Biology subjects

Lundeberg, J.

Publications and source records attributed to Lundeberg, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Spatial immune profiling reveals lymphocyte confinement to myeloid-mesenchymal niches and heterogeneous therapeutic T-cell opportunity in pediatric ependymoma

Pediatric posterior fossa group A (PFA) ependymoma is an aggressive, chemo-resistant brain tumor with a poor 10-year overall survival and no therapeutic options at relapse. Although conventionally classified as immune-cold, the spatial organization and therapeutic relevance of its rare immune infiltrates remain poorly characterized. Here, we present a spatially resolved immune analysis of 14 PFA ependymoma sections using published Visium spatial transcriptomics data analyzed through a multi-stage immune-focused computational framework combining reference-guided deconvolution, functional lymphocyte state scoring, ligand-receptor inference, and {beta}/{gamma}{delta} T cell immunotherapy opportunity scoring. Lymphocyte-associated hotspots were identified in 6 of 14 sections and, among confidently classified hotspots, were dominated by inflammatory-associated and retention-associated transcriptional states rather than cytotoxic or exhausted programs. These hotspots were preferentially confined to myeloid- and mesenchymal-rich tumor zones with near-complete exclusion from epithelial regions; ligand-receptor analysis identified four candidate restriction axes (SPP1-CD44, FN1-integrin, collagen VI-integrin, and APP-CD74). Immunotherapy opportunity scoring revealed heterogeneous and spatially compartmentalized {beta} and {gamma}{delta} T cell recognition landscapes across sections: {beta}T ligand availability and immunosuppressive tone both rose in the mesenchymal compartment, while the two components of {gamma}{delta} phosphoantigen-mediated recognition were spatially and transcriptionally dissociated from one another. Together, these findings reframe PFA ependymoma not as a monolithic immune desert but as a tumor type with spatially compartmentalized, mechanistically tractable immune niches, and provide a generalizable computational strategy for mapping immune architecture and T cell therapy susceptibility in low-infiltrated pediatric CNS tumors.

cancer biology↗

bioRxiv2026

In utero barcoding of mouse mesoderm reveals the clonal architecture of organ mesenchyme

The mesoderm coordinates organogenesis and generates the cardiovascular, visceral and musculoskeletal systems, yet in mammals this germ layer is difficult to access, impeding its genetic manipulation and study. Here, we establish embryonic day (E) 7.5 exocoelomic cavity nano-injection as a scalable route for selective genetic targeting of mouse mesoderm. Combining in utero lentiviral barcoding with single-cell and spatial transcriptomics, we reconstructed mesodermal and neural crest clonal relationships across >590,000 cells and 31,000 multicellular clones from E9.5-E10.5 embryos and E16.5 livers and hearts. In liver, capsular mesothelium was closely related to hepatic stellate cells, whereas fibroblast and vascular smooth muscle lineages diverged earlier. In heart, epicardium was clonally coupled to three spatially restricted mesenchymal sub-lineages but not to cardiomyocytes, and lineage tracing separated epicardial- and neural crest-derived populations within transcriptionally convergent valve mesenchyme. This approach enables Cre-independent manipulation of mammalian mesoderm and resolves organ mesenchyme development at clonal resolution.

developmental biology↗

bioRxiv2026

Highly resolved tumor architecture via matched spatial and nucleus transcriptomics from a single tissue section

Spatial transcriptomics often relies on reference-based deconvolution to infer cell types in a tissue context; however, public single-cell datasets can miss patient-specific biology. Here we introduce SIMPlex, a method that generates matched spatial and single-nucleus gene-expression profiles from the same 5 um FFPE section. We demonstrate context-matched profiles across mouse brain, breast cancer and prostate cancer tissues, resolving fine-grained cell-states with distinct spatial signatures. By extracting both spatial and nuclear layers, SIMPlex maximises the information recovered from a single tissue section, an advantage for scarce archival and clinical specimens.

molecular biology↗

bioRxiv2026

Spatial mapping of pediatric brain tumors across diagnoses and relapses

We present a spatial transcriptomic atlas of 19 pediatric brain tumor patients spanning nine major and rare diagnoses, including seven relapses, revealing their spatial cellular and molecular organization. Each tumor section resolves into 2 - 4 recurrent spatial archetypes across 11 biological themes, with some mirroring developmental lineage patterns - for example, oligodendrocyte-lineage programs in pilocytic astrocytomas. Spatially inferred copy-number analysis identifies relapse-associated putative clones. In one rare embryonal tumor, spatial niches in the primary tumor harboring putative clones colocalized with an archetype enriched for nervous system development and glioblast-lineage programs. In one ependymoma and one pilocytic astrocytoma, relapse-associated putative clones preferentially localized to the vasculature, suggesting regrowth during relapse may be seeded by clonal selection of residual tumor cells within specialized microenvironmental niches. This resource provides an open-access spatially resolved map via an interactive viewer to inform research on pediatric brain tumor ecosystems, relapse biology, and therapeutic strategies.

cancer biology↗

bioRxiv2026

Pubertal maturation and chemotherapy-associated disruption of the pediatric ovary revealed by multimodal single-cell profiling

Ovarian tissue cryopreservation enables fertility preservation in females undergoing gonadotoxic therapies, restoring fertility in adults. Although offered even before puberty, the childhood ovary and its vulnerability to therapy remain poorly characterized. Here, ovarian tissue from 16 patients undergoing fertility preservation (aged 1-16 years) and 11 adult controls (aged 22-32 years) was analyzed using single-cell RNA sequencing, spatial transcriptomics, and multiplex immunostaining. In chemotherapy-naive samples, 13 somatic cell populations underwent extracellular matrix remodeling, vascular, neural, and stromal maturation during puberty, whereas changes in germline related to chromatin remodeling. Spatial transcriptomics resolved 23 clusters across, revealing distinct tissue organization and follicular niche composition between children and adults. Chemotherapy exposure depleted perifollicular and vascular cells, suppressed intercellular signaling, and dysregulated over half of puberty-associated genes, converging on stress responses and extracellular matrix remodeling, with SEPTIN7 as a potential biomarker. These findings uncover critical developmental vulnerabilities of the pediatric ovary relevant to fertility preservation.

developmental biology↗

bioRxiv2025

Human growth plates house resting zone sub-populations with features of quiescent stem cells

Incomplete mapping of gene expression within human (epiphyseal) growth plates contributes to the challenges of diagnosing and treating patients with skeletal growth disorders. To address this issue, we applied spatially resolved transcriptomics to rare growth plate biopsies obtained from healthy adolescents. In addition to identifying novel markers of each zone of the human growth plate, spatial profiling revealed that the expression of genes associated with poorly understood growth disorders, including NKX3-2, SGMS2 and WNK4, is restricted to specific human growth plate zones. By elaborating on the low transcriptional activity of resting zone chondrocytes, we found that a subset of these cells exists in a functionally quiescent state in vivo, as determined by their predominantly nuclear mRNA, abundant heterochromatin, and ability to exit the G0 phase under specific conditions - features shared with skeletal stem cells in mouse growth plates. Additionally, we identified distinct and overlapping sub-populations of human resting zone chondrocytes; an exploration of their hierarchy determined that CHRDL2 and/or SFRP5-positive sub-populations are among the least quiescent resting zone cells. In summary, we generated the most comprehensive gene expression characterization of the human growth plate, which revealed novel zone-specific markers, new primary growth disorders, candidate pharmacological targets, and led us to uncover sub-populations of resting zone chondrocytes with features of quiescent stem cells. These results contribute to a better understanding of the cellular and molecular mechanisms governing human height and can facilitate improved diagnosis and treatment strategies of patients with skeletal growth disorders.

molecular biology↗

bioRxiv2025

Spatially resolved integrative analysis of transcriptomic and metabolomic changes in tissue injury studies

Recent developments in spatially resolved -omics have enabled studies linking gene expression and metabolite levels to tissue morphology, offering new insights into biological pathways. By capturing multiple modalities on matched tissue sections, one can better probe how different biological entities interact in a spatially coordinated manner. However, such cross-modality integration presents experimental and computational challenges. To align multimodal datasets into a shared coordinate system and facilitate enhanced integration and analysis, we propose MAGPIE (Multi-modal Alignment of Genes and Peaks for Integrated Exploration), a framework for co-registering spatially resolved transcriptomics, metabolomics, and tissue morphology from the same or consecutive sections. We illustrate the generalisability and scalability of MAGPIE on spatial multi-omics data from multiple tissues, combining Visium with both MALDI and DESI mass spectrometry imaging. MAGPIE was also applied to newly generated multimodal datasets created using specialised experimental sampling strategy to characterise the metabolic and transcriptomic landscape in an in vivo model of drug-induced pulmonary fibrosis, to showcase the linking of small-molecule co-detection with endogenous responses in lung tissue. MAGPIE highlights the refined resolution and increased interpretability of spatial multimodal analyses in studying tissue injury, particularly in pharmacological contexts, and offers a modular, accessible computational workflow for data integration.

bioinformatics↗

bioRxiv2024

Profiling of epithelial functional states and fibroblast phenotypes in hormone therapy-naive localised prostate cancer

Localised prostate cancers (PCa) are heterogeneous and multifocal, with diverse outcomes. Current prognostic methods are epithelium-centric, overlooking the complex cellular landscape within the tumour microenvironment (TME), which remains incompletely characterised. We performed a comprehensive analysis of cancerous and adjacent-benign cores from 24 patients with hormone therapy-naive localised PCa using single-cell RNA-sequencing. By integrating copy number variation and transcriptional signatures, we classified epithelial cells across a malignant spectrum, revealing widespread molecular perturbation. We found an expansion of Club cell phenotypes, suggestive of Luminal dedifferentiation. We also performed a detailed annotation of stromal phenotypes, focusing on fibroblasts, and identified a novel peri-neural fibroblast population. Spatial transcriptomics elucidated the precise anatomical distribution of CAFs within the PCa TME. This study provides a valuable foundation for advancing our understanding of PCa pathobiology and developing a comprehensive cellular model of the disease. Statement of SignificanceOur study leverages single-cell RNA-sequencing and spatial transcriptomics to provide a comprehensive cellular annotation of hormone therapy-naive localised PCa. We reveal widespread molecular perturbations in epithelial cells and map distinct fibroblast populations to specific anatomical niches. Notably, we identify a novel peri-neural phenotype associated with nerves, which merits further functional characterisation and exploration as a potential therapeutic target.

cancer biology↗

bioRxiv2024

MEA-seqX: High-resolution Profiling of Large-scale Electrophysiological and Transcriptional Network Dynamics

Concepts of brain function imply congruence and mutual causal influence between molecular events and neuronal activity. Decoding entangled information from concurrent molecular and electrophysiological network events demands innovative methodology bridging scales and modalities. Our MEA-seqX platform, integrating high-density microelectrode arrays, spatial transcriptomics, optical imaging, and advanced computational strategies, enables the simultaneous recording and analysis of molecular and electrical network activities at the level of individual cells. Applied to a mouse hippocampal model of experience-dependent plasticity, MEA-seqX unveiled massively enhanced nested dynamics between transcription and function. Graph-theoretic analysis revealed an increase in densely connected bimodal hubs, marking the first observation of coordinated spatiotemporal dynamics in hippocampal circuitry at both molecular and functional levels. This platform also identified different cell types based on their distinct bimodal profiles. Machine-learning algorithms accurately predicted network-wide electrophysiological features from spatial gene expression, demonstrating a previously inaccessible convergence across modalities, time, and scales.

neuroscience↗

bioRxiv2024

AAnet resolves a continuum of spatially-localized cell states to unveil tumor complexity

Identifying functionally important cell states and structure within a heterogeneous tumor remains a significant biological and computational challenge. Moreover, current clustering or trajectory-based computational models are ill-equipped to address the notion that cancer cells reside along a phenotypic continuum. To address this, we present Archetypal Analysis network (AAnet), a neural network that learns key archetypal cell states within a phenotypic continuum of cell states in single-cell data. Applied to single-cell RNA sequencing data from pre-clinical models and a cohort of 34 clinical breast cancers, AAnet identifies archetypes that resolve distinct biological cell states and processes, including cell proliferation, hypoxia, metabolism and immune interactions. Notably, archetypes identified in primary tumors are recapitulated in matched liver, lung and lymph node metastases, demonstrating that a significant component of intratumoral heterogeneity is driven by cell intrinsic properties. Using spatial transcriptomics as orthogonal validation, AAnet-derived archetypes show discrete spatial organization within tumors, supporting their distinct archetypal biology. We further reveal that ligand:receptor cross-talk between cancer and adjacent stromal cells contributes to intra-archetypal biological mimicry. Finally, we use AAnet archetype identifiers to validate GLUT3 as a critical mediator of a hypoxic cell archetype harboring a cancer stem cell population, which we validate in human triple-negative breast cancer specimens. AAnet is a powerful tool to reveal functional cell states within complex samples from multimodal single-cell data.

bioinformatics↗

bioRxiv2024

Spatial Dynamics of the Developing Human Heart

Heart development relies on a topologically defined interplay between a diverse array of cardiac cells. We finely curated spatial and single-cell measurements with subcellular imaging-based transcriptomics validation to explore spatial dynamics during early human cardiogenesis. Analyzing almost 80,000 individual cells and 70,000 spatially barcoded tissue regions between the 5.5th and 14th postconceptional weeks, we identified 31 coarse- and 72 fine-grained cell states and mapped them to highly resolved cardiac cellular niches. We provide novel insight into the development of the cardiac pacemaker-conduction system, heart valves, and atrial septum, and decipher heterogeneity of the hitherto elusive cardiac fibroblast population. Furthermore, we describe the formation of cardiac autonomic innervation and present the first spatial account of chromaffin cells in the fetal human heart. We support independent exploration of our datasets by an open-access, spatially centric interactive viewer. In summary, our study delineates the cellular and molecular landscape of the developing hearts architecture, offering links to genetic causes of heart disease.

developmental biology↗

bioRxiv2024

High-parametric protein maps reveal the spatial organization in early-developing human lung

The respiratory system, including the lungs, is essential for terrestrial life. While recent research has advanced our understanding of lung development, much still relies on animal models and transcriptome analyses. In this study conducted within the Human Developmental Cell Atlas (HDCA) initiative, we describe the protein-level spatiotemporal organization of the lung during the first trimester of human gestation. Using high-parametric tissue imaging with a 30-plex antibody panel, we analyzed human lung samples from 6 to 13 post-conception weeks, generating data from over 2 million cells across five developmental timepoints. We present a resource detailing spatially resolved cell type composition of the developing human lung, including proliferative states, immune cell patterns, spatial arrangement traits, and their temporal evolution. This represents an extensive single-cell resolved protein-level examination of the developing human lungs and provides a valuable resource for further research into the developmental roots of human respiratory health and disease.

developmental biology↗

bioRxiv2023

Host-Pathogen Interactions in the Plasmodium-Infected Mouse Liver at Spatial and Single-Cell Resolution

Upon infecting its vertebrate host, the malaria parasite initially invades the liver where it undergoes massive replication, whilst remaining clinically silent. The spatial coordination of factors regulating immune responses and metabolic zonation during malaria infection, in the true tissue context, remains unexplored. Here, we perform spatial transcriptomics in combination with single-nuclei RNA-sequencing (snRNA-seq) over multiple time points during liver infection to delineate transcriptional programs of host-pathogen interactions across P. berghei-infected liver tissues. Our data suggest changes in gene expression related to lipid metabolism in response to Plasmodium infection in the proximity of infected hepatocytes, such as the modulation of the expression of genes involved in peroxisome proliferator-activated receptor pathway signaling. The data further indicate the presence of inflammatory hotspots with distinct cell type compositions and differential liver inflammation programs along the lobular axis in the malaria-infected tissues. Furthermore, a significant upregulation of genes involved in inflammation is observed in liver tissues of control mice injected with mosquito salivary gland components, which is considerably delayed compared to P. berghei infected mice. Our study establishes a benchmark for investigating transcriptome changes during host-parasite interactions in tissues, it provides informative insights regarding in vivo study design linked to infection, and provides a useful tool for the discovery and validation of de novo intervention strategies aimed at malaria liver stage infection.

molecular biology↗

bioRxiv2023

Translational mapping of spatially resolved transcriptomes in human and mouse pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with poor prognosis and limited treatment options. Efforts to identify effective treatments are thwarted by limited understanding of IPF pathogenesis and poor translatability of available preclinical models. To address these limitations, we generated spatially resolved transcriptome maps of human IPF and bleomycin-induced mouse lung fibrosis. We uncovered distinct fibrotic niches in the IPF lung, characterized by aberrant alveolar epithelial cells in a microenvironment dominated by TGF{beta} signaling alongside factors such as p53 and ApoE. We also identified a clear divergence between the arrested alveolar regeneration in the IPF fibrotic niches, and the active tissue repair in the acutely fibrotic mouse lung. Our study offers in-depth insights into the IPF transcriptional landscape and proposes alveolar regeneration as a promising therapeutic strategy for IPF.

molecular biology↗

bioRxiv2023

snPATHO-seq: unlocking the pathology archives

Formalin-fixed paraffin-embedded (FFPE) samples are valuable but underutilized in single-cell omics research due to their low DNA and RNA quality. In this study, leveraging recent single-cell genomic technology advances, we introduce a versatile method to derive high-quality single-nucleus transcriptomic data from FFPE samples.

molecular biology↗

bioRxiv2023

Spatial landmark detection and tissue registration with deep learning

Spatial landmarks are crucial in describing histological features between samples or sites, tracking regions of interest in microscopy, and registering tissue samples within a common coordinate framework. Although other studies have explored unsupervised landmark detection, existing methods are not well-suited for histological image data as they often require a large number of images to converge, are unable to handle non-linear deformations between tissue sections, and are ineffective for z-stack alignment, other modalities beyond image data, or multimodal data. We address these challenges by introducing a new landmark detection and registration method, utilizing neural-network-guided thin-plate splines. Our proposed method is evaluated on a diverse range of datasets, demonstrating superior performance in both accuracy and stability compared to existing approaches.

bioinformatics↗

bioRxiv2023

Spatial transcriptomic analysis of virtual prostate biopsy reveals confounding effect of heterogeneity on genomic signature scoring

Genetic signatures have added a molecular dimension to prognostics and therapeutic decision-making. However, tumour heterogeneity in prostate cancer and current sampling methods could confound accurate assessment. Based on previously published spatial transcriptomic data from multifocal prostate cancer, we created virtual biopsy models that mimic conventional biopsy placement and core size. We then analysed the gene expression of different prognostic signatures (OncotypeDx(R), Decipher(R), Prostadiag(R)) using a step-wise approach increasing resolution from pseudo-bulk analysis of the whole biopsy, to differentiation by tissue subtype (benign, stroma, tumour), followed by distinct tumour grade and finally clonal resolution. The gene expression profile of virtual tumour biopsies revealed clear differences between grade groups and tumour clones, compared to a benign control, which were not reflected in bulk analyses. This suggests that bulk analyses of whole biopsies or tumour-only areas, as used in clinical practice, may provide an inaccurate assessment of gene profiles. The type of tissue, the grade of the tumour and the clonal composition all influence the gene expression in a biopsy. Clinical decision making based on biopsy genomics should be made with caution while we await more precise targeting and cost-effective spatial analyses. Patient summaryProstate cancers are very variable, including within a single tumour. Current genetic scoring systems, which are sometimes used to make decisions for how to treat patients with prostate cancer, are based on sampling methods which do not reflect these variations. We found, using state-of-the-art spatial genetic technology to simulate accurate assessment of variation in biopsies, that the current approaches miss important details which could negatively impact clinical decisions. Take home messageVirtual biopsies from spatial transcriptomic analysis of a whole prostate reveal that current genomic risk scores potentially deliver misleading results as they are based on bulk analysis of prostate biopsies and ignore tumour heterogeneity.

genomics↗

bioRxiv2023

Spatial Multimodal Analysis of Transcriptomes and Metabolomes in Tissues

We present a spatial omics approach that merges and expands the capabilities of independently performed in situ assays on a single tissue section. Our spatial multimodal analysis combines histology, mass spectrometry imaging, and spatial transcriptomics to facilitate precise measurements of mRNA transcripts and low-molecular weight metabolites across tissue regions. We demonstrate the potential of our method using murine and human brain samples in the context of dopamine and Parkinsons disease.

genomics↗