Biology subjects

Brombacher, E.

Publications and source records attributed to Brombacher, E..

5 recordsLinked to original sources

bioRxiv2024

RTF: An R package for modelling time course data

SummaryThe retarded transient function (RTF) approach has been introduced as a complementary approach to employing ordinary differential equations (ODEs) for modelling dynamics typically observed for cellular signalling processes. Here, we introduce the R package to the RTF approach, which has originally been implemented within the MATLAB-based Data2Dynamics modelling framework. The package enables modelling of time and dose dependencies and includes model reduction to minimize overfitting. It can be applied not only to experimental data but also to trajectories of ODE models, to characterize the dynamics or to identify major targets of experimental perturbations in the low-dimensional representations calculated by the package. Availability and ImplementationThe R package RTF is available at https://github.com/kreutz-lab/RTF. Contactclemens.kreutz@uniklinik-freiburg.de

bioinformatics↗

bioRxiv2024

Dynamic modelling of signalling pathways when ODEsare not feasible

MotivationMathematical modelling plays a crucial role in understanding inter- and intracellular signalling processes. Currently, ordinary differential equations (ODEs) are the predominant approach in systems biology for modelling such pathways. While ODE models offer mechanistic interpretability, they also suffer from limitations, including the need to consider all relevant compounds, resulting in large models difficult to handle numerically and requiring extensive data. ResultsIn previous work, we introduced the retarded transient function (RTF) as an alternative method for modelling temporal responses of signalling pathways. Here, we extend the RTF approach to integrate concentration or dose-dependencies into the modelling of dynamics. With this advancement, RTF modelling now fully encompasses the application range of ordinary differential equation (ODE) models, which comprises predictions in both time and concentration domains. Moreover, characterizing dose-dependencies provides an intuitive way to investigate and characterize signalling differences between biological conditions or cell-types based on their response to stimulating inputs. To demonstrate the applicability of our extended approach, we employ data from time- and dose-dependent inflammasome activation in bone-marrow derived macrophages (BMDMs) treated with nigericin sodium salt. Our results show the effectiveness of the extended RTF approach as a generic framework for modelling dose-dependent kinetics in cellular signalling. The approach results in intuitively interpretable parameters that describe signal dynamics and enables predictive modelling of time- and dose-dependencies even if only individual cellular components are quantified. AvailabilityThe presented approach is available within the MATLAB-based Data2Dynamics modelling toolbox at https://github.com/Data2Dynamics and https://zenodo.org/records/14008247 and as R code at https://github.com/kreutz-lab/RTF. Contactclemens.kreutz@uniklinik-freiburg.de

systems biology↗

bioRxiv2023

Metabolic sensor AMPK licenses CD103+ dendritic cells to induce Treg responses

Dendritic cells (DCs) play a crucial role in promoting tolerance through priming of regulatory T cells (Treg). Several studies indicate DC tolerogenicity is dependent on catabolic metabolism. However, the role of AMP-activated Kinase (AMPK), a key energy and nutrient sensor driving catabolic metabolism, in this process is unclear. We found that human retinoic acid-induced tolerogenic CD103+ DCs (RA-DCs) display increased AMPK signaling. Interestingly, RA-DCs, but not vitamin-D3- or dexamethasone-induced tolerogenic DCs, required AMPK for Treg induction. Mechanistically, AMPK underpinned RA-driven tolerogenicity by promoting RALDH activity in a FoxO3-dependent manner. Correspondingly, mice deficient for AMPK in DCs (CD11c{Delta}AMPK1) harbored reduced frequencies of intestinal CD103+CD11b+ DCs with impaired RALDH activity. Importantly, upon infection with parasitic worm Schistosoma mansoni, that elicits strong Th2 and Treg responses, CD11c{Delta}AMPK1 mice showed a defect in Treg accumulation and concomitantly, displayed an impaired ability to control Type 2 immunity-driven granulomatous inflammation against the parasite eggs. Together, our findings identify AMPK as a key regulator of tolerance by CD103+ DCs. SummaryDendritic cells (DCs) are critical for inducing tolerance. However, how metabolic cues control their tolerogenicity is still poorly understood. Patente et al demonstrate that AMPK is crucial for Treg induction by retinoic acid-primed tolerogenic CD103+ DCs.

immunology↗

bioRxiv2022

The performance of deep generative models for learning joint embeddings of single-cell multi-omics data

Recent extensions of single-cell studies to multiple data modalities raise new questions regarding experimental design. For example, the challenge of sparsity in single-omics data might be partly resolved by compensating for missing information across modalities. In particular, deep learning approaches, such as deep generative models (DGMs), can potentially uncover complex patterns via a joint embedding. Yet, this also raises the question of sample size requirements for identifying such patterns from single-cell multi-omics data. Here, we empirically examine the quality of DGM-based integrations for varying sample sizes. We first review the existing literature and give a short overview of deep learning methods for multi-omics integration. Next, we consider eight popular tools in more detail and examine their robustness to different cell numbers, covering two of the most common multi-omics types currently favored. Specifically, we use data featuring simultaneous gene expression measurements at the RNA level and protein abundance measurements for cell surface proteins (CITE-seq), as well as data where chromatin accessibility and RNA expression are measured in thousands of cells (10x Multiome). We examine the ability of the methods to learn joint embeddings based on biological and technical metrics. Finally, we provide recommendations for the design of multi-omics experiments and discuss potential future developments.

bioinformatics↗

bioRxiv2020

Tail-Robust Quantile Normalization

High-throughput biological data - such as mass spectrometry-based proteomics data - suffer from systematic non-biological variance, which is introduced by systematic errors such as batch effects. This hinders the estimation of real biological signals and, thus, decreases the power of statistical tests and biases the identification of differentially expressed sample classes. To remove such unintended variation, while retaining the biological signal of interest, the analysis workflows for mass spectrometry-based quantification typically comprises normalization steps prior to the statistical analysis of the data. Several normalization methods, such as quantile normalization, have originally been developed for microarray data. However, unlike microarray data, proteomics data may contain features, in the form of protein intensities, that are consistently highly abundant across experimental conditions and, hence, are encountered in the tails of the protein intensity distribution. If such proteins are present, statistical inferences of the intensity profiles of the normalized features are impeded through the increased number of false positive findings due to the biased estimation of the variance of the data. Thus, we developed a, freely available, novel approach: tail-robust quantile normalization. It extends the traditional quantile normalization to preserve the biological signals of features in the tails of the distribution over experimental conditions and to account for sample-dependent missing values.

bioinformatics↗