Poincaré maps for analyzing complex hierarchies in single-cell data.

The need to understand cell developmental processes spawned a plethora of computational methods for discovering hierarchies from scRNAseq data. However, existing techniques are based on Euclidean geometry, a suboptimal choice for modeling complex cell trajectories with multiple branches. To overcome this fundamental representation issue we propose Poincaré maps, a method that harness the power of hyperbolic geometry into the realm of single-cell data analysis. Often understood as a continuous extension of trees, hyperbolic geometry enables the embedding of complex hierarchical data in only two dimensions while preserving the pairwise distances between points in the hierarchy. This enables the use of our embeddings in a wide variety of downstream data analysis tasks, such as visualization, clustering, lineage detection and pseudotime inference. When compared to existing methods - unable to address all these important tasks using a single embedding - Poincaré maps produce state-of-the-art two-dimensional representations of cell trajectories on multiple scRNAseq datasets.

Time-resolved serial femtosecond crystallography at the European XFEL.

The European XFEL (EuXFEL) is a 3.4-km long X-ray source, which produces femtosecond, ultrabrilliant and spatially coherent X-ray pulses at megahertz (MHz) repetition rates. This X-ray source has been designed to enable the observation of ultrafast processes with near-atomic spatial resolution. Time-resolved crystallographic investigations on biological macromolecules belong to an important class of experiments that explore fundamental and functional structural displacements in these molecules. Due to the unusual MHz X-ray pulse structure at the EuXFEL, these experiments are challenging. Here, we demonstrate how a biological reaction can be followed on ultrafast timescales at the EuXFEL. We investigate the picosecond time range in the photocycle of photoactive yellow protein (PYP) with MHz X-ray pulse rates. We show that difference electron density maps of excellent quality can be obtained. The results connect the previously explored femtosecond PYP dynamics to timescales accessible at synchrotrons. This opens the door to a wide range of time-resolved studies at the EuXFEL.

The Karabo distributed control system.

The Karabo distributed control system has been developed to address the challenging requirements of the European X-ray Free Electron Laser facility, including complex and custom-made hardware, high data rates and volumes, and close integration of data analysis for distributed processing and rapid feedback. Karabo is a pluggable, distributed application management system forming a supervisory control and data acquisition environment as part of a distributed control system. Karabo provides integrated control of hardware, monitoring, data acquisition and data analysis on distributed hardware, allowing rapid control feedback based on complex algorithms. Services exist for access control, data logging, configuration management and situational awareness through alarm indicators. The flexible framework enables quick response to the changing requirements in control and analysis, and provides an efficient environment for development, and a single interface to make all changes immediately available to operators and experimentalists.

Comprehensive characterization of ureagenesis in the spfash mouse, a model of human ornithine transcarbamylase deficiency, reveals age-dependency of ammonia detoxification.

The most common ureagenesis defect is X-linked ornithine transcarbamylase (OTC) deficiency which is a main target for novel therapeutic interventions. The spf ash mouse model carries a variant (c.386G>A, p.Arg129His) that is also found in patients. Male spf ash mice have a mild biochemical phenotype with low OTC activity (5%-10% of wild-type), resulting in elevated urinary orotic acid but no hyperammonemia. We recently established a dried blood spot method for in vivo quantification of ureagenesis by Gas chromatography-mass spectrometry (GC-MS) using stable isotopes. Here, we applied this assay to wild-type and spf ash mice to assess ureagenesis at different ages. Unexpectedly, we found an age-dependency with a higher capacity for ammonia detoxification in young mice after weaning. A parallel pattern was observed for carbamoylphosphate synthetase 1 and OTC enzyme expression and activities, which may act as pacemaker of this ammonia detoxification pathway. Moreover, high ureagenesis in younger mice was accompanied by elevated periportal expression of hepatic glutamine synthetase, another main enzyme required for ammonia detoxification. These observations led us to perform a more extensive analysis of the spf ash mouse in comparison to the wild-type, including characterization of the corresponding metabolites, enzyme activities in the liver and plasma and the gut microbiota. In conclusion, the comprehensive enzymatic and metabolic analysis of ureagenesis performed in the presented depth was only possible in animals. Our findings suggest such analyses being essential when using the mouse as a model and revealed age-dependent activity of ammonia detoxification.

Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series.

Stochastic chemical reaction networks constitute a model class to quantitatively describe dynamics and cell-to-cell variability in biological systems. The topology of these networks typically is only partially characterized due to experimental limitations. Current approaches for refining network topology are based on the explicit enumeration of alternative topologies and are therefore restricted to small problem instances with almost complete knowledge. We propose the reactionet lasso, a computational procedure that derives a stepwise sparse regression approach on the basis of the Chemical Master Equation, enabling large-scale structure learning for reaction networks by implicitly accounting for billions of topology variants. We have assessed the structure learning capabilities of the reactionet lasso on synthetic data for the complete TRAIL induced apoptosis signaling cascade comprising 70 reactions. We find that the reactionet lasso is able to efficiently recover the structure of these reaction systems, ab initio, with high sensitivity and specificity. With only < 1% false discoveries, the reactionet lasso is able to recover 45% of all true reactions ab initio among > 6000 possible reactions and over 102000 network topologies. In conjunction with information rich single cell technologies such as single cell RNA sequencing or mass cytometry, the reactionet lasso will enable large-scale structure learning, particularly in areas with partial network structure knowledge, such as cancer biology, and thereby enable the detection of pathological alterations of reaction networks. We provide software to allow for wide applicability of the reactionet lasso.