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Introduction: Cleft lip ± cleft palate (CL/P) is one of the most common birth defects. Although research has identified multiple genetic risk loci for different types of CL/P (i.e., syndromic or non-syndromic forms), determining the respective causal genes and understanding the relevant functional networks remain challenging. The recent introduction of single-cell RNA sequencing (scRNA-seq) has provided novel opportunities to study gene expression patterns at cellular resolution. The aims of our study were to: (i) aggregate available scRNA-seq data from embryonic mice and provide this as a resource for the craniofacial community; and (ii) demonstrate the value of these data in terms of the investigation of the gene expression patterns of CL/P candidate genes.Methods and Results: First, two published scRNA-seq data sets from embryonic mice were re-processed, i.e., data representing the murine time period of craniofacial development: (i) facial data from embryonic day (E) E11.5; and (ii) whole embryo data from E9.5–E13.5 from the Mouse Organogenesis Cell Atlas (MOCA). Marker gene expression analyses demonstrated that at E11.5, the facial data were a high-resolution representation of the MOCA data. Using CL/P candidate gene lists, distinct groups of genes with specific expression patterns were identified. Among others we identified that a co-expression network including Irf6, Grhl3 and Tfap2a in the periderm, while it was limited to Irf6 and Tfap2a in palatal epithelia, cells of the ectodermal surface, and basal cells at the fusion zone. The analyses also demonstrated that additional CL/P candidate genes (e.g., Tpm1, Arid3b, Ctnnd1, and Wnt3) were exclusively expressed in Irf6+ facial epithelial cells (i.e., as opposed to Irf6- epithelial cells). The MOCA data set was finally used to investigate differences in expression profiles for candidate genes underlying different types of CL/P. These analyses showed that syndromic CL/P genes (syCL/P) were expressed in significantly more cell types than non-syndromic CL/P candidate genes (nsCL/P).Discussion: The present study illustrates how scRNA-seq data can empower research on craniofacial development and disease.

Despite its high prevalence, the cellular and molecular mechanisms of chronic obstructive pulmonary disease (COPD) are far from being understood. Here, we determine disease-related changes in cellular and molecular compositions within the alveolar space and peripheral blood of a cohort of COPD patients and controls. Myeloid cells were the largest cellular compartment in the alveolar space with invading monocytes and proliferating macrophages elevated in COPD. Modeling cell-to-cell communication, signaling pathway usage, and transcription factor binding predicts TGF-β1 to be a major upstream regulator of transcriptional changes in alveolar macrophages of COPD patients. Functionally, macrophages in COPD showed reduced antigen presentation capacity, accumulation of cholesteryl ester, reduced cellular chemotaxis, and mitochondrial dysfunction, reminiscent of impaired immune activation.

Zusammenfassung Die individuelle Zelle stellt die fundamentale Einheit des Lebens dar. Ihre Funktionsweise ist seit Jahrhunderten Gegenstand der biomedizinischen Forschung. Seit Kurzem erlaubt die sogenannte Einzelzellsequenzierung revolutionäre neue Einblicke in die Komposition von Geweben, die Interaktion zwischen Zellen und den Ablauf dynamischer Prozesse. Die damit verbundenen Daten, z. B. aus der Transkriptomik, stellen Analysten jedoch vor neue Herausforderungen. Die Daten sind typischerweise sehr groß, verrauscht und stark mit anderen Informationen vernetzt, sodass herkömmliche Verfahren ausscheiden. Neue, speziell adaptierte Algorithmen aus dem Feld der künstlichen Intelligenz können hier Abhilfe schaffen. In Verbindung stellen Einzelzellsequenzierung und künstliche Intelligenz mächtige neue Werkzeuge für die biomedizinische Forschung dar und erlauben Einblicke in höchster Auflösung. Wir geben einen kurzen Überblick über beide Technologien und liefern Beispiele für ihre Anwendung im medizinischen Bereich. Wir demonstrieren anschließend beispielhaft, wie Methoden der künstlichen Intelligenz erfolgreich für die Analyse von Einzelzelltranskriptomdaten eingesetzt werden können. Da der korrekte und gewinnbringende Einsatz dieser Methoden weiterhin detailliertes Hintergrundwissen verlangt, ist ein intensivierter Austausch zwischen den Disziplinen unumgänglich. Wir gehen deshalb abschließend auf den Einsatz neuer Plattformen zur Kollaboration und zum Wissensaustausch ein.

Network theory provides novel concepts that promise an improved characterization of interacting dynamical systems. Within this framework, evolving networks can be considered as being composed of nodes, representing systems, and of time-varying edges, representing interactions between these systems. This approach is highly attractive to further our understanding of the physiological and pathophysiological dynamics in human brain networks. Indeed, there is growing evidence that the epileptic process can be regarded as a large-scale network phenomenon. We here review methodologies for inferring networks from empirical time series and for a characterization of these evolving networks. We summarize recent findings derived from studies that investigate human epileptic brain networks evolving on timescales ranging from few seconds to weeks. We point to possible pitfalls and open issues, and discuss future perspectives., Comment: In press (Physica D)

Epilepsy can be regarded as a network phenomenon with functionally and/or structurally aberrant connections in the brain. Over the past years, concepts and methods from network theory substantially contributed to improve the characterization of structure and function of these epileptic networks and thus to advance understanding of the dynamical disease epilepsy. We extend this promising line of research and assess—with high spatial and temporal resolution and using complementary analysis approaches that capture different characteristics of the complex dynamics—both strength and direction of interactions in evolving large-scale epileptic brain networks of 35 patients that suffered from drug-resistant focal seizures with different anatomical onset locations. Despite this heterogeneity, we find that even during the seizure-free interval the seizure onset zone is a brain region that, when averaged over time, exerts strongest directed influences over other brain regions being part of a large-scale network. This crucial role, however, manifested by averaging on the population-sample level only – in more than one third of patients, strongest directed interactions can be observed between brain regions far off the seizure onset zone. This may guide new developments for individualized diagnosis, treatment and control.

We advance our approach of analyzing the dynamics of interacting complex systems with the nonlinear dynamics of interacting nonlinear elements. We replace the widely used lattice-like connection topology of cellular neural networks (CNN) by complex topologies that include both short- and long-ranged connections. With an exemplary time-resolved analysis of asymmetric nonlinear interdependences between the seizure generating area and its immediate surrounding we provide first evidence for complex CNN connection topologies to allow for a faster network optimization together with an improved approximation accuracy of directed interactions.

We propose a straightforward extension of symbolic transfer entropy to enable the investigation of delayed directional relationships between coupled dynamical systems from time series. Analyzing time series from chaotic model systems, we demonstrate the applicability and limitations of our approach. Our findings obtained from applying our method to infer delayed directed interactions in the human epileptic brain underline the importance of our approach for improving the construction of functional network structures from data.

Epilepsy can be regarded as a network phenomenon with functionally and/or structurally aberrant connections in the brain. Over the past years, concepts and methods from network theory substantially contributed to improve the characterization of structure and function of these epileptic networks and thus to advance understanding of the dynamical disease epilepsy. We extend this promising line of research and assess—with high spatial and temporal resolution and using complementary analysis approaches that capture different characteristics of the complex dynamics—both strength and direction of interactions in evolving large-scale epileptic brain networks of 35 patients that suffered from drug-resistant focal seizures with different anatomical onset locations. Despite this heterogeneity, we find that even during the seizure-free interval the seizure onset zone is a brain region that, when averaged over time, exerts strongest directed influences over other brain regions being part of a large-scale network. This crucial role, however, manifested by averaging on the population-sample level only – in more than one third of patients, strongest directed interactions can be observed between brain regions far off the seizure onset zone. This may guide new developments for individualized diagnosis, treatment and control.

Drug-resistant, multifocal, non-resectable epilepsies are among the most difficult epileptic disorders to manage. An approach to control previously uncontrollable seizures in epilepsy patients would consist of identifying seizure precursors in critical brain areas combined with delivering a counteracting influence to prevent seizure generation. Predictability of seizures with acceptable levels of sensitivity and specificity, even in an ambulatory setting, has been repeatedly shown, however, in patients with a single seizure focus only. We did a study to assess feasibility of state-of-the-art, electroencephalogram-based seizure-prediction techniques in patients with uncontrollable multifocal seizures. We obtained significant predictive information about upcoming seizures in more than two thirds of patients. Unexpectedly, the emergence of seizure precursors was confined to non-affected brain areas. Our findings clearly indicate that epileptic networks, spanning lobes and hemispheres, underlie generation of seizures. Our proof-of-concept study is an important milestone towards new therapeutic strategies based on seizure-prediction techniques for clinical practice.

Inferring strength and direction of interactions from electroencephalographic (EEG) recordings is of crucial importance to improve our understanding of dynamical interdependencies underlying various physiological and pathophysiological conditions in the human epileptic brain. We here use approaches from symbolic analysis to investigate—in a time-resolved manner—weighted and directed, short- to long-ranged interactions between various brain regions constituting the epileptic network. Our observations point to complex spatial–temporal interdependencies underlying the epileptic process and their role in the generation of epileptic seizures, despite the massive reduction of the complex information content of multi-day, multi-channel EEG recordings through symbolization. We discuss limitations and potential future improvements of this approach.