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RNA velocity has opened up new ways of studying cellular differentiation in single-cell RNA-sequencing data. It describes the rate of gene expression change for an individual gene at a given time point based on the ratio of its spliced and unspliced messenger RNA (mRNA). However, errors in velocity estimates arise if the central assumptions of a common splicing rate and the observation of the full splicing dynamics with steady-state mRNA levels are violated. Here we present scVelo, a method that overcomes these limitations by solving the full transcriptional dynamics of splicing kinetics using a likelihood-based dynamical model. This generalizes RNA velocity to systems with transient cell states, which are common in development and in response to perturbations. We apply scVelo to disentangling subpopulation kinetics in neurogenesis and pancreatic endocrinogenesis. We infer gene-specific rates of transcription, splicing and degradation, recover each cell’s position in the underlying differentiation processes and detect putative driver genes. scVelo will facilitate the study of lineage decisions and gene regulation. scVelo reconstructs transient cell states and differentiation pathways from single-cell RNA-sequencing data.

A cell type's transcriptome defines the active genes that control its biology. Two groups used single-cell RNA sequencing to define the transcriptomes for essentially all cell types of a complete animal, the regenerative planarian Schmidtea mediterranea. Because pluripotent stem cells constantly differentiate to rejuvenate any part of the body of this species, all developmental lineages are active in adult animals. Fincher et al. determined the transcriptomes for most, if not all, planarian cell types, including some that were previously unknown. They also identified transition states and genes governing positional information. Plass et al. used single-cell transcriptomics and computational algorithms to reconstruct a lineage tree capturing the developmental progressions from stem to differentiated cells. They could then predict gene programs that are specifically turned on and off along the tree, and they used this approach to study how the cell types behaved during regeneration. These whole-animal transcriptome “atlases” are a powerful way to study metazoan biology. Science , this issue p. eaaq1736 , p. eaaq1723 Single-cell analysis reveals major planarian cell types, a differentiation tree, and genes for specific differentiation events. Flatworms of the species Schmidtea mediterranea are immortal—adult animals contain a large pool of pluripotent stem cells that continuously differentiate into all adult cell types. Therefore, single-cell transcriptome profiling of adult animals should reveal mature and progenitor cells. By combining perturbation experiments, gene expression analysis, a computational method that predicts future cell states from transcriptional changes, and a lineage reconstruction method, we placed all major cell types onto a single lineage tree that connects all cells to a single stem cell compartment. We characterized gene expression changes during differentiation and discovered cell types important for regeneration. Our results demonstrate the importance of single-cell transcriptome analysis for mapping and reconstructing fundamental processes of developmental and regenerative biology at high resolution.

In this paper, a novel optical measurement principle for deformation, especially torsion, is presented. A laser beam is guided via total internal reflection (TIR) in a prism rod. Every single reflection causes an increasing change in the beam path, which can be measured by its effect on the outcoupling position of the laser. With a diameter of the prism rod of 10 mm and a length of 120 mm, the system achieves torsion sensitivities between 350 µm/° and more than 7000 µm/°, depending on the actual torsion angle φ. A decency level of sensitivity is defined for comparison, which is exceeded by a factor of ~55 at φ=0. The presented principle of TIR prism rods can be adapted to measure different load cases. Using two laser beams, bending and torsion can be distinguished and combined load cases analyzed. The resulting system can be integrated into machine elements, such as screws, to perform condition monitoring on mechanically loaded components.

Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells ( https://github.com/theislab/Scanpy ). Along with Scanpy , we present AnnData , a generic class for handling annotated data matrices ( https://github.com/theislab/anndata ).

Single-cell transcriptomics is a versatile tool for exploring heterogeneous cell populations, but as with all genomics experiments, batch effects can hamper data integration and interpretation. The success of batch-effect correction is often evaluated by visual inspection of low-dimensional embeddings, which are inherently imprecise. Here we present a user-friendly, robust and sensitive k-nearest-neighbor batch-effect test (kBET; https://github.com/theislab/kBET) for quantification of batch effects. We used kBET to assess commonly used batch-regression and normalization approaches, and to quantify the extent to which they remove batch effects while preserving biological variability. We also demonstrate the application of kBET to data from peripheral blood mononuclear cells (PBMCs) from healthy donors to distinguish cell-type-specific inter-individual variability from changes in relative proportions of cell populations. This has important implications for future data-integration efforts, central to projects such as the Human Cell Atlas.

Single-cell RNA-seq quantifies biological heterogeneity across both discrete cell types and continuous cell transitions. Partition-based graph abstraction (PAGA) provides an interpretable graph-like map of the arising data manifold, based on estimating connectivity of manifold partitions ( https://github.com/theislab/paga ). PAGA maps preserve the global topology of data, allow analyzing data at different resolutions, and result in much higher computational efficiency of the typical exploratory data analysis workflow. We demonstrate the method by inferring structure-rich cell maps with consistent topology across four hematopoietic datasets, adult planaria and the zebrafish embryo and benchmark computational performance on one million neurons.

Retinal ischemia and reperfusion injuries (IRI) permanently affect neuronal tissue and function by apoptosis and inflammation due to the limited regenerative potential of neurons. Recently, evidence emerged that the noble gas Argon exerts protective properties, while lacking any detrimental or adverse effects. We hypothesized that Argon inhalation after IRI would exert antiapoptotic effects in the retina, thereby protecting retinal ganglion cells (RGC) of the rat's eye. IRI was performed on the left eyes of rats (n = 8) with or without inhaled Argon postconditioning (25, 50 and 75 Vol%) for 1 hour immediately or delayed after ischemia (i.e. 1.5 and 3 hours). Retinal tissue was harvested after 24 hours to analyze mRNA and protein expression of Bcl-2, Bax and Caspase-3, NF-κB. Densities of fluorogold-prelabeled RGCs were analyzed 7 days after injury in whole-mounts. Histological tissue samples were prepared for immunohistochemistry and blood was analyzed regarding systemic effects of Argon or IRI. Statistics were performed using One-Way ANOVA. IRI induced RGC loss was reduced by Argon 75 Vol% inhalation and was dose-dependently attenuated by lower concentrations, or by delayed Argon inhalation (1504±300 vs. 2761±257; p<0.001). Moreover, Argon inhibited Bax and Bcl-2 mRNA expression significantly (Bax: 1.64±0.30 vs. 0.78±0.29 and Bcl-2: 2.07±0.29 vs. 0.99±0.22; both p<0.01), as well as caspase-3 cleavage (1.91±0.46 vs. 1.05±0.36; p<0.001). Expression of NF-κB was attenuated significantly. Immunohistochemistry revealed an affection of Müller cells and astrocytes. In addition, IRI induced leukocytosis was reduced significantly after Argon inhalation at 75 Vol%. Immediate and delayed Argon postconditioning protects IRI induced apoptotic loss of RGC in a time- and dose-dependent manner, possibly mediated by the inhibition of NF-κB. Further studies need to evaluate Argon's possible role as a therapeutic option.

We show that deep convolutional neural networks combined with nonlinear dimension reduction enable reconstructing biological processes based on raw image data. We demonstrate this by reconstructing the cell cycle of Jurkat cells and disease progression in diabetic retinopathy. In further analysis of Jurkat cells, we detect and separate a subpopulation of dead cells in an unsupervised manner and, in classifying discrete cell cycle stages, we reach a sixfold reduction in error rate compared to a recent approach based on boosting on image features. In contrast to previous methods, deep learning based predictions are fast enough for on-the-fly analysis in an imaging flow cytometer. The interpretation of information-rich, high-throughput single-cell data is a challenge requiring sophisticated computational tools. Here the authors demonstrate a deep convolutional neural network that can classify cell cycle status on-the-fly.

Recent advances in multiplexed single‐cell transcriptomics experiments facilitate the high‐throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible. Therefore, computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep‐learning approaches for single‐cell response modeling. CPA learns to in silico predict transcriptional perturbation response at the single‐cell level for unseen dosages, cell types, time points, and species. Using newly generated single‐cell drug combination data, we validate that CPA can predict unseen drug combinations while outperforming baseline models. Additionally, the architecture's modularity enables incorporating the chemical representation of the drugs, allowing the prediction of cellular response to completely unseen drugs. Furthermore, CPA is also applicable to genetic combinatorial screens. We demonstrate this by imputing in silico 5,329 missing combinations (97.6% of all possibilities) in a single‐cell Perturb‐seq experiment with diverse genetic interactions. We envision CPA will facilitate efficient experimental design and hypothesis generation by enabling in silico response prediction at the single‐cell level and thus accelerate therapeutic applications using single‐cell technologies. Synopsis The compositional perturbation autoencoder (CPA) is a deep learning model for predicting the transcriptomic responses of single cells to single or combinatorial treatments from drugs and genetic manipulations. CPA can be trained on highly multiplexed, single‐cell experiments with thousands of conditions to predict unmeasured phenotypes (e.g., specific dose responses). It can generalize to predict responses to small molecules never seen in the training by adding priors on chemical space. Validations using a newly generated combinatorial drug perturbation dataset demonstrate the accuracy of CPA in predicting unseen drug combinations. CPA is also applicable to genetic combinatorial screens, as shown by imputing in silico 5,329 missing combinations in a single‐cell perturb‐seq experiment with diverse genetic interactions. Graphical Abstract The compositional perturbation autoencoder (CPA) is a deep learning model for predicting the transcriptomic responses of single cells to single or combinatorial treatments from drugs and genetic manipulations.

We present a new impurity solver for dynamical mean-field theory based on imaginary-time evolution of matrix product states. This converges the self-consistency loop on the imaginary-frequency axis and obtains real-frequency information in a final real-time evolution. Relative to computations on the real-frequency axis, required bath sizes are much smaller and no entanglement is generated, so much larger systems can be studied. The power of the method is demonstrated by solutions of a three-band model in the single- and two-site dynamical mean-field approximation. Technical issues are discussed, including details of the method, efficiency as compared to other matrix-product-state-based impurity solvers, bath construction and its relation to real-frequency computations and the analytic continuation problem of quantum Monte Carlo methods, the choice of basis in dynamical cluster approximation, and perspectives for off-diagonal hybridization functions.