<p>  </p> <p>The growth of land plants depends on a group of pluripotent stem cells in a tissue called the meristem. Seed plants initiate and maintain different types of meristems at the asexual sporophyte stage, and they generate sexual gametophytes, which are dependent on their sporophytes and are devoid of a meristem. In contrast, aside from forming indeterminate meristems at the sporophyte stage, seedless vascular plants, including ferns, also develop meristems in their gametophytes to drive gametophyte development and formation of sexual organs. To date, compared to the well-characterized cell behaviors and regulatory pathways in the meristems of seed plants, the molecular and cellular basis of meristem development in seedless ferns is still poorly understood. </p> <p>In several seed plants, the HAIRY MERISTEM (HAM) family transcription factors play important roles in maintaining the indeterminacy of shoot apical meristems and promoting the <em>de novo</em> formation of axillary meristems. In the first part of this dissertation, through constructing a comprehensive phylogeny, I found that HAM family members are widely present in land plants and duplicated in a common ancestor of flowering plants, leading to the formation of two distinct groups: type I and type II. In addition, HAM members from different seed plants and seedless plants are able to replace the roles of the Arabidopsis type-II <em>HAM</em> genes, maintaining established shoot apical meristems and promoting the initiation of new stem cell niches in Arabidopsis. Furthermore, preliminary functional studies of the <em>HAM </em>homolog (<em>CrHAM</em>) in the model fern<em> Ceratopteris richardii</em> suggest that CrHAM is required for maintaining the indeterminacy of multicellular meristems in Ceratopteris gametophytes. Collectively, these results indicate that HAM family members may serve as common regulators in control of meristem development in both seed plants and seedless vascular plants. </p> <p>In the remaining chapter of this dissertation, long-term time-lapse confocal imaging was performed using Ceratopteris stable transgenic plants, in which each individual cell (nucleus) was labelled with a fluorescent marker. Real-time lineage, identity, and division activity of each single cell from meristem initiation to establishment in Ceratopteris gametophytes were then determined. Additionally, cell fate and lineage alterations during <em>de novo</em> formation of new meristems were examined by mechanical perturbations. These quantitative analyses lead to the conclusion that in Ceratopteris gametophytes, initiation and proliferation of multicellular meristems relies on a few marginal cell lineages. Once established, the meristem maintains an actively dividing zone during gametophyte development. Within the meristem, cell division is independent of cell lineages and marginal cells are more actively dividing than inner cells. The meristem also triggers differentiation of adjacent cells into egg-producing archegonia in a position-dependent manner. </p> <p>In summary, this work provides insight into the evolution of key stem-cell regulators and advances the understanding of diversified meristem development in land plants. </p>

Understanding the complex mechanisms to assemble a functional brain demands sophisticated experimental designs. Drosophila melanogaster, a model organism equipped with powerful genetic tools and evolutionarily conserved developmental programs, is ideal for such mechanistic studies. Valuable insights were learned from research in Drosophila ventral nerve cord, such as spatial patterning, temporal coding, and lineage diversification. However, the blueprint of Drosophila cerebrum development remains largely unknown. Neural progenitor cells, called neuroblasts (NBs), serially and stereotypically produce neurons and glia in the Drosophila cerebrum. Neuroblasts inherit specific sets of early patterning genes, which likely determine their individual identities when neuroblasts delaminate from neuroectoderm. Unique neuroblasts may hence acquire the abilities to differentially interpret the temporal codes and deposit characteristic progeny lineages. We believe resolving this age-old speculation requires a tracing system that links patterning genes to neuroblasts and corresponding lineages, and further allows specific manipulations. Using modern transgenic systems, one can immortalize transient NB gene expressions into continual labeling of their offspring. Having a collection of knockin drivers that capture endogenous gene expression patterns would open the door for tracing specific NBs and their progenies based on the combinatorial expression of various early patterning genes. Anticipating the need for a high throughput gene targeting system, we created Golic+ (gene targeting during oogenesis with lethality inhibitor and CRISPR/Cas “plus”), which features efficient homologous recombination in cystoblasts and a lethality selection for easy targeting candidate recovery. Using Golic+, we successfully generated T2AGal4 knock-ins for 6 representative early patterning genes, including lab, unpg, hkb, vnd, ind, and msh. They faithfully recapitulated the expression patterns of the targeted genes. After preserving initial NB expressions by triggering irreversible genetic labeling, we revealed the lineages founded by the NBs expressing a particular early patterning gene. Identifying the neuroblasts and lineages that express a particular early patterning gene should elucidate the genetic origin of neuroblast diversity. We believe such an effort will lead to a deeper understanding of brain development and evolution.

Nous nous intéressons à des modèles statistiques pour les données indexées par des arborescences. Dans le contexte de l'équipe Virtual Plants, équipe hôte de cette thèse, les applications d'intérêt portent sur le développement de la plante et sa modulation par des facteurs environnementaux et génétiques. Nous nous restreignons donc à des applications issues du développement de la plante, à la fois au niveau microscopique avec l'étude de la lignée cellulaire du tissu biologique servant à la croissance des plantes, et au niveau macroscopique avec le mécanisme de production de branches. Le catalogue de modèles disponibles pour les données indexées par des arborescences est beaucoup moins important que celui disponible pour les données indexées par des chemins. Cette thèse vise donc à proposer un cadre de modélisation statistique pour l'étude de patterns pour données indexées par des arborescences. À cette fin, deux classes différentes de modèles statistiques, les modèles de Markov et de détection de ruptures, sont étudiées. / We address statistical models for tree-indexed data.Tree-indexed data can be seen as a generalization of path-indexed data since directed path graphs are directed tree graphs where there is at most one child per vertex.In the context of the Virtual Plants team, host team of this thesis, applications of interest focus on plant development and its modulation by environmental and genetic factors.We thus focus on plant developmental applications, both at the microscopic level with the study of the cell lineage in the biological tissue responsible for the plant growth, and at the macroscopic level with the mechanism of production of branches. The catalog of models available for tree-indexed data is far less important than the one available for path-indexed data.This thesis therefore aims at proposing a statistical modeling framework for studying patterns in tree-indexed data.To this end, two different classes of statistical models, Markov and change-point models, are investigated.

No / Androgens stimulate many hair follicles to alter hair colour and size via the hair growth cycle; in androgenetic alopecia tiny, pale hairs gradually replace large, pigmented ones. Since stem cell factor (SCF) is important in embryonic melanocyte migration and maintaining adult rodent pigmentation, we investigated SCF/c-Kit signalling in human hair follicles to determine whether this was altered in androgenetic alopecia. Quantitative immunohistochemistry detected three melanocyte-lineage markers and c-Kit in four focus areas: the epidermis, infundibulum, hair bulb (where pigment is formed) and mid-follicle outer root sheath (ORS). Colocalisation confirmed melanocyte c-Kit expression; cultured follicular melanocytes also exhibited c-Kit. Few ORS cells expressed differentiated melanocyte markers or c-Kit, but NKI/beteb antibody, which also recognises early melanocyte-lineage antigens, identified fourfold more cells, confirmed by colocalisation. Occasional similar bulbar cells were seen. Melanocyte distribution, concentration and c-Kit expression were unaltered in balding follicles. Androgenetic alopecia cultured dermal papilla cells secreted less SCF, measured by ELISA, than normal cells. This identifies three types of melanocyte-lineage cells in human follicles. The c-Kit expression by dendritic, pigmenting, bulbar melanocytes and rounded, differentiated, non-pigmenting ORS melanocytes implicate SCF in maintaining pigmentation and migration into regenerating hair bulbs. Less differentiated, c-Kit-independent cells in the mid-follicle ORS stem cell niche and occasionally in the bulb, presumably a local reserve for long scalp hair growth, implicate other factors in activating stem cells. Androgens appear to reduce alopecia hair colour by inhibiting dermal papilla SCF production, impeding bulbar melanocyte pigmentation. These results may facilitate new treatments for hair colour changes in hirsutism, alopecia or greying.

Thesis (Ph. D.)--University of Oklahoma. / Bibliography: leaves 182-199.

Stem cells differentiate through an organized hierarchy of intermediate cell types to terminally differentiated cell types. This process is largely guided by master transcriptional regulators, but it also depends on the expression of many other types of genes. The discrete cell types in the differentiation hierarchy are often identified based on the expression or non-expression of certain marker genes. Historically, these have often been various cell-surface proteins, which are fairly easy to assay biochemically but are not necessarily causative of the cell type, in the sense of being master transcriptional regulators. This raises important questions about how gene expression across the whole genome controls or reflects cell state, and in particular, differentiation hierarchies. Traditional approaches to understanding gene expression patterns across multiple conditions, such as principal components analysis or K-means clustering, can group cell types based on gene expression, but they do so without knowledge of the differentiation hierarchy. Hierarchical clustering and maximization of parsimony can organize the cell types into a tree, but in general this tree is different from the differentiation hierarchy. Using hematopoietic differentiation as an example, we demonstrate how many genes other than marker genes are able to discriminate between different branches of the differentiation tree by proposing two models for detecting genes that are up-regulated or down-regulated in distinct lineages. We then propose a novel approach to solving the following problem: Given the differentiation hierarchy and gene expression data at each node, construct a weighted Euclidean distance metric such that the minimum spanning tree with respect to that metric is precisely the given differentiation hierarchy. We provide a set of linear constraints that are provably sufficient for the desired construction and a linear programming framework to identify sparse sets of weights, effectively identifying genes that are most relevant for discriminating different parts of the tree. We apply our method to microarray gene expression data describing 38 cell types in the hematopoiesis hierarchy, constructing a sparse weighted Euclidean metric that uses just 175 genes. These 175 genes are different than the marker genes that were used to identify the 38 cell types, hence offering a novel alternative way of discriminating different branches of the tree. A DAVID functional annotation analysis shows that the 175 genes reflect major processes and pathways active in different parts of the tree. However, we find that there are many alternative sets of weights that satisfy the linear constraints. Thus, in the style of random-forest training, we also construct metrics based on random subsets of the genes and compare them to the metric of 175 genes. Our results show that the 175 genes frequently appear in the random metrics, implicating their significance from an empirical point of view as well. Finally, we show how our linear programming method is able to identify columns that were selected to build minimum spanning trees on the nodes of random variable-size matrices.