Biology subjects

Theis, F.

Publications and source records attributed to Theis, F..

28 records · Page 2Linked to original sources

bioRxiv2021

Jointly learning T-cell receptor and transcriptomic information to decipher the immune response

Recent advancements in single-cell immune profiling that enable the measurement of the transcriptome and T-cell receptor (TCR) sequences simultaneously have emerged as a promising approach to study immune responses at cellular resolution. Yet, combining these different types of information from multiple datasets into a joint representation is complicated by the unique characteristics of each modality and the technical effects between datasets. Here, we present mvTCR, a multimodal generative model to learn a unified representation across modalities and datasets for joint analysis of single-cell immune profiling data. We show that mvTCR allows the construction of large-scale and multimodal T-cell atlases by distilling modality-specific properties into a shared view, enabling unique and improved data analysis. Specifically, we demonstrated mvTCRs potential by revealing and separating SARS-CoV-2-specific T-cell clusters from bystanders that would have been missed in individual unimodal data analysis. Finally, mvTCR can enable automated analysis of new datasets when combined with transfer-learning approaches. Overall, mvTCR provides a principled solution for standard analysis tasks such as multimodal integration, clustering, specificity analysis, and batch correction for single-cell immune profiling data.

bioinformatics↗

bioRxiv2021

Cellular drivers of injury response and regeneration in the adult zebrafish heart

Myocardial infarction is a leading cause of death worldwide, as the adult human heart does not have the ability to regenerate efficiently after insults. In contrast, the adult zebrafish heart has a high capacity for regeneration, and understanding the mechanisms of regenerative processes in fish allows identification of novel therapeutic strategies. While several pro-regenerative factors have been described, the cell types orchestrating heart regeneration remain largely elusive. To overcome this conceptual limitation, we dissected cell type diversity in the regenerating zebrafish heart based on single cell transcriptomics and spatiotemporal analysis. We discovered a dramatic induction of several pro-regenerative cell types with fibroblast characteristics. To understand the cascade of events leading to heart regeneration, we determined the origin of these cell types by high-throughput lineage tracing. We found that pro-regenerative fibroblasts are derived from two separate sources, the epicardium and the endocardium. Mechanistically, we identified Wnt signaling as a key regulator of the endocardial regenerative response. In summary, our results uncover specialized fibroblast cell types as major drivers of heart regeneration, thereby opening up new possibilities to interfere with the regenerative capacity of the vertebrate heart.

systems biology↗

bioRxiv2018

Meeting the challenges of high-dimensional data analysis in immunology

Recent advances in cytometry have radically altered the fate of single-cell proteomics by allowing a more accurate understanding of complex biological systems. Mass cytometry (CyTOF) provides simultaneous single-cell measurements that are crucial to understand cellular heterogeneity and identify novel cellular subsets. High-dimensional CyTOF data were traditionally analyzed by gating on bivariate dot plots, which are not only laborious given the quadratic increase of complexity with dimension but are also biased through manual gating. This review aims to discuss the impact of new analysis techniques for in-depths insights into the dynamics of immune regulation obtained from static snapshot data and to provide tools to immunologists to address the high dimensionality of their single-cell data.

immunology↗

bioRxiv2018

MetaMap, an interactive webtool for the exploration of metatranscriptomic reads in human disease-related RNA-seq data

MotivationThe MetaMap resource contains metatranscriptomic expression data from screening >17,000 RNA-seq samples from >400 archived human disease-related studies for viral and microbial reads, so-called \"metafeatures\". However, navigating this set of large and heterogeneous data is challenging, especially for researchers without bioinformatic expertise. Therefore, a user-friendly interface is needed that allows users to visualize and statistically analyse the data.\n\nResultsWe developed an interactive frontend to facilitate the exploration of the MetaMap resource. The webtool allows users to query the resource by searching study abstracts for keywords or browsing expression patterns for specific metafeatures. Moreover, users can manually define sample groupings or use the existing annotation for downstream analysis. The web tool provides a large variety of analyses and visualizations including dimension reduction, differential abundance analysis and Krona visualizations. The MetaMap webtool represents a valuable resource for hypothesis generation regarding the impact of the microbiome in human disease.\n\nAvailabilityThe presented web tool can be accessed at https://github.com/theislab/MetaMap

bioinformatics↗

bioRxiv2018

pheno-seq - linking 3D phenotypes of clonal tumor spheroids to gene expression

3D-culture systems have advanced cancer modeling by reflecting physiological characteristics of in-vivo tissues, but our understanding of functional intratumor heterogeneity including visual phenotypes and underlying gene expression is still limited. Single-cell RNA-sequencing is the method of choice to dissect transcriptional tumor cell heterogeneity in an unbiased way, but this approach is limited in correlating gene expression with contextual cellular phenotypes.\n\nTo link morphological features and gene expression in 3D-culture systems, we present pheno-seq for integrated high-throughput imaging and transcriptomic profiling of clonal tumor spheroids. Specifically, we identify characteristic EMT expression signatures that are associated with invasive growth behavior in a 3D breast cancer model. Additionally, pheno-seq determined transcriptional programs containing lineage-specific markers that can be linked to heterogeneous proliferative capacity in a patient-derived 3D model of colorectal cancer. Finally, we provide evidence that pheno-seq identifies morphology-specific genes that are missed by scRNA-seq and inferred single-cell regulatory states without acquiring additional single cell expression profiles. We anticipate that directly linking molecular features with patho-phenotypes of cancer cells will improve the understanding of intratumor heterogeneity and consequently be useful for translational research.

genomics↗

bioRxiv2018

Multi-Experiment Nonlinear Mixed Effect Modeling of Single-Cell Translation Kinetics after Transfection

Single-cell time-lapse studies have advanced the quantitative understanding of cell-to-cell variability. However, as the information content of individual experiments is limited, methods to integrate data collected under different conditions are required.\n\nHere we present a multi-experiment nonlinear mixed effect modeling approach for mechanistic pathway models, which allows the integration of multiple single-cell perturbation experiments. We apply this approach to the translation of green fluorescent protein after transfection using a massively parallel read-out of micropatterned single-cell arrays. We demonstrate that the integration of data from perturbation experiments allows the robust reconstruction of cell-to-cell variability, i.e., parameter densities, while each individual experiment provides insufficient information. Indeed, we show that the integration of the datasets on the population level also improves the estimates for individual cells by breaking symmetries, although each of them is only measured in one experiment. Moreover, we confirmed that the suggested approach is robust with respect to batch effects across experimental replicates and can provide mechanistic insights into the nature of batch effects. We anticipate that the proposed multi-experiment nonlinear mixed effect modeling approach will serve as a basis for the analysis of cellular heterogeneity in single-cell dynamics.

systems biology↗

bioRxiv2018

FASTGenomics: An analytical ecosystem for single-cell RNA sequencing data

Recent technological advances enable genomics of individual cells, the building blocks of all living organisms. Single cell data characteristics differ from those of bulk data, which led to a plethora of new analytical strategies. However, solutions are only useful for experts and currently, there are no widely accepted gold standards for single cell data analysis. To meet the requirements of analytical flexibility, ease of use and data security, we developed FASTGenomics (https://fastgenomics.org) as a powerful, efficient, versatile, robust, safe and intuitive analytical ecosystem for single-cell transcriptomics.

bioinformatics↗

bioRxiv2017

Subtle Changes in Clonal Dynamics Underlie the Age-Related Decline in Neurogenesis

Neural stem cells in the adult murine brain have only a limited capacity to self-renew, and the number of neurons they generate drastically declines with age. How cellular dynamics sustain neurogenesis and how alterations with age may result in this decline, are both unresolved issues. Therefore, we clonally traced neural stem cell lineages using confetti reporters in young and old adult mice. To understand underlying mechanisms, we derived mathematical population models of adult neurogenesis that explain the observed clonal cell type abundances. Models fitting the data best consistently show self renewal of transit amplifying progenitors and rapid neuroblast cell cycle exit. Most importantly, we identified an increase of asymmetric stem cell divisions at the expense of symmetric stem cell differentiation with age. Beyond explaining existing longitudinal population data, our model identifies a particular cellular strategy underlying adult neural stem cell homeostasis that gives insights into the aging of a stem cell compartment.

neuroscience↗

bioRxiv2017

Efficient parameterization of large-scale mechanistic models enables drug response prediction for cancer cell lines

The response of cancer cells to drugs is determined by various factors, including the cells mutations and gene expression levels. These factors can be assessed using next-generation sequencing. Their integration with vast prior knowledge on signaling pathways is, however, limited by the availability of mathematical models and scalable computational methods. Here, we present a computational framework for the parameterization of large-scale mechanistic models and its application to the prediction of drug response of cancer cell lines from exome and transcriptome sequencing data. With this framework, we parameterized a mechanistic model describing major cancer-associated signaling pathways (>1200 species and >2600 reactions) using drug response data. For the parameterized mechanistic model, we found a prediction accuracy, which exceeds that of the considered statistical approaches. Our results demonstrate for the first time the massive integration of heterogeneous datasets using large-scale mechanistic models, and how these models facilitate individualized predictions of drug response. We anticipate our parameterized model to be a starting point for the development of more comprehensive, curated models of signaling pathways, accounting for additional pathways and drugs.

systems biology↗

bioRxiv2017

Common patterns of gene regulation associated with Cesarean section and the development of islet autoimmunity -- indications of immune cell activation

BackgroundBirth by Cesarean section increases the risk of developing type 1 diabetes later in life; however, the underlying molecular mechanisms of this effect remain unclear. We aimed to elucidate common regulatory processes observed after Cesarean section and the development of islet autoimmunity, which precedes type 1 diabetes, by investigating the transcriptome of blood cells in the developing immune system.\n\nMethodsWe analyzed gene expression of peripheral blood mononuclear cells taken at several time points from children with increased familial and genetic risk for type 1 diabetes (n = 109). We investigated effects of Cesarean section on gene expression profiles of children in the first year of life using a generalized additive mixed model to account for the longitudinal data structure. To investigate the effect of islet autoimmunity, we compared gene expression differences between children after initiation of islet autoimmunity and age-matched children who did not develop islet autoantibodies. Finally, we compared both results to identify common regulatory patterns of Cesarean section and islet autoimmunity at the gene expression level.\n\nResultsWe identified two differentially expressed pathways in children born by Cesarean section: the pentose phosphate pathway and pyrimidine metabolism, both involved in nucleotide synthesis and cell proliferation. Islet autoantibody analysis revealed multiple differentially expressed pathways generally involved in immune processes, including both of the above-mentioned nucleotide synthesis pathways. Comparison of global gene expression signatures showed that transcriptomic changes were systematically and significantly correlated between Cesarean section and islet autoimmunity. In addition, signatures of both Cesarean section and islet autoimmunity correlated with transcriptional changes observed during activation of isolated CD4+ T lymphocytes.\n\nConclusionsWe identified coherent gene expression signatures for Cesarean section, an early risk factor for type 1 diabetes, and islet autoantibodies positivity, an obligatory stage of autoimmune response prior to the development of type 1 diabetes. Both transcriptional signatures were correlated with changes in gene expression during the activation of CD4+ T lymphocytes, reflecting common molecular changes in immune cell activation.

genomics↗