A5065507596- Bioinformatics and Genomic Networks
- Single-cell and spatial transcriptomics
- Computational Drug Discovery Methods
- Gene expression and cancer classification
- Immune cells in cancer
- Cell Image Analysis Techniques
- Colorectal Cancer Surgical Treatments
- Gene Regulatory Network Analysis
- Colorectal Cancer Screening and Detection
- SARS-CoV-2 and COVID-19 Research
- Cancer Genomics and Diagnostics
- Genetic factors in colorectal cancer
- Complex Network Analysis Techniques
- interferon and immune responses
- Molecular Biology Techniques and Applications
- Microbial Metabolic Engineering and Bioproduction
- Machine Learning in Bioinformatics
- Advanced Proteomics Techniques and Applications
- Prostate Cancer Treatment and Research
- Immunotherapy and Immune Responses
- Cancer Immunotherapy and Biomarkers
- Genetics, Aging, and Longevity in Model Organisms
- Mass Spectrometry Techniques and Applications
- Cardiovascular Function and Risk Factors
- vaccines and immunoinformatics approaches
Roche (Switzerland)
2021-2025
University Hospital Heidelberg
2020-2024
Heidelberg University
2020-2024
Aix-Marseille Université
2018-2024
Inserm
2019-2024
Centre de Génétique Médicale de Marseille
2019-2024
Institut Polytechnique de Bordeaux
2018-2019
Centre National de la Recherche Scientifique
2018-2019
Centrale Marseille
2018-2019
Institut de Mathématiques de Marseille
2019
Abstract The growing availability of single-cell data, especially transcriptomics, has sparked an increased interest in the inference cell-cell communication. Many computational tools were developed for this purpose. Each them consists a resource intercellular interactions prior knowledge and method to predict potential communication events. Yet impact choice on resulting predictions is largely unknown. To shed light this, we systematically compare 16 resources 7 methods, plus consensus...
Abstract Motivation Recent years have witnessed an exponential growth in the number of identified interactions between biological molecules. These are usually represented as large and complex networks, calling for development appropriated tools to exploit functional information they contain. Random walk with restart (RWR) is state-of-the-art guilt-by-association approach. It explores network vicinity gene/protein seeds study their functions, based on premise that nodes related similar...
Molecular knowledge of biological processes is a cornerstone in omics data analysis. Applied to single-cell data, such analyses provide mechanistic insights into individual cells and their interactions. However, intercellular communication scarce, scattered across resources, not linked intracellular processes. To address this gap, we combined over 100 resources covering interactions roles proteins inter- signaling, as well transcriptional post-transcriptional regulation. We added protein...
Abstract The consensus molecular subtypes (CMS) of colorectal cancer (CRC) is the most widely-used gene expression-based classification and has contributed to a better understanding disease heterogeneity prognosis. Nevertheless, CMS intratumoral restricts its clinical application, stressing necessity further characterizing composition architecture CRC. Here, we used Spatial Transcriptomics (ST) in combination with single-cell RNA sequencing (scRNA-seq) decipher spatially resolved cellular In...
Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well interactions between proteins. However, modeling across biological contexts remains challenging for existing algorithms. Here we introduce PINNACLE, a geometric deep learning approach that generates context-aware representations. Leveraging multiorgan single-cell atlas, PINNACLE learns on contextualized interaction networks to produce 394,760 representations from...
Network embedding approaches are gaining momentum to analyse a large variety of networks. Indeed, these have demonstrated their effectiveness in tasks such as community detection, node classification, and link prediction. However, very few network methods been specifically designed handle multiplex networks, i.e. networks composed different layers sharing the same set nodes but having types edges. Moreover, our knowledge, existing cannot embed multiple from multiplex-heterogeneous several...
Abstract We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression tumor samples, through a community-wide DREAM Challenge. assess six published and 22 community-contributed methods using in vitro silico transcriptional profiles admixed cancer healthy cells. Several predict most cell types well, though they either were not trained to all functional CD8+ T states or do so with low accuracy. address this gap, including deep learning-based approach, whose...
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 drug repurposing.
Background The immune status of a patient’s tumor microenvironment (TME) may guide therapeutic interventions with cancer immunotherapy and help identify potential resistance mechanisms. Currently, patients’ is mostly classified based on CD8+tumor-infiltrating lymphocytes. An unmet need exists for comparable reliable precision immunophenotyping tools that would facilitate clinical treatment-relevant decision-making the understanding how to overcome Methods We systematically analyzed CD8...
Gene therapy using recombinant adeno-associated virus vectors holds great promise, but how serotype, sex, and liver zonation influence transduction transcriptomic changes is not fully understood. In this proof-of-concept study, we utilized Visium spatial transcriptomics combined with single-nucleus RNA sequencing to map the distribution impacts of rAAV2- rAAV9-CMV-EGFP in male female mouse livers. Spatial enabled precise transgene mapping, showing rAAV2's periportal rAAV9's pericentral...
Spatial transcriptomics technologies currently lack scalable and cost-effective options to profile tissues in three dimensions. Technological advances microcomputed tomography enabled non-destructive volumetric imaging of tissue blocks with sub-micron resolution at a centimetre scale. Here, we present X-Pression, deep convolutional neural network-based framework designed reconstruct 3D expression signatures cellular niches from data. By training on singular 2D section paired spatial...
Breast cancer remains one of the prominent causes death worldwide. Although chemotherapeutic agents often result in substantial reduction primary or metastatic tumours, remaining drug-tolerant tumour cell populations, known as minimal residual disease (MRD), pose a significant risk recurrence and therapy resistance. In this study, we describe spatiotemporal organisation response MRD BRCA1;p53-deficient mouse mammary tumours human clinical samples using multimodal approach. By integrating...
Abstract The growing availability of single-cell data has sparked an increased interest in the inference cell-cell communication from this data. Many tools have been developed for purpose. Each them consists a resource intercellular interactions prior knowledge and method to predict potential events. Yet impact choice on resulting predictions is largely unknown. To shed light this, we created framework, available at https://github.com/saezlab/ligrec_decoupler , facilitate comparative...
Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well interactions between proteins. However, modeling across biological contexts remains challenging for existing algorithms. Here, we introduce Pinnacle, a geometric deep learning approach that generates context-aware representations. Leveraging multi-organ single-cell atlas, Pinnacle learns on contextualized interaction networks to produce 394,760 representations...
Background Prostate cancer is a major public health issue, mainly because patients relapse after androgen deprivation therapy. Proteomic strategies, aiming to reflect the functional activity of cells, are nowadays among leading approaches tackle challenges not only better diagnosis, but also unraveling mechanistic details related disease etiology and progression. Methods We conducted here large SILAC-based Mass Spectrometry experiment map proteomes phosphoproteomes four widely used prostate...
<ns4:p>The identification of communities, or modules, is a common operation in the analysis large biological networks. The <ns4:italic>Disease Module Identification DREAM challenge</ns4:italic> established framework to evaluate clustering approaches biomedical context, by testing association communities with GWAS-derived trait and disease genes. We implemented here several extensions MolTi software that detects optimizing multiplex (and monoplex) network modularity. In particular, now runs...
Comparing SARS-CoV-2 infection-induced gene expression signatures to drug treatment-induced is a promising bioinformatic tool repurpose existing drugs against SARS-CoV-2. The general hypothesis of signature-based repurposing that with inverse similarity disease signature can reverse phenotype and thus be effective it. However, in the case viral infection diseases, like SARS-CoV-2, infected cells also activate adaptive, antiviral pathways, so relationship between more ambiguous. To address...
Abstract Molecular knowledge of biological processes is a cornerstone in the analysis omics data. Applied to single-cell data, such analyses can provide mechanistic insights into individual cells and their interactions. However, intercellular communication scarce, scattered across different resources, not linked intracellular processes. To address this gap, we combined over 100 resources single database. It covers interactions roles proteins inter- signal transduction, as well...
Abstract The heterogeneity of colorectal cancer (CRC) contributes to substantial differences in patient response standard therapies. consensus molecular subtypes (CMS) CRC is the most widely-used gene expression-based classification and has contributed a better understanding disease prognosis. Nevertheless, CMS intratumoral restricts its clinical application, stressing necessity further characterizing composition architecture CRC. Here, we used Spatial Transcriptomics (ST) combination with...
Comorbidities are expected to impact the pathophysiology of heart failure (HF) with preserved ejection fraction (HFpEF). However, comorbidity profiles usually reduced a few comorbid disorders. Systems medicine approaches can model phenome-wide improve our understanding HFpEF and infer associated genetic profiles.We retrospectively explored 569 comorbidities in 29,047 HF patients, including 8062 6585 (HFrEF) patients from German university hospital. We assessed differences between subtypes...
<ns4:p>The identification of communities, or modules, is a common operation in the analysis large biological networks. The <ns4:italic>Disease Module Identification DREAM challenge</ns4:italic> established framework to evaluate clustering approaches biomedical context, by testing association communities with GWAS-derived trait and disease genes. We implemented here several extensions MolTi software that detects optimizing multiplex (and monoplex) network modularity. In particular, now runs...
Dissecting tissue compartments in spatial transcriptomics (ST) remains challenging due to limited resolution and dependence on single-cell reference data. We present Chrysalis, a computational method that rapidly uncovers through spatially variable gene (SVG) detection archetypal analysis without requiring external Additionally, it offers unique visualisation approach for swift characterisation provides access the underlying expression signatures, enabling identification of functionally...