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Biology subjects

Heyn, H.

Publications and source records attributed to Heyn, H..

At least 19 recordsLinked to original sources

C1q from C1q+ tumor-associated myeloid cells promotes resistance to T-cell engagers and CAR T-cells and is induced by LIF and glucocorticoids

Immunotherapies, particularly T-cell engagers (TCEs) and CAR T-cells, have shown limited efficacy in solid tumors, partly due to an immunosuppressive tumor microenvironment (TME). However, the molecular mechanisms by which the TME impairs immunotherapy remain poorly understood. Here, we found that C1q generated by C1q tumor-associated myeloid cells (TAMs) plays a fundamental role in shaping the immunosuppressive TME in glioblastoma (GBM), one of the most aggressive tumors. C1q suppressed T-cell activation and impaired the activity of T-cell engagers (TCEs) and CAR T-cells. Genetic ablation of C1qa improved anti-tumor responses to TCEs and CAR T-cells. We used innovative patient-derived tumor tissue cultures (PDTTCs), which preserve an intact TME, from 19 GBM patients and identified the LIF cytokine as the main inducer of C1q. Moreover, we discovered that glucocorticoids cooperate with LIF to induce C1q. The identified C1q TAM signature overlapped with an anti-LIF gene signature, was associated with poor prognosis, and was enriched in mesenchymal GBMs with NF1 mutations. The blockade of LIF using an anti-LIF neutralizing antibody decreased the presence of C1q+ TAMs, and we found that the regulation of C1q by anti-LIF is conserved between human and mouse. Using the C1qa-/- GBM mouse model, we showed that C1q mediates the anti-tumor immune response induced by LIF blockade. Our findings identify C1q TAMs as key immunosuppressive players in GBM, impairing CAR T-cells and TCE activity, and position them as therapeutic targets to improve immunotherapy responses in this devastating disease.

cancer biology↗

Site-Specific Entry Factors Define Cellular Susceptibility to SARS-CoV-2 in Human Tissues

SARS-CoV-2 primarily infects the respiratory epithelium through entry factors ACE2 and TMPRSS2. However, alternative viral entry receptors and/or modulators of viral entry factors expression may contribute to infection in respiratory as well as non-respiratory tissues, including the kidneys and colon. Using single-cell analyses, we identified novel candidates for spike-mediated viral entry in epithelial cells from diverse fresh human tissues exposed to a labeled spike-pseudotyped virus. Systematic viral tracking revealed tissue-specific membrane receptors that contribute to viral entry, functionally validated in human tissues and partially predicted by molecular modeling. We found ADAM17 and IL1R1, together with canonical molecules, to significantly facilitate viral entry in the lung parenchyma, while entry into the renal cortex was less dependable on canonical factors, but strongly modulated by JAK1/2 engagement. These findings uncover distinct viral entry mechanisms across tissues and suggest novel organ-specific therapeutic targets for patients at risk.

microbiology↗

Multi-omic profiling of pleural effusions: a reservoir-incubator for non-invasive insights into tumor heterogeneity

Pleural effusions (PEs) are common in advanced cancer and are routinely sampled for cytological evaluation; however, their full potential as a source of tumor and immune information remains underexplored. Although malignant PEs (MPEs) are increasingly used for liquid biopsy, their cellular complex interactions have not been resolved at high resolution. Here, we present the first comprehensive single-cell and multi-omic atlas of PEs, integrating scRNA-seq, TCR-seq and whole-exome sequencing with lung, breast, or ovarian cancer, as well as non-malignant heart failure controls. We profiled over 200,000 cells and identified more than 30 immune, stromal, and tumor subpopulations across disease contexts. The tumor cell signature in MPEs recapitulates the genomic and transcriptomic features of matched primary tumors, supporting their use for non-invasive tumor profiling. In contrast, immune landscapes varied by disease: lung cancer MPEs were enriched in exhausted CD8 T cells, breast MPEs showed CD4 T helper cell dominance, and ovarian and heart failure PEs harbored tissue-resident myeloid cell populations. These data reveal that PEs are dynamic, disease-specific ecosystems shaped by tumor-driven immune remodeling. By leveraging their cellular diversity, we position MPEs as a powerful platform for studying metastatic adaptation and immune responses in vivo, with direct implications for diagnostics, immune monitoring, and precision oncology.

cancer biology↗

Dysregulation of transcription networks regulating oligodendrogenesis in age-related decline in CNS remyelination

In demyelinating diseases like multiple sclerosis (MS), efficient remyelination is critical for functional recovery. Remyelination efficiency declines with age, and is linked to progressive disability. The gene regulatory network underlying remyelination, and how it is altered with aging, remains unclear. Here we present a comparative single-nucleus RNA and ATAC sequencing analysis of remyelination in young and aged mice. We identified gene modules dynamically expressed throughout oligodendrocyte differentiation, revealing age-dependent changes in key processes related to myelination. Multi-omic analysis allowed us to map the regulatory network driving efficient remyelination within oligodendrocyte lineage cells in young mice. We highlight key transcription factors in the network dysregulated with age, and we describe similar dysregulations in MS lesions. Modifying the expression of these transcription factors in primary oligodendrocyte progenitor cells impacts differentiation. These findings provide a foundational understanding of this regenerative process in the context of aging and in chronic demyelinating diseases.

neuroscience↗

Latent plasticity of the human pancreas across development, health, and disease.

The pancreas plays a central role in major human diseases, yet our understanding of its cellular diversity and plasticity remains incomplete. Here, we present a single-cell multiomics atlas of the human pancreas, profiling over four million cells and nuclei from 57 donors across fetal development, adult homeostasis, and type 2 diabetes (T2D). Integrating sc/snRNA-seq, snATAC-seq, VASA-seq, spatial transcriptomics (Xenium), and multiplexed proteomics (CODEX), we resolve gene expression, chromatin accessibility, and spatial organization at high resolution. We identify transcriptionally plastic centroacinar-like cells (pCACs) in adults with fetal-like features, delineate endocrine and exocrine lineage trajectories during development, and uncover HNF1A-defined beta cell epigenetic states. In T2D, we observe shifts in beta cell subtypes and altered regulatory programs. Glucose perturbation of healthy islets reveals cell-type-specific adaptation and stress responses. This atlas provides a foundational framework to understand pancreas biology and the role of cellular plasticity in regeneration and disease.

genomics↗

A transcriptional map of human tonsil architecture:beyond the sum of (single cell) parts

The tonsil is a highly compartmentalized organ in which different microanatomical structures orchestrate designated (immune) functions. We use this already well-studied tissue to survey spatial molecular imaging data (CosMx SMI) for studying immune responses in native tissue context; and, to demonstrate the advantages of SMI for faithfully recapitulating cellular composition in direct comparison with single-cell RNA sequencing. While SMI data still poses many analytical challenges and lacks standardization, we established a versatile analysis pipeline focused on the profitable particularities of these data: considering organization (microenvironment), interactions (signaling), and function (higher-order structures) across scales. Specifically, we resolve ~ 2M cells into 52 subpopulations across immune and, in particular, structural compartments. Various spatial niches partition tonsillar tissue into architecturally and functionally distinct regions, which we characterize through cell-cell colocalization and communication analyses, while performing various non-standard analyses at the level of spatial features. These topological readouts may help elucidate where certain immunological processes occur (e.g., class switch recombination); and, where signaling pathways are active (e.g., TNF and galectin, which have been implicated in diverse lymphomas). In all, we provide an analytical framework for Spatial Immunology, and showcase alternative views that such techniques and concomitant computational approaches can bring on tissue composition and architecture.

immunology↗

In Situ Inference of Copy Number Variations in Image-Based Spatial Transcriptomics

Copy number variations (CNVs) drive cancer progression. So far, spatial CNV inference has relied on whole transcriptome-based sequencing technologies. However, advances in image-based spatial transcriptomics (iST) now enable high-plex gene measurement in situ. Here, we introduce an approach that adapts CNV inference to iST data, enabling spatial mapping of malignant clones and the tumor microenvironment, at single-cell resolution. Additionally, we assess how panel size and detection efficiency influence CNV inference.

bioinformatics↗

Tracing colorectal malignancy transformation from cell to tissue scale

The transformation of normal intestinal epithelium into colorectal cancer (CRC) involves coordinated changes across molecular, cellular, and architectural scales; yet, how these layers integrate remains poorly resolved. Here, we survey colorectal tumorigenesis by combining whole-transcriptome spatial molecular imaging (WTx CosMx SMI) with single-nucleus RNA-sequencing (snPATHO-seq) and digital histopathology on colon samples containing reference mucosa, adenomas and carcinomas, as well as a metastatic lymph node. Leveraging (discrete) histological annotations and (continuous) data-driven trajectories, we quantify the dynamics of cellular density, heterogeneity, function and signaling along the reference-adenoma-carcinoma axis, which is concordant in its spatial and molecular definition. This combination of analytical approaches across different data views let us chart tissue transformation across dimensions (physical/transcriptional) and scales (cell/tissue). We resolve [~]3.5 million cells into 43 epithelial, immune, and stromal subpopulations that exhibit a bi-furcating tumor evolution: On the one hand, LGR5+ stem-like epithelial cells are enriched in highly homogeneous proliferative tumor cores. On the other hand, MMP7+ fetal-like states are restricted to immunosuppressive invasive fronts, rich in cancer-associated fibroblasts (CAFs) and tumor-associated macrophages. These subpopulations form concentric spatial layers that organize transformed regions, and their compound aligns with histological malignancy. We further define transition crypts - single colonic crypts with divided histological and transcriptional makeup - that arise from rare crypt fusion or abrupt transformation events. Finally, we trace MMP7+ fetal-like tumor cell states and concomitant myofibroblast-like FAP+ CAFs to lymphovascular invasion sites and matched lymph node metastases, thereby recapitulating invasive programs at single-cell resolution and across sites. In all, we present single cell- and WTx-resolved spatial data that are among the first of their kind. These open up spatial-centric, out-of-the-box analytical avenues to resolve the molecular, cellular and architectural dynamics that attend tissue transformation during CRC onset, progression and dissemination.

cancer biology↗

Systematic evaluation of single-cell multimodal data integration for comprehensive human reference atlas.

The integration of multimodal single-cell data enables comprehensive organ reference atlases, yet its impact remains largely unexplored, particularly in complex tissues. We generated a benchmarking dataset for the renal cortex by integrating 3 and 5 scRNA-seq with joint snRNA-seq and snATAC-seq, profiling 119,744 high-quality nuclei/cells from 19 donors. To align cell identities and enable consistent comparisons, we developed the interpretable machine learning tool scOMM (single-cell Omics Multimodal Mapping) and systematically assessed integration strategies. "Horizontal" integration of scRNA and snRNA-seq improved cell-type identification, while "vertical" integration of snRNA-seq and snATAC-seq had an additive effect, enhancing resolution in homogeneous populations and difficult-to-identify states. Global integration was especially effective in identifying adaptive states and rare cell types, including WFDC2-expressing Thick Ascending Limb and Norn cells, previously undetected in kidney atlases. Our work establishes a robust framework for multimodal reference atlas generation, advancing single-cell analysis and extending its applicability to diverse tissues.

genomics↗

Decoding the immune response in leptomeningeal disease through single-cell sequencing of cerebrospinal fluid

AbstractAssessing anti-tumor immune responses and immune microenvironments in central nervous system (CNS) neoplasms, such as brain tumors and leptomeningeal disease (LMD), provides prognostic insights and predictive biomarkers. Liquid biopsy of the cerebrospinal fluid (CSF) represents a promising minimally-invasive approach, but its ability to reflect immune responses against tumors remains unclear. Here, we used single-cell sequencing of CSF cells and spatial transcriptomics of CNS lesions to compare and contrast LMD patients with CNS lymphoma (CNSL), glioblastoma (GB) and brain metastases (BrM), to neuroinflammatory CNS disorders. We identified disease-specific CSF environments, reflecting parenchymal tumor microenvironment features. CNSL showed robust T cell responses, while BrM and GB were dominated by both blood-derived and tissue-resident myeloid cells. Longitudinal CSF sampling unveiled mechanisms of disease progression and therapy resistance, highlighting the potential of CSF liquid biopsies for uncovering disease biology, discovering cellular biomarkers and developing personalized therapies for CNS neoplasms.

immunology↗

Spatial Flux Balance Analysis reveals tissue-of-origin and spatially dependent metabolic rewiring in renal and colorectal cancer

1To fully understand how cancer metabolism differs between primary tumors and metastases, resolving cell metabolism with spatial precision is essential. Yet, spatial fluxomics lags behind advancements in spatial transcriptomics. To address this gap, we generated high-resolution spatial transcriptomics datasets from paired primary colorectal tumors and liver metastases, designed to capture metabolic adaptations across distinct tumor sites. Concurrently, we developed the Spatial Flux Balance Analysis (spFBA) computational framework to leverage them. Since broad metabolic differences between tumors and healthy tissues are established, we first validated spFBA on a publicly available renal cancer dataset, including tumor-normal interface samples. spFBA detected cancer metabolic hallmarks, like enhanced glucose uptake and metabolic growth, but with unprecedented resolution, revealing lactate production with sustained oxygen consumption at the tumor interface and with reduced respiration in the core. Next, applying spFBA to our colorectal cancer dataset, we provided biological insights, confirming that metastases mimic the metabolic traits of their tissue of origin. Additionally, our approach uncovered the first in vivo evidence of lactate-consuming cancer cells, marking a significant advance in understanding cancer metabolism. spFBA stands out as a powerful approach to unravel the spatial metabolic complexity of cancer and beyond, leveraging the expanding landscape of spatial transcriptomics datasets.

systems biology↗

Spatio-temporal T cell tracking for personalized TCR-T designs in childhood cancer

Immune checkpoint inhibition (ICI) has revolutionized oncology, offering extended survival and long-term remission in previously incurable cancers. While highly effective in tumors with high mutational burden, lowly mutated cancers, including pediatric malignancies, present low response rate and limited predictive biomarkers. Here, we present a framework for the identification and validation of tumor-reactive T cells as a biomarker to quantify ICI efficacy and as candidates for a personalized TCR-T cell therapy. Therefore, we profiled a pediatric malignant rhabdoid tumor patient with complete remission after ICI therapy using deep single-cell T cell receptor (TCR) repertoire sequencing of the tumor microenvironment (TME) and the peripheral blood. Specifically, we tracked T cell dynamics longitudinally from the tumor to cells in circulating over a time course of 12 months, revealing a systemic response and durable clonal expansion of tumor-resident and ICI-induced TCR clonotypes. We functionally validated tumor reactivity of TCRs identified from the TME and the blood by co-culturing patient-derived tumor cells with TCR-engineered autologous T cells. Here, we observed unexpectedly high frequencies of tumor-reactive TCR clonotypes in the TME and confirmed T cell dynamics in the blood post-ICI to predict tumor-reactivity. These findings strongly support spatio-temporal tracking of T cell activity in response to ICI to inform therapy efficacy and to serve as a source of tumor-reactive TCRs for personalized TCR-T designs.

genomics↗

STAMP: Single-Cell Transcriptomics Analysis and Multimodal Profiling through Imaging

We introduce Single-Cell Transcriptomics Analysis and Multimodal Profiling (STAMP), a scalable profiling approach of individual cells. Leveraging transcriptomics and proteomics imaging platforms, STAMP eliminates sequencing costs, to enable single-cell genomics from hundreds to millions of cells at an unprecedented low cost. Stamping cells in suspension onto imaging slides, STAMP supports single-modal (RNA or protein) and multimodal (RNA and protein) profiling and flexible, ultra-high-throughput formats. STAMP allows the analysis of a single or multiple samples within the same experiment, enhancing experimental flexibility, throughput and scale. We tested STAMP with diverse sample types, including peripheral blood mononuclear cells (PBMCs), dissociated cancer cells and differentiated embryonic stem cell cultures, as well as whole cells and nuclei. Combining RNA and protein profiling, we applied immuno-phenotyping of millions of blood cells simultaneously. We also used STAMP to identify ultra-rare cell populations, simulating clinical applications to identify circulating tumor cells (CTCs). Performing in vitro differentiation studies, we further showed its potential for large-scale perturbation studies. Together, STAMP establishes a new standard for cost-effective, scalable single-cell analysis. Without the need for sequencing, STAMP makes high-resolution profiling more affordable and accessible. Designed to meet the needs of research labs, diagnostic cores and pharmaceutical companies, STAMP holds the promise to transform our capacity to map human biology, diagnose diseases and drug discovery.

genomics↗

Integrative single-cell multi-omics of CD19CARpos and CARneg T cells suggest drivers of immunotherapy response in B-cell neoplasias

How phenotypic, clonal, and functional heterogeneity of CAR-T-cells impact clinical outcomes remain understudied. Here, we integrated clonal kinetics with transcriptomic heterogeneity resolved by single-cell omics to explore cellular dynamics response of both non-transduced (CARneg) and transduced (CARpos)T-cells. CARneg and CARposT-cells were longitudinally interrogated in the manufactured infusion product (IP) and in-vivo at CAR-T cell expansion peak in five B-ALL patients treated with CD19CAR-T-cells (varni-cel). Significant differences were found in the cellular dynamics between CARpos and CARnegT-cells in response to therapy. CARposT-cells in the IP exhibited a significant higher CD4:CD8 ratio than CARnegT-cells, and the CD4:CD8 CARposT-cell composition impacted therapy outcome as confirmed in a larger cohort of 24 varni-cel-treated B-ALL patients. Conversely, an inverted trend in the CD4:CD8 CARposT-cell ratio was consistently observed at the expansion peak, with clonally expanding CD8+ effector memory and cytotoxic T-cells being the most abundant populations. Expanded cytotoxic CARpos{gamma}{delta}T cells emerged at the expansion peak, and the extent of their in-vivo expansion positively correlated with treatment efficacy, which was validated in a large cohort of B-ALL patients (n=18) treated with varni-cell and B-cell lymphoma patients (n=58) treated with either lisa-cel or axi-cel. Our data provide insights into the complexity and diversity of T-cell responses following CAR-T cell therapy and suggest drivers of immunotherapy response.

immunology↗

scDrugPrio: A framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases

BackgroundIneffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. MethodsHere, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs. ResultsscDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohns disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohns disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. ConclusionWe propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrios potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).

immunology↗

Quantifying Adaptive Evolution of the Human Immune Cell Landscape

The human immune system is under constant evolutionary pressure, primarily through its role as first line of defence against pathogens. Accordingly, population genomics studies have shown that immune-related genes have a high rate of adaptive evolution. These studies, however, are mainly based on protein-coding genes without cellular context, leaving the adaptive role of cell types and states uncharted. Inferring the rate of protein-coding genes adaptation in developing and adult immune cells at cellular resolution, we found cell types from both the lymphoid and myeloid compartments to harbour significantly increased adaptation rates. Specific cell states, such as foetal Pre-Pro B cells and adult T resident memory CD8+ cells show highly elevated rates of adaptation. We further analysed activated cell states, specifically, iPSC-derived macrophages responding to various challenges, including pro- and anti-inflammatory cytokines or bacterial and viral infections, the latter simulating the evolutionary arms race between humans and pathogens. Here, we found positive selection to be concentrated in early immune responses, suggesting benefits for the host to adapt to early stages of infection to control pathogen numbers and spread. Together, our study reveals spatio-temporal and functional biases in human immune populations with evidence of rapid adaptive evolution and provides a retrospect of forces that shaped the complexity, architecture, and function of the human body.

immunology↗

The cellular landscape of the endochondral bone highlights its multipotent and immunomodulatory features during the transition to extrauterine life

The cellular complexity of the endochondral bone underlies its essential and pleiotropic roles during organismal life. While the adult bone has received significant attention, we still lack a deep understanding of the perinatal bone cellulome. Here, we have profiled the full composition of the murine endochondral bone at the single-cell level during the transition from fetal to newborn life and in comparison to the adult tissue, with particular emphasis on the mesenchymal compartment. The perinatal bone contains different fibroblastic clusters with blastema-like characteristics in organizing and supporting skeletogenesis, angiogenesis, and hematopoiesis. Our data also suggests dynamic inter- and intra-compartment interactions as well as a bone marrow milieu that seems prone to anti-inflammation, which we hypothesize is necessary to ensure the proper program of lymphopoiesis and the establishment of central and peripheral tolerance in early life. Our study provides an integrative roadmap for the future design of genetic and cellular functional assays to validate cellular interactions and lineage relationships within the perinatal bone.

cell biology↗

FixNCut: Single-cell genomics through reversible tissue fixation and dissociation

The use of single-cell technologies for clinical applications requires disconnecting sampling from downstream processing steps. Early sample preservation can further increase robustness and reproducibility by avoiding artifacts introduced during specimen handling. We present FixNCut, a methodology for the reversible fixation of tissue followed by dissociation that overcomes current limitations. We applied FixNCut to human and mouse tissues to demonstrate the preservation of RNA integrity, sequencing library complexity, and cellular composition, while diminishing stress-related artifacts. Besides single-cell RNA sequencing, FixNCut is compatible with multiple single-cell and spatial technologies, making it a versatile tool for robust and flexible study designs.

bioinformatics↗