Biology subjects

Eils, R.

Publications and source records attributed to Eils, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Evolution of SARS-CoV-2 reshapes upper airway immunity in children

Children were relatively protected from severe COVID-19 during the early pandemic a phenomenon linked to rapid and heightened innate immune responses in the upper-airways. Although adult upper-airway antiviral responses varied across successive SARS-CoV-2 waves, whether viral evolution similarly reshaped pediatric mucosal immunity remains unclear. Here we used single-cell transcriptomics to profile 229,533 upper-airway cells from 68 unvaccinated children including 17 uninfected controls, spanning the wild-type SARS-CoV-2, Alpha, Delta and Omicron waves. Epithelial interferon responses were primarily determined by time since symptom onset but were, unlike in adults, independent of disease severity. During early infection, anti-viral responses differed across variants without following a progressive pattern of attenuation. Alpha and Delta elicited the strongest interferon programs across epithelial and immune cell populations, whereas Omicron exhibited a shift towards macrophage cytokine signaling and T-cell cytotoxicity. These findings reveal variant-specific remodeling of pediatric upper-airway immunity that fundamentally differs from that described in adults.

immunology↗

bioRxiv2026

The Human Pancreas Cell Atlas defines a healthy reference framework for disease contextualization and translational benchmarking

A central challenge in single-cell biology is distinguishing disease-associated remodeling from normal cellular heterogeneity. Addressing this challenge requires healthy reference frameworks that capture cellular diversity across individuals, technologies, and biological contexts. Here we present the Human Pancreas Cell Atlas (HPCA), a reference atlas of the healthy human pancreas integrating 815,126 single-cell and single-nucleus transcriptomes from 109 donors across 12 studies, diverse technologies, and demographics. Using benchmarked integration and community-driven annotations, HPCA defines 94 cell types and transcriptional states spanning endocrine, exocrine, immune, and stromal compartments. The atlas identifies rare endocrine populations, including a putative, spatially supported polyhormonal alpha-beta-delta state, and provides a unified framework for interpreting pancreatic cellular variation across diverse biological and demographic covariates. Projection of disease and model-system datasets onto HPCA contextualized endocrine and epithelial remodeling relative to healthy pancreatic states. Diabetes-associated endocrine cells remained embedded within the healthy endocrine state space while exhibiting disease-specific changes, as supported by spatial and eQTL concordance analyses. Integration with a pancreatic ductal adenocarcinoma atlas resolved injury-associated and malignant epithelial ecosystem regions across donors. Finally, the HPCA enables quantitative benchmarking of murine diabetes models and stem-cell-derived islets against human pancreatic reference states. Together, the HPCA establishes a healthy transcriptional coordinate system for interpreting disease-associated pathophysiology, experimental perturbation, and regenerative fidelity, illustrating how reference atlases can function as analytical frameworks rather than static cell catalogs.

bioinformatics↗

bioRxiv2026

Nonlinear mixed-effect models and tailored parametrization schemes enables integration of single cell and bulk data

Experimental methods for characterizing single cells and cell populations have improved tremendously over the past decades. This progress has enabled the development of quantitative, mechanistic models for cellular processes based on either single cell or bulk data. However, coherent statistical frameworks for the model-based integration of different data types at the single-cell and population levels are still missing. In this work, we present a mathematical modeling approach for integrating single-cell time-lapse, single-cell snapshot, single-cell time-to-event and population-average data. Utilizing a formulation based on nonlinear mixed-effect modeling, we enable the description of multiple data types, with and without single-cell resolution, and we propose a tailored parameter estimation method. Furthermore, we propose a tailored parameter estimation scheme that facilitates the assessment of underlying process parameters. Our study demonstrates that the proposed approach can reliably integrate diverse data types, thereby improving parameter identifiability and prediction accuracy. Applying this framework of extrinsic apoptosis reveals that simultaneously considering multiple data types can be essential, particularly when experimental constraints limit data availability. The proposed approach is broadly applicable and may significantly advance our understanding of complex biological processes.

systems biology↗

bioRxiv2026

Atlas-scale spatially aware clustering with support for 3D and multimodal data using SpatialLeiden

Here we extend SpatialLeiden, our spatial clustering algorithm, to enable generalised atlas-scale multi-sample, 3D serial-section, and multimodal spatial omics via flexible neighbour-graph multiplexing on batch-corrected latent spaces. It delivers coherent domains aligning with brain atlases across >100 samples, stable 3D reconstruction of cancer tissue structures, and integrated multimodal features, outperforming specialized tools in modularity and scalability on standard hardware. SpatialLeiden is compatible with scverse for broad and intuitive adoption.

bioinformatics↗

bioRxiv2025

Long-read single-cell RNA sequencing uncovers cell-type specific transcript regulation in COVID-19

SARS-CoV-2 infection leads to extensive host transcriptomic changes, but the role of alternative splicing in shaping the immune response remains underexplored. Here, we present the first application of long-read single-cell RNA sequencing on nasopharyngeal swabs from COVID-19 patients and healthy controls to resolve transcript-level changes across cell types. Our analysis identified major epithelial cell types and pronounced immune infiltration, with cell-type annotations concordant with those from short-read data. By enabling isoform-level resolution, our nanopore sequencing approach revealed cell-type specific alternative splicing, undetectable with short-read sequencing. For example, although gene-level expression of the key immune and apoptosis regulators, IFNAR2 and FAIM, did not differ between COVID-19 patients and healthy controls we identified marked shifts in isoform usage. Between moderate and critical cases, we observed cell-type specific differential transcript usage in the T cell signaling kinase FYN and the immune-regulatory transcription factor IRF2. As some of these splicing alterations yield functionally distinct isoforms, we hypothesize that alternative splicing modulates immune signaling and apoptosis, fine-tuning the host response to SARS-CoV-2 infection. Our study demonstrates the unique power of long-read single-cell transcriptomics to uncover isoform-resolved regulatory changes, offering novel insights into the role of alternative splicing in shaping immune responses to viral infections.

genomics↗

bioRxiv2025

Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering

Spatial omics technologies have revolutionized the study of tissue architecture and cellular heterogeneity by integrating molecular profiles with spatial localization. In spatially resolved transcriptomics, delineating higher-order anatomical structures is critical for understanding how cellular organization affects tissue and organ function. Since 2020, more than 50 spatially aware clustering (SAC) methods have been developed for this purpose. However, the reliability of current benchmarks is undermined by their narrow focus on Visium and brain tissue datasets, as well as incorrect interpretation of manual annotation as ground truth. Here, we present SACCELERATOR, a community-driven, extensible framework that standardizes data formatting, method integration, and metric evaluation, and is designed to rapidly incorporate new methods and datasets. SACCELERATOR currently includes 22 SAC methods applied to 15 datasets spanning 9 technologies and diverse tissue types. Our analysis revealed substantial limitations in the generalizability and reproducibility of SAC methods across tissues and platforms. We also demonstrate that anatomical labels commonly used as ground truths are often biased, potentially error-prone, and, in some cases, unsuitable for benchmarking efforts. Rather than scoring and comparing methods, we propose a consensus-guided workflow that aggregates clustering results to generate consensus representations. Descriptive spatial metrics highlight areas of high entropy where method disagreement is highest, enabling targeted feedback for tissue experts. Applied to brain and cancer datasets, this approach uncovered biologically meaningful patterns overlooked by individual methods and manual annotations. Our results underscore the need for iterative, expert-in-the-loop analysis and reveal that traditional evaluation metrics do not always capture the subjective qualities of results. By improving tissue annotation and addressing key benchmarking limitations, SACCELERATOR provides a robust foundation for advancing spatial omics research.

genomics↗

bioRxiv2025

Aberrant brain-type neuronal programs in large-cell pancreatic neuroendocrine carcinoma

High-grade pancreatic neuroendocrine carcinoma (panNEC) is a rare, aggressive cancer with limited tissue availability. Single-nucleus transcriptomics of five large-cell panNECs revealed two clinically relevant neuroendocrine cell states: one highly proliferative with an aberrant brain-specific neuronal differentiation program and another stress-responsive state enriched for heat stress, hypoxia, and glycolysis. Our findings suggest potentially new therapeutic vulnerabilities, highlighting the need to evaluate the efficacy of combination therapies.

cancer biology↗

bioRxiv2025

Biologically Guided Variational Inference for Interpretable Multimodal Single-Cell Integration and Mechanistic Discovery

Multi-omics technologies allow detailed characterization of cell types and states across omics layers as well as chemical and genetic perturbations. Variational autoencoders have become a cornerstone of single-cell data integration; however, they are often implemented as black-box models, requiring post hoc interpretation using known markers, pathways, or regulators to make sense of their latent representations. NetworkVI fundamentally flips this paradigm by incorporating biological knowledge directly into the model architecture. By embedding co-regulation networks derived from topologically associated domains and structured ontologies such as the Gene Ontology (GO), NetworkVI introduces a biologically-informed inductive bias that promotes the preservation of meaningful variation during integration while enforcing interpretability at both the gene and GO levels. NetworkVI achieves state-of-the-art data integration, modality imputation, and cell label transfer across bimodal and trimodal datasets. Beyond integration, here we show that NetworkVI facilitates ontology-guided hypothesis generation by exploiting established associations between genes, structured cellular programs, and regulatory domains to interpretably model cellular identities. Furthermore, decomposition of GO activation spaces resolves lineage-specific functional states within immune cell types, including quiescent, inflammatory, and transitional monocyte subpopulations, that are invisible to transcriptomic clustering. NetworkVI prioritizes GO-term programs associated with immunosenescence, consistent with age-associated immune dysregulation and reveals candidate immune evasion mechanisms consistent with CD58 loss in a Perturb-CITE-seq melanoma dataset.

bioinformatics↗

bioRxiv2025

2D, or not 2D? Investigating Vertical Signal Integrity of Tissue Slices

Imaging-based spatially resolved transcriptomics can localize transcripts within tissue sections in 3D. Cell segmentation assigns transcripts to cells and precedes annotation of cell function. However, cell segmentation is usually performed in 2D, thus unable to deal with spatial doublets arising from overlapping cells, resulting in segmented cells containing transcripts originating from multiple cell types. Here we present a computational tool called ovrlpy that identifies overlapping cells, tissue folds, and inaccurate cell-segmentation by analyzing transcript localization in 3D.

bioinformatics↗

bioRxiv2024

Rewired type I IFN signaling is linked to age-dependent differences in COVID-19

Advanced age is the most important risk factor for severe disease or death from COVID-19, but a thorough mechanistic understanding of the molecular and cellular underpinnings is lacking. Multi-omics analysis of samples from SARS-CoV-2 infected persons aged 1 to 84 years, revealed a rewiring of type I interferon (IFN) signaling with a gradual shift from signal transducer and activator of transcription 1 (STAT1) to STAT3 activation in monocytes, CD4+ T cells and B cells with increasing age. Diversion of interferon IFN signaling was associated with increased expression of inflammatory markers, enhanced release of inflammatory cytokines, and delayed contraction of infection-induced CD4+ T cells. A shift from IFN-responsive germinal center B (GCB) cells towards CD69high GCB and atypical B cells corresponded to the formation of IgA in children while complement fixing IgG was dominant in adults. Our data provide a mechanistic basis for inflammation-prone responses to infections and associated pathology during aging. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=144 SRC="FIGDIR/small/619479v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@723896org.highwire.dtl.DTLVardef@e12b2aorg.highwire.dtl.DTLVardef@d8de22org.highwire.dtl.DTLVardef@1df96ec_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2024

SpatialLeiden - Spatially-aware Leiden clustering

Clustering can identify the natural structure that is inherent to measured data. For single-cell omics, clustering finds cells with similar molecular phenotype after which cell types are annotated. Leiden clustering is the algorithm of choice in the single-cell community. However, in the field of spatial omics, Leiden has been considered a non-spatial clustering method. Here, we show that by integrating spatial embeddings Leiden clustering is rendered into a computationally highly performant, spatially aware clustering method that compares well with state-of-the art clustering methods.

bioinformatics↗

bioRxiv2024

Sainsc: a computational tool for segmentation-free analysis of in-situ capture

Spatially resolved transcriptomics has become the method of choice to characterise the complexity of biomedical tissue samples. Until recently, scientists have been restricted to profiling methods with high spatial resolution but for a limited set of genes or methods that can profile transcriptome-wide but at low spatial resolution. Through recent developments, there are now methods which offer subcellular spatial resolution and full transcriptome coverage. However, utilizing the high spatial and gene resolution of these new methods remains elusive due to several factors including low detection efficiency, high computational cost and difficulties in delineating cell borders. Here we present Sainsc (Segmentation-free analysis of in-situ capture data), which combines a cell-segmentation free approach with efficient data processing of transcriptome-wide nanometer resolution spatial data. Sainsc can generate cell-type maps with accurate cell-type assignment at a subcellular level, together with corresponding maps of the assignment scores that facilitate the interpretation in the local confidence of cell-type assignment. We demonstrate its utility and accuracy across different tissues and profiling methods. Compared to other methods, Sainsc requires lower computational resources and has scalable performance, enabling interactive data exploration. Sainsc is compatible with common data analysis frameworks and is available as open-source software in multiple programming languages.

bioinformatics↗

bioRxiv2023

Enhanced Airway Epithelial Response to SARS-CoV-2 Infection in Children is Critically Tuned by the Cross-Talk Between Immune and Epithelial Cells

To cope with novel virus infections to which no prior adaptive immunity exists, the body strongly relies on the innate immune system. In such cases, including infections with SARS-CoV-2, children tend to fair better than adults. In the context of COVID-19, it became evident that a rapid interferon response at the site of primary infection is key for successful control of the virus and prevention of severe disease. The airway epithelium of children was shown to exhibit a primed state already at homeostasis and to respond particularly well to SARS-CoV-2 infection. However, the underlying mechanism for this priming remained elusive. Here we show that interactions between airway mucosal immune cells and epithelial cells are stronger in children, and via cytokine-mediated signaling lead to IRF-1-dependent upregulation of the viral sensors RIG-I and MDA5. Based on a cellular in vitro model we show that stimulated human peripheral blood mononuclear cells (PBMC) can induce a robust interferon-beta response towards SARS-CoV-2 in a lung epithelial cell line otherwise unresponsive to this virus. This is mediated by type I interferon, interferon-gamma and TNF, and requires induction of both, RIG-I and MDA5. In single cell-analysis of nasal swab samples the same cytokines are found to be elevated in mucosal immune cells of children, correlating with elevated epithelial expression of viral sensors. In vitro analysis of PBMC derived from healthy adolescents and adults confirm that immune cells of younger individuals show increased cytokine production and potential to prime epithelial cells. In co-culture with SARS-CoV-2-infected A549 cells, PBMC from adolescents significantly enhance the antiviral response. Taken together, our study suggests that higher numbers and a more vigorous activity of innate immune cells in the airway mucosa of children tune the set-point of the epithelial antiviral system. This likely is a major contributor to the robust immune response to SARS-CoV-2 in children. Our findings shed light on the molecular underpinnings of the stunning resilience of children towards severe COVID-19, and may propose a novel concept for immunoprophylactic treatments.

immunology↗

bioRxiv2022

Disturbed trophoblast transition links preeclampsia progression from placenta to the maternal syndrome

Pre-eclampsia (PE) is a syndrome that affects multiple organ systems and is the most severe hypertensive disorder in pregnancy. It frequently leads to preterm delivery, maternal and fetal morbidity and mortality and life-long complications1. We currently lack efficient screening tools2, 3 and early therapies4, 5 to address PE. To investigate the early stages of early onset PE, and identify candidate markers and pathways, we performed spatio-temporal multi-omics profiling of human PE placentae and healthy controls and validated targets in early gestation in a longitudinal clinical cohort. We used a single-nuclei RNA-seq approach combined with spatial proteo- and transcriptomics and mechanistic in vitro signalling analyses to bridge the gap from late pregnancy disease to early pregnancy pathomechanisms. We discovered a key disruption in villous trophoblast differentiation, which is driven by the increase of transcriptional coactivator p300, that ultimately ends with a senescence-associated secretory phenotype (SASP) of trophoblasts. We found a significant increase in the senescence marker activin A in preeclamptic maternal serum in early gestation, before the development of clinical symptoms, indicating a translation of the placental syndrome to the maternal side. Our work describes a new disease progression, starting with a disturbed transition in villous trophoblast differentiation. Our study identifies potential pathophysiology-relevant biomarkers for the early diagnosis of the disease as well as possible targets for interventions, which would be crucial steps toward protecting the mother and child from gestational mortality and morbidity and an increased risk of cardiovascular disease later in life.

systems biology↗

bioRxiv2022

Transcriptionally defined morphological subtypes of pancreatic ductal adenocarcinoma

Tumour heterogeneity remains a major obstacle to effective and precise therapy for pancreatic ductal adenocarcinoma (PDAC), the most common pancreatic cancer. Several transcriptional subtypes of PDAC with differential prognosis have been described, but they co-occur within tumours and are difficult to distinguish in routine clinical workflows. To investigate the relationship between transcriptional PDAC subtypes, local tissue morphology and the tumour microenvironment, we employed in situ sequencing to profile single cells in their spatial tissue context. We identify five transcriptional subtypes of PDAC cells occurring in three distinct morphological patterns, including secretory tumour cell monolayers, invasive tumour cells with high expression of cell adhesion molecules CEACAM5 and CEACAM6, and spatially distributed tumour cells associated with inflammatory-type fibroblasts. Analysis of bulk RNA-sequencing datasets of the TCGA-PAAD and PACA-AU cohorts according to these spatio-transcriptional subtypes confirmed their prognostic significance. Our results thus indicate an automatable substratification based on spatially-resolved transcriptomics of PDAC and identify distinct subtypes of classical PDAC, representing most cases of this devastating malignancy.

cancer biology↗

bioRxiv2022

Profibrotic priming of airway cell types and drug responses in early-stage idiopathic pulmonary fibrosis

Early genetic studies hinted the role of airway epithelial cells in the development of idiopathic pulmonary fibrosis (IPF), while recent single-cell RNA sequencing (scRNA-seq) atlases utilized explant IPF lungs and therefore represent late-stage disease. Here, we used air liquid interface (ALI) cultures of primary cells taken from the subsegmental bronchi of newly diagnosed IPF patients, reflecting early-stage fibrosis, to interrogate the transcriptional landscape of the airway mucosa. Profiling of 129,986 cells identified a shared proinflammatory state in epithelial cells and an early activation state of fibroblasts. Moreover, IPF basal cells initiated awry repair mechanisms and primed the airway mucosa for TGF-{beta} activation. Treatment with nintedanib, pirfenidone, both established antifibrotic drugs, and saracatinib, an Src kinase inhibitor that can limit IPF progression, only significantly affected certain IPF signatures. This study provides insight into the early disease mechanisms of IPF and may serve as a resource to further investigate pharmacological inhibition effects.

molecular biology↗

bioRxiv2022

Temporal control of the integrated stress response by a stochastic molecular switch

Stress granules (SGs) are formed in the cytosol as an acute response to environmental cues and activation of the integrated stress response (ISR), a central signaling pathway controlling protein synthesis. Using chronic virus infection as stress model, we previously uncovered a unique temporal control of the ISR resulting in recurrent phases of SG assembly and disassembly. Here, we elucidate the molecular network generating this fluctuating stress response, by integrating quantitative experiments with mathematical modeling, and find that the ISR operates as a stochastic switch. Key elements controlling this switch are the cooperative activation of the stress-sensing kinase PKR, the ultrasensitive response of SG formation to the phosphorylation of the translation initiation factor eIF2, and negative feedback via GADD34, a stress-induced subunit of protein phosphatase 1. We identify GADD34 mRNA levels as the molecular memory of the ISR that plays a central role in cell adaptation to acute and chronic stress.

cell biology↗

bioRxiv2021

SSAM-lite: a light-weight web app for rapid analysis of spatially resolved transcriptomics data

1The combination of a cells transcriptional profile and location defines its function in a spatial context. Spatially resolved transcriptomics (SRT) has emerged as the assay of choice for characterizing cells in situ. SRT methods can resolve gene expression up to single-molecule resolution. A particular computational problem with single-molecule SRT methods is the correct aggregation of mRNA molecules into cells. Traditionally, aggregating mRNA molecules into cell-based features begins with the identification of cells via segmentation of the nucleus or the cell membrane. However, recently a number of cell-segmentation-free approaches have emerged. While these methods have been demonstrated to be more performant than segmentation-based approaches, they are still not easily accessible since they require specialized knowledge of programming languages and access to large computational resources. Here we present SSAM-lite, a tool that provides an easy-to-use graphical interface to perform rapid and segmentation-free cell-typing of SRT data in a web browser. SSAM-lite runs locally and does not require computational experts or specialized hardware. Analysis of a tissue slice of the mouse somatosensory cortex took less than a minute on a laptop with modest hardware. Parameters can interactively be optimized on small portions of the data before the entire tissue image is analyzed. A server version of SSAM-lite can be run completely offline using local infrastructure. Overall, SSAM-lite is portable, lightweight, and easy to use, thus enabling a broad audience to investigate and analyze single-molecule SRT data. Availability and ImplementationSSAM-lite is an open-source browser-based web application with source code freely available on Github via https://github.com/HiDiHlabs/ssam-lite. Stable releases can be accessed via https://ssam-lite.bihealth.org and https://ssam-lite.netlify.app, and developmental releases can be accessed via https://dev--ssam-lite.netlify.app. The source code for a locally deployable server version, SSAM-lite-server, is available on GitHub via https://github.com/HiDiHlabs/ssam-lite-server. Both versions require a modern browser with JavaScript and WebGL support. Detailed user guides and documentation can be found at https://ssam-lite.readthedocs.io.

bioinformatics↗