Biology subjects

Hasenauer, J.

Publications and source records attributed to Hasenauer, J..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Pseudo-time trajectory of single-cell lipidomics: Suggestion for experimental setup and computational analysis

Cellular heterogeneity is a fundamental facet of cell biology, influencing cellular signaling, metabolism, and gene regulation. Its accurate quantification requires measurements at the single-cell level. Most high-throughput single-cell technologies provide only a snapshot of cellular heterogeneity at a specific time point because the measurement is destructive. This limits our current ability to understand the dynamics of cellular behavior and quantify cell-specific parameters. We propose an experimental setup combined with a model-based analysis framework, enabling the extraction of longitudinal data from a single destructive measurement. Although broadly applicable, we focus on lipid metabolism, a domain where obtaining longitudinal single-cell data has remained elusive due to technical constraints. Our method leverages multiple labels whose measurements are linked to a shared dynamic. This allows the estimation of cell-specific parameters and the quantification of heterogeneity. This framework establishes a foundation for future investigations, providing a roadmap toward a deeper understanding of dynamic cellular processes.

systems biology↗

bioRxiv2025

NKG7 is a stable marker of cytotoxicity across immune contexts and within the tumor microenvironment

Cytotoxicity is a cornerstone of immune defense, critical for combating tumors and infections. This process relies on the coordinated action of granzymes and pore-forming proteins, with Granzyme B (GZMB) and Perforin (PRF1) being key markers and the most widely studied molecules pertaining to cytotoxicity. However, other human granzymes and cytotoxic components remain underexplored, despite growing evidence of their distinct, context-dependent roles. Natural Killer Cell Granule Protein 7 (NKG7) has recently emerged as a crucial cytotoxicity regulator, yet its expression patterns and function are poorly understood. Using large publicly available single-cell RNA sequencing atlases, we performed a comprehensive profiling of cytotoxicity across immune subsets and tissues. Our analysis highlights NKG7 expression as a strong marker of cytotoxicity, exhibiting a strong correlation with overall cytotoxic activity (r = 0.97) and surpassing traditional markers such as Granzyme B and Perforin in reliability. Furthermore, NKG7 expression is notably consistent across diverse immune subsets and tissues, reinforcing its versatility and robustness as a cytotoxicity marker. These findings position NKG7 as an invaluable tool for evaluating immune responses and a reliable indicator of cytotoxic functionality across biological and clinical contexts.

immunology↗

bioRxiv2024

Universal differential equations for systems biology: Current state and open problems

Universal Differential Equations (UDEs) combine mechanistic differential equations with data-driven artificial neural networks, forming a flexible framework for modelling complex biological systems. This hybrid approach leverages prior knowledge and data to uncover unknown processes and deliver accurate predictions. However, UDEs face challenges in efficient and reliable training due to stiff dynamics and noisy, sparse data common in biology, and in ensuring the interpretability of the parameters of the mechanistic model. We investigate these challenges and evaluate UDE performance on realistic biological scenarios, providing a systematic training pipeline. Our results demonstrate the versatility of UDEs in systems biology and reveal that noise and limited data significantly degrade performance, but regularisation can improve accuracy and interpretability. By addressing key challenges and offering practical solutions, this work advances UDE methodology and underscores its potential in tackling complex problems in systems biology.

systems biology↗

bioRxiv2024

cOmicsArt - a customizable Omics Analysis and reporting tool

MotivationThe availability of bulk-omic data is steadily increasing, necessitating collaborative efforts between experimental and computational researchers. While software tools with graphical user interfaces (GUIs) enable rapid and interactive data assessment, they are limited to pre-implemented methods, often requiring transitions to custom code for further adjustments. However, most available tools lack GUI-independent reproducibility such as direct integration with R, resulting in very limited support for transition. ResultsWe introduce the customizable Omics Analysis and reporting tool - cOmicsArt. cOmicsArt aims to enhance collaboration through integration of GUI-based analysis with R. The GUI allows researchers to perform user-friendly exploratory and statistical analyses with interactive visualizations and automatic documentation. Downloadable R scripts and results ensure reproducibility and seamless integration with R, supporting both novice and experienced programmers by enabling easy customizations and serving as a foundation for more advanced analyses. This versatility also allows for usage in educational settings guiding students from GUI-based analysis to R Code. AvailabilitycOmicsArt is freely available at https://shiny.iaas.uni-bonn.de/cOmicsArt/ User documentation is available at https://icb-dcm.github.io/cOmicsArt/ Source code is available on GitHub https://github.com/ICB-DCM/cOmicsArt A docker image can be retrieved from https://hub.docker.com/r/pauljonasjost/comicsart/tags A snapshot upon publication can be found on Zenodo: https://zenodo.org/records/13740904 A screen recording of cOmicsArt is available at: https://www.youtube.com/watch?v=pTGjtIYQOakp Contactjan.hasenauer@uni-bonn.de Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2024

Combined Stochastic Models for the Evaluation of Cancer Progression and Patient Trajectories

Cancer is a major burden of disease around the globe and one of the leading causes of premature death. The key to improve patient outcomes in modern clinical cancer research is to gain insights into dynamics underlying cancer evolution in order to facilitate the search for effective therapies. However, most cancer data analysis tools are designed for controlled trials and cannot leverage routine clinical data, which are available in far greater quantities. In addition, many cancer models focus on single disease processes in isolation, disregarding interaction This work proposes a unified stochastic modelling framework for cancer progression that combines (stochastic) processes for tumour growth, metastatic seeding, and patient survival to provide a comprehensive understanding of cancer progression. In addition, our models aim to use non-equidistantly sampled data collected in clinical routine to analyse the whole patient trajectory over the course of the disease. The model formulation features closed-form expressions of the likelihood functions for parameter inference from clinical data. The efficacy of our model approach is demonstrated through a simulation study involving four exemplary models, which utilise both analytic and numerical likelihoods. The results of the simulation studies demonstrate the accuracy and computational efficiency of the analytic likelihood formulations. We found that estimation can retrieve the correct model parameters and reveal the underlying data dynamics, and that this modelling framework is flexible in choosing the precise parameterisation. This work can serve as a foundation for the development of combined stochastic models for guiding personalized therapies in oncology.

systems biology↗

bioRxiv2024

C-COMPASS: A Neural Network Tool for Multi-Omic Classification of Cell Compartments

Functional compartmentalization in eukaryotic cells is essential for maintaining physiological processes. The development of systematic organelle proteomic techniques, such as Protein Correlation Profiling (PCP) and Localization of Organelle Proteins by Isotope Tagging (LOPIT), has enhanced our understanding of organelle dynamics and protein localization. However, the complexity of the data and the need for advanced computational skills limit their accessibility. To address this, we introduce C-COMPASS, an open-source software featuring a user-friendly interface that utilizes a neural network-based regression model to predict the spatial distribution of proteins across cellular compartments. C-COMPASS manages complex multilocalization patterns and integrates protein abundance information to model organelle composition changes under various biological conditions. Using C-COMPASS, we mapped the organelle proteomic landscapes of humanized liver mice in different metabolic states and modeled changes in organelle composition to provide insights into cellular adaptations. Additionally, we extended cellular maps to the lipid level by co-generating protein and lipid profiles. C-COMPASS trains neural networks with marker protein profiles to predict lipid localizations, enabling parallel mapping of lipid and protein localization. This approach overcomes the lack of knowledge of organelle-specific lipid markers and identifies previously unknown organelle-specific lipid species. C-COMPASS offers a comprehensive solution for studying organelle dynamics at the multi-omics level, designed to be accessible without requiring extensive computational expertise or specialized high-performance computing equipment.

cell biology↗

bioRxiv2024

N-Cadherin mediated cell rearrangements shape embryonic macrophage cluster

Drosophila embryonic macrophages are highly motile phagocytic and secretory cells which are essential for embryonic development. Embryonic migration and dispersal of these cells along pre-determined routes is invariably tied to their functions such that misrouting or delay in macrophage migration has serious consequences for embryogenesis and adult homeostasis. In this study, we describe early steps of macrophage migration from their site of origin in the head mesoderm to the germband and show that hemocytes start moving as an epithelial-like cluster with N-Cadherin based Adherens junctions. The cells within the cluster move in a co-ordinated manner and exhibit cell rearrangements mediated through junction shrinkage. We found that N-Cadherin is dynamically relocalized during cluster extension and modulating N-Cadherin levels results in hemocyte dispersal away from their main axis of migration which affects migration into the germband. We therefore elucidate a novel multi-tiered mechanism which ensures that macrophages are positioned appropriately to regulate their distribution in the embryo.

developmental biology↗

bioRxiv2024

Anabolic lipid metabolism regulates adipose type 2 innate lymphoid cell differentiation to maintain metabolic health

Group 2 innate lymphoid cells (ILC2) residing in the adipose tissue play an important role in maintaining the metabolic health and energy balance of the organisms. In obesity ILC2 numbers are reduced and their function is impaired, leading to the progression of metabolic inflammation. However, which events impact on ILC2 biology in the adipose tissue in obesity remains unresolved. Here, we find that high fat diet (HFD)-induced obesity in mice results in the metabolic reprogramming of adipose ILC2, impairing mitochondrial function and the expression of the enzyme Acetyl-CoA carboxylase 1 (ACC1). Investigating a possible connection between ACC1 and obesity-induced changes in ILC2, we show that fatty acids directly reduce the expression of ACC1, while pharmacological inhibition of ACC1 diminishes mitochondrial function and ILC2 metabolism. Furthermore, deletion of ACC1 in ILC2 phenocopies the overall reduction and functional impairment of ILC2 observed in obesity, which ultimately leads to increased triglycerides in circulation, adipose tissue hypertrophy and inflammation, even in the absence of HFD. Through single-cell RNA sequencing analysis we uncover that HFD-feeding or deletion of ACC1 results in the accumulation of undifferentiated ILC2 and ILC progenitors in the adipose tissue, suggesting that ACC1 may primarily regulate the maturation of ILC2. Together, these results reveal that obesity could predominately impair adipose ILC2 differentiation and activation by impacting on the expression of ACC1, rather than inducing cell death through lipid overload and lipotoxicity.

immunology↗

bioRxiv2024

Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data

Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. The parameters of these models are commonly estimated from experimental data. Yet, experimental data generated from different techniques do not provide direct information about the state of the system but a non-linear (monotonic) transformation of it. For such semi-quantitative data, when this transformation is unknown, it is not apparent how the model simulations and the experimental data can be compared. Here, we propose a versatile spline-based approach for the integration of a broad spectrum of semi-quantitative data into parameter estimation. We derive analytical formulas for the gradients of the hierarchical objective function and show that this substantially increases the estimation efficiency. Subsequently, we demonstrate that the method allows for the reliable discovery of unknown measurement transformations. Furthermore, we show that this approach can significantly improve the parameter inference based on semi-quantitative data in comparison to available methods. Modelers can easily apply our method by using our implementation in the open-source Python Parameter EStimation TOolbox (pyPESTO).

systems biology↗

bioRxiv2024

From Planning Stage To FAIR Data: A Practical Metadatasheet For Biomedical Scientists

Datasets consist of measurement data and metadata. Metadata provides context, essential for understanding and (re-)using data. Various metadata standards exist for different methods, systems and contexts. However, relevant information resides at differing stages across the data-lifecycle. Often, this information is defined and standardized only at publication stage, which can lead to data loss and workload increase. In this study, we developed Metadatasheet, a metadata standard based on interviews with members of two biomedical consortia and systematic screening of data repositories. It aligns with the data-lifecycle allowing synchronous metadata recording within Microsoft Excel, a widespread data recording software. Additionally, we provide an implementation, the Metadata Workbook, that offers user-friendly features like automation, dynamic adaption, metadata integrity checks, and export options for various metadata standards. By design and due to its extensive documentation, the proposed metadata standard simplifies recording and structuring of metadata for biomedical scientists, promoting practicality and convenience in data management. This framework can accelerate scientific progress by enhancing collaboration and knowledge transfer throughout the intermediate steps of data creation.

bioinformatics↗

bioRxiv2023

Early-stage idiopathic pulmonary fibrosis is characterized by bronchoalveolar accumulation of SPP1+ macrophages

Patients affected by idiopathic pulmonary fibrosis (IPF), a progressive chronic and eventually fatal lung disease with unknown cause, suffer from delayed diagnosis and limited personalized treatment options due to the lack of predictive and staging relevant disease markers. Prior studies focused on the cellular and transcriptomic changes found in lung tissue during terminal IPF-associated lung fibrosis. In clinical routine, bronchoscopy is applied for diagnosis and staging of suspected IPF affected individuals, thus providing us with a clinically applicable window, to study the cellular and transcriptional changes within affected individuals at the time of diagnosis. Here we study a cohort consisting of 11 IPF affected individuals and 11 healthy controls. We investigate their single cell transcriptomic profile alongside the surface phenotype of alveolar-resident immune cells. Single cell transcriptional analysis reveals accumulation of SPP1+ macrophages (SPP1+M{varphi}) within the alveolar space during early stage diagnosed IPF, in the absence of a decline in lung function. SPP1+M{varphi} were characterized by high expression of lipid storage and handling genes, accumulation of intracellular cholesterol and expression of proinflammatory cytokine expression. In silico developmental trajectory analysis revealed that SPP1+M{varphi} are likely derived from classical monocytes. Finally, to confirm the clinical diagnostic value of SPP1+ M{varphi}, we developed a flow cytometry panel to rapidly identify and test for the presence of SPP1+ M{varphi} in bronchoalveolar lavage samples. The new panel has confirmed the increased frequency of SPP1+ M{varphi} in early-IPF in an independent validation cohort. Taken together, we show that SPP1+ M{varphi} are associated with early IPF pathogenesis in the absence of a decline in lung function, thus providing a clinically valuable markers for diagnosis of early IPF in clinical practice. HighlightsO_LIIPF patients can be stratified into early and advanced disease groups based on clinical features. C_LIO_LISingle cell transcriptomic and high dimensional flow cytometry enabled mapping of the myeloid compartment across the continuum of idiopathic pulmonary fibrosis associated disease states. C_LIO_LISPP1+M{varphi} accumulated during early clinical stage of IPF. C_LIO_LIDevelopment of a clinically applicable SPP1M{varphi} identification panel. C_LI

immunology↗

bioRxiv2023

Privacy-preserving impact evaluation using Differences-in-Differences

Difference-in-Differences (DID) is a widely used tool for causal impact evaluation but is constrained by data privacy regulations when applied to sensitive personal information, such as individual-level performance records or healthcare data, that must not be shared with data analysts. Obtaining consent can reduce sample sizes or exclude treated/untreated groups, diminishing statistical power or making estimation impossible. Federated Learning, which shares aggregated statistics to ensure privacy, can address these concerns, but advanced federated DID software packages remain scarce. We derived and developed a federated version of the Callaway and SantAnna DID, implemented within the DataSHIELD platform. Our package adheres to DataSHIELDs security measures and adds extra protections, enhancing data privacy and confidentiality. It reproduces point estimates, asymptotic standard errors, and bootstrapped standard errors equivalent to the non-federated implementation. We demonstrate this functionality on simulated data and real-world data from a malaria intervention in Mozambique. By leveraging federated estimates, we increase effective sample sizes leading to reduced estimation uncertainty, and enable estimation when single data owners cannot share the data but only have access to the treated or untreated group.

bioinformatics↗

bioRxiv2023

An amortized approach to non-linear mixed-effects modeling based on neural posterior estimation

Non-linear mixed-effects models are a powerful tool for studying heterogeneous populations in various fields, including biology, medicine, economics, and engineering. Here, the aim is to find a distribution over the parameters that describe the whole population using a model that can generate simulations for an individual of that population. However, fitting these distributions to data is computationally challenging if the description of individuals is complex and the population is large. To address this issue, we propose a novel machine learning-based approach: We exploit neural density estimation based on conditional normalizing flows to approximate individual-specific posterior distributions in an amortized fashion, thereby allowing for efficient inference of population parameters. Applying this approach to problems from cell biology and pharmacology, we demonstrate its unseen flexibility and scalability to large data sets compared to established methods.

systems biology↗

bioRxiv2023

A more expressive spline representation for SBML models improves code generation performance in AMICI

AbstractsSpline interpolants are commonly used for discretizing and estimating functions in mathematical models. While splines can be encoded in the Systems Biology Markup Language (SBML) using piecewise functions, the resulting formulas are very complex and difficult to derive by hand. Tools to create such formulas exist but only deal with numeric data and thus cannot be used for function estimation. Similarly, simulation tools suffer from several limitations when handling splines. For example, in the AMICI library splines with large numbers of nodes lead to long model import times. We have developed a set of SBML annotations to mark assignment rules as spline formulas. These compact representations are human-readable and easy to edit, in contrast to the piecewise representation. Different boundary conditions and extrapolation methods can also be specified. By extending AMICI to create and recognize these annotations, model import can be sped up significantly. This allows practitioners to increase the expressivity of their models. While the performance improvement is limited to AMICI, our tools for creating spline formulas can be used for other tools as well and our syntax for compact spline representation may be a starting point for an SBML-native way to represent spline interpolants.

bioinformatics↗

bioRxiv2023

FitMultiCell: Simulating and parameterizing computational models of multi-scale and multi-cellular processes

MotivationBiological tissues are dynamic and highly organized. Multi-scale models are helpful tools to analyze and understand the processes determining tissue dynamics. These models usually depend on parameters that need to be inferred from experimental data to achieve a quantitative understanding, to predict the response to perturbations, and to evaluate competing hypotheses. However, even advanced inference approaches such as Approximate Bayesian Computation (ABC) are difficult to apply due to the computational complexity of the simulation of multi-scale models. Thus, there is a need for a scalable pipeline for modeling, simulating, and parameterizing multi-scale models of multi-cellular processes. ResultsHere, we present FitMultiCell, a computationally efficient and user-friendly open-source pipeline that can handle the full workflow of modeling, simulating, and parameterizing for multi-scale models of multi-cellular processes. The pipeline is modular and integrates the modeling and simulation tool Morpheus and the statistical inference tool pyABC. The easy integration of high-performance infrastructure allows to scale to computationally expensive problems. The introduction of a novel standard for the formulation of parameter inference problems for multi-scale models additionally ensures reproducibility and reusability. By applying the pipeline to multiple biological problems, we demonstrate its broad applicability, which will benefit in particular image-based systems biology. AvailabilityFitMultiCell is available open-source at https://gitlab.com/fitmulticell/fit. Contactjan.hasenauer@uni-bonn.de Supplementary informationSupplementary data are available at https://doi.org/10.5281/zenodo.7646287 online.

systems biology↗

bioRxiv2023

Missing data in amortized simulation-based neural posterior estimation

Amortized simulation-based neural posterior estimation provides a novel machine learning based approach for solving parameter estimation problems. It has been shown to be computationally efficient and able to handle complex models and data sets. Yet, the available approach cannot handle the in experimental studies ubiquitous case of missing data, and might provide incorrect posterior estimates. In this work, we discuss various ways of encoding missing data and integrate them into the training and inference process. We implement the approaches in the BayesFlow methodology, an amortized estimation framework based on invertible neural networks, and evaluate their performance on multiple test problems. We find that an approach in which the data vector is augmented with binary indicators of presence or absence of values performs the most robustly. Accordingly, we demonstrate that amortized simulation-based inference approaches are applicable even with missing data, and we provide a guideline for their handling, which is relevant for a broad spectrum of applications.

systems biology↗

bioRxiv2022

Posterior marginalization accelerates Bayesian inference for dynamical systems

Bayesian inference is an important method in the life and natural sciences for learning from data. It provides information about parameter uncertainties, and thereby the reliability of models and their predictions. Yet, generating representative samples from the Bayesian posterior distribution is often computationally challenging. Here, we present an approach that lowers the computational complexity of sample generation for problems with scaling, offset and noise parameters. The proposed method is based on the marginalization of the posterior distribution, which reduces the dimensionality of the sampling problem. We provide analytical results for a broad class of problems and show that the method is suitable for a large number of applications. Subsequently, we demonstrate the benefit of the approach for various application examples from the field of systems biology. We report a substantial improvement up to 50 times in the effective sample size per unit of time, in particular when applied to multi-modal posterior problems. As the scheme is broadly applicable, it will facilitate Bayesian inference in different research fields.

systems biology↗

bioRxiv2022

IntestLine: a Shiny-based application to map the rolled intestinal tissue onto a line

To allow the comprehensive histological analysis of the whole intestine in one image, the tissue is often rolled to a spiral before imaging. This Swiss-rolling technique facilitates robust experimental procedures, but it limits the possibilities to comprehend changes along the intestine. Here, we present IntestLine, a Shiny-based open-source application to map imaging data of intestinal tissues in spiral shape onto a line. The mapping of intestinal tissues improves the visualization of the whole intestine in both proximal-distal and serosa-luminal axis, and facilitates the observation of location-specific cell types and markers. In summary, IntestLine serves as a tool to visualize and characterize intestine in future imaging studies.

bioinformatics↗