Publications by authors named "Julio Saez Rodriguez"

Article Synopsis
  • Glioblastomas are aggressive brain tumors that resist treatment and utilize neuron-tumor connections to promote their growth, with cholinergic neurons playing a key role in this invasion.
  • The study utilized rabies viruses for retrograde tracing to reveal how glioblastomas integrate into brain circuits, showing that radiotherapy can enhance neuron-tumor connectivity, complicating treatment efforts.
  • By disrupting neuron-tumor connections, researchers discovered a potential therapeutic approach that could halt glioblastoma progression, emphasizing the need to target these synapses for better treatment outcomes.
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The gastrointestinal tract is a multi-organ system crucial for efficient nutrient uptake and barrier immunity. Advances in genomics and a surge in gastrointestinal diseases has fuelled efforts to catalogue cells constituting gastrointestinal tissues in health and disease. Here we present systematic integration of 25 single-cell RNA sequencing datasets spanning the entire healthy gastrointestinal tract in development and in adulthood.

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Summary: The inference of kinase activity from phosphoproteomics data can point to causal mechanisms driving signalling processes and potential drug targets. Identifying the kinases whose change in activity explains the observed phosphorylation profiles, however, remains challenging, and constrained by the manually curated knowledge of kinase-substrate associations. Recently, experimentally determined substrate sequence specificities of human kinases have become available, but robust methods to exploit this new data for kinase activity inference are still missing.

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Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system. Inflammation is gradually compartmentalized and restricted to specific tissue niches such as the lesion rim. However, the precise cell type composition of such niches, their interactions and changes between chronic active and inactive stages are incompletely understood.

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Background: Ischemia-reperfusion injury (IRI) inevitably occurs during kidney transplantation and extended ischemia is associated with delayed graft function and poor outcomes. Remote ischemic preconditioning (RIPC) is a simple, noninvasive procedure aimed at reducing IRI and improving graft function. Experimental studies have implicated the kynurenine pathway as a protective mechanism behind RIPC.

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Inflammation, fibrosis and metabolic stress critically promote heart failure with preserved ejection fraction (HFpEF). Exposure to high-fat diet and nitric oxide synthase inhibitor N[w]-nitro-l-arginine methyl ester (L-NAME) recapitulate features of HFpEF in mice. To identify disease-specific traits during adverse remodeling, we profiled interstitial cells in early murine HFpEF using single-cell RNAseq (scRNAseq).

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Mechanisms underlying the cardiovascular-kidney-metabolic (CKM) syndrome are unknown, although key small molecule metabolites may be involved. Bulk and spatial metabolomics identified adenine to be upregulated and specifically enriched in coronary blood vessels in hearts from patients with diabetes and left ventricular hypertrophy. Single nucleus gene expression studies revealed that endothelial methylthioadenosine phosphorylase (MTAP) was increased in human hearts with hypertrophic cardiomyopathy.

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The growing availability of single-cell and spatially resolved transcriptomics has led to the development of many approaches to infer cell-cell communication, each capturing only a partial view of the complex landscape of intercellular signalling. Here we present LIANA+, a scalable framework built around a rich knowledge base to decode coordinated inter- and intracellular signalling events from single- and multi-condition datasets in both single-cell and spatially resolved data. By extending and unifying established methodologies, LIANA+ provides a comprehensive set of synergistic components to study cell-cell communication via diverse molecular mediators, including those measured in multi-omics data.

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Article Synopsis
  • The study evaluates various deconvolution methods that estimate immune cell levels in tumor samples based on gene expression data during a DREAM Challenge.
  • While many established methods perform adequately for most immune cell types, they struggle with accurately assessing all states of functional CD8+ T cells.
  • Community-contributed methods, particularly a deep learning approach, have shown promising results and could enhance the development of deconvolution techniques, especially in identifying functional CD4+ T cell states.
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From the catalytic breakdown of nutrients to signaling, interactions between metabolites and proteins play an essential role in cellular function. An important case is cell-cell communication, where metabolites, secreted into the microenvironment, initiate signaling cascades by binding to intra- or extracellular receptors of neighboring cells. Protein-protein cell-cell communication interactions are routinely predicted from transcriptomic data.

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Understanding the interplay of the proteome and the metabolome helps to understand cellular regulation and response. To enable robust inferences from such multi-omics analyses, we introduced and evaluated a workflow for combined proteome and metabolome analysis starting from a single sample. Specifically, we integrated established and individually optimized protocols for metabolomic and proteomic profiling (EtOH/MTBE and autoSP3, respectively) into a unified workflow (termed MTBE-SP3), and took advantage of the fact that the protein residue of the metabolomic sample can be used as a direct input for proteome analysis.

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Transcriptomics is widely used to assess the state of biological systems. There are many tools for the different steps, such as normalization, differential expression, and enrichment. While numerous studies have examined the impact of method choices on differential expression results, little attention has been paid to their effects on further downstream functional analysis, which typically provides the basis for interpretation and follow-up experiments.

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Correlation is not causation: this simple and uncontroversial statement has far-reaching implications. Defining and applying causality in biomedical research has posed significant challenges to the scientific community. In this perspective, we attempt to connect the partly disparate fields of systems biology, causal reasoning, and machine learning to inform future approaches in the field of systems biology and molecular medicine.

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Article Synopsis
  • Precise gene expression control is crucial for maintaining cell function, and its decline with age contributes to changes in cellular health and related diseases.
  • Gene regulatory networks model the interactions that govern gene expression through mechanisms like signal transduction and transcription factors, reflecting how genes coexpress under various conditions.
  • Advances in technology have improved our ability to analyze these networks, leading to better insights into cellular aging and disease, with applications from large-scale multi-omics datasets.
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Impairment of the central nervous system (CNS) poses a significant health risk for astronauts during long-duration space missions. In this study, we employed an innovative approach by integrating single-cell multiomics (transcriptomics and chromatin accessibility) with spatial transcriptomics to elucidate the impact of spaceflight on the mouse brain in female mice. Our comparative analysis between ground control and spaceflight-exposed animals revealed significant alterations in essential brain processes including neurogenesis, synaptogenesis and synaptic transmission, particularly affecting the cortex, hippocampus, striatum and neuroendocrine structures.

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Single-cell transcriptomics and spatially-resolved imaging/sequencing technologies have revolutionized biomedical research. However, they suffer from lack of spatial information and a trade-off of resolution and gene coverage, respectively. We propose DOT, a multi-objective optimization framework for transferring cellular features across these data modalities, thus integrating their complementary information.

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In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities.

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Image-based deep learning models are used to extract new information from standard hematoxylin and eosin pathology slides; however, biological interpretation of the features detected by artificial intelligence (AI) remains a challenge. High-grade serous carcinoma of the ovary (HGSC) is characterized by aggressive behavior and chemotherapy resistance, but also exhibits striking variability in outcome. Our understanding of this disease is limited, partly due to considerable tumor heterogeneity.

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Article Synopsis
  • The rise of single-cell analysis tools makes benchmarks crucial for guiding analysis and method improvement.
  • Current benchmarks suffer from issues like lack of standardization and limited adaptability, affecting their usefulness over time.
  • Open Problems is introduced as a dynamic, community-driven benchmarking platform that addresses these issues by encompassing 10 current single-cell tasks to enhance method selection and evaluation.
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In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. Here, we integrate two tools, LIANA and Tensor-cell2cell, which, when combined, can deploy multiple existing methods and resources to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this work, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights.

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Complex disease phenotypes often span multiple molecular processes. Functional characterization of these processes can shed light on disease mechanisms and drug effects. Thermal Proteome Profiling (TPP) is a mass-spectrometry (MS) based technique assessing changes in thermal protein stability that can serve as proxies of functional protein changes.

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Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing.

Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms.

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The immune system comprises diverse specialized cell types that cooperate to defend the host against a wide range of pathogenic threats. Recent advancements in single-cell and spatial multi-omics technologies provide rich information about the molecular state of immune cells. Here, we review how the integration of single-cell and spatial multi-omics data with prior knowledge-gathered from decades of detailed biochemical studies-allows us to obtain functional insights, focusing on gene regulatory processes and cell-cell interactions.

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