriptional programs e.g., by differentiation from stem cells into various tissues or by adapting to micro-environmental requirements. We here complement developmental trajectories in cell-state space by trajectories in gene-state space to more clearly address this latter aspect. Such trajectories can be generated using self-organizing maps machine learning. The method transforms multidimensional gene expression patterns into two dimensional data landscapes, which resemble the metaphoric Waddington epigenetic landscape. Trajectories in this landscape visualize transcriptional programs passed by cells along their developmental paths from stem cells to differentiated tissues. In addition, we generated developmental “vector fields” using RNA-velocities to forecast changes of RNA abundance in the expression landscapes. We applied the method to tissue development of planarian as an illustrative example. Gene-state space trajectories complement our data portrayal approach by (pseudo-)temporal information about changing transcriptional programs of the cells. Future applications can be seen in the fields of tissue and cell differentiation, ageing and tumor progression and also, using other data types such as genome, methylome, and also clinical and epidemiological phenotype data.

Cell differentiation is the process of a cell developing from one cell type to another. It is of interest to analyse the differentiation from stem cells to different types of mature cells, and discover what genes are involved in regulating the differentiation to specific cells, for instance to get insights to what is causing certain diseases and find potential treatments.  In this project, two mathematical models are developed for analysing blood cell differentiation (haematopoiesis) with methods based on optimal transportation. Optimal transportation is about moving one mass distribution to another at minimal cost. Modelling a sample of cells as point masses placed in a space based on the cells' gene expressions, accessed by single-cell RNA sequencing, optimal transportation is used to find transitions between cells that costs the least in terms of changes in gene expression. With this, cell-to-cell trajectories, from haematopoietic stem cells to mature blood cells, are obtained. With the first model, cells are divided into groups based on their maturity, which is determined by using diffusion pseudotime, and optimal transportation is preformed between groups. The resulting trajectories suggest that haematopoietic stem cells possibly can develop into the same mature cell type in different ways, and that the cell fate for some cell types is decided late on in development. In future work, the gene regulation along the obtained trajectories can be analysed. The second model is developed to be more general than the first, and not be dependent on a group division before preforming optimal transportation. / Celldifferentiering är processen då en cell utvecklas från en celltyp till en annan. Det är av intresse att analysera differentieringen från stamcell till olika typer av mogna celler, och undersöka vilka gener som har betydelse i regleringen av differentieringen till specifika celler, bland annat för att få en inblick i vad som orsakar vissa sjukdomar och hitta potentiella botemedel. I detta projekt utvecklas två matematiska modeller för att analysera blodcellsutveckling (hematopoes) med metoder som är baserade på optimal transport. Optimal transport handlar om att förflytta en massfördelning till en annan till lägst kostnad. Genom att modellera celler som punktmassor, placerade i ett rum baserat på cellernas genuttryck som fås genom singel-cell RNA-sekvensering, används optimal transport för att hitta förflyttningar mellan celler som kostar minst i termer av förändringar i genuttryck. Från detta skapas vägar mellan celler, från hematopoetiska stamceller till mogna celler. I den första modellen delas cellerna upp i grupper baserat på deras mognadsgrad, som bestäms genom att använda pseudotid baserad på en diffusionsavbildning, och optimal transport används sedan mellan grupperna. De resulterande vägarna visar på att hematopoetiska stamceller möjligen kan utvecklas till samma typ av mogen cell på olika sätt, och att cellödet för vissa typer av celler bestäms sent i utvecklingen. I framtida arbete kan genregleringen längs de funna vägarna analyseras. Den andra modellen utvecklas för att vara mer generell än den första, och inte bero på en gruppuppdelning innan optimal transport används.