Biology subjects

Dries, R.

Publications and source records attributed to Dries, R..

13 recordsLinked to original sources

bioRxiv2026

dbverse scales spatial omics analysis with embedded analytical databases

Spatial omics datasets are increasing in size and complexity, exceeding the memory of standard computers and thereby limiting data analysis. Here we present dbverse, a framework for larger-than-memory matrix, spatial and genomic data analysis in embedded analytical databases. Benchmarks show dbverse provides orders of magnitude runtime improvements relative to established in-memory and file-backed methods for core operations in single-cell and spatial omics analysis. We integrated dbverse with Giotto Suite, scaling end-to-end preprocessing of millions of cells and enabling spatial alternative polyadenylation analysis as demonstrated on a Visium HD 3' ovarian clear cell carcinoma sample. The dbverse framework provides an interoperable database foundation for larger-than-memory spatial omics analysis on ordinary computers.

bioinformatics↗

bioRxiv2026

Integrative Single-cell and Spatial Transcriptomic Analysis of Osteosarcoma Reveals Conserved and Distinct Ecosystems Across Sites and Species

Osteosarcoma is a heterogeneous malignancy, exhibiting significant variability among patients, individual cancer cells within a tumor, and the stromal cells that compose primary and metastatic lesions. To facilitate the study of this complex disease, we compiled a unique cross-species single-cell transcriptomic dataset totaling over a million cells/nuclei from human specimens, canine specimens, patient-derived xenografts/PDX, and syngeneic mouse models at both primary (bone) and metastatic (lung) sites. Using a rigorous process for multi-species alignment and annotation, we identified six conserved tumor cell transcriptional states organized along hierarchical differentiation trajectories from progenitor to differentiated phenotypes. Parallel analysis of tumor-associated cells identified conserved macrophage, fibroblast, and endothelial populations that exhibit species- and site-specific reprogramming. Validation by mapping cell types using spatial transcriptomics revealed structured neighborhood architectures that were reproduced across multiple samples. Cell-cell interaction analysis revealed similarities and differences in tumor-host networks across primary and metastatic sites and across species. This analysis enabled pathway-specific assessment of tumor-host communication fidelity across osteosarcoma model systems relative to humans, revealing canine osteosarcoma as a more faithful model. Metastatic lung lesions, counterintuitively, exhibited more intense and complex extracellular matrix (ECM) signaling than primary bone tumors. A key example was tumor-derived fibronectin (FN1), which engages integrin and syndecan receptors on lung epithelial cells, driving a pathological mesenchymal and profibrotic state that promotes fibrotic niche formation and metastatic lung colonization. Together, this cross-species resource delineates both conserved and divergent tumor microenvironment programs, demonstrates how model-aware analyses uncover previously unrecognized tumor-host interactions, and underscores the need for therapies that co-target tumor heterogeneity and its supportive metastatic niche. Statement of SignificanceWe show that the tumor microenvironment in human osteosarcoma patients has biological phenotypes that are conserved across both patients and species. This points towards underlying molecular mechanisms that could be therapeutically targeted.

cancer biology↗

bioRxiv2025

Clinical feasibility of spatial transcriptomics using discarded tissue from diagnostic breast biopsies

Spatial transcriptomics holds potential to transform cancer diagnostics, yet significant barriers still limit its clinical translation. First, access to primary patient tissue is often restricted by logistical challenges and patient hesitancy. Second, it remains uncertain whether high-quality spatial transcriptomics data can be generated from clinical biopsy sections as these are collected primarily for diagnostic purposes that do not prioritize RNA-quality. We investigated whether discarded tissue slices, generated during standard pathology procedures, could be repurposed for spatial transcriptomics and alleviate concerns about both tissue availability and quality. Here, we established a pipeline to collect and perform spatial in situ transcriptomics on discarded biopsy material from a breast cancer patient, and digitized matched pathology images, including hematoxylin and eosin and traditional histochemistry stains from adjacent sections. Our results show that spatial transcriptomics data from discarded tissue are concordant with the original pathology report, while also providing additional insights such as accurate cell type annotation, detailed spatial architecture, and quantification of biological processes relevant to breast cancer progression. Altogether, our approach using discarded pathology tissue sections provides a practical and scalable solution that would maximize the scientific value of existing clinical specimens and enable high-resolution tumor microenvironment mapping.

genomics↗

bioRxiv2025

Distinct malignant cell states and myeloid glutamate signaling associated with aggressive pancreatic neuroendocrine tumors

Pancreatic neuroendocrine tumors (PNET) are rare malignancies of the endocrine pancreas with diverse clinical outcomes. While some PNETs are indolent, others are aggressive and metastasize quickly. However, clinically-relevant molecular stratification for PNET to predict outcomes and guide therapeutic decision-making is limited. Thus, there is an urgent need to understand the molecular heterogeneity of PNETs to refine prognostication and discover novel therapeutic vulnerabilities. We performed single-nucleus RNA sequencing on resected primary and metastatic PNETs (n = 20), including two PNETs with neoadjuvant treatment. We inferred gene expression programs (GEPs) of malignant and non-malignant cells and investigated associations with clinical outcomes. Next, we inferred interactions in the tumor microenvironment (TME) and performed transwell assays for functional validation. Finally, we explored genomic and transcriptomic evolution in a unique case study of an untreated primary PNET with two asynchronous hepatic metastases. A malignant GEP enriched for neural/synaptic signaling genes was associated with worse overall survival, broad chromosomal loss of heterozygosity, and alternative lengthening of telomeres. Another malignant GEP enriched for VEGF signaling increased throughout metastatic progression in our case study. We found that macrophage-derived glutamate drives polarization towards an immunosuppressive phenotype and activates the MAPK/ERK pathway in malignant cells to increase migratory capacity. This study provides a detailed single-nucleus transcriptomic classification of malignant, stromal, and immune cell types and states in PNETs, their interactions in the TME, and associations with clinical outcomes. The refined molecular taxonomy of PNET may guide the development of more efficacious biomarkers and therapeutic strategies.

cancer biology↗

bioRxiv2024

The MYCN 5' UTR as a therapeutic target in neuroblastoma

Tumor cell amplification of the MYCN transcription factor is seen in half of patients with high-risk neuroblastoma, where it functions as an oncogenic driver associated with metastatic disease and poor survival. Yet, direct targeting of MYCN has been met with little success, prompting efforts to inhibit its expression at multiple levels. MYCN-amplified neuroblastoma cells have an increased requirement for protein synthesis to meet the overwhelming transcriptional burden imposed by oncogenic MYCN. Here, we take advantage of this vulnerability to interrogate the therapeutic potential of inhibiting the activity of the eukaryotic translation initiation factor 4A1 (eIF4A1), an RNA-helicase responsible for resolving structural barriers such as polypurine preponderance within 5' untranslated regions (UTRs). We observed that eIF4A1 is a key regulator of transcript-specific mRNA recruitment in MYCN-overexpressing neuroblastomas and MYCN-associated transcripts rank highly in polypurine-rich 5' UTR sequences, the majority of which have critical roles in cell proliferation. Using CMLD012824, a novel synthetic amidino-rocaglate (ADR) derivative, we demonstrate selectively increased eIF4A1 affinity for polypurine-rich 5' UTRs, including the MYCN mRNA, leading to translation inhibition and cytotoxicity in human neuroblastoma cell lines and animal models. Through ribosome profiling and PAR-CLIP analysis, we show that ADR-mediated clamping of eIF4A1 onto mRNA spans the full lengths of target transcripts, whereas translational inhibition is mediated selectively through 5' UTR binding. Both cap-dependent and cap-independent translation of MYCN are disrupted, pointing to the ability of CMLD012824 to disrupt non-canonical translation initiation. Our studies provide insights into the functional role of eIF4A1 in meeting the increased protein synthesis demands of MYCN-amplified neuroblastoma and suggest that its disruption may be therapeutically beneficial in this disease.

cancer biology↗

bioRxiv2024

Aberrant activation of wound healing programs within the metastatic niche facilitates lung colonization by osteosarcoma cells

PurposeLung metastasis is responsible for nearly all deaths caused by osteosarcoma, the most common pediatric bone tumor. How malignant bone cells coerce the lung microenvironment to support metastatic growth is unclear. The purpose of this study is to identify metastasis-specific therapeutic vulnerabilities by delineating the cellular and molecular mechanisms underlying osteosarcoma lung metastatic niche formation. Experimental designUsing single-cell transcriptomics (scRNA-seq), we characterized genome- and tissue-wide molecular changes induced within lung tissues by disseminated osteosarcoma cells in both immunocompetent murine models of metastasis and patient samples. We confirmed transcriptomic findings at the protein level and determined spatial relationships with multi-parameter immunofluorescence and spatial transcriptomics. Based on these findings, we evaluated the ability of nintedanib, a kinase inhibitor used to treat patients with pulmonary fibrosis, to impair metastasis progression in both immunocompetent murine osteosarcoma and immunodeficient human xenograft models. Single-nucleus and spatial transcriptomics was used to perform molecular pharmacodynamic studies that define the effects of nintedanib on tumor and non-tumor cells within the metastatic microenvironment. ResultsOsteosarcoma cells induced acute alveolar epithelial injury upon lung dissemination. scRNA-seq demonstrated that the surrounding lung stroma adopts a chronic, non-resolving wound-healing phenotype similar to that seen in other models of lung injury. Accordingly, metastasis-associated lung demonstrated marked fibrosis, likely due to the accumulation of pathogenic, pro-fibrotic, partially differentiated epithelial intermediates and macrophages. Our data demonstrated that nintedanib prevented metastatic progression in multiple murine and human xenograft models by inhibiting osteosarcoma-induced fibrosis. ConclusionsFibrosis represents a targetable vulnerability to block the progression of osteosarcoma lung metastasis. Our data support a model wherein interactions between osteosarcoma cells and epithelial cells create a pro-metastatic niche by inducing tumor deposition of extracellular matrix proteins such as fibronectin that is disrupted by the anti-fibrotic TKI nintedanib. Our data shed light on the non-cell autonomous effects of TKIs on metastasis and provide a roadmap for using single-cell and spatial transcriptomics to define the mechanism of action of TKI on metastases in animal models. Statement of translational relevanceTherapies that block metastasis have the potential to save the majority of lives lost due to solid tumors. Disseminated tumor cells must integrate into the foreign, inhospitable microenvironments they encounter within secondary organs to facilitate metastatic colonization and progression. Our study elucidated that disseminated osteosarcoma cells survive within the lung by co-opting and amplifying the lungs endogenous wound-healing response program. This osteosarcoma-induced wound response results in fibrosis of the surrounding microenvironment. Our data implicates fibrosis and abnormal wound healing as key drivers of osteosarcoma lung metastasis that can be targeted therapeutically to disrupt metastasis progression.

cancer biology↗

bioRxiv2023

Giotto Suite: a multi-scale and technology-agnostic spatial multi-omics analysis ecosystem

Emerging spatial omics technologies continue to advance the molecular mapping of tissue architecture and the investigation of gene regulation and cellular crosstalk, which in turn provide new mechanistic insights into a wide range of biological processes and diseases. Such technologies provide an increasingly large amount of information content at multiple spatial scales. However, representing and harmonizing diverse spatial datasets efficiently, including combining multiple modalities or spatial scales in a scalable and flexible manner, remains a substantial challenge. Here, we present Giotto Suite, a suite of open-source software packages that underlies a fully modular and integrated spatial data analysis toolbox. At its core, Giotto Suite is centered around an innovative and technology-agnostic data framework embedded in the R software environment, which allows the representation and integration of virtually any type of spatial omics data at any spatial resolution. In addition, Giotto Suite provides both scalable and extensible end-to-end solutions for data analysis, integration, and visualization. Giotto Suite integrates molecular, morphology, spatial, and annotated feature information to create a responsive and flexible workflow for multi-scale, multi-omic data analyses, as demonstrated here by applications to several state-of-the-art spatial technologies. Furthermore, Giotto Suite builds upon interoperable interfaces and data structures that bridge the established fields of genomics and spatial data science, thereby enabling independent developers to create custom-engineered pipelines. As such, Giotto Suite creates an immersive ecosystem for spatial multi-omic data analysis.

bioinformatics↗

bioRxiv2023

Matrix and analysis metadata standards (MAMS) to facilitate harmonization and reproducibility of single-cell data

A large number of genomic and imaging datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While much effort has been devoted to capturing information related to biospecimen information and experimental procedures, the metadata standards that describe data matrices and the analysis workflows that produced them are relatively lacking. Detailed metadata schema related to data analysis are needed to facilitate sharing and interoperability across groups and to promote data provenance for reproducibility. To address this need, we developed the Matrix and Analysis Metadata Standards (MAMS) to serve as a resource for data coordinating centers and tool developers. We first curated several simple and complex "use cases" to characterize the types of featureobservation matrices (FOMs), annotations, and analysis metadata produced in different workflows. Based on these use cases, metadata fields were defined to describe the data contained within each matrix including those related to processing, modality, and subsets. Suggested terms were created for the majority of fields to aid in harmonization of metadata terms across groups. Additional provenance metadata fields were also defined to describe the software and workflows that produced each FOM. Finally, we developed a simple listlike schema that can be used to store MAMS information and implemented in multiple formats. Overall, MAMS can be used as a guide to harmonize analysis-related metadata which will ultimately facilitate integration of datasets across tools and consortia. MAMS specifications, use cases, and examples can be found at https://github.com/single-cell-mams/mams/.

bioinformatics↗

bioRxiv2021

Cell lineage as a predictor of immune response in neuroblastoma

Immunotherapy for patients with neuroblastoma has met with limited success, partly due to an incomplete understanding of the mechanisms underlying immune responsiveness in this clinically and genetically heterogenic tumor. Here, we undertook an unbiased analysis using dimension reduction and UMAP visualization of transcriptional signatures derived from 498 primary neuroblastoma tumors. Four distinct clusters based on differentially expressed genes emerged, of which one, representing about 30% and comprising mainly of MYCN-nonamplified tumors, was notable for the high expression of genes associated with both immune response activation and suppression. This capacity to elicit a productive immune response resided exclusively in tumors with dominant populations of undifferentiated, neural crest-like or mesenchymal cells; by contrast, tumors comprising primarily of committed, adrenergic neuron-like cells were less immunogenic. Mesenchymal neuroblastoma cells were enriched for innate and adaptive immune gene signatures, demonstrated engagement with cytotoxic T and natural killer cells, and induced immune cell infiltration in an immunocompetent mouse model. Transcriptional or targeted therapy-induced reprogramming of adrenergic cells to the mesenchymal state led to reactivation of tumor cell-intrinsic immune genes. Key immune response genes in adrenergic tumor cells were found to be epigenetically silenced by the PRC2 complex, and such repression could be relieved by either mesenchymal cell state reprogramming or EZH2 inhibition, leading to increased activation of natural killer cells by the tumor cells. These data identify cell lineage as a major determinant of the immunogenic potential in neuroblastoma that could be used to stratify patients who are most likely to benefit from immunotherapy.

cancer biology↗

bioRxiv2020

Multi-Modal Profiling of Human Fetal Liver-Derived Hematopoietic Stem Cells Reveals the Molecular Signature of Engraftment Potential

The human hematopoietic stem cell (HSC) harbors remarkable regenerative potential that can be harnessed therapeutically. During early development, HSCs in the fetal liver (FL) undergo active expansion while simultaneously retaining robust engraftment capacity, yet the underlying molecular program responsible for their efficient engraftment remains unclear. We profiled 26,407 FL cells at both transcriptional and protein levels including over 7,000 highly enriched and functional FL HSCs to establish a detailed molecular signature of engraftment potential. Integration of transcript and linked cell surface marker expression revealed a generalizable signature defining functional FL HSCs and allowed for the stratification of enrichment strategies with high translational potential. This comprehensive, multi-modal profiling of engraftment capacity connects a critical biological function at a key developmental timepoint with its underlying molecular drivers, serving as a useful resource for the field.

cell biology↗

bioRxiv2019

Giotto, a pipeline for integrative analysis and visualization of single-cell spatial transcriptomic data

The rapid development of novel spatial transcriptomic and proteomic technologies has provided new opportunities to investigate the interactions between cells and their native microenvironment. However, effective use of such technologies requires the development of innovative computational tools that are easily accessible and intuitive to use. Here we present Giotto, a comprehensive, flexible, robust, and open-source toolbox for spatial transcriptomic and proteomic data analysis and visualization. The data analysis module provides end-to-end analysis by implementing a wide range of algorithms for characterizing cell-type distribution, spatially coherent gene expression patterns, and interactions between each cell and its surrounding neighbors. Furthermore, Giotto can also be used in conjunction with external single-cell RNAseq data to infer the spatial enrichment of cell types from data that do not have single-cell resolution. The data visualization module allows users to interactively visualize the gene expression data, analysis outputs, and additional imaging features, thereby providing a user-friendly workspace to explore multiple modalities of information for biological investigation. These two modules can be used iteratively for refined analysis and hypothesis development. We applied Giotto to a wide range of public datasets encompassing diverse technologies and platforms, thereby demonstrating its general applicability for spatial transcriptomic and proteomic data analysis and visualization.

bioinformatics↗

bioRxiv2018

CDK12 loss in cancer cells affects DNA damage response genes through premature cleavage and polyadenylation

The cyclin-dependent kinase 12 (CDK12) modulates transcription elongation by phosphorylating the carboxy-terminal domain of RNA polymerase II and appears to selectively affect the expression of DNA damage response (DDR) and mRNA processing genes. Yet, the mechanism(s) by which it achieves this selectivity remains unclear. Using a highly selective CDK12/13 inhibitor, THZ531, and nascent RNA sequencing, we show that CDK12 inhibition results in gene length-dependent elongation defects, leading to premature cleavage and polyadenylation (PCPA) as well as loss of expression of long (>45 kb) genes, a substantial proportion of which participate in the DDR. This early termination phenotype correlated with an increased proportion of intronic polyadenylation sites, a feature that was especially prominent among DDR genes. Finally, phosphoproteomic analysis indicated that pre-mRNA processing factors, including those involved in PCPA, are direct phosphotargets of CDKs 12 and 13. These results support a model in which DDR genes are uniquely susceptible to CDK12 inhibition due primarily to their relatively longer lengths and lower ratios of U1 snRNP binding to intronic polyadenylation sites.

cancer biology↗

bioRxiv2018

Decomposing spatially dependent and cell type specific contributions to cellular heterogeneity

Both the intrinsic regulatory network and spatial environment are contributors of cellular identity and result in cell state variations. However, their individual contributions remain poorly understood. Here we present a systematic approach to integrate both sequencing-and imaging-based single-cell transcriptomic profiles, thereby combining whole-transcriptomic and spatial information from these assays. We applied this approach to dissect the cell-type and spatial domain associated heterogeneity within the mouse visual cortex region. Our analysis identified distinct spatially associated signatures within glutamatergic and astrocyte cell compartments, indicating strong interactions between cells and their spatial environment. Using these signatures as a guide to analyze single cell RNAseq data, we identified previously unknown, but spatially associated subpopulations. As such, our integrated approach provides a powerful tool for dissecting the roles of intrinsic regulatory networks and spatial environment in the maintenance of cellular states.

bioinformatics↗