Lymph node (LN) function requires the organization of cells into higher-order spatial units. However, the principles governing LN architecture in health and disease remain poorly understood. Here, we used single-cell and spatial mapping to investigate the mechanisms directing immune cell organization in human LNs and its disruption in architecturally distinct lymphoma entities: indolent follicular lymphoma (FL) and aggressive diffuse large B cell lymphoma (DLBCL). Our data substantiate the central role of LN-resident stromal cells in chemokine-driven lymphocyte zonation and reveal an inflammatory feedback loop fueled by tumor-reactive T cells that triggers stromal remodeling, progressive loss of homeostatic chemokine gradients, and tissue disorganization from a non-malignant state to FL and DLBCL. Loss of homeostatic chemokines was associated with adverse patient survival, identifying the underlying architectural rearrangement as a key event during lymphomagenesis. Collectively, our results highlight the principles of LN organization and suggest how lymphoma-induced microenvironmental reprogramming drives the loss of tissue organization.

Spatially resolved transcriptomics (SRT) is transforming how we study tissues by measuring gene expression in cells in their spatial context. However, the field lacks robust methodological guidance on one of its most fundamental analytical steps: how to accurately segment cells and assign spatially localized transcripts to them. Major technical challenges include sparse molecular signals, transcript displacement, complex cellular morphologies, and the projection of three-dimensional tissue architecture onto two-dimensional imaging planes. These challenges make segmentation a major source of uncertainty, with errors that can propagate through downstream analyses and ultimately lead to misleading biological interpretations. Here, we argue that segmentation should be treated as a central unresolved problem in spatial omics rather than a routine preprocessing step. We review current approaches, highlight key methodological limitations, including the lack of appropriate metrics and gold-standard benchmarks, and propose a community-driven path forward. Establishing shared evaluation frameworks, scalable benchmark datasets, and transparent reporting standards will be essential for transforming SRT into a robust and reproducible foundation for biological discovery and clinical translation.

Abstract 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 analyze 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 SOFA’s effectiveness for discovering novel patterns by applying it to cancer, brain development and heart failure multi-omic data sets.

Bispecific antibodies (bsAbs) such as glofitamab represent a promising therapeutic approach for relapsed/refractory B-cell non-Hodgkin's lymphoma (R/R B-NHL), but resistance mechanisms remain poorly understood. This study aimed to identify predictive markers of bsAbs resistance based on the response of 3D patient-derived lymphoma spheroids (PDLS) established from 39 R/R B-NHL samples. PDLS were treated with glofitamab for 3 days and B-cell depletion was quantified to assess the ex-vivo treatment response. Comprehensive immune profiling was performed on patient samples using multiparametric flow cytometry, single-cell RNA sequencing, CODEX spatial proteomics and functional assays. High responders to glofitamab possessed CD8+ T-cells with consistently higher cytotoxic and activation signatures across effector differentiation states, while low responders showed enrichment of exhausted CD8+ T-cell with enhanced expression of exhaustion markers (TIGIT, LAG3, PD1). Furthermore, low responders exhibited elevated functional CD4+ T-follicular helper (Tfh) cells in close proximity to malignant B-cell thus promoting their survival through IL21 and CXCL13 signaling pathways. Analysis of pretreatment RNA-seq data from 48 R/R B-NHL patients confirmed that high Tfh abundance is associated with poor glofitamab response. In PDLS, anti-TIGIT co-treatment enhanced glofitamab efficacy in low responders, and Tfh depletion experiments confirmed that reducing Tfh activity increased B-cell depletion. Together, these findings identify CD8+ T-cell exhaustion and functionally activated Tfh cells as key factors associated with glofitamab resistance in R/R B-NHL. This work supports their potential use as predictive biomarkers for selecting patients with higher probability of response and provides a foundation for future combination therapeutic strategies.

Chronic lymphocytic leukemia (CLL) is characterized by the accumulation of clonal B cells. Although targeted therapies have improved outcomes, resistance remains a challenge, particularly in high-risk patients with TP53 mutations or unmutated immunoglobulin heavy-chain variable region (IGHV) genes (U-CLL). Ferroptosis, a regulated, iron-dependent form of cell death, may represent an exploitable vulnerability in CLL; however, its mechanisms and clinical relevance remain poorly understood. Here, we identified IGHV status and microenvironmental cues as determinants of ferroptosis sensitivity. Using CLL cell lines, patient samples, and in vivo models, we show that CLL cells exhibit elevated basal levels of lipid peroxides and labile iron, predisposing them to ferroptosis. However, stromal interactions enhance cystine import and glutathione synthesis, thereby mitigating susceptibility to ferroptosis. Mechanistically, BTK inhibition sensitizes CLL cells to ferroptosis by increasing the transferrin receptor (TFRC, CD71) and increasing the intracellular Fe²⁺ level. High TFRC expression was associated with improved survival in two independent CLL patient cohorts, supporting its therapeutic and prognostic relevance. Combining ibrutinib with the GPX4 inhibitor RSL3 enhances ferroptosis and improves antileukemic efficacy in vivo. CLL cells with mutated IGHV genes (M-CLL) display greater TFRC expression and ferroptosis sensitivity than U-CLL cells do. This resistance can be overcome by ibrutinib-mediated TFRC induction or via metabolic targeting of fatty acid metabolism. Notably, ACSL1 is selectively upregulated in U-CLL cells and represents a targetable metabolic enhancer of ferroptosis sensitivity, as shown in vivo. Our findings reveal that TFRC and ACSL1 are functionally distinct yet targetable nodes that govern ferroptosis vulnerability in CLL patients and may guide novel therapeutic strategies for high-risk patients.

Highly multiplexed immunofluorescence imaging visualizes and quantifies protein levels at single-cell resolution in intact tissues at low cost and high scalability. Analysis of these data involves multiple steps with many method and parameter choices that must be adapted to the data and analytical objectives. There is an unmet need for a toolbox that offers flexible end-to-end coverage of the workflow. Here we present 'spatialproteomics', a Python package that addresses these challenges. Spatialproteomics enables the processing and analysis of large imaging data, including steps such as segmentation, image processing and cell-type classification, while synchronizing shared coordinates across data modalities. We demonstrate spatialproteomics on images of reactive lymph nodes and B cell non-Hodgkin lymphomas from 132 patients. We showcase an end-to-end analysis from raw images to statistical characterization of how cell type composition and spatial distribution vary across indolent and aggressive lymphomas. Furthermore, we show how spatialproteomics can process Gigapixel whole-slide images.

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.

Identifying gene expression differences in heterogeneous tissues across conditions is a fundamental biological task, enabled by multi-condition single-cell RNA sequencing (RNA-seq). Current data analysis approaches divide the constituent cells into clusters meant to represent cell types, but such discrete categorization tends to be an unsatisfactory model of the underlying biology. Here, we introduce latent embedding multivariate regression (LEMUR), a model that operates without, or before, commitment to discrete categorization. LEMUR (1) integrates data from different conditions, (2) predicts each cell's gene expression changes as a function of the conditions and its position in latent space and (3) for each gene, identifies a compact neighborhood of cells with consistent differential expression. We apply LEMUR to cancer, zebrafish development and spatial gradients in Alzheimer's disease, demonstrating its broad applicability.

Established genetic biomarkers in chronic lymphocytic leukemia (CLL) have been useful in predicting response to chemoimmunotherapy but are less predictive of response to targeted therapies. With several such targeted therapies now approved for CLL, identifying novel, non-genetic predictive biomarkers of response may help to select the optimal therapy for individual patients. We coupled data from a functional precision medicine technique called BH3-profiling, which assesses cellular cytochrome c loss levels as indicators for survival dependence on anti-apoptotic proteins, with multi-omics data consisting of targeted and whole-exome sequencing, genome-wide DNA methylation profiles, RNA-sequencing, protein and functional analyses, to identify biomarkers for treatment response in CLL patients. We initially studied 73 CLL patients from a discovery cohort. We found that greater dependence on the anti-apoptotic BCL-2 protein was associated with prognostically favorable genetic biomarkers. Furthermore, BCL-2 dependence was strongly associated with gene expression patterns and signaling pathways that suggest a more targeted drug-sensitive milieu and was predictive of drug responses. We subsequently demonstrated that these associations were causal in cell lines and additional CLL patient samples. To validate the findings from our discovery cohort and in vitro studies, we utilized primary CLL cells from 54 additional patients treated on a prospective, phase-2 clinical trial of the BTK inhibitor ibrutinib given in combination with chemoimmunotherapy (fludarabine, cyclophosphamide, rituximab) and confirmed in this independent dataset that higher BCL-2 dependence predicted favorable clinical response, independent of the genetic background of the CLL cells. We comprehensively defined BCL-2 dependence as a potential functional and predictive biomarker of treatment response in CLL, underscoring the importance of characterizing apoptotic signaling in CLL to stratify patients beyond genetic markers and identifying novel combinations to exploit BCL-2 dependence therapeutically. Our approach has the potential to help optimize targeted therapy combinations for CLL patients.

Bispecific antibodies (BsAb) have emerged as a leading treatment modality in patients suffering from B-cell non-Hodgkin’s lymphoma (B-NHL). However, treatment failure is common and may potentially be attributed to pre-existing or emerging T-cell exhaustion. CD39 catalyzes—together with CD73—the hydrolysis of immunogenic ATP into immunosuppressive adenosine and thus actively promotes an immunosuppressive micromilieu. Previously, we and others demonstrated that CD39+ T-cell subsets may have an adverse impact on the efficacy of T-cell-engaging immunotherapies. In this study, we applied an autologous ex vivo culture model of primary lymph node-derived T cells to investigate the potential of anti-CD39 or anti-CD73 blocking antibodies as T-cell enhancing combination partners of an anti-CD20 BsAb. Existing single-cell data of patient samples examined in this study were used to detect potential biomarkers predicting combination benefits. Combining anti-CD20 BsAb with anti-CD39 or anti-CD73 blocking antibodies induced synergistic effects on tumor cell killing, T-cell expansion and secretion of cytokines, including granzyme B, perforin, interleukin-10, interferon-γ, and tumor necrosis factor-α. We discovered that blockade of the CD39/CD73 pathway was particularly effective in patients with a high proportion of Programmed cell death protein 1 (PD-1)+ T-cell immunoglobulin and mucin-domain containing-3 (TIM3)+ exhausted T cells. Also, expression of CD39 in effector memory T cells indicated superior treatment benefit ex vivo. In summary, our study holds significant relevance as it introduces the combination of bispecific and anti-CD39 or anti-CD73 antibodies as a synergistic treatment approach in B-NHL, while also suggesting potential indicators to identify patients that might benefit from this treatment.

Chromothripsis is a frequent form of genome instability, whereby a presumably single catastrophic event generates extensive genomic rearrangements of one or multiple chromosome(s). However, little is known about the heterogeneity of chromothripsis across different clones from the same tumour, as well as changes in response to treatment. Here we analyse single-cell genomic and transcriptomic alterations linked with chromothripsis in human p53-deficient medulloblastoma and neural stem cells (n = 9). We reconstruct the order of somatic events, identify early alterations likely linked to chromothripsis and depict the contribution of chromothripsis to malignancy. We characterise subclonal variation of chromothripsis and its effects on extrachromosomal circular DNA, cancer drivers and putatively druggable targets. Furthermore, we highlight the causative role and the fitness consequences of specific rearrangements in neural progenitors. Chromothripsis (CT) is a type of genome instability which is prevalent in medulloblastoma with germline TP53 mutations (Li-Fraumeni syndrome, LFS). Here the authors combine single-cell genomic and transcriptomic analyses to reveal the clonal heterogeneity and functional consequences of CT in LFS medulloblastoma.

Genetic variants (both coding and noncoding) can impact gene function and expression, driving disease mechanisms such as cancer progression. The systematic study of endogenous genetic variants is hindered by inefficient precision editing tools, combined with technical limitations in confidently linking genotypes to gene expression at single-cell resolution. We developed single-cell DNA–RNA sequencing (SDR-seq) to simultaneously profile up to 480 genomic DNA loci and genes in thousands of single cells, enabling accurate determination of coding and noncoding variant zygosity alongside associated gene expression changes. Using SDR-seq, we associate coding and noncoding variants with distinct gene expression in human induced pluripotent stem cells. 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 provides a powerful platform to dissect regulatory mechanisms encoded by genetic variants, advancing our understanding of gene expression regulation and its implications for disease. This study introduces SDR-seq, a droplet-based single-cell DNA–RNA sequencing platform, enabling the study of gene expression profiles linked to both noncoding and coding variants.

Summary Highly 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 Implementation The source code for spatialproteomics is freely available at under the MIT license. Contact wolfgang.huber{at}embl.org, Peter-Martin.Bruch{at}med.uni-duesseldorf.de ### Competing Interest Statement The authors have declared no competing interest. European Union’s Horizon 2020 Research and Innovation Programme, , 964264

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: ### Competing Interest Statement Dmitry Kuchenov is currently employed at Soley Therapeutics, but this work was completed independently and is unrelated to research activities of the the company.

Topologically associating domains (TADs) and chromatin architectural loops impact promoter-enhancer interactions, with CCCTC-binding factor (CTCF) defining TAD borders and loop anchors. TAD boundaries and loops progressively strengthen upon embryonic stem (ES) cell differentiation, underscoring the importance of chromatin topology in ontogeny. However, the mechanisms driving this process remain unclear. Here we show a widespread increase in CTCF-RNA-binding protein (RBP) interactions upon ES to neural stem (NS) cell differentiation. While dispensable in ES cells, RBPs reinforce CTCF-anchored chromatin topology in NS cells. We identify Pantr1, a non-coding RNA, as a key facilitator of CTCF-RBP interactions, promoting chromatin maturation. Using acute CTCF degradation, we find that, through its insulator function, CTCF helps maintain neuronal gene silencing in NS cells by acting as a barrier to untimely gene activation during development. Altogether, we reveal a fundamental mechanism driving developmentally linked chromatin structural consolidation and the contribution of this process to the control of gene expression in differentiation.

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 . ![GRAPHICAL ABSTRACT][1] GRAPHICAL ABSTRACT ### Competing Interest Statement The authors have declared no competing interest. [1]: pending:yes

Recurrent mutations in the third base of U1 spliceosomal RNA responsible for marked splicing and expression abnormalities have been described in chronic lymphocytic leukemia (CLL) and some solid tumors. However, the clinical significance of these mutations in large and independent CLL cohorts as well as their presence in other B-cell neoplasms is unknown. Here we characterized U1 mutations in 1670 CLL and 363 mature B-cell lymphomas. We confirmed that the g.3A>C U1 mutation is found in 3.5% of CLL, which conferred rapid disease progression independently of the main biological and clinical prognostic markers of the disease. Additionally, a recurrent g.9C>T mutation was found in 1.5% of CLL causing downstream splicing alterations and associated with adverse prognosis. We also identified a g.4C>T mutation in 10% of diffuse large B-cell lymphomas of the germinal center subtype and a g.7A>G mutation in 30% of EBV-negative Burkitt lymphomas, both of which altered the splicing pattern of multiple genes. This study reveals novel, recurrent, and tumor-specific U1 mutations in mature B-cell neoplasms with biological and prognostic implications, thus establishing U1 as a novel pan-B-cell malignancy driver gene.

Introduction Microenvironmental profiling in lymphoma has identified clinically relevant subgroups through transcriptional and genomic profiling. We further enhance the characterization of DLBCL by examining the variations in cellular composition and spatial architecture and correlating these findings with clinical outcomes following chemoimmunotherapy. Method Using CO-Detection by indEXing (CODEX) we characterized the cellular composition of aggressive B-cell non-Hodgkin lymphoma samples from 193 patients at initial diagnosis which were consecutively treated with chemoimmunotherapy in the RICOVER-60 trial (NCT00052936), one of the landmark studies establishing R-CHOP as the standard treatment for DLBCL. Samples were assembled in tissue microarrays and stained with 54-plex antibody panel targeting microenvironmental cells along with important functional markers of malignant B cells. Genetic and clinical annotations were integrated to represent known tumor cell intrinsic features. Results Our analysis revealed that the cellular composition of the microenvironment in diffuse large B-cell lymphoma (DLBCL) varied significantly among samples but remained consistent between technical replicates. This heterogeneity was observed across all major non-lymphoma cell types: T cells ranged from 0.7% to 85% of all cells, with a mean frequency of 29%, followed by tumor-associated macrophages (5%), stromal cells (4%), dendritic cells (2%), and other myeloid cells (4%). We further focused on lymphoma-infiltrating T-cells, classifying them based on our recently published large T-cell Cite-seq dataset (Roider, Nat. Cell Biol. 2024). Our thorough examination of T-cell fingerprints in relation to clinical endpoints revealed among other findings that higher infiltration of cytotoxic T-cells is associated with favorable outcomes, whereas the frequency of exhausted cytotoxic T-cells correlated with poor outcomes. Additionally, we will report on the associations between DLBCL genotypes and specific T-cell phenotypes. Beyond the cell type composition, we investigated the spatial interactions between distinct cell types and found that seven distinct cellular neighborhoods could robustly be identified across patient samples based on the 30 nearest neighboring cells. The composition and organization of cellular neighborhoods was distinct between patients, likely representing different patterns of interaction between the lymphoma and its microenvironment. Associating the observed spatial organization in cellular neighborhoods, we observed significant differences with regard to the known genetic subgroups of DLBCL. Conclusion Our results highlight the importance of microenvironmental factors for clinical outcome after chemoimmunotherapy in DLBCL. T cell phenotypes represent biological factors which impact clinical outcomes but are currently undervalued in clinical diagnostics since they are not represented in typical genetic tests. Additionally, our data suggest that the microenvironmental differences which have previously been described based on transcriptional profiling might also have an impact on the spatial architecture of the lymphoma. This would enable the integration into clinical diagnostic tests and might unravel the mechanisms by which the lymph node organization is disturbed in DLBCL.