Examining the development of brain structure in utero with fetal MRI, acquired as part of the Developing Human Connectome Project

The human brain is an incredibly complex organ, and the study of it traverses many scales across space and time. The development of the brain is a protracted process that begins embryonically but continues into adulthood. Although neural circuits have the capacity to adapt and are modulated throughout life, the major structural foundations are laid in utero during the fetal period, through a series of rapid but precisely timed, dynamic processes. These include neuronal proliferation, migration, differentiation, axonal pathfinding, and myelination, to name a few. The fetal origins of disease hypothesis proposed that a variety of non-communicable diseases emerging in childhood and adulthood could be traced back to a series of risk factors effecting neurodevelopment in utero (Barker 1995). Since this publication, many studies have shown that the structural scaffolding of the brain is vulnerable to external environmental influences and the perinatal developmental window is a crucial determinant of neurological health later in life. However, there remain many fundamental gaps in our understanding of it. The study of human brain development is riddled with biophysical, ethical, and technical challenges. The Developing Human Connectome Project (dHCP) was designed to tackle these specific challenges and produce high quality open-access perinatal MRI data, to enable researchers to investigate normal and abnormal neurodevelopment (Edwards et al., 2022). This thesis will focus on investigating the diffusion-weighted and anatomical (T2) imaging data acquired in the fetal period, between the second to third trimester (22 – 37 gestational weeks). The limitations of fetal MR data are ill-defined due to a lack of literature and therefore this thesis aims to explore the data through a series of critical and strategic analysis approaches that are mindful of the biophysical challenges associated with fetal imaging. A variety of analysis approaches are optimised to quantify structural brain development in utero, exploring avenues to relate the changes in MR signal to possible neurobiological correlates. In doing so, the work in this thesis aims to improve mechanistic understanding about how the human brain develops in utero, providing the clinical and medical imaging community with a normative reference point. To this aim, this thesis investigates fetal neurodevelopment with advanced in utero MRI methods, with a particular emphasis on diffusion MRI. Initially, the first chapter outlines a descriptive, average trajectory of diffusion metrics in different white matter fiber bundles across the second to third trimester. This work identified unique polynomial trajectories in diffusion metrics that characterise white matter development (Wilson et al., 2021). Guided by previous literature on the sensitivity of DWI to cellular processes, I formulated a hypothesis about the biophysical correlates of diffusion signal components that might underpin this trend in transitioning microstructure. This hypothesis accounted for the high sensitivity of the diffusion signal to a multitude of simultaneously occurring processes, such as the dissipating radial glial scaffold, commencement of pre-myelination and arborization of dendritic trees. In the next chapter, the methods were adapted to address this hypothesis by introducing another dimension, and charting changes in diffusion properties along developing fiber pathways. With this approach it was possible to identify compartment-specific microstructural maturation, refining the spatial and temporal specificity (Wilson et al., 2023). The results reveal that the dynamic fluctuations in the components of the diffusion signal correlate with observations from previous histological work. Overall, this work allowed me to consolidate my interpretation of the changing diffusion signal from the first chapter. It also serves to improve understanding about how diffusion signal properties are affected by processes in transient compartments of the fetal brain. The third chapter of this thesis addresses the hypothesis that cortical gyrification is influenced by both underlying fiber connectivity and cytoarchitecture. Using the same fetal imaging dataset, I analyse the tissue microstructural change underlying the formation of cortical folds. I investigate correlations between macrostructural surface features (curvature, sulcal depth) and tissue microstructural measures (diffusion tensor metrics, and multi-shell multi-tissue decomposition) in the subplate and cortical plate across gestational age, exploring this relationship both at the population level and within subjects. This study provides empirical evidence to support the hypotheses that microstructural properties in the subplate and cortical plate are altered with the development of sulci. The final chapter explores the data without anatomical priors, using a data-driven method to extract components that represent coordinated structural maturation. This analysis aims to examine if brain regions with coherent patterns of growth over the fetal period converge on neonatal functional networks. I extract spatially independent features from the anatomical imaging data and quantify the spatial overlap with pre-defined neonatal resting state networks. I hypothesised that coherent spatial patterns of anatomical development over the fetal period would map onto the functional networks observed in the neonatal period. Overall, this thesis provides new insight about the developmental contrast over the second to third trimester of human development, and the biophysical correlates affecting T2 and diffusion MR signal. The results highlight the utility of fetal MRI to research critical mechanisms of structural brain maturation in utero, including white matter development and cortical gyrification, bridging scales from neurobiological processes to whole brain macrostructure. their gendered constructions relating to women

INTEGRATIVE BIOINFORMATICS APPROACHES TO ELUCIDATING PROSTATE CANCER CELL HETEROGENEITY, PLASTICITY, AND TREATMENT RESPONSE

Prostate cancer (PCa) is the most common non-cutaneous tumor in American men, and the second leading cause of cancer-related deaths. PCa-related deaths can be attributed to heterogeneous tumors containing metastatic and therapy-resistant cancer cells. Cancer stem cells (CSC) are an important contributor to this tumor heterogeneity, which are present in primary tumors and become enriched in castration resistant PCa (CRPC). Our lab has demonstrated that the prostate cancer stem cells (PCSCs) are enriched in the phenotypically undifferentiated PCa cell population that lacks the expression of differentiation marker prostate-specific antigen (PSA). Our work has also demonstrated that PCa cells manifest significant plasticity such that phenotypically differentiated PSA+ PCa cells can be reprogrammed to the castrationresistant, stem-like state by chronic castration or overexpression of the stemness factor NANOG. Therefore, my overarching hypothesis is that PSA-/lo cells possess intrinsic molecular and epigenetic features that regulate their aggressiveness and stemness and contribute to tumor progression and therapy resistance, and that these properties can be gained through epigenetic reprogramming of more differentiated PSA+ PCa cell population. Throughout my Ph.D. thesis research, I employed an integrative bioinformatics approach to test this hypothesis

Distinct Wnt-driven primitive streak-like populations reflect in vivo lineage precursors

During gastrulation, epiblast cells are pluripotent and their fate is thought to be constrained principally by their position. Cell fate is progressively restricted by localised signalling cues from areas including the primitive streak. However, it is unknown whether this restriction accompanies, at the individual cell level, a reduction in potency. Investigation of these early transition events in vitro is possible via the use of epiblast stem cells (EpiSCs), self-renewing pluripotent cell lines equivalent to the postimplantation epiblast. Strikingly, mouse EpiSCs express gastrulation stage regional markers in self-renewing conditions. Here, we examined the differentiation potential of cells expressing such lineage markers. We show that undifferentiated EpiSC cultures contain a major subfraction of cells with reversible early primitive streak characteristics, which is mutually exclusive to a neural-like fraction. Using in vitro differentiation assays and embryo grafting we demonstrate that primitive streak-like EpiSCs are biased towards mesoderm and endoderm fates while retaining pluripotency. The acquisition of primitive streak characteristics by self-renewing EpiSCs is mediated by endogenous Wnt signalling. Elevation of Wnt activity promotes restriction towards primitive streak-associated lineages with mesendodermal and neuromesodermal characteristics. Collectively, our data suggest that EpiSC pluripotency encompasses a range of reversible lineage-biased states reflecting the birth of pioneer lineage precursors from a pool of uncommitted EpiSCs similar to the earliest cell fate restriction events taking place in the gastrula stage epiblast

Generalizations of the Multicut Problem for Computer Vision

Graph decomposition has always been a very important concept in machine learning and computer vision. Many tasks like image and mesh segmentation, community detection in social networks, as well as object tracking and human pose estimation can be formulated as a graph decomposition problem. The multicut problem in particular is a popular model to optimize for a decomposition of a given graph. Its main advantage is that no prior knowledge about the number of components or their sizes is required. However, it has several limitations, which we address in this thesis: Firstly, the multicut problem allows to specify only cost or reward for putting two direct neighbours into distinct components. This limits the expressibility of the cost function. We introduce special edges into the graph that allow to define cost or reward for putting any two vertices into distinct components, while preserving the original set of feasible solutions. We show that this considerably improves the quality of image and mesh segmentations. Second, multicut is notorious to be NP-hard for general graphs, that limits its applications to small super-pixel graphs. We define and implement two primal feasible heuristics to solve the problem. They do not provide any guarantees on the runtime or quality of solutions, but in practice show good convergence behaviour. We perform an extensive comparison on multiple graphs of different sizes and properties. Third, we extend the multicut framework by introducing node labels, so that we can jointly optimize for graph decomposition and nodes classification by means of exactly the same optimization algorithm, thus eliminating the need to hand-tune optimizers for a particular task. To prove its universality we applied it to diverse computer vision tasks, including human pose estimation, multiple object tracking, and instance-aware semantic segmentation. We show that we can improve the results over the prior art using exactly the same data as in the original works. Finally, we use employ multicuts in two applications: 1) a client-server tool for interactive video segmentation: After the pre-processing of the video a user draws strokes on several frames and a time-coherent segmentation of the entire video is performed on-the-fly. 2) we formulate a method for simultaneous segmentation and tracking of living cells in microscopy data. This task is challenging as cells split and our algorithm accounts for this, creating parental hierarchies. We also present results on multiple model fitting. We find models in data heavily corrupted by noise by finding components defining these models using higher order multicuts. We introduce an interesting extension that allows our optimization to pick better hyperparameters for each discovered model. In summary, this thesis extends the multicut problem in different directions, proposes algorithms for optimization, and applies it to novel data and settings.Die Zerlegung von Graphen ist ein sehr wichtiges Konzept im maschinellen Lernen und maschinellen Sehen. Viele Aufgaben wie Bild- und Gittersegmentierung, Kommunitätserkennung in sozialen Netzwerken, sowie Objektverfolgung und Schätzung von menschlichen Posen können als Graphzerlegungsproblem formuliert werden. Der Mehrfachschnitt-Ansatz ist ein populäres Mittel um über die Zerlegungen eines gegebenen Graphen zu optimieren. Sein größter Vorteil ist, dass kein Vorwissen über die Anzahl an Komponenten und deren Größen benötigt wird. Dennoch hat er mehrere ernsthafte Limitierungen, welche wir in dieser Arbeit behandeln: Erstens erlaubt der klassische Mehrfachschnitt nur die Spezifikation von Kosten oder Belohnungen für die Trennung von zwei Nachbarn in verschiedene Komponenten. Dies schränkt die Ausdrucksfähigkeit der Kostenfunktion ein und führt zu suboptimalen Ergebnissen. Wir fügen dem Graphen spezielle Kanten hinzu, welche es erlauben, Kosten oder Belohnungen für die Trennung von beliebigen Paaren von Knoten in verschiedene Komponenten zu definieren, ohne die Menge an zulässigen Lösungen zu verändern. Wir zeigen, dass dies die Qualität von Bild- und Gittersegmentierungen deutlich verbessert. Zweitens ist das Mehrfachschnittproblem berüchtigt dafür NP-schwer für allgemeine Graphen zu sein, was die Anwendungen auf kleine superpixel-basierte Graphen einschränkt. Wir definieren und implementieren zwei primal-zulässige Heuristiken um das Problem zu lösen. Diese geben keine Garantien bezüglich der Laufzeit oder der Qualität der Lösungen, zeigen in der Praxis jedoch gutes Konvergenzverhalten. Wir führen einen ausführlichen Vergleich auf vielen Graphen verschiedener Größen und Eigenschaften durch. Drittens erweitern wir den Mehrfachschnitt-Ansatz um Knoten-Kennzeichnungen, sodass wir gemeinsam über Zerlegungen und Knoten-Klassifikationen mit dem gleichen Optimierungs-Algorithmus optimieren können. Dadurch wird der Bedarf der Feinabstimmung einzelner aufgabenspezifischer Löser aus dem Weg geräumt. Um die Allgemeingültigkeit dieses Ansatzes zu überprüfen, haben wir ihn auf verschiedenen Aufgaben des maschinellen Sehens, einschließlich menschliche Posenschätzung, Mehrobjektverfolgung und instanz-bewusste semantische Segmentierung, angewandt. Wir zeigen, dass wir Resultate von vorherigen Arbeiten mit exakt den gleichen Daten verbessern können. Abschließend benutzen wir Mehrfachschnitte in zwei Anwendungen: 1) Ein Nutzer-Server-Werkzeug für interaktive Video Segmentierung: Nach der Vorbearbeitung eines Videos zeichnet der Nutzer Striche auf mehrere Einzelbilder und eine zeit-kohärente Segmentierung des gesamten Videos wird in Echtzeit berechnet. 2) Wir formulieren eine Methode für simultane Segmentierung und Verfolgung von lebenden Zellen in Mikroskopie-Aufnahmen. Diese Aufgabe ist anspruchsvoll, da Zellen sich aufteilen und unser Algorithmus dies in der Erstellung von Eltern-Hierarchien mitberücksichtigen muss. Wir präsentieren außerdem Resultate zur Mehrmodellanpassung. Wir berechnen Modelle in stark verrauschten Daten indem wir mithilfe von Mehrfachschnitten höherer Ordnung Komponenten finden, die diesen Modellen entsprechen. Wir führen eine interessante Erweiterung ein, die es unserer Optimierung erlaubt, bessere Hyperparameter für jedes entdeckte Modell auszuwählen. Zusammenfassend erweitert diese Arbeit den Mehrfachschnitt-Ansatz in unterschiedlichen Richtungen, schlägt Algorithmen zur Inferenz in den resultierenden Modellen vor und wendet ihn auf neuartigen Daten und Umgebungen an

Models of <i>KPTN</i>-related disorder implicate mTOR signalling in cognitive and overgrowth phenotypes

KPTN-related disorder is an autosomal recessive disorder associated with germline variants in KPTN (previously known as kaptin), a component of the mTOR regulatory complex KICSTOR. To gain further insights into the pathogenesis of KPTN-related disorder, we analysed mouse knockout and human stem cell KPTN loss-of-function models. Kptn -/- mice display many of the key KPTN-related disorder phenotypes, including brain overgrowth, behavioural abnormalities, and cognitive deficits. By assessment of affected individuals, we have identified widespread cognitive deficits (n = 6) and postnatal onset of brain overgrowth (n = 19). By analysing head size data from their parents (n = 24), we have identified a previously unrecognized KPTN dosage-sensitivity, resulting in increased head circumference in heterozygous carriers of pathogenic KPTN variants. Molecular and structural analysis of Kptn-/- mice revealed pathological changes, including differences in brain size, shape and cell numbers primarily due to abnormal postnatal brain development. Both the mouse and differentiated induced pluripotent stem cell models of the disorder display transcriptional and biochemical evidence for altered mTOR pathway signalling, supporting the role of KPTN in regulating mTORC1. By treatment in our KPTN mouse model, we found that the increased mTOR signalling downstream of KPTN is rapamycin sensitive, highlighting possible therapeutic avenues with currently available mTOR inhibitors. These findings place KPTN-related disorder in the broader group of mTORC1-related disorders affecting brain structure, cognitive function and network integrity.</p

Interplay between astrocytic and neuronal networks during virtual navigation in the mouse hippocampus

Encoding of spatial information in hippocapal place cells is believed to contribute to spatial cognition during navigation. Whether the processing of spatial information is exclusively limited to neuronal cells or it involves other cell types, e.g. glial cells, in the brain is currently unknown. In this thesis work, I developed an analysis pipeline to tackle this question using statistical methods and Information Theory approaches. I applied these analytical tools to two experimental data sets in which neuronal place cells in the hippocampus were imaged using two-photon microscopy, while selectively manipulating astrocytic calcium dynamics with pharmacogenetics during virtual navigation. Using custom analytical methods, we observed that pharmacogenetic perturbation of astrocytic calcium dynamics, through clozapine-n-oxyde (CNO) injection, induced a significant increase in neuronal place field and response profile width compared to control conditions. The distributions of neuronal place field and response profile center were also significantly different upon perturbation of astrocytic calcium dynamics compared to control conditions. Moreover, we found contrasting effect of astrocytic calcium dynamics perturbation on neuronal content of spatial information in the two data sets. In the first data set, we found that CNO injection resulted in a significant increase in the average information content in all neurons. In the second data set, we instead found that mutual information values were not significantly different upon CNO application compared to controls. Although the presented results are still preliminary and more experiments and analyses are needed, these findings suggest that astrocytic calcium dynamics may actively control the way hippocampal neuronal networks encode spatial information during virtual navigation. These data thus suggest a complex and tight interplay between neuronal and astrocytic networks during higher cognitive functions

Skin cell heterogeneity and dynamics during morphogenesis, tissue homeostasis, and regeneration

Skin is our protective barrier against various environmental harms. For the skin to fulfill its crucial function, it relies on multiple cell types working in concert; but most importantly it relies on skin-resident epithelial stem cells. These cells ensure an intact barrier through constant replacement of the epidermis and they ensure proper hair production through cyclical regeneration of hair follicles. This combination of constant and cyclical renewal within one tissue makes skin a prime model system for the study of adult tissue stem cells. The overall aim of this thesis was to transcriptionally dissect this well-established model system in a systematic and unbiased way. The majority of data presented in this thesis is based on the combination of single-cell RNA sequencing and in situ stainings of mRNA. This combination allows us to appreciate the genome-wide transcriptional heterogeneity while still being able to place the identified cell populations in their spatial tissue context. In Paper I, the first whole-transcriptome study of skin at the single-cell level, we examined the vectors describing cellular heterogeneity within the epidermal compartment of mouse skin during its resting stage (telogen). In Paper II, we expanded on this analysis by including full-thickness skin during rest (telogen) and growth (anagen). This allowed for an unbiased census of all major cell types contained in the skin, and it furthermore enabled us to study how skin achieves and accommodates hair growth. In Paper III, we studied the role of dermal fibroblasts in early embryonic skin development. We uncovered unexpected heterogeneity among embryonic fibroblasts and explored their supportive functions for skin maturation. Moreover, we identified novel keratinocyte subpopulations and closely analyzed epidermal fate decisions. In Paper IV, we monitored transcriptional adaptations of two distinct epidermal stem cell populations during their contribution to wound healing. This allowed us to answer fundamental questions about stem cell plasticity and the dynamics of cell adaptations following injury. In sum, this thesis uncovers the dynamic and heterogeneous nature of mouse skin during adult tissue homeostasis, embryonic development, and tissue regeneration after injury. Most importantly, we provide new insights into how stem cell identity is shaped and how developmental as well as regenerative processes are orchestrated

Sox10 regulates enteric neural crest cell migration in the developing gut

Concurrent Sessions 1: 1.3 - Organs to organisms: Models of Human Diseases: abstract no. 1417th ISDB 2013 cum 72nd Annual Meeting of the Society for Developmental Biology, VII Latin American Society of Developmental Biology Meeting and XI Congreso de la Sociedad Mexicana de Biologia del Desarrollo. The Conference's web site is located at http://www.inb.unam.mx/isdb/Sox10 is a HMG-domain containing transcription factor which plays important roles in neural crest cell survival and differentiation. Mutations of Sox10 have been identified in patients with Waardenburg-Hirschsprung syndrome, who suffer from deafness, pigmentation defects and intestinal aganglionosis. Enteric neural crest cells (ENCCs) with Sox10 mutation undergo premature differentiation and fail to colonize the distal hindgut. It is unclear, however, whether Sox10 plays a role in the migration of ENCCs. To visualize the migration behaviour of mutant ENCCs, we generated a Sox10NGFP mouse model where EGFP is fused to the N-terminal domain of Sox10. Using time-lapse imaging, we found that ENCCs in Sox10NGFP/+ mutants displays lower migration speed and altered trajectories compared to normal controls. This behaviour was cell-autonomous, as shown by organotypic grafting of Sox10NGFP/+ gut segments onto control guts and vice versa. ENCCs encounter different extracellular matrix (ECM) molecules along the developing gut. We performed gut explant culture on various ECM and found that Sox10NGFP/+ ENCCs tend to form aggregates, particularly on fibronectin. Time-lapse imaging of single cells in gut explant culture indicated that the tightly-packed Sox10 mutant cells failed to exhibit contact inhibition of locomotion. We determined the expression of adhesion molecule families by qPCR analysis, and found integrin expression unaffected while L1-cam and selected cadherins were altered, suggesting that Sox10 mutation affects cell adhesion properties of ENCCs. Our findings identify a de novo role of Sox10 in regulating the migration behaviour of ENCCs, which has important implications for the treatment of Hirschsprung disease.postprin

Analysis of craniofacial defects in Six1/Eya1-associated Branchio-Oto-Renal Syndrome

Poster Session I - Morphogenesis: 205/B10117th ISDB 2013 cum 72nd Annual Meeting of the Society for Developmental Biology, 7th Latin American Society of Developmental Biology Meeting and 11th Congreso de la Sociedad Mexicana de Biologia del Desarrollo.Branchio-Oto-Renal (BOR) syndrome patients exhibit craniofacial and renal anomalies as well as deafness. BOR syndrome is caused by mutations in Six1 or Eya1, both of which regulate cell proliferation and differentiation. The molecular mechanism underlying the craniofacial and branchial arch (BA) defects in BOR syndrome is unclear. We have found that Hoxb3 is up-regulated in the second branchial arch (BA2) of Six1-/- mutants. Moreover, Hoxb3 over-expression in transgenic mice leads to BA abnormalities which are similar to the BA defects in Six1-/- or Eya1-/- mutants, suggesting a regulatory relationship among Six1, Eya1 and Hoxb3 genes. The aim of this study is to investigate the molecular mechanism underlying abnormal BA development in BOR syndrome using Six1 and Eya1 mutant mice. Two potential Six1 binding sites were identified on the Hoxb3 gene. In vitro and in vivo Chromatin IP assays showed that Six1 could directly bind to one of the sites specifically. Furthermore, using a chick in ovo luciferase assay we showed that Six1 could suppress gene expression through one of the specific binding sites. On the other hand, in Six1-/- mutants, we found that the Notch ligand Jag1 was up-regulated in BA2. Similarly, in Hoxb3 transgenic mice, ectopic expression of Jag1 could be also detected in BA2. To investigate the activation of Notch signaling pathway, we found that Notch intracellular domain (NICD), a direct indicator of Notch pathway activation, was up-regulated in BAs of Six1-/-; Eya1-/- double mutants. Our results indicate that Hoxb3 and Notch signaling pathway are involved in mediating the craniofacial defects of Six1/Eya1-associated Branchio-Oto-Renal Syndrome.postprin