Decoding the heterogeneity of skin in homeostasis and regeneration at single-cell resolution

The skin plays a critical role in securing homeostasis in the mammalian body. Its epidermis forms a tight barrier, which separates the internal from the external environment, thereby shielding the body from physical and chemical insult. Due to the exposed position of skin as the outermost organ of the body, skin cells need to be replaced continuously. Cellular maintenance and regeneration of the skin and its associated hair follicles is orchestrated by a variety of stem cell populations. Because of its regenerative properties, the mouse skin is one of the most important model organs in stem cell research and regenerative medicine. The skin is a complex multicellular system composed of a large variety of molecularly and functionally distinct cell populations. The physiology of the skin is a result of the intricate interplay of these diverse cell types. Accordingly, knowledge about the cellular composition of the skin is an essential step in understanding its biology. For a long time, cell populations in the skin were defined based on the expression of individual molecular markers, thus making a comprehensive analysis of cellular heterogeneity impossible. In this thesis, I describe how we used single-cell transcriptomics to create systematic cell type maps of the skin in order to analyze complex molecular processes at single-cell resolution. In the first part of this thesis, I provide an overview of the morphology, function and cellular heterogeneity of the skin. I put particular emphasis on the skin as a self-maintaining tissue and model organ for stem cell research, describing regenerative process such as skin barrier maintenance, cyclical regeneration of hair follicles and cutaneous wound healing in great detail. Then, I introduce single-cell RNA-sequencing as a technique, which has revolutionized the way we analyze and conceptualize cellular heterogeneity in complex tissues. Next, I portray how we championed the application of single-cell transcriptomics in skin biology with three key papers. In Paper I, we used single-cell RNA-sequencing to analyze the mouse epidermis including hair follicles during its resting stage (telogen). We discovered previously unknown cellular heterogeneity in the epidermis and demonstrated that the complexity of this tissue is the result of just two vectors of variation: differentiation stage and spatial position. In Paper II, we analyzed the complete mouse skin, including both epidermal and stromal cells, during hair growth (anagen) and rest (telogen). In addition to describing novel cell types in the stromal part of the skin, we model cellular differentiation and lineage specification in the growing hair follicle at unprecedented resolution. In Paper III, we use single-cell transcriptomics to track molecular changes in different stem cell populations during wound healing and answer several key questions related to stem cell identity and plasticity during regenerative processes. In the last section of this thesis, I demonstrate that our studies have not just allowed us to analyze the cellular heterogeneity of the mouse skin at unprecedented detail, but have also enabled us to address a variety of critical questions such as how stem cell identity is shaped and how regenerative processes are orchestrated in the skin. I thus outline how our endeavors mark the first step towards a systems biology of the skin

Automatic alignment for three-dimensional tomographic reconstruction

In tomographic reconstruction, the goal is to reconstruct an unknown object from a collection of line integrals. Given a complete sampling of such line integrals for various angles and directions, explicit inverse formulas exist to reconstruct the object. Given noisy and incomplete measurements, the inverse problem is typically solved through a regularized least-squares approach. A challenge for both approaches is that in practice the exact directions and offsets of the x-rays are only known approximately due to, e.g. calibration errors. Such errors lead to artifacts in the reconstructed image. In the case of sufficient sampling and geometrically simple misalignment, the measurements can be corrected by exploiting so-called consistency conditions. In other cases, such conditions may not apply and we have to solve an additional inverse problem to retrieve the angles and shifts. In this paper we propose a general algorithmic framework for retrieving these parameters in conjunction with an algebraic reconstruction technique. The proposed approach is illustrated by numerical examples for both simulated data and an electron tomography dataset

Context-Aware Verification of DMN

The Decision Model and Notation (DMN) standard is a user-friendly notation for decision logic. To verify correctness of DMN decision tables, many tools are available. However, most of these look at a table in isolation, with little or no regards for its context. In this work, we argue for the importance of context, and extend the formal verification criteria to include it. We identify two forms of context, namely in-model context and background knowledge. We also present our own context-aware verification tool, implemented in our DMN-IDP interface, and show that this context-aware approach allows us to perform more thorough verification than any other available tool

Populous: A tool for populating ontology templates

We present Populous, a tool for gathering content with which to populate an ontology. Domain experts need to add content, that is often repetitive in its form, but without having to tackle the underlying ontological representation. Populous presents users with a table based form in which columns are constrained to take values from particular ontologies; the user can select a concept from an ontology via its meaningful label to give a value for a given entity attribute. Populated tables are mapped to patterns that can then be used to automatically generate the ontology's content. Populous's contribution is in the knowledge gathering stage of ontology development. It separates knowledge gathering from the conceptualisation and also separates the user from the standard ontology authoring environments. As a result, Populous can allow knowledge to be gathered in a straight-forward manner that can then be used to do mass production of ontology content.Comment: in Adrian Paschke, Albert Burger begin_of_the_skype_highlighting end_of_the_skype_highlighting, Andrea Splendiani, M. Scott Marshall, Paolo Romano: Proceedings of the 3rd International Workshop on Semantic Web Applications and Tools for the Life Sciences, Berlin,Germany, December 8-10, 201

Developing a kidney and urinary pathway knowledge base

<p>Abstract</p> <p>Background</p> <p>Chronic renal disease is a global health problem. The identification of suitable biomarkers could facilitate early detection and diagnosis and allow better understanding of the underlying pathology. One of the challenges in meeting this goal is the necessary integration of experimental results from multiple biological levels for further analysis by data mining. Data integration in the life science is still a struggle, and many groups are looking to the benefits promised by the Semantic Web for data integration.</p> <p>Results</p> <p>We present a Semantic Web approach to developing a knowledge base that integrates data from high-throughput experiments on kidney and urine. A specialised KUP ontology is used to tie the various layers together, whilst background knowledge from external databases is incorporated by conversion into RDF. Using SPARQL as a query mechanism, we are able to query for proteins expressed in urine and place these back into the context of genes expressed in regions of the kidney.</p> <p>Conclusions</p> <p>The KUPKB gives KUP biologists the means to ask queries across many resources in order to aggregate knowledge that is necessary for answering biological questions. The Semantic Web technologies we use, together with the background knowledge from the domain’s ontologies, allows both rapid conversion and integration of this knowledge base. The KUPKB is still relatively small, but questions remain about scalability, maintenance and availability of the knowledge itself.</p> <p>Availability</p> <p>The KUPKB may be accessed via <url>http://www.e-lico.eu/kupkb</url>.</p