Comprehensive single cell mRNA profiling reveals a detailed roadmap for pancreatic endocrinogenesis.

Deciphering mechanisms of endocrine cell induction, specification and lineage allocation in vivo will provide valuable insights into how the islets of Langerhans are generated. Currently, it is ill defined how endocrine progenitors segregate into different endocrine subtypes during development. Here, we generated a novel neurogenin 3 (Ngn3)-Venus fusion (NVF) reporter mouse line, that closely mirrors the transient endogenous Ngn3 protein expression. To define an in vivo roadmap of endocrinogenesis, we performed single cell RNA sequencing of 36,351 pancreatic epithelial and NVF+ cells during secondary transition. This allowed Ngn3low endocrine progenitors, Ngn3high endocrine precursors, Fev+ endocrine lineage and hormone+ endocrine subtypes to be distinguished and time-resolved, and molecular programs during the step-wise lineage restriction steps to be delineated. Strikingly, we identified 58 novel signature genes that show the same transient expression dynamics as Ngn3 in the 7260 profiled Ngn3-expressing cells. The differential expression of these genes in endocrine precursors associated with their cell-fate allocation towards distinct endocrine cell types. Thus, the generation of an accurately regulated NVF reporter allowed us to temporally resolve endocrine lineage development to provide a fine-grained single cell molecular profile of endocrinogenesis in vivo.

Parametric and non-parametric gradient matching for network inference: a comparison.

BACKGROUND: Reverse engineering of gene regulatory networks from time series gene-expression data is a challenging problem, not only because of the vast sets of candidate interactions but also due to the stochastic nature of gene expression. We limit our analysis to nonlinear differential equation based inference methods. In order to avoid the computational cost of large-scale simulations, a two-step Gaussian process interpolation based gradient matching approach has been proposed to solve differential equations approximately. RESULTS: We apply a gradient matching inference approach to a large number of candidate models, including parametric differential equations or their corresponding non-parametric representations, we evaluate the network inference performance under various settings for different inference objectives. We use model averaging, based on the Bayesian Information Criterion (BIC), to combine the different inferences. The performance of different inference approaches is evaluated using area under the precision-recall curves. CONCLUSIONS: We found that parametric methods can provide comparable, and often improved inference compared to non-parametric methods; the latter, however, require no kinetic information and are computationally more efficient.

PEITH(Θ): perfecting experiments with information theory in Python with GPU support.

Motivation: Different experiments provide differing levels of information about a biological system. This makes it difficult, a priori, to select one of them beyond mere speculation and/or belief, especially when resources are limited. With the increasing diversity of experimental approaches and general advances in quantitative systems biology, methods that inform us about the information content that a given experiment carries about the question we want to answer, become crucial. Results: PEITH(Θ) is a general purpose, Python framework for experimental design in systems biology. PEITH(Θ) uses Bayesian inference and information theory in order to derive which experiments are most informative in order to estimate all model parameters and/or perform model predictions. Availability and implementation: https://github.com/MichaelPHStumpf/Peitho. Contact: m.stumpf@imperial.ac.uk or juliane.liepe@mpibpc.mpg.de.

Human cortical neurogenesis is altered via glucocorticoid-mediated regulation of ZBTB16 expression.

Glucocorticoids are important for proper organ maturation, and their levels are tightly regulated during development. Here, we use human cerebral organoids and mice to study the cell-type-specific effects of glucocorticoids on neurogenesis. We show that glucocorticoids increase a specific type of basal progenitors (co-expressing PAX6 and EOMES) that has been shown to contribute to cortical expansion in gyrified species. This effect is mediated via the transcription factor ZBTB16 and leads to increased production of neurons. A phenome-wide Mendelian randomization analysis of an enhancer variant that moderates glucocorticoid-induced ZBTB16 levels reveals causal relationships with higher educational attainment and altered brain structure. The relationship with postnatal cognition is also supported by data from a prospective pregnancy cohort study. This work provides a cellular and molecular pathway for the effects of glucocorticoids on human neurogenesis that relates to lasting postnatal phenotypes.

An integrated cell atlas of the lung in health and disease.

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas.

Data-driven dynamical modelling of a pathogen-infected plant gene regulatory network: A comparative analysis.

Recent advances in synthetic biology have enabled the design of genetic feedback control circuits that could be implemented to build resilient plants against pathogen attacks. To facilitate the proper design of these genetic feedback control circuits, an accurate model that is able to capture the vital dynamical behaviour of the pathogen-infected plant is required. In this study, using a data-driven modelling approach, we develop and compare four dynamical models (i.e. linear, Michaelis-Menten with Hill coefficient (Hill Function), standard S-System and extended S-System) of a pathogen-infected plant gene regulatory network (GRN). These models are then assessed across several criteria, i.e. ease of identifying the type of gene regulation, the predictive capability, Akaike Information Criterion (AIC) and the robustness to parameter uncertainty to determine its viability of balancing between biological complexity and accuracy when modelling the pathogen-infected plant GRN. Using our defined ranking score, we obtain the following insights to the modelling of GRN. Our analyses show that despite commonly used and provide biological relevance, the Hill Function model ranks the lowest while the extended S-System model ranks highest in the overall comparison. Interestingly, the performance of the linear model is more consistent throughout the comparison, making it the preferred model for this pathogen-infected plant GRN when considering data-driven modelling approach.

Cell-Type-Specific Impact of Glucocorticoid Receptor Activation on the Developing Brain: A Cerebral Organoid Study.

OBJECTIVE: A fine-tuned balance of glucocorticoid receptor (GR) activation is essential for organ formation, with disturbances influencing many health outcomes. In utero, glucocorticoids have been linked to brain-related negative outcomes, with unclear underlying mechanisms, especially regarding cell-type-specific effects. An in vitro model of fetal human brain development, induced human pluripotent stem cell (hiPSC)-derived cerebral organoids, was used to test whether cerebral organoids are suitable for studying the impact of prenatal glucocorticoid exposure on the developing brain. METHODS: The GR was activated with the synthetic glucocorticoid dexamethasone, and the effects were mapped using single-cell transcriptomics across development. RESULTS: The GR was expressed in all cell types, with increasing expression levels through development. Not only did its activation elicit translocation to the nucleus and the expected effects on known GR-regulated pathways, but also neurons and progenitor cells showed targeted regulation of differentiation- and maturation-related transcripts. Uniquely in neurons, differentially expressed transcripts were significantly enriched for genes associated with behavior-related phenotypes and disorders. This human neuronal glucocorticoid response profile was validated across organoids from three independent hiPSC lines reprogrammed from different source tissues from both male and female donors. CONCLUSIONS: These findings suggest that excessive glucocorticoid exposure could interfere with neuronal maturation in utero, leading to increased disease susceptibility through neurodevelopmental processes at the interface of genetic susceptibility and environmental exposure. Cerebral organoids are a valuable translational resource for exploring the effects of glucocorticoids on early human brain development.

Sfaira accelerates data and model reuse in single cell genomics.

Single-cell RNA-seq datasets are often first analyzed independently without harnessing model fits from previous studies, and are then contextualized with public data sets, requiring time-consuming data wrangling. We address these issues with sfaira, a single-cell data zoo for public data sets paired with a model zoo for executable pre-trained models. The data zoo is designed to facilitate contribution of data sets using ontologies for metadata. We propose an adaption of cross-entropy loss for cell type classification tailored to datasets annotated at different levels of coarseness. We demonstrate the utility of sfaira by training models across anatomic data partitions on 8 million cells.