Cell migration is a fundamental biological process essential for embryonal development, immune function, and cancer metastasis, with migration velocity representing a key parameter of this behaviour. Today, cell migration velocity can be measured in high-throughput assays that generate complex, hierarchically structured datasets with technical noise, batch effects, and biological variability, introducing significant uncertainty in velocity estimates. Current statistical approaches often fail to rigorously quantify this uncertainty, limiting reproducibility and comparisons across independent experimental datasets. Here, we present cellmig, a specialized computational tool that addresses this challenge. It implements established Bayesian hierarchical modeling within an accessible workflow tailored for high-throughput live cell migration assays, to separate biological signals from technical variation while explicitly quantifying uncertainty in migration velocity. cellmig provides a robust framework for analyzing cell migration assays, including dose-response studies and large-scale screens with multiple biological and technical replicates. By modeling biological variability (e.g., compound-dependent effects) and technical confounders (e.g., batch variability) within a unified Bayesian framework, cellmig estimates condition-specific effects on cell velocity with probabilistic uncertainty intervals, avoiding common pitfalls associated with null-hypothesis testing. Through exhaustive benchmarking against commonly used approaches in the field, we demonstrate that cellmig achieves improved sensitivity in detecting subtle migration effects and enhanced robustness against technical variability. Additionally, its generative models enable simulation of migration velocities under various assumptions, aiding experimental planning. We validated cellmig through a tiered strategy: (1) benchmarking on two independent experimental datasets and (2) deployment on a large-scale high-throughput screen that discovered new chemical biology. Our results demonstrate that cellmig can detect subtle dose-dependent velocity changes, maintain robustness against systematic variability and batch effects, and facilitate reliable integration of multi-experiment datasets. In summary, cellmig enhances reproducibility, reliability, and biological insight in high-throughput migration studies, facilitating quantitative inter-dataset comparisons. cellmig is implemented as an open-source R package and is freely available on Bioconductor (https://bioconductor.org/packages/cellmig).

Glioblastoma (GBM) is a highly heterogeneous, invasive brain tumor with profound metabolic plasticity. Patient-derived cultures provide valuable preclinical models, yet the stability and clinical relevance of their metabolic phenotypes during ex vivo maintenance remain unclear. Here, we performed longitudinal functional profiling of mitochondrial respiration, substrate utilization, and migration in primary patient-derived GBM cultures at one and five weeks ex vivo using Seahorse Bioanalyzer assays and wound-healing assays, complemented by exploratory integration with routine clinical parameters (MGMT promoter methylation, p53 expression, sex, and age). Early profiling identified two mitochondrial subgroups distinguished by spare and maximal respiratory capacity, with MGMT promoter methylation observed exclusively in the high-respiration cluster. Over time, most cultures retained their initial phenotype, whereas a subset transitioned from low to high respiration, indicating dynamic adaptation under standardized conditions. Extended culture was accompanied by patient-specific shifts in fuel utilization, including increased glutamine dependency and reduced inter-sample variability in glucose and fatty-acid parameters. Exploratory analyses suggested an association between higher age and lower fatty-acid oxidation capacity and indicated constrained fatty-acid dependency in p53-positive cultures. Migration assays revealed marked inter-sample heterogeneity and a 2-6 times faster gap closure in male-derived than female-derived cultures. Notably, early migratory behavior showed no strong association with OCR-defined metabolic state, and early fuel-flex parameters did not recapitulate OCR cluster structure. Together, these data provide a standardized longitudinal functional benchmark showing that patient-derived GBM cultures preserve intrinsic metabolic heterogeneity while undergoing time-dependent adaptation, and that migration represents an additional, partially independent functional axis. These findings are hypothesis-generating and support stratified, time-sensitive follow-up studies of metabolic and invasive phenotypes in GBM.

ABSTRACT Targeted therapies in gastrointestinal stromal tumors (GIST) often fail due to heterogeneous resistance mutations arising across metastatic sites. Efficient, rational design of mutation-specific therapies requires the ability to quantify treatment resistance across many genotypes in parallel. Here, we present BARcode MIXture analysis (BARMIX), a platform combining multiplexed experiments with DNA-barcoded cancer cell mixtures in vitro and in vivo , and a probabilistic framework for quantitative assessment of genotype-specific treatment resistance. BARMIX efficiently and accurately recapitulated known clinical resistance patterns in GIST and matched resistance measurements from individual cell lines in vitro and in vivo . This experimental-computational approach provides a scalable and broadly applicable strategy for quantifying treatment responses in complex cell populations, enabling systematic preclinical testing of new drugs and combinations to identify mutation-specific therapeutic options for precision oncology in GIST and beyond.

Extracellular matrix (ECM) is the main component of cartilage, making it an ideal environment to study cell-matrix interactions. Among ECM constituents, heparan sulfate (HS)-carrying proteoglycans (PGs) are of particular interest since they are not only structural components but are also involved in cell matrix adhesion and signalling processes. We previously demonstrated that transgenic mice with a clonal loss of HS synthesis in chondrocytes ( Col2-rtTA-Cre;Ext1e2fl/e2f l) develop clusters of enlarged cells in the articular cartilage (AC), which are surrounded by a glycosaminoglycan (GAG)-rich ECM. This led to the questions how HS regulate the molecular composition and mechanical properties of the ECM, how they sense alterations in the HS structure and how they respond to it. We stained tissue sections of Col2-rtTA-Cre;Ext1e2fl/e2f animals and detected increased levels of chondroitin sulfate (CS), Aggrecan (Acan), Perlecan (Pcan), Matrilin (Matn)-3 and-4, Collagen type II (Col2) and Col9, while Col12 was abolished in the HS-deficient clusters. We assessed the stiffness of the mutant matrix by Atomic Force Microscopy (AFM) and found that it was markedly softer than the surrounding, HS-containing tissue. Likely in response to this altered texture, HS-deficient clones showed increased protein levels of Integrin pathway components. To model a loss of HS-function in vitro , we treated murine embryonic fibroblasts (MEFs) with the HS-antagonist Surfen . Treatment during cell adhesion resulted in impaired cell-substrate adhesion, increased formation of filopodia-like membrane protrusions, decreased cell polarisation and migration, reduced formation of FA and SF, and a translocation of YAP into the cytoplasm. Similarly, we observed reduced cell polarisation in HS-deficient CHO pgsD-667 cells, which could not be rescued by external presentation of HS. When MEFs were treated with Surfen after the completion of the initial cell adhesion process, inhibition of HS-function led to an increased formation of FA and SF, in line with the increased levels of Integrin pathway components observed in HS-deficient chondrocytes in vivo . We detected high levels of Yes1-associated protein (YAP) in the HS-deficient clusters, and we investigated the effect of YAP modulation on high density micromass cultures from primary murine chondroprogenitors. YAP activation induced an increased GAG synthesis similar to Surfen, while YAP inactivation partially abolished the effect of Surfen, showing that YAP acts downstream of HS function and controls GAG synthesis. Taken together, we demonstrated that HS-function is essential for Integrin-dependent cell-matrix interactions. Information on the impaired cell matrix adhesion upon loss of HS is conveyed into the nucleus via YAP, which at least partially controls the synthesis of GAGs in chondrocytes. ### Competing Interest Statement The authors have declared no competing interest.

MOTIVATION:In biological research, complex and noisy biological systems with small effects are often studied with small sample sizes. Such a setting is ideal for Bayesian analysis as it supplements new data with prior knowledge and emphasizes uncertainty quantification. Unfortunately, the proper application of Bayesian analysis requires a degree of computational expertise beyond the training of many biologists. RESULTS:We have developed BAYAS (BAYesian Analysis Simplified), a web-based tool that provides programming-free access to Bayesian workflows for numerous use cases. BAYAS comes with three modules: Planning for Bayesian determination of sample sizes; Evaluation for Bayesian analysis of experimental data; Report to make analyses transparent and reproducible. AVAILABILITY AND IMPLEMENTATION:BAYAS can be accessed freely at https://bayas.zmb.uni-due.de/app/bayas (server) and https://github.com/GitCJW/bayas_bioinformatics or https://doi.org/10.5281/zenodo.15052467 (source).

BACKGROUND:Histone modifications are key epigenetic regulators of cell differentiation and have been intensively studied in many cell types and tissues. Nevertheless, we still lack a thorough understanding of how combinations of histone marks at the same genomic location, so-called chromatin states, are linked to gene expression, and how these states change in the process of differentiation. To receive insight into the epigenetic changes accompanying the differentiation along the chondrogenic lineage we analyzed two publicly available datasets representing (1) the early differentiation stages from embryonic stem cells into chondrogenic cells and (2) the direct differentiation of mature chondrocyte subtypes. RESULTS:We used ChromHMM to define chromatin states of 6 activating and repressive histone marks for each dataset and tracked the transitions between states that are associated with the progression of differentiation. As differentiation-associated state transitions are likely limited to a reduced set of genes, one challenge of such global analyses is the identification of these rare transitions within the large-scale data. To overcome this problem, we have developed a relativistic approach that quantitatively relates transitions of chromatin states on defined groups of tissue-specific genes to the background. In the early lineage, we found an increased transition rate into activating chromatin states on mesenchymal and chondrogenic genes while mature chondrocytes are mainly enriched in transition between activating states. Interestingly, we also detected a complex extension of the classical bivalent state (H3K4me3/H3K27me3) consisting of several activating promoter marks besides the repressive mark H3K27me3. Within the early lineage, mesenchymal and chondrogenic genes undergo transitions from this state into active promoter states, indicating that the initiation of gene expression utilizes this complex combination of activating and repressive marks. In contrast, at mature differentiation stages the inverse transition, the gain of H3K27me3 on active promoters, seems to be a critical parameter linked to the initiation of gene repression in the course of differentiation. CONCLUSIONS:Our results emphasize the importance of a relative analysis of complex epigenetic data to identify chromatin state transitions associated with cell lineage progression. They further underline the importance of serial analysis of such transitions to uncover the diverse regulatory potential of distinct histone modifications like H3K27me3.

Merkel cell carcinoma (MCC) is an aggressive skin cancer with neuroendocrine differentiation marked by high cellular plasticity, often manifesting as rapid therapy resistance. Although the cell-of-origin is presumed to be epithelial, epidermal localization of MCC is rarely observed, largely because in situ MCC is typically an incidental finding. Nevertheless, a subset of MCC tumors exhibits epidermotropism, wherein tumor cells are present in the epidermis. The behavior of cancer cells is profoundly influenced by the tumor microenvironment and interactions with neighboring cells. Notably, the normal counterparts of the cancer’s cell-of-origin have been shown to attenuate tumor aggressiveness. Thus, epidermotropic MCC presents a unique opportunity to explore the potential role of epidermal microenvironment in modulating tumor cell behavior. While the epidermotropic tumor nests share histological resemblance with their dermal counterparts, their transcriptomic profiles remain unexplored. Here, we employed high-definition spatial and single-cell transcriptomics to dissect the gene expression profiles of epidermotropic MCC cells, comparing them to MCC cells in the tumor core and those adjacent to blood vessels. Notably, epidermotropic MCC cells exhibit a transcriptomic signature reminiscent of cutaneous squamous cell carcinoma, characterized by upregulation of genes encoding keratins, S100A proteins, as well as calmodulin-like proteins 3 and 5. Mechanistically, this keratinocytic differentiation is associated with enhanced p63 activity, leading to the upregulation of PERP. Collectively, our study demonstrates that MCC cells can adopt a keratinocytic differentiation program in response to microenvironmental cues, underscoring the remarkable phenotypic plasticity of this malignancy and the importance of the microenvironment for tumor cell characteristics.

Motivation Accurate quantification of live cell migration, including velocity, is essential for understanding biological processes such as development, immune function, and cancer metastasis. High-throughput migration assays generate complex, hierarchically structured datasets with technical noise, batch effects, and biological variability, introducing significant uncertainty into velocity estimates. Current statistical approaches often fail to rigorously quantify this uncertainty, undermining reproducibility and comparability across independent experimental datasets. To address this challenge, we present cellmig , a computational tool that employs Bayesian hierarchical modeling to separate biological signals from technical variation while explicitly quantifying uncertainty in migration velocity. Results cellmig provides a robust framework for analyzing cell migration assays, including dose-response studies and large-scale screens with multiple biological and technical replicates. By modeling biological variability (e.g., compound-dependent effects) and technical confounders (e.g., batch variability) within a unified Bayesian framework, cellmig estimates condition-specific effects on cell velocity with probabilistic uncertainty intervals, avoiding common pitfalls associated with null-hypothesis testing. Additionally, its generative models enable simulation of migration velocities under various assumptions, aiding experimental planning. In summary, cellmig enhances reproducibility, reliability, and biological insight in high-throughput migration studies, facilitating inter-dataset comparisons. Availability and Implementation cellmig is an open-source R package, freely available at: Contact barbara.gruener{at}uk-essen.de, daniel.hoffmann{at}uni-due.de, simo.kitanovski{at}uni-due.de ### Competing Interest Statement The authors have declared no competing interest.

Multiplets-droplets that capture more than one cell-are a known artefact in droplet-based single-cell RNA sequencing (scRNA-seq), yet their prevalence and impact remain underestimated. In this study, we assess the frequency of multiplets across diverse publicly available datasets and evaluate how well commonly used detection tools are able to identify them. Using cell hashing data to determine a lower bound of the true multiplet rate, we demonstrate that commonly used heuristic estimations systematically underestimate multiplet rates, and that existing tools-despite optimized parameters-detect only a small subset of cell-hashing multiplets. We further refine a Poisson-based model to estimate the true multiplet rate, revealing that actual rates can exceed heuristic predictions by more than twofold. Downstream analyses are significantly affected by multiplets: they are not confined to isolated clusters but are distributed throughout the transcriptional landscape, where they distort clustering and cell type annotation. In differential gene expression analysis, multiplets inflated artefactual signals while expected cell-type markers remained stable, leading to shifts in effect sizes and partial loss of significant genes despite high overall fold-change correlation. Using both quantitative and qualitative approaches, we visualize these effects and show that cell-hashing-informed multiplet removal eliminates artefactual clusters and improves annotation clarity, whereas computationally detected multiplets fail to fully remove artefacts in the most common experimental contexts. Our findings confirm that multiplet contamination remains a pervasive and under-addressed issue in scRNA-seq analysis. Since most datasets lack multiplexing, researchers must often rely on heuristics and limited tools, leaving many multiplets unidentified. We advocate for more robust multiplet-detection strategies, including multimodal validation, to ensure more accurate and interpretable scRNA-seq results.

High-throughput sequencing (HTseq) characterizes complex entities at the level of nucleic acid sequences, e. g. the transcriptome in a biopsy at cellular resolution, or an immune-receptor-repertoire. The complexity of HTseq data makes analysis challenging. Suitable methods are often missing. We therefore develop computational tools for quantitative visualization (quantitative modelling of data, then visualization of meaningful summaries) as illustrated with two examples, scBubble-tree and ClustIRR.

Although selection of escape mutations in CD8 T cell epitopes has been previously described in HBV infection, the overall impact of CD8 T cell selection pressure and its influence on HBV sequence diversity remains unclear. Here, we applied whole-genome sequencing to HBV isolates from 532 HLA class I genotyped patients and detected HLA-associated mutations in viral genomes (HAMs) using a Bayesian model (HAMdetector).

BACKGROUND:Visualization approaches transform high-dimensional data from single cell RNA sequencing (scRNA-seq) experiments into two-dimensional plots that are used for analysis of cell relationships, and as a means of reporting biological insights. Yet, many standard approaches generate visuals that suffer from overplotting, lack of quantitative information, and distort global and local properties of biological patterns relative to the original high-dimensional space. RESULTS:We present scBubbletree, a new, scalable method for visualization of scRNA-seq data. The method identifies clusters of cells of similar transcriptomes and visualizes such clusters as "bubbles" at the tips of dendrograms (bubble trees), corresponding to quantitative summaries of cluster properties and relationships. scBubbletree stacks bubble trees with further cluster-associated information in a visually easily accessible way, thus facilitating quantitative assessment and biological interpretation of scRNA-seq data. We demonstrate this with large scRNA-seq data sets, including one with over 1.2 million cells. CONCLUSIONS:To facilitate coherent quantification and visualization of scRNA-seq data we developed the R-package scBubbletree, which is freely available as part of the Bioconductor repository at: https://bioconductor.org/packages/scBubbletree/.

BACKGROUND & AIMS:Immune responses by CD8 T cells are essential for control of HBV replication. Although selection of escape mutations in CD8 T-cell epitopes has previously been described in HBV infection, its overall influence on HBV sequence diversity and correlation with markers of HBV replication remain unclear. METHODS:Whole-genome sequencing was applied to HBV isolates from 532 patients with chronic HBV infection and high-resolution HLA class I genotyping. Using a Bayesian model (HAMdetector) for identification of HLA-associated mutational states (HAMs), the frequency and location of residues under CD8 T-cell selection pressure were determined and the levels of adaptation of individual isolates were quantified. RESULTS:Using previously published thresholds for the identification of HAMs, a total of 295 residues showed evidence of CD8 T-cell escape, the majority of which were located in previously unidentified epitopes. Interestingly, HAMs were highly enriched in the HBV core protein compared to all other proteins. When individual HBV isolates were compared, different levels of adaptation to HLA class I immune pressure were noted. The level of adaptation increased with patient age and correlated with markers of replication, with low levels of adaptation in HBeAg-positive infection. Furthermore, the levels of adaptation negatively correlated with HBV viral load and HBsAg levels, consistent with high levels of HLA class I-associated selection pressure in patients with low replication levels. CONCLUSIONS:HBV sequence diversity is shaped by HLA class I-associated selection pressure with the HBV core protein being a predominant target of selection. Importantly, different levels of adaptation to immune pressure were observed between HBV infection stages, which need to be considered in the context of T-cell-based therapies. IMPACT AND IMPLICATIONS:The immune response mediated by CD8 T cells plays a critical role in controlling HBV infection and shows promise for therapeutic strategies aimed at achieving a functional cure. This study demonstrates that mutational escape within CD8 T-cell epitopes is common in HBV and represents a key factor in the failure of immune control. Notably, the HBV core protein emerges as the primary target of CD8 T-cell selection pressure. Additionally, the observed correlation between HBV adaptation levels and viral replication markers indicates that CD8 T-cell immunity may influence transitions between phases of chronic HBV infection.

To understand the biological relevance and mode of action of artificial protein ligands, crystal structures with their protein targets are essential. Here, we describe and investigate all known crystal structures that contain a so-called “molecular tweezer” or one of its derivatives with an attached natural ligand on the respective target protein. The aromatic ring system of these compounds is able to include lysine and arginine side chains, supported by one or two phosphate groups that are attached to the half-moon-shaped molecule. Due to their marked preference for basic amino acids and the fully reversible binding mode, molecular tweezers are able to counteract pathologic protein aggregation and are currently being developed as disease-modifying therapies against neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease. We analyzed the corresponding crystal structures with 14-3-3 proteins in complex with mono- and diphosphate tweezers. Furthermore, we solved crystal structures of two different tweezer variants in complex with the enzyme Δ1-Pyrroline-5-carboxyl-dehydrogenase (P5CDH) and found that the tweezers are bound to a lysine and methionine side chain, respectively. The different binding modes and their implications for affinity and specificity are discussed, as well as the general problems in crystallizing protein complexes with artificial ligands.

Functional cure for chronic hepatitis B (CHB) remains challenging due to the lack of direct intervention methods for hepatic inflammation. Multi-omics research offers a promising approach to understand hepatic inflammation mechanisms in CHB. A Bayesian linear model linked gene expression with clinical parameters, and population-specific expression analysis (PSEA) refined bulk gene expression into specific cell types across different clinical phases. These models were integrated into our analysis of key factors like inflammatory cells, immune activation, T cell exhaustion, chemokines, receptors, and interferon-stimulated genes (ISGs). Validation through multi-immune staining in liver specimens from CHB patients bolstered our findings. In CHB patients, increased gene expression related to immune cell activation and migration was noted. Marker genes of macrophages, T cells, immune-negative regulators, chemokines, and ISGs showed a positive correlation with serum alanine aminotransferase (ALT) levels but not hepatitis B virus DNA levels. The PSEA model confirmed T cells as the source of exhausted regulators, while macrophages primarily contributed to chemokine expression. Upregulated ISGs (ISG20, IFI16, TAP2, GBP1, PSMB9) in the hepatitis phase were associated with T cell and macrophage infiltration and positively correlated with ALT levels. Conversely, another set of ISGs (IFI44, ISG15, IFI44L, IFI6, MX1) mainly expressed by hepatocytes and B cells showed no correlation with ALT levels. Our study presents a multi-omics analysis integrating bulk transcriptomic, single-cell sequencing data, and clinical data from CHB patients to decipher the cause of intrahepatic inflammation in CHB. The findings confirm that macrophages secrete chemokines like CCL20, recruiting exhausted T cells into liver tissue; concurrently, hepatocyte innate immunity is suppressed, hindering the antiviral effects of ISGs.

Purpose (the aim of the study): Cartilage matrix mainly consists of two components: Collagens, giving it tensile strength, and proteoglycans, conveying its elastic properties. Heparan sulfate (HS)-carrying proteoglycans are of particular interest since they are not only structural components but also regulate signalling processes. We demonstrated that transgenic mice with a clonal loss of HS synthesis in chondrocytes (Col2-rtTA-Cre;Ext1e2fl/e2fl) develop clusters of enlarged cells in the articular cartilage, which are surrounded by a matrix with increased glycosaminoglycan (GAG) content.

Merkel cell carcinoma (MCC) is a highly aggressive skin cancer associated with integration of Merkel cell polyomavirus (MCPyV). MCPyV-encoded T-antigens (TAs) are pivotal for sustaining MCC's oncogenic phenotype, i.e., repression of TAs results in reactivation of the RB pathway and subsequent cell cycle arrest. However, the MCC cell line LoKe, characterized by a homozygous loss of the RB1 gene, exhibits uninterrupted cell cycle progression after shRNA-mediated TA repression. This unique feature allows an in-depth analysis of the effects of TAs beyond inhibition of the RB pathway, revealing the decrease in expression of stem cell-related genes upon panTA-knockdown. Analysis of gene regulatory networks identified members of the E2F family (E2F1, E2F8, TFDP1) as key transcriptional regulators that maintain stem cell properties in TA-expressing MCC cells. Furthermore, minichromosome maintenance (MCM) genes, which encodes DNA-binding licensing proteins essential for stem cell maintenance, were suppressed upon panTA-knockdown. The decline in stemness occurred simultaneously with neural differentiation, marked by the increased expression of neurogenesis-related genes such as neurexins, BTG2, and MYT1L. This upregulation can be attributed to heightened activity of PBX1 and BPTF, crucial regulators of neurogenesis pathways. The observations in LoKe were confirmed in an additional MCPyV-positive MCC cell line in which RB1 was silenced before panTA-knockdown. Moreover, spatially resolved transcriptomics demonstrated reduced TA expression in situ in a part of a MCC tumor characterized by neural differentiation. In summary, TAs are critical for maintaining stemness of MCC cells and suppressing neural differentiation, irrespective of their impact on the RB-signaling pathway.

Post-injury dysfunction of humoral immunity accounts for infections and poor outcomes in cardiovascular diseases. Among immunoglobulins (Ig), IgA, the most abundant mucosal antibody, is produced by plasma B cells in intestinal Peyer's patches (PP) and lamina propria. Here we show that patients with stroke and myocardial ischemia (MI) had strongly reduced IgA blood levels. This was phenocopied in experimental mouse models where decreased plasma and fecal IgA were accompanied by rapid loss of IgA-producing plasma cells in PP and lamina propria. Reduced plasma IgG was detectable in patients and experimental mice 3-10 d after injury. Stroke/MI triggered the release of neutrophil extracellular traps (NETs). Depletion of neutrophils, NET degradation or blockade of NET release inhibited the loss of IgA+ cells and circulating IgA in experimental stroke and MI and in patients with stroke. Our results unveil how tissue-injury-triggered systemic NET release disrupts physiological Ig secretion and how this can be inhibited in patients. Tuz et al. report that stroke and myocardial infarction induce the release of neutrophil extracellular traps (NETs), triggering the loss of B cells and a decrease in immunoglobulin A secretion, and that inhibition of NETs prevents the loss of immunoglobulin A in mice and in patients with stroke.

Motivation: Visualization approaches transform high-dimensional data from single cell RNA sequencing (scRNA-seq) experiments into two-dimensional plots that are used for analysis of cell relationships, and as a means of reporting biological insights. Yet, many standard approaches generate visuals that suffer from overplotting, lack of quantitative information, and distort global and local properties of biological patterns relative to the original high-dimensional space. Results: We present scBubbletree, a new, scalable method for visualization of scRNA-seq data. The method identifies clusters of cells of similar transcriptomes and visualizes such clusters as "bubbles" at the tips of dendrograms (bubble trees), corresponding to quantitative summaries of cluster properties and relationships. scBubbletree stacks bubble trees with further cluster-associated information in a visually easily accessible way, thus facilitating quantitative assessment and biological interpretation of scRNA-seq data. Availability and Implementation: the R package scBubbletree is freely available at: https://bioconductor.org/packages/scBubbletree/ Contact: simo.kitanovski@uni-due.de, daniel.hoffmann@uni-due.de

Sterile tissue injury after stroke causes lymphocyte contraction in lymphoid tissues and may decrease circulating IgA-levels. Intestinal Peyer’s patches (PP) harbor large numbers of IgA+ B cell precursors and plasma cells. Whether and how tissue injury triggers PP-B cell death, thereby mediating IgA-loss, is unknown. We found decreased circulating IgA levels in stroke and myocardial infarction patients. Experimental stroke and myocardial infarction in mice phenocopied the human situation. Decreased plasma and fecal IgA were accompanied by rapid and macroscopic shrinkage of PP caused by substantial losses of PP-resident IgA + precursors and plasma cells in mice. Tissue injury induced neutrophil activation endowed with the release of toxic neutrophil extracellular traps (NETs). Antibody-mediated or genetically-induced neutrophil loss, digestion of NETs, or inhibition of their release by the Gasdermin D blockade completely prevented lymphocyte loss and PP shrinkage. We also identified NETs in the plasma of stroke and myocardial infarction patients. Hence, tissue injury induces systemic NET-release, which might be targeted to maintain immune homeostasis at mucosal barriers.