Biology subjects

Huber, W.

Publications and source records attributed to Huber, W..

At least 19 recordsLinked to original sources

bioRxiv2025

Cancer pathway connectivity resolved by drug perturbation and RNA sequencing

Pathway inhibitors are a backbone of cancer treatment. The configuration of pathway dependencies varies from tumour to tumour. Better treatment for individual patients could be designed if the pathway wiring were readily measurable. Here, we characterise the transcriptional responses of 116 lymphoma patient samples exposed to ten drug perturbations. We used factor analysis to decompose individual and shared drug effects, thereby generating a pathway connectivity map of chronic lymphocytic leukemia (CLL). The expression profiles of the major disease subgroups, defined by IGHV mutation status, became more similar to each other after BTK inhibition, consistent with B-cell receptor (BCR) signalling as a driver of their phenotypic difference. An even stronger convergence was observed with combined IRAK4 and BTK inhibition, indicating cooperation of BCR and toll-like receptor (TLR) signalling in CLL. We identified genetic aberrations (BRAF, TP53, deletion 17p, deletion 15q, trisomy 12) that modulated drug effects in CLL and constituted specific interaction patterns. IRAK4 inhibition effects depended on the presence of trisomy 12, a finding that suggests that the trisomy 12 driver event in CLL acts by gene dosage-dependent IRAK4 upregulation and amplification of the BCR/TLR cooperation. Our results highlight the potential of systematic drug perturbation assays with transcriptome readout to map pathway interconnectivity and functionally annotate tumour drivers.

systems biology↗

bioRxiv2025

A quantitative approach to discover nonlinear signaling linking combinatorial environments with cellular responses

Cells constantly integrate diverse inputs from the extracellular environment, yet our understanding of how cells effectively process information from multiple cues at the same time remains limited. We utilized an integrated imaging platform and RNAseq analysis to investigate the combined effects of growth factors on cellular signaling and gene expression. Paired stimuli by receptor ligands revealed diverse signaling signatures-- ranging from antagonism to synergy--driving global signaling programs and gene expression. Notably, correlation networks based on signaling signatures identified vulnerabilities in cancer cells when compared to synergistic drug combinations. Profiling kinase and phosphatase activities uncovered a crucial interplay, where cellular sensitivity and phospho-turnover dynamics are modulated by input history through coordinated basal kinase and phosphatase activities. Our novel methodology sheds light on cellular processing of multiple cues, elucidating intricate mechanisms underlying cellular adaptation to extracellular environmental variations. Based on previous preprint: doi: https://doi.org/10.1101/346957

cell biology↗

bioRxiv2025

TurbOmics: a web-based platform for the analysis of metabolomics data using a multi-omics integrative approach

In recent years, multi-omics integration has proven highly effective for the holistic characterization of biological systems, with the development of numerous bioinformatic platforms. However, these tools face limitations when incorporating metabolomics data, including absence of support for untargeted metabolomics annotation and dependence on predefined knowledge bases. Furthermore, the advanced algorithms required for multi-omics integration typically demand programming skills and statistical background, restricting their use to specialized users. To advance towards resolving these challenges, we present TurbOmics, a user-friendly web-based platform that enables researchers with diverse backgrounds to analyze metabolomics, proteomics, and transcriptomics data using advanced algorithms for multi-omics integration, while addressing key challenges associated with metabolomics data. Users can upload quantitative data and include additional information, such as metabolite identification or lipid classes, that streamline the interpretation of complex results. TurbOmics can be used sequentially with our previously published tool, TurboPutative, to simplify, reduce and prioritize the list of putative annotations from untargeted metabolomics datasets. Thanks to its flexible and interactive interface, researchers can perform exploratory data analysis and multi-omics integration using the Multi-Omics Factor Analysis, Pathway Integrative Analysis and Enrichment Analysis modules. We believe that TurbOmics will make multi-omics analysis more accessible to the research community. The platform is freely available at https://proteomics.cnic.es/TurboPutative/TurbOmicsApp.html. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=149 SRC="FIGDIR/small/653072v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@26a2e6org.highwire.dtl.DTLVardef@90bfa8org.highwire.dtl.DTLVardef@116d6e4org.highwire.dtl.DTLVardef@76134b_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGRAPHICAL ABSTRACTC_FLOATNO C_FIG

bioinformatics↗

bioRxiv2025

Spatialproteomics - an interoperable toolbox for analyzing highly multiplexed fluorescence image data

SummaryHighly multiplexed immunofluorescence imaging is a recent method to characterize tissues at single-cell resolution on the protein level, offering low cost, high scalability, and the ability to analyze paraffin-embedded tissue samples. However, the analysis of these data involves a sequence of steps, including segmentation, image processing, marker quantification, cell type classification, and neighborhood analysis, each of which involves a multitude of method and parameter choices that need to be adapted to the data and analytical objective at hand. Moreover, variations in data quality can be high and unpredictable, which necessitates further flexibility and interactivity. While individual components exist, there is an unmet need for a coherent toolbox that offers end-to-end coverage of the workflow, flexibility, and automation. We present spatialproteomics, a Python package that addresses these challenges. Built on top of xarray and dask, spatialproteomics can process images that are larger than the working memory. It supports synchronization of shared coordinates across data modalities such as images, segmentation masks, and expression matrices, which facilitates easy and safe subsetting and transformation. We demonstrate spatialproteomics on a set of images of reactive lymph nodes or different forms of B cell Non-Hodgkin lymphomas (BNHL) from 132 patients. We showcase an end-to-end analysis from raw images to statistical characterization of cell type composition and spatial distribution across indolent and aggressive lymphomas. Furthermore, we show how spatialproteomics can process gigapixel whole slide images. Altogether, we propose spatialproteomics as an easy-to-install, easy-to-learn, comprehensive toolbox for constructing powerful end-to-end image analysis solutions for highly multiplexed immunofluorescence imaging. Availability and ImplementationThe source code for spatialproteomics is freely available at https://github.com/sagar87/spatialproteomics under the MIT license. Contactwolfgang.huber@embl.org, Peter-Martin.Bruch@med.uni-duesseldorf.de

bioinformatics↗

bioRxiv2024

Semi-supervised Omics Factor Analysis (SOFA) disentangles known sources of variation from latent factors in multi-omics data

A fundamental design pattern in biomolecular studies is to assay the same set of samples (organisms, tissue biopsies, or individual cells) by multiple different omics assays. Group Factor Analysis (GFA) and its adaptation to high-dimensional settings, Multi-Omics Factor Analysis (MOFA), are widely used as a first-line approach to analyse such data and are effective in detecting patterns of correlation, organize them into so-called latent factors, and identify common and assay-specific factors. However, in many applications a subset of the found factors just rediscovers already known covariates (e.g., disease subtypes, environmental covariates) while others may represent genuine novelty. Here, we present Semi-supervised Omics Factor Analysis (SOFA), a method that incorporates known covariates into the model upfront and focuses the factor discovery on novel sources of variation. We show SOFAs effectiveness for discovering novel patterns by applying it to cancer, brain development and heart failure multi-omic data sets.

bioinformatics↗

bioRxiv2024

Joint Modeling of Cellular Heterogeneity and Condition Effects with scPCA in Single-Cell RNA-Seq

Single-cell RNA sequencing (scRNA-seq) in multi-condition experiments enables the systematic assessment of treatment effects. Analyzing scRNA-seq data relies on linear dimensionality reduction (DR) methods like principal component analysis (PCA). These methods decompose high-dimensional gene expression profiles into tractable factor representations and prototypical gene expression patterns (components), facilitating the study of cell type variation. However, integrating study covariates within linear DR frameworks remains a challenging task. We present scPCA, a flexible DR framework that jointly models cellular heterogeneity and conditioning variables, allowing it to recover an integrated factor representation and reveal transcriptional changes across conditions and components of the decomposition. We show that scPCA extracts an interpretable latent representation by analyzing unstimulated and IFN-beta-treated PBMCs, and showcase that the model may be employed to effectively address batch effects. We examine age-related changes in rodent lung cell populations, uncovering a previously unreported surge in Ccl5 expression in T cells. We illustrate how scPCA may be employed to identify coordinated transcriptional changes across multiple time-points in depolarized visual cortex neurons. Finally, we show that scPCA elucidates transcriptional shifts in CRISPR-Cas9 chordin knockout zebrafish fish single-cell data despite large difference cell abundance across conditions. Since scPCA introduces a general approach to account for conditioning variables in high-dimensional data, it may also be applicable to datasets other than scRNA-seq.

bioinformatics↗

bioRxiv2024

Deep learning-based predictions of gene perturbation effects do not yet outperform simple linear methods

Advanced deep-learning methods, such as foundation models, promise to learn representations of biology that can be employed to predict in silico the outcome of unseen experiments, such as the effect of genetic perturbations on the transcriptomes of human cells. To see whether current models already reach this goal, we benchmarked five foundation models and two other deep learning models against deliberately simplistic linear baselines. For combinatorial perturbations of two genes for which only the individual single perturbations had been seen, we find that the deep learning-based approaches did not perform better than a simple additive model. For perturbations of genes that had not yet been seen, the deep learning-based approaches did not outper-form the baseline of predicting the mean across the training perturbations. We hypothesize that the poor performance is partially because the pre-training data is observational; we show that a simple linear model reliably outperforms all other models when pre-trained on another perturbation dataset. While the promise of deep neural networks for the representation of biological systems and prediction of experimental outcomes is plausible, our work highlights the need for clear setting of objectives and for critical benchmarking to direct research efforts. Contactconstantin.ahlmann@embl.de

bioinformatics↗

bioRxiv2024

Proliferation and differentiation of intestinal stem cells depends on the zinc finger transcription factor BCL11/Chronophage

The molecular programs that drive proliferation and differentiation of intestinal stem cells (ISCs) are essential for organismal fitness. Notch signalling regulates the binary fate decision of ISCs, favouring enterocyte commitment when Notch activity is high and enteroendocrine cell (EE) fate when activity is low. However, the gene regulatory mechanisms that underlie this process on an organ scale remain poorly understood. Here, we find that the expression of the C2H2-type zinc-finger transcription factor Chronophage (Cph), homologous to mammalian BCL11, increases specifically along the ISC-to-EE lineage when Notch is inactivated. We show that the expression of Cph is regulated by the Achaete-Scute Complex (AS-C) gene, scute, which directly binds to multiple sites within the Cph locus to promote its expression. Our genetic and single-cell RNA sequencing experiments demonstrate that Cph maintains the ISC and EE populations and is necessary to remodel the transcriptome of progenitor cells with low Notch activity. By identifying and functionally validating Cph target genes, we uncover a novel role for sugar free frosting (sff) in directing proliferative and lineage commitment steps of ISCs. Our results shed light on the mechanisms by which Cph sustains intestinal epithelial homeostasis and could represent a conserved strategy for balancing proliferation and differentiation in different tissues and species.

developmental biology↗

bioRxiv2024

Functional phenotyping of genomic variants using multiomic scDNA-scRNA-seq

Genomic variation ranging from single nucleotide polymorphisms to structural variants can impact gene function and expression, contributing to disease mechanisms such as cancer progression. The systematic study of this variation is hindered by inefficient precision editing tools making it challenging to confidently link genotype and gene expression in pooled screens. Additionally, assessing heterogenous variants in primary tumor samples at scale is difficult with current single-cell technologies. We developed droplet-based multiomic targeted scDNA-scRNAseq (SDR-seq) to precisely link genotypes with gene expression profiles in high-throughput. SDR-seq simultaneously assesses up to 480 RNA and gDNA targets with high coverage and sensitivity across thousands of cells. Using SDR-seq, we associate coding and non-coding variants with distinct gene expression profiles in human iPSCs. Furthermore, we demonstrate that in primary B-cell lymphoma samples, cells with a higher mutational burden exhibit elevated B-cell receptor signaling and tumorigenic gene expression. SDR-seq has broad potential for gaining functional insights into regulatory mechanisms encoded by genetic variants at diverse loci, advancing our ability to study gene expression regulation and its implications for disease.

molecular biology↗

bioRxiv2024

Restraining Wnt activation and intestinal tumorigenesis by a Rab35 dependent GTPase relay

Maintenance of homeostatic processes ensure curtailment of intestinal tumorigenesis. Inactivating mutations to Adenomatous Polyposis Coli (Apc) result in aberrantly activated Wnt signalling and initiates colorectal cancer (CRC) in approx. 80% of cases, yet our understanding of the subcellular mechanisms that modulate dysregulated pathway activity is limited. Here, using a conditional in vivo genetic screen, we identify Rab35 GTPase as a novel tumour suppressor that modulates regional Wnt activity after loss of Apc in progenitor cells. Single cell analysis revealed that progenitor cells respond to Apc depletion by increasing the expression of a GTPase activating protein, which we named blackbelt, and triggering Rab35 disassociation from the plasma membrane. Mechanistically, we demonstrate that Rab35 controls the localisation and activation of the Rho GTPase, Cdc42, which functions as a relay to regulate JNK signalling. This in turn tunes the Wnt pathway upstream of {beta}-catenin to direct proliferation and differentiation of progenitor cells. Importantly, we show that maintaining active JNK signalling is important for the propagation of Apc mutant mouse colon organoids. Our findings highlight a novel GTPase cascade that sustains aberrant Wnt activity in specific segments of the intestine and provides impetus to therapeutically exploit this pathway to target CRC.

cancer biology↗

bioRxiv2023

A single-sample workflow for joint metabolomic and proteomic analysis of clinical specimens

Understanding the interplay of the proteome and the metabolome aids in understanding cellular phenotypes. To enable more robust inferences from such multi-omics analyses, combining proteomic and metabolomic datasets from the same sample provides major benefits by reducing technical variation between extracts during the pre-analytical phase, decreasing sample variation due to varying cellular content between aliquots, and limiting the required sample amount. We evaluated the advantages, practicality and feasibility of a single-sample workflow for combined proteome and metabolome analysis. In the workflow, termed MTBE-SP3, we combined a fully automated protein lysis and extraction protocol (autoSP3) with a semi-automated biphasic 75% EtOH/MTBE extraction for quantification of polar/non-polar metabolites. Additionally, we compared the resulting proteome of various biological matrices (FFPE tissue, fresh-frozen tissue, plasma, serum and cells) between autoSP3 and MTBE-SP3. Our analysis revealed that the single-sample workflow provided similar results to those obtained from autoSP3 alone, with an 85-98% overlap of proteins detected across the different biological matrices. Additionally, it provides distinct advantages by decreasing (tissue) heterogeneity by retrieving metabolomics and proteomic data from the identical biological material, and limiting the total amount of required material. Lastly, we applied MTBE-SP3 to a lung adenocarcinoma cohort of 10 patients. Integrating the metabolic and proteomic alterations between tumour and non-tumour adjacent tissue yielded consistent data independent of the method used. This revealed mitochondrial dysfunction in tumor tissue through deregulation of OGDH, SDH family enzymes and PKM. In summary, MTBE-SP3 enables the facile and confident parallel measurement of proteins and metabolites obtained from the same sample. This workflow is particularly applicable for studies with limited sample availability and offers the potential to enhance the integration of metabolomic and proteomic datasets.

cancer biology↗

bioRxiv2023

A single-cell multi-omic and spatial atlas of nodal B-cell lymphomas reveals B-cell maturation drives intratumor heterogeneity

Intratumor heterogeneity underpins cancer pathogenesis and evolution, although it is typically considered independent from the differentiation processes that drive physiological cell-type diversity. As cancer types and subtypes arise from different cell types, we investigated whether cellular differentiation influences intratumor heterogeneity. Nodal B-cell non-Hodgkin lymphomas are a diverse set of cancers originating from different stages of B-cell maturation. Through single-cell transcriptome and surface epitope profiling (CITE-Seq) of diffuse large B-cell, mantle cell, follicular, and marginal zone lymphomas in addition to reactive lymph nodes from 51 patients, we found multiple B-cell maturation states within tumors. Intratumor maturation states emerged from the same clone, revealing divergent differentiation from a shared cell of origin. Maturation state composition varied across subtypes and tumors, which encompassed mixed cell-of-origin diagnostic subtypes. Through highly multiplexed immunohistochemistry (CODEX) of samples from 19 of these patients, we found that intratumor maturation states inhabited distinct spatial niches, displaying cellular interactions and regulatory networks typical of their maturation states while harboring different genetic variants. By deconvoluting intratumor maturation states from a microarray dataset of 507 patients, we identified risk groups within diagnoses with striking differences in survival, including IgM memory-enriched germinal center B-cell (M = 1.9 vs >10 years, p = 0.00039) and activated B-cell (M = 2.4 vs 9.6 years, p = 0.016) diffuse large B-cell lymphoma, and dark zone-enriched follicular lymphoma (M = 8.6 vs 13 years; p = 0.0019). Our findings reveal cellular differentiation remains plastic in B-cell lymphomas, driving tumor variation, evolution, and response. Key PointsO_LICellular differentiation remains plastic in B-cell lymphomas, driving tumor variation, evolution, and response. C_LIO_LIIntratumor maturation states occupy unique immune niches, harbor distinct genetic variants, and are tied to different survival outcomes. C_LI

cancer biology↗

bioRxiv2023

Feature selection by replicate reproducibility and non-redundancy

MotivationA fundamental step in many analyses of high-dimensional data is dimension reduction. Two basic approaches are introduction of new, synthetic coordinates, and selection of extant features. Advantages of the latter include interpretability, simplicity, transferability and modularity. A common criterion for unsupervised feature selection is variance or dynamic range. However, in practice it can occur that high-variance features are noisy, that important features have low variance, or that variances are simply not comparable across features because they are measured in unrelated numeric scales or physical units. Moreover, users may want to include measures of signal-to-noise ratio and non-redundancy into feature selection. ResultsHere, we introduce the RNR algorithm, which selects features based on (i) the reproducibility of their signal across replicates and (ii) their non-redundancy, measured by linear independence. It takes as input a typically large set of features measured on a collection of objects with two or more replicates per object. It returns an ordered list of features i1, i2, ..., ik, where feature i1 is the one with the highest reproducibility across replicates, i2 that with the highest reproducibility across replicates after projecting out the dimension spanned by i1, and so on. Applications to microscopy based imaging of cells and proteomics experiments highlight benefits of the approach. AvailabilityThe RNR method is implemented in the R package FeatSeekR and is available via Bioconductor (Huber et al., 2015) under the GPL-3 open source license. Contacttuemay.capraz@embl.de

bioinformatics↗

bioRxiv2023

Proteomic profiling identifies Serpin B9 as mediator of resistance to CAR T-cell and bispecific antibody treatment in B-cell lymphoma

Although T-cell-engaging therapies are highly effective in patients with relapsed and/or refractory B-cell non-Hodgkin lymphoma (B-NHL), responses are often not durable. To identify tumor-intrinsic drivers of resistance, we quantified in-vitro response to CD19-directed chimeric antigen receptor T-cells (CD19-CAR) and bispecific antibodies (BsAb) across 46 B-NHL cell lines and measured their proteomic profiles at baseline. Among the proteins associated with poor in-vitro response was Serpin B9, an endogenous granzyme B inhibitor. Knock-out of SERPINB9 in cell lines with high intrinsic expression rendered them more susceptible to CD19-CAR and CD19-BsAb. Overexpression in cell lines with low intrinsic expression attenuated responses. Polatuzumab, vorinostat, lenalidomide, or checkpoint inhibitors improved response to CD19-CAR, although independently of Serpin B9 expression. Besides providing an important resource of therapy response and proteomic profiles, this study refines our understanding of resistance in T-cell engaging therapies, and suggests clinically relevant combination regimes.

cancer biology↗

bioRxiv2023

MsQuality - an interoperable open-source packagefor the calculation of standardized quality metricsof mass spectrometry data

MotivationMultiple factors can impact accuracy and reproducibility of mass spectrometry data. There is a need to integrate quality assessment and control into data analytic workflows. ResultsThe MsQuality package calculates 40 low-level quality metrics based on the controlled mzQC vocabulary defined by the HUPO-PSI on a single mass spectrometry-based measurement of a sample. It helps to identify low-quality measurements and track data quality. Its use of community-standard quality metrics facilitates comparability of quality assessment and control (QA/QC) criteria across datasets. AvailabilityThe R package MsQuality is available through Bioconductor at https://bioconductor.org/packages/MsQuality. Contactnaake@embl.de Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2023

SpatialData: an open and universal data framework for spatial omics

Spatially resolved omics technologies are transforming our understanding of biological tissues. However, handling uni- and multi-modal spatial omics datasets remains a challenge owing to large volumes of data, heterogeneous data types and the lack of unified spatially-aware data structures. Here, we introduce SpatialData, a framework that establishes a unified and extensible multi-platform file-format, lazy representation of larger-than-memory data, transformations, and alignment to common coordinate systems. SpatialData facilitates spatial annotations and cross-modal aggregation and analysis, the utility of which is illustrated via multiple vignettes, including integrative analysis on a multi-modal Xenium and Visium breast cancer study.

bioinformatics↗

bioRxiv2023

Analysis of multi-condition single-cell data with latent embedding multivariate regression

Identifying gene expression differences in heterogeneous tissues across experimental or observational conditions is a fundamental biological task, enabled by single-cell assays such as multi-condition sc-RNA-seq. Current data analysis approaches divide the constituent cells into clusters meant to represent cell types, and identify differentially expressed genes for each cluster. However, such discrete categorization tends to be an unsatisfactory model of the underlying biology. Use of more gradual representations of cell type or cell state promises higher statistical power, better usability and better interpretability. Here, we introduce Latent Embedding Multivariate Regression (LEMUR), a generative model that enables differential expression analysis using a continuous low-dimensional latent space parameterization of cell type and state diversity. It operates without, or before, commitment to discrete categorization. LEMUR (1) integrates data from the different conditions, (2) predicts how each cells gene expression would change as a function of the conditions and its position in latent space, and (3) for each gene, identifies compact neighborhoods of cells with consistent differential expression. Unlike statically defined clusters, these neighborhoods adapt to the underlying gene expression changes. We assess LEMURs performance on a compendium of single-cell datasets and show applications to the identification of tumor subpopulations with distinct drug responses, the interplay between cell state and developmental time in zebrafish embryos, and the discovery of cell state x environment interactions in a spatial single-cell study of plaques in Alzheimers disease. LEMUR is broadly applicable as a first-line analysis approach to multi-condition sc-RNA-seq data. Software availabilityhttps://bioconductor.org/packages/lemur Contactconstantin.ahlmann@embl.de

bioinformatics↗

bioRxiv2022

High-throughput profiling of drug interactions in Gram-positive bacteria

Drug combinations present a powerful strategy to tackle antimicrobial resistance, but have not been systematically tested in many bacterial species. Here, we used an automated high-throughput setup to profile [~] 8000 combinations between 65 antibacterial drugs in three Gram-positive species: the model species, Bacillus subtilis and two prominent pathogens, Staphylococcus aureus and Streptococcus pneumoniae. Thereby, we recapitulate previously known drug interactions, but also identify ten times more interactions than previously reported in the pathogen S. aureus, including two synergies that were also effective in multi-drug resistant clinical S. aureus isolates in vitro and in vivo. Interactions were largely species-specific and mostly synergistic for drugs targeting the same cellular process, as observed also for Gram-negative species1. Yet, the dominating synergies are clearly distinct between Gram-negative and Gram-positive species, and are driven by different bottlenecks in drug uptake and vulnerabilities of their cell surface structures. To further explore interactions of commonly prescribed non-antibiotic drugs with antibiotics, we tested 2728 of such combinations in S. aureus, detecting a plethora of unexpected antagonisms that could compromise the efficacy of antimicrobial treatments in the age of polypharmacy. We uncovered even more synergies than antagonisms, some of which we could demonstrate as effective combinations in vivo against multi-drug resistant clinical isolates. Among them, we showed that the antiaggregant ticagrelor interferes with purine metabolism and changes the surface charge of S. aureus, leading to strong synergies with cationic antibiotics. Overall, this exemplifies the untapped potential of approved non-antibacterial drugs to be repurposed as antibiotic adjuvants. All data can be browsed through an interactive interface (https://apps.embl.de/combact/).

microbiology↗