Fabian J. Theis

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

Recent technical developments have enabled the transcriptomes of hundreds of cells to be assayed in an unbiased manner, opening up the possibility that new subpopulations of cells can be found. However, the effects of potential confounding factors, such as the cell cycle, on the heterogeneity of gene expression and therefore on the ability to robustly identify subpopulations remain unclear. We present and validate a computational approach that uses latent variable models to account for such hidden factors. We show that our single-cell latent variable model (scLVM) allows the identification of otherwise undetectable subpopulations of cells that correspond to different stages during the differentiation of naive T cells into T helper 2 cells. Our approach can be used not only to identify cellular subpopulations but also to tease apart different sources of gene expression heterogeneity in single-cell transcriptomes.

An atlas of genetic influences on human blood metabolites.

Genome-wide association scans with high-throughput metabolic profiling provide unprecedented insights into how genetic variation influences metabolism and complex disease. Here we report the most comprehensive exploration of genetic loci influencing human metabolism thus far, comprising 7,824 adult individuals from 2 European population studies. We report genome-wide significant associations at 145 metabolic loci and their biochemical connectivity with more than 400 metabolites in human blood. We extensively characterize the resulting in vivo blueprint of metabolism in human blood by integrating it with information on gene expression, heritability and overlap with known loci for complex disorders, inborn errors of metabolism and pharmacological targets. We further developed a database and web-based resources for data mining and results visualization. Our findings provide new insights into the role of inherited variation in blood metabolic diversity and identify potential new opportunities for drug development and for understanding disease.

Sparse component analysis and blind source separation of underdetermined mixtures

In this letter, we solve the problem of identifying matrices S /spl isin/ /spl Ropf//sup n/spl times/N/ and A /spl isin/ /spl Ropf//sup m/spl times/n/ knowing only their multiplication X = AS, under some conditions, expressed either in terms of A and sparsity of S (identifiability conditions), or in terms of X (sparse component analysis (SCA) conditions). We present algorithms for such identification and illustrate them by examples.

MicroRNA Loss Enhances Learning and Memory in Mice

Dicer-dependent noncoding RNAs, including microRNAs (miRNAs), play an important role in a modulation of translation of mRNA transcripts necessary for differentiation in many cell types. In vivo experiments using cell type-specific Dicer1 gene inactivation in neurons showed its essential role for neuronal development and survival. However, little is known about the consequences of a loss of miRNAs in adult, fully differentiated neurons. To address this question, we used an inducible variant of the Cre recombinase (tamoxifen-inducible CreERT2) under control of Camk2a gene regulatory elements. After induction of Dicer1 gene deletion in adult mouse forebrain, we observed a progressive loss of a whole set of brain-specific miRNAs. Animals were tested in a battery of both aversively and appetitively motivated cognitive tasks, such as Morris water maze, IntelliCage system, or trace fear conditioning. Compatible with rather long half-life of miRNAs in hippocampal neurons, we observed an enhancement of memory strength of mutant mice 12 weeks after the Dicer1 gene mutation, before the onset of neurodegenerative process. In acute brain slices, immediately after high-frequency stimulation of the Schaffer collaterals, the efficacy at CA3-to-CA1 synapses was higher in mutant than in control mice, whereas long-term potentiation was comparable between genotypes. This phenotype was reflected at the subcellular and molecular level by the elongated filopodia-like shaped dendritic spines and an increased translation of synaptic plasticity-related proteins, such as BDNF and MMP-9 in mutant animals. The presented work shows miRNAs as key players in the learning and memory process of mammals.

The dynamic range of the human metabolome revealed by challenges

Metabolic challenge protocols, such as the oral glucose tolerance test, can uncover early alterations in metabolism preceding chronic diseases. Nevertheless, most metabolomics data accessible today reflect the fasting state. To analyze the dynamics of the human metabolome in response to environmental stimuli, we submitted 15 young healthy male volunteers to a highly controlled 4 d challenge protocol, including 36 h fasting, oral glucose and lipid tests, liquid test meals, physical exercise, and cold stress. Blood, urine, exhaled air, and breath condensate samples were analyzed on up to 56 time points by MS‐and NMR‐based methods, yielding 275 metabolic traits with a focus on lipids and amino acids. Here, we show that physiological challenges increased interindividual variation even in phenotypically similar volunteers, revealing metabotypes not observable in baseline metabolite profiles; volunteer‐specific metabolite concentrations were consistently reflected in various biofluids; and readouts from a systematic model of β‐oxidation (e.g., acetylcarnitine/palmitylcarnitine ratio) showed significant and stronger associations with physiological parameters (e.g., fat mass) than absolute metabolite concentrations, indicating that systematic models may aid in understanding individual challenge responses. Due to the multitude of analytical methods, challenges and sample types, our freely available metabolomics data set provides a unique reference for future metabolomics studies and for verification of systems biology models.—Krug, S., Kastenmüller, G., Stückler, F., Rist, M. J., Skurk, T., Sailer, M., Raffler, J., Römisch‐Margl, W., Adamski, J., Prehn, C., Frank, T., Engel, K‐H., Hofmann, T., Luy, B., Zimmermann, R., Moritz, F., Schmitt‐Kopplin, P., Krumsiek, J., Kremer, W., Huber, F., Oeh, U., Theis, F. J., Szymczak, W., Hauner, H., Suhre, K., Daniel, H. The dynamic range of the human metabolome revealed by challenges. FASEB J. 26, 2607‐2619 (2012). www.fasebj.org

Discovery of Sexual Dimorphisms in Metabolic and Genetic Biomarkers

Metabolomic profiling and the integration of whole-genome genetic association data has proven to be a powerful tool to comprehensively explore gene regulatory networks and to investigate the effects of genetic variation at the molecular level. Serum metabolite concentrations allow a direct readout of biological processes, and association of specific metabolomic signatures with complex diseases such as Alzheimers disease and cardiovascular and metabolic disorders has been shown. There are well-known correlations between sex and the incidence, prevalence, age of onset, symptoms, and severity of a disease, as well as the reaction to drugs. However, most of the studies published so far did not consider the role of sexual dimorphism and did not analyse their data stratified by gender. This study investigated sex-specific differences of serum metabolite concentrations and their underlying genetic determination. For discovery and replication we used more than 3,300 independent individuals from KORA F3 and F4 with metabolite measurements of 131 metabolites, including amino acids, phosphatidylcholines, sphingomyelins, acylcarnitines, and C6-sugars. A linear regression approach revealed significant concentration differences between males and females for 102 out of 131 metabolites (p-values<3.8×10−4; Bonferroni-corrected threshold). Sex-specific genome-wide association studies (GWAS) showed genome-wide significant differences in beta-estimates for SNPs in the CPS1 locus (carbamoyl-phosphate synthase 1, significance level: p<3.8×10−10; Bonferroni-corrected threshold) for glycine. We showed that the metabolite profiles of males and females are significantly different and, furthermore, that specific genetic variants in metabolism-related genes depict sexual dimorphism. Our study provides new important insights into sex-specific differences of cell regulatory processes and underscores that studies should consider sex-specific effects in design and interpretation.

Live imaging of astrocyte responses to acute injury reveals selective juxtavascular proliferation

Astrocytes are thought to have important roles after brain injury, but their behavior has largely been inferred from postmortem analysis. To examine the mechanisms that recruit astrocytes to sites of injury, we used in vivo two-photon laser-scanning microscopy to follow the response of GFP-labeled astrocytes in the adult mouse cerebral cortex over several weeks after acute injury. Live imaging revealed a marked heterogeneity in the reaction of individual astrocytes, with one subset retaining their initial morphology, another directing their processes toward the lesion, and a distinct subset located at juxtavascular sites proliferating. Although no astrocytes actively migrated toward the injury site, selective proliferation of juxtavascular astrocytes was observed after the introduction of a lesion and was still the case, even though the extent was reduced, after astrocyte-specific deletion of the RhoGTPase Cdc42. Thus, astrocyte recruitment after injury relies solely on proliferation in a specific niche.

Characterization of transcriptional networks in blood stem and progenitor cells using high-throughput single-cell gene expression analysis

Cellular decision-making is mediated by a complex interplay of external stimuli with the intracellular environment, in particular transcription factor regulatory networks. Here we have determined the expression of a network of 18 key haematopoietic transcription factors in 597 single primary blood stem and progenitor cells isolated from mouse bone marrow. We demonstrate that different stem/progenitor populations are characterized by distinctive transcription factor expression states, and through comprehensive bioinformatic analysis reveal positively and negatively correlated transcription factor pairings, including previously unrecognized relationships between Gata2, Gfi1 and Gfi1b. Validation using transcriptional and transgenic assays confirmed direct regulatory interactions consistent with a regulatory triad in immature blood stem cells, where Gata2 may function to modulate cross-inhibition between Gfi1 and Gfi1b. Single-cell expression profiling therefore identifies network states and allows reconstruction of network hierarchies involved in controlling stem cell fate choices, and provides a blueprint for studying both normal development and human disease.

Hypergraphs and cellular networks

3,41Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany, 2Institute for Mathematical Optimization, Faculty of Mathematics, Otto-von-Guericke University Magdeburg, Magdeburg, Germany, 3Institute for Bioinformatics and Systems Biology, Helmholtz Zentrum Mu¨nchen—German Research Center forEnvironmental Health, Neuherberg, Germany, 4Max Planck Institute for Dynamics and Self-Organization, Go¨ttingen, Germany

Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data

BackgroundWith the advent of high-throughput targeted metabolic profiling techniques, the question of how to interpret and analyze the resulting vast amount of data becomes more and more important. In this work we address the reconstruction of metabolic reactions from cross-sectional metabolomics data, that is without the requirement for time-resolved measurements or specific system perturbations. Previous studies in this area mainly focused on Pearson correlation coefficients, which however are generally incapable of distinguishing between direct and indirect metabolic interactions.ResultsIn our new approach we propose the application of a Gaussian graphical model (GGM), an undirected probabilistic graphical model estimating the conditional dependence between variables. GGMs are based on partial correlation coefficients, that is pairwise Pearson correlation coefficients conditioned against the correlation with all other metabolites. We first demonstrate the general validity of the method and its advantages over regular correlation networks with computer-simulated reaction systems. Then we estimate a GGM on data from a large human population cohort, covering 1020 fasting blood serum samples with 151 quantified metabolites. The GGM is much sparser than the correlation network, shows a modular structure with respect to metabolite classes, and is stable to the choice of samples in the data set. On the example of human fatty acid metabolism, we demonstrate for the first time that high partial correlation coefficients generally correspond to known metabolic reactions. This feature is evaluated both manually by investigating specific pairs of high-scoring metabolites, and then systematically on a literature-curated model of fatty acid synthesis and degradation. Our method detects many known reactions along with possibly novel pathway interactions, representing candidates for further experimental examination.ConclusionsIn summary, we demonstrate strong signatures of intracellular pathways in blood serum data, and provide a valuable tool for the unbiased reconstruction of metabolic reactions from large-scale metabolomics data sets.