Biology subjects

Vigneau, S.

Publications and source records attributed to Vigneau, S..

12 recordsLinked to original sources

bioRxiv2026

Metastatic Osteosarcoma is Characterized by Loss of Osteoblastic Lineage Fidelity

Osteosarcoma (OS) is a rare and aggressive bone cancer with limited therapeutic progress in several decades. To characterize the transcriptional diversity of malignant cell states in OS, we analyzed single-nucleus RNA sequencing data from 24 tumors, including both primary and metastatic lesions. We identified a canonical osteoblastic cell state that predominated in primary tumor cells, but was dampened in metastases. Integrating tumor data with data from embryonic skeletal development and an experimental osteoblast differentiation model revealed that metastatic OS cells resemble poorly specified mesenchyme enriched for non-osseous programs. Clonal populations in metastatic samples acquired mesenchymal cell states distinct from paired primary tumors. Similar cell state shifts were evident in post-treatment primary tumors relative to paired pre-treatment samples. Our results suggest that localized post-treatment and metastatic OS malignant cells lose osteoblastic lineage fidelity, which may represent a unifying axis of disease evolution and reveal new therapeutic vulnerabilities.

Cancer Biology↗

bioRxiv2025

Mapping the Transcriptional Landscape of Drug Responses in Primary Human Cells Using High-Throughput DRUG-seq

To advance our understanding of drug action in physiologically-relevant systems, we developed a high-throughput transcriptomic atlas of compound responses in primary human cell types. Leveraging the scalable and cost-effective Digital RNA with the pertUrbation of Genes (DRUG-seq) assay, we profiled gene expression responses to 89 pharmacologically-active compounds across six concentrations in four distinct primary cell types: aortic smooth muscle cells (AoSMCs), skeletal muscle myoblasts (SkMMs), dermal fibroblasts, and melanocytes. Through rigorous quality control and normalization, we generated reproducible and cell type-resolved transcriptomic signatures, enabling the discovery of both shared and divergent regulatory programs. This dataset revealed core cellular responses, such as brefeldin A-mediated ER stress across all cell types, as well as lineage-specific effects, including dexamethasone-induced hypoxia signaling in AoSMCs, complex inflammatory responses linked to epithelial-to-mesenchymal transition pathways in SkMMs, TGF-{beta}-modulated states in fibroblasts, and dabrafenib-driven transcriptional shifts towards quiescence in melanocytes. By integrating systematic perturbations with primary models, this dataset serves as a resource for building systems-level models of drug response and mechanism. Ultimately, we aim to accelerate predictive pharmacology by enabling high-throughput data generation grounded in human biology and readily usable by artificial intelligence models.

systems biology↗

bioRxiv2024

Single-cell epigenetic profiling reveals an interferon response-high program associated with BAP1 deficiency in kidney cancer

Renal cell carcinoma (RCC) is characterized by recurrent somatic mutations in epigenetic regulators, which stratify patients into clinically significant subgroups with distinct prognoses and treatment responses. However, the cell type-specific epigenetic landscape of RCC--broadly and in the context of these mutations--is incompletely understood. To investigate these open questions, we integrated single nucleus ATAC sequencing data from RCC tumors across four independent cohorts. In clear cell RCC tumors, we identified four shared malignant epigenetic programs related to angiogenesis, proximal tubule-like features, interferon (IFN) signaling, and one that lacked distinct genomic regions with increased accessibility. Among the mutated epigenetic regulators, BAP1 mutation exhibited the most significant impact on chromatin accessibility in tumor cells, and the associated epigenetic changes were linked to IFN response. We identify multiple potential sources of elevated IFN signaling in these lesions, such as increased immune infiltration and increased accessibility and expression of an IFN-associated ERV, ERV3-16A3_LTR. We find that the expression of ERV3-16A3_LTR may itself be a negative prognostic biomarker in ccRCC. Our findings highlight the convergence of malignant epigenetic programs across ccRCC tumors and suggest that BAP1 loss, potentially through ERV3-16A3_LTR dysregulation, is associated with an IFN response-high epigenetic program.

cancer biology↗

bioRxiv2024

A spatial cell atlas of neuroblastoma reveals developmental, epigenetic and spatial axis of tumor heterogeneity

Neuroblastoma is a pediatric cancer arising from the developing sympathoadrenal lineage with complex inter- and intra-tumoral heterogeneity. To chart this complexity, we generated a comprehensive cell atlas of 55 neuroblastoma patient tumors, collected from two pediatric cancer institutions, spanning a range of clinical, genetic, and histologic features. Our atlas combines single-cell/nucleus RNA-seq (sc/scRNA-seq), bulk RNA-seq, whole exome sequencing, DNA methylation profiling, spatial transcriptomics, and two spatial proteomic methods. Sc/snRNA-seq revealed three malignant cell states with features of sympathoadrenal lineage development. All of the neuroblastomas had malignant cells that resembled sympathoblasts and the more differentiated adrenergic cells. A subset of tumors had malignant cells in a mesenchymal cell state with molecular features of Schwann cell precursors. DNA methylation profiles defined four groupings of patients, which differ in the degree of malignant cell heterogeneity and clinical outcomes. Using spatial proteomics, we found that neuroblastomas are spatially compartmentalized, with malignant tumor cells sequestered away from immune cells. Finally, we identify spatially restricted signaling patterns in immune cells from spatial transcriptomics. To facilitate the visualization and analysis of our atlas as a resource for further research in neuroblastoma, single cell, and spatial-omics, all data are shared through the Human Tumor Atlas Network Data Commons at www.humantumoratlas.org.

cancer biology↗

bioRxiv2023

A cellular and spatial atlas of TP53-associated tissue remodeling in lung adenocarcinoma

TP53 is the most frequently mutated gene across many cancers and is associated with shorter survival in lung adenocarcinoma (LUAD). To define how TP53 mutations affect the LUAD tumor microenvironment (TME), we constructed a multi-omic cellular and spatial tumor atlas of 23 treatment-naive human lung tumors. We found that TP53-mutant (TP53mut) malignant cells lose alveolar identity and upregulate highly proliferative and entropic gene expression programs consistently across resectable LUAD patient tumors, genetically engineered mouse models, and cell lines harboring a wide spectrum of TP53 mutations. We further identified a multicellular tumor niche composed of SPP1+ macrophages and collagen-expressing fibroblasts that coincides with hypoxic, pro-metastatic expression programs in TP53mut tumors. Spatially correlated angiostatic and immune checkpoint interactions, including CD274-PDCD1 and PVR-TIGIT, are also enriched in TP53mut LUAD tumors, which may influence response to checkpoint blockade therapy. Our methodology can be further applied to investigate mutation-specific TME changes in other cancers.

cancer biology↗

bioRxiv2023

Inference of single cell profiles from histology stains with the Single-Cell omics from Histology Analysis Framework (SCHAF)

Tissue biology involves an intricate balance between cell-intrinsic processes and interactions between cells organized in specific spatial patterns, which can be respectively captured by single cell profiling methods, such as single cell RNA-seq (scRNA-seq) and spatial transcriptomics, and histology imaging data, such as Hematoxylin-and-Eosin (H&E) stains. While single cell profiles provide rich molecular information, they can be challenging to collect routinely in the clinic and either lack spatial resolution or high gene throughput. Conversely, histological H&E assays have been a cornerstone of tissue pathology for decades, but do not directly report on molecular details, although the observed structure they capture arises from molecules and cells. Here, we leverage vision transformers and adversarial deep learning to develop the Single Cell omics from Histology Analysis Framework (SCHAF), which generates a tissue samples spatially-resolved whole transcriptome single cell omics dataset from its H&E histology image. We demonstrate SCHAF on a variety of tissues-- including lung cancer, metastatic breast cancer, placentae, and whole mouse pups--training with matched samples analyzed by sc/snRNA-seq, H&E staining, and, when available, spatial transcriptomics. SCHAF generated appropriate single cell profiles from histology images in test data, related them spatially, and compared well to ground-truth scRNA-Seq, expert pathologist annotations, or direct spatial transcriptomic measurements, with some limitations. SCHAF opens the way to next-generation H&E analyses and an integrated understanding of cell and tissue biology in health and disease.

bioinformatics↗

bioRxiv2022

Intertumoral lineage diversity and immunosuppressive transcriptional programs in well-differentiated gastroenteropancreatic neuroendocrine tumors

Neuroendocrine tumors (NETs) are rare cancers that may arise in the gastrointestinal tract and pancreas. The fundamental mechanisms driving gastroenteropancreatic (GEP) NET growth remain incompletely elucidated; however, the heterogeneous clinical behavior of GEP-NETs suggests that both cellular lineage dynamics and tumor microenvironment influence tumor pathophysiology. Here, we investigated the single-cell transcriptomes of tumor and immune cells from patients with gastroenteropancreatic NETs. Malignant GEP-NET cells expressed genes and regulons associated with normal, gastrointestinal endocrine cell differentiation and fate determination stages. While tumor and lymphoid compartments sparsely expressed immunosuppressive targets, infiltrating myeloid cells were enriched for alternative immunotherapy pathways including VSIR, Tim3/Gal9, and SIGLEC10. Finally, analysis of paired primary and metastatic tissue specimens from small intestinal NETs demonstrated transcriptional transformation between the primary tumor and its distant metastasis. Our findings highlight the transcriptomic heterogeneity that distinguishes the cellular landscapes of GEP-NET anatomic subtypes and reveal potential avenues for future precision medicine therapeutics.

cancer biology↗

bioRxiv2020

Diversity of developing peripheral glia revealed by single cell RNA sequencing

The peripheral nervous system responds to a wide variety of sensory stimuli, a process that requires great neuronal diversity. These diverse peripheral sensory neurons are closely associated with glial cells that originate from the neural crest (NC). However, the molecular nature and origins of diversity among peripheral glia is not understood. Here we used single cell RNA sequencing to profile and compare developing and mature glia from somatosensory lumbar dorsal root ganglia (DRG) and auditory spiral ganglia (SG). We found that the glial precursors (GPs) differ in their transcriptional profile and prevalence in these two systems. Despite their unique features, somatosensory and auditory GPs undergo convergent differentiation to generate myelinating and non-myelinating Schwann cells that are molecularly uniform. By contrast, although satellite glia surround the neuronal cell bodies in both ganglia, we found that those in the SG express multiple myelination-associated genes, while DRG satellite cells express components that suppress myelination. Lastly, we identified a set of glial signature genes that are also expressed by placode-derived supporting cells, providing new insights into commonalities among glia across the nervous system. This comprehensive survey of gene expression in peripheral glia constitutes a valuable resource for understanding how glia acquire specialized functions and how their roles differ across sensory modalities.

neuroscience↗

bioRxiv2020

Transcriptional mediators of treatment resistance in lethal prostate cancer

Metastatic castration resistant prostate cancer (mCRPC) is primarily treated with therapies that prevent transcriptional activity of the androgen receptor (AR), cause DNA damage, or prevent cell division. Clinical resistance to these therapies, including second-generation androgen-targeting compounds such as enzalutamide and abiraterone, is nearly universal. Other treatment modalities, including immune checkpoint inhibitors, have provided minimal benefit except in rare subsets of patients1,2. Both tumour intrinsic and extrinsic cellular programs contributing to therapeutic resistance remain areas of active investigation. Here we use full-length single-cell RNA-sequencing (scRNA-seq) to identify the transcriptional states of cancer and immune cells in the mCRPC microenvironment. Within cancer cells, we identified transcriptional patterns that mediate a significant proportion of inherited risk for prostate cancer, extensive heterogeneity in AR splicing within and between tumours, and vastly divergent regulatory programs between adenocarcinoma and small cell carcinoma. Moreover, upregulation of TGF-{beta} signalling and epithelial-mesenchymal transition (EMT) were both associated with resistance to enzalutamide. We found that some lymph node metastases, but no bone metastases, were heavily infiltrated by dysfunctional CD8+ T cells, including cells undergoing dramatic clonal expansion during enzalutamide treatment. Our findings suggest avenues for rational therapeutic approaches targeting both tumour-intrinsic and immunological pathways to combat resistance to current treatment options.

genomics↗

bioRxiv2020

Mechanism of monoallelic expression and allelic rheostat role of DNA methylation

In diploid cells, maternal and paternal copies of genes usually have similar transcriptional activity. Mammalian allele-specific epigenetic mechanisms such as X-chromosome inactivation (XCI) and imprinting were historically viewed as rare exceptions to this rule. The discovery of mitotically stable monoallelic autosomal expression (MAE) a decade ago revealed an additional allele-specific mode regulating thousands of mammalian genes. However, despite its prevalence, the mechanistic basis of MAE remains unknown. To uncover the mechanism of MAE maintenance, we devised a small-molecule screen for reactivation of silenced alleles across multiple loci using targeted RNA sequencing. Contrary to previous reports, we identified DNA methylation as a key mechanism of MAE mitotic maintenance. In contrast with the binary choice of the active allele in XCI, stringent transcriptome-wide analysis revealed MAE as a regulatory mode with tunable control of allele-specific expression, dependent on the extent of DNA methylation. In a subset of MAE genes, allelic imbalance was insensitive to changes in DNA methylation, implicating additional mechanisms in MAE maintenance in these loci. Our findings identify a key mechanism of MAE maintenance, reveal tunability of this mode of gene regulation, and provide the essential platform for probing the biological role of MAE in development and disease.

genomics↗

bioRxiv2019

A single-cell and single-nucleus RNA-seq toolbox for fresh and frozen human tumors

Single cell genomics is essential to chart the complex tumor ecosystem. While single cell RNA-Seq (scRNA-Seq) profiles RNA from cells dissociated from fresh tumor tissues, single nucleus RNA-Seq (snRNA-Seq) is needed to profile frozen or hard-to-dissociate tumors. Each strategy requires modifications to fit the unique characteristics of different tissue and tumor types, posing a barrier to adoption. Here, we developed a systematic toolbox for profiling fresh and frozen clinical tumor samples using scRNA-Seq and snRNA-Seq, respectively. We tested eight tumor types of varying tissue and sample characteristics (resection, biopsy, ascites, and orthotopic patient-derived xenograft): lung cancer, metastatic breast cancer, ovarian cancer, melanoma, neuroblastoma, pediatric sarcoma, glioblastoma, pediatric high-grade glioma, and chronic lymphocytic leukemia. Analyzing 212,498 cells and nuclei from 39 clinical samples, we evaluated protocols by cell quality, recovery rate, and cellular composition. We optimized protocols for fresh tissue dissociation for different tumor types using a decision tree to account for the technical and biological variation between clinical samples. We established methods for nucleus isolation from OCT embedded and fresh-frozen tissues, with an optimization matrix varying mechanical force, buffer, and detergent. scRNA-Seq and snRNA-Seq from matched samples recovered the same cell types and intrinsic expression profiles, but at different proportions. Our work provides direct guidance across a broad range of tumors, including criteria for testing and selecting methods from the toolbox for other tumors, thus paving the way for charting tumor atlases.

genomics↗

bioRxiv2018

MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin

SummaryA large fraction of human and mouse autosomal genes are subject to random monoallelic expression (MAE), an epigenetic mechanism characterized by allele-specific gene expression that varies between clonal cell lineages. MAE is highly cell-type specific, and mapping it in a large number of cell and tissue types can provide insight into its biological function. Its detection, however, remains challenging. We previously reported that a sequence-independent chromatin signature identifies, with high sensitivity and specificity, genes subject to MAE in multiple tissue types using readily available ChIP-seq data. Here we present an implementation of this method as a user-friendly, open-source software pipeline for monoallelic gene inference from chromatin (MaGIC).\n\nAvailability and implementationThe source code for the MaGIC pipeline and the Shiny app is available at https://github.com/gimelbrantlab/magic.\n\nContactsebastien_vigneau@dfci.harvard.edu, gimelbrant@mail.dfci.harvard.edu

bioinformatics↗