Biology subjects

Zacharias, H. U.

Publications and source records attributed to Zacharias, H. U..

8 recordsLinked to original sources

bioRxiv2026

Encephalitic alphavirus infection reduces neurite outgrowth and downregulates Rho GTPase pathways in human iPSC-derived neurospheres

Venezuelan equine encephalitis virus (VEEV) is a re-emerging neurotropic arbovirus, which affects the developing human brain, potentially leading to miscarriage, stillbirth, or long-term neurological sequelae. Currently, the mechanisms underlying these sequelae remain largely elusive. Using human induced pluripotent stem cell (iPSC)-derived neurospheres as a model for brain development, we show that both human neurons and glial cells are susceptible to VEEV infection. Microscopy-based quantification reveals that VEEV impairs neurite outgrowth in the human neurospheres. To mechanistically assess these observations, we performed multi-omics analyses of VEEV-infected neurospheres and demonstrate translational shutoff including downregulation of Rho GTPase pathways. The transcriptional and proteome profile is indicative of reduced actin cytoskeleton dynamics and consequently migratory defects of cells in the neurospheres. Shotgun secretomics moreover revealed downmodulation of neurite outgrowth associated proteins including midkine (MDK), amyloid-beta precursor protein (APP) and microtubule-associated protein 1B (MAP1B). Our findings reveal mechanistic insights into how VEEV infection impairs neuronal development and demonstrate that neurospheres are a powerful platform for assessing teratogenic effects of viruses on brain development.

neuroscience↗

bioRxiv2026

HIDE-Deconv: A hierarchical deconvolution framework for multiscale characterization of cellular remodeling

Most deconvolution methods estimate cellular composition at a single level of cellular resolution despite biological processes often manifesting within fine-grained cellular subpopulations. We present HIDE-Deconv, a hierarchical deconvolution framework that jointly optimizes cellular compositions across multiple levels of a cell-type hierarchy while maintaining consistency between resolutions. In benchmark experiments, HIDE-Deconv achieved the highest overall predictive performance among evaluated methods. Analyses of lung adenocarcinoma, sepsis, COVID-19 and systemic lupus erythematosus revealed biologically relevant cellular remodeling that remained concealed at broader levels of cellular resolution. HIDE-Deconv is available as an open-source framework at https://github.com/dvoelkl/HIDE-deconv.

bioinformatics↗

bioRxiv2025

HIDE: Hierarchical cell-type Deconvolution

MotivationCell-type deconvolution is a computational approach to infer cellular distributions from bulk transcriptomics data. Several methods have been proposed, each with its own advantages and disadvantages. Reference based approaches make use of archetypic transcriptomic profiles representing individual cell types. Those reference profiles are ideally chosen such that the observed bulks can be reconstructed as a linear combination thereof. This strategy, however, ignores the fact that cellular populations arise through the process of cellular differentiation, which entails the gradual emergence of cell groups with diverse morphological and functional characteristics. ResultsHere, we propose Hierarchical cell-type Deconvolution (HIDE), a cell-type deconvolution approach which incorporates a cell hierarchy for improved performance and interpretability. This is achieved by a hierarchical procedure that preserves estimates of major cell populations while inferring their respective subpopulations. We show in simulation studies that this procedure produces more reliable and more consistent results than other state-of-the-art approaches. Finally, we provide an example application of HIDE to explore breast cancer specimens from TCGA. AvailabilityA python implementation of HIDE is available at zenodo (doi:10.5281/zenodo.14724906). Supplementary informationSupplementary material is available at Bioinformatics online.

bioinformatics↗

bioRxiv2024

Deconvolution of omics data in Python with Deconomix -- cellular compositions, cell-type specific gene regulation, and background contributions

BackgroundGene expression profiles derived from heterogeneous bulk samples contain signals from various cell populations. Cell-type deconvolution approaches are computational tools to reverse engineer the composition of bulks in term of cell populations. Accurate estimates of cell compositions are crucial for identifying cell populations relevant for disease. Moreover, analyses, such as the identification of differentially expressed genes, can be confounded by cellular composition, as differences in gene expression may arise from both variations in cellular composition and gene regulation. ResultsWe present Deconvolution of omics data (Deconomix) - a comprehensive toolbox for the cell-type deconvolution of bulk transcriptomics data, available as a Python package and standalone graphical user interface. Deconomix stands apart from competing solutions with rich functionality and highly efficient implementations. It facilitates (A) the inference of cellular compositions from bulk transcriptomics data, (B) the machine learning-based optimization of gene weights to resolve small cell populations and to disentangle phenotypically related cells, (C) the inference of background contributions which otherwise would deteriorate cell-type deconvolution, and (D) population estimates of cell-type specific gene regulation. To showcase the application of Deconomix, we present a case study on breast cancer data from TCGA, highlighting subtype-specific cellular compositions and cell-type-specific gene-regulatory programs. ConclusionWe present Deconomix, a comprehensive Python package including a graphical user interface for the inference of cellular compositions, cell-type-specific gene regulation, and background contributions from bulk transcriptomics data. O_TEXTBOXKey PointsO_LIDeconomix optimizes gene weights to disentangle small cell populations and phenotypically related cells. C_LIO_LIDeconomix estimates cell compositions, unknown background contributions and cell-type specific gene regulation from bulk transcriptomics data. C_LIO_LIA Python package is available as code repository and installable via PyPI [1]. C_LIO_LIA standalone graphical user interface is available as code repository [2]. C_LIO_LIAn exemplary analysis of a breast cancer case study is provided as tutorial [3]. C_LI C_TEXTBOX

bioinformatics↗

bioRxiv2024

CODEX: COunterfactual Deep learning for the in-silico EXploration of cancer cell line perturbations

MotivationHigh-throughput screens (HTS) provide a powerful tool to decipher the causal effects of chemical and genetic perturbations on cancer cell lines. Their ability to evaluate a wide spectrum of interventions, from single drugs to intricate drug combinations and CRISPR-interference, has established them as an invaluable resource for the development of novel therapeutic approaches. Nevertheless, the combinatorial complexity of potential interventions makes a comprehensive exploration intractable. Hence, prioritizing interventions for further experimental investigation becomes of utmost importance. ResultsWe propose CODEX as a general framework for the causal modeling of HTS data, linking perturbations to their downstream consequences. CODEX relies on a stringent causal modeling strategy based on counterfactual reasoning. As such, CODEX predicts drug-specific cellular responses, comprising cell survival and molecular alterations, and facilitates the in-silico exploration of drug combinations. This is achieved for both bulk and single-cell HTS. We further show that CODEX provides a rationale to explore complex genetic modifications from CRISPR-interference in silico in single cells. Availability and ImplementationOur implementation of CODEX is publicly available at https://github.com/sschrod/CODEX. All data used in this article are publicly available.

bioinformatics↗

bioRxiv2023

PriOmics: integration of high-throughput proteomic data with complementary omics layers using mixed graphical modeling with group priors

Mass spectrometry (MS)-based high-throughput proteomics data cover abundances of 1,000s of proteins and facilitate the study of co- and post-translational modifications (CTMs/PTMs) such as acetylation, ubiquitination, and phosphorylation. Yet, it remains an open question how to holistically explore such data and their relationship to complementary omics layers or phenotypical information. Network inference methods aim for a holistic analysis of data to reveal relationships between molecular variables and to resolve underlying regulatory mechanisms. Among those, graphical models have received increased attention as they can distinguish direct from indirect relationships, aside from their generalizability to diverse data types. We propose PriOmics as a graphical modeling approach to integrate proteomics data with complementary omics layers and pheno- and genotypical information. PriOmics models intensities of individual peptides and incorporates their protein affiliation as prior knowledge in order to resolve statistical relationships between proteins and CTMs/PTMs. We show in simulation studies that PriOmics improves the recovery of statistical associations compared to the state of the art and demonstrate that it can disentangle regulatory effects of protein modifications from those of respective protein abundances. These findings are substantiated in a dataset of Diffuse Large B-Cell Lymphomas (DLBCLs) where we integrate SWATH-MS-based proteomics data with transcriptomic and phenotypic information. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/566517v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@19bcac0org.highwire.dtl.DTLVardef@11c126forg.highwire.dtl.DTLVardef@1fe6631org.highwire.dtl.DTLVardef@e73187_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

Adaptive Digital Tissue Deconvolution

MotivationThe inference of cellular compositions from bulk and spatial transcriptomics data increasingly complements data analyses. Multiple computational approaches were suggested and recently, machine learning techniques were developed to systematically improve estimates. Such approaches allow to infer additional, less abundant cell types. However, they rely on training data which do not capture the full biological diversity encountered in transcriptomics analyses; data can contain cellular contributions not seen in the training data and as such, analyses can be biased or blurred. Thus, computational approaches have to deal with unknown, hidden contributions. Moreover, most methods are based on cellular archetypes which serve as a reference; e.g., a generic T-cell profile is used to infer the proportion of T-cells. It is well known that cells adapt their molecular phenotype to the environment and that pre-specified cell archetypes can distort the inference of cellular compositions. ResultsWe propose Adaptive Digital Tissue Deconvolution (ADTD) to estimate cellular proportions of pre-selected cell types together with possibly unknown and hidden background contributions. Moreover, ADTD adapts prototypic reference profiles to the molecular environment of the cells, which further resolves cell-type specific gene regulation from bulk transcriptomics data. We verify this in simulation studies and demonstrate that ADTD improves existing approaches in estimating cellular compositions. In an application to bulk transcriptomics data from breast cancer patients, we demonstrate that ADTD provides insights into cell-type specific molecular differences between breast cancer subtypes. Availability and implementationA python implementation of ADTD and a tutorial are available at Gitlab and zenodo (doi:10.5281/zenodo.7548362). Contactmichael.altenbuchinger@bioinf.med.uni-goettingen.de and sushma.grellscheid@uib.no Supplementary informationSupplementary material is available at Bioinformatics online.

bioinformatics↗

bioRxiv2022

SpaCeNet: Spatial Cellular Networks from omics data

Advances in omics technologies have allowed spatially resolved molecular profiling of single cells, providing a window not only into the diversity and distribution of cell types within a tissue, but also into the effects of interactions between cells in shaping the transcriptional landscape. Cells send chemical and mechanical signals which are received by other cells, where they can subsequently initiate context-specific gene regulatory responses. These interactions and their responses shape the individual molecular phenotype of a cell in a given microenvironment. RNAs or proteins measured in individual cells together with the cells spatial distribution provide invaluable information about these mechanisms and the regulation of genes beyond processes occurring independently in each individual cell. "SpaCeNet" is a method designed to elucidate both the intracellular molecular networks (how molecular variables affect each other within the cell) and the intercellular molecular networks (how cells affect molecular variables in their neighbors). This is achieved by estimating conditional independence relations between captured variables within individual cells and by disentangling these from conditional independence relations between variables of different cells. A python implementation of SpaCeNet is publicly available at https://github.com/sschrod/SpaCeNet.

bioinformatics↗