A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets
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ABSTRACT:
PROVIDER: PRJNA749249 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR15224029.fastq.gz | Fastqsanger.gz | |||
| SRR15224030.fastq.gz | Fastqsanger.gz | |||
| SRR15224031.fastq.gz | Fastqsanger.gz | |||
| SRR15224032.fastq.gz | Fastqsanger.gz | |||
| SRR15224033.fastq.gz | Fastqsanger.gz |
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Project description:A major health care problems today is that drug treatment is ineffective for many patients. This may depend on the involvement of thousands of genes, which vary between patients with the same diagnosis at different time points. Identification and prioritization of upstream regulatory (UR) genes for precision medicine are therefore great challenges. In this study, we present a framework to model dynamic cellulome- and genome-wide changes in digital twins, to prioritize UR genes. We used seasonal allergic rhinitis (SAR) as a disease model because the external trigger (pollen) is known, so that the disease process can be mimicked by time series analyses of in vitro allergen-stimulated PBMC from SAR patients. Based on single cell profiling data from different stages of the disease, we constructed network models, hypothesizing that the directed molecular interactions between cell types could be traced to find an UR cell type and gene. This hypothesis was rejected because the cellular interactions formed multi-directional networks, rather than linear hierarchies. Instead, we were successfully able to rank and prioritize the URs based on their relative effects on the different cell types. We propose that this ranking strategy is a scalable approach for organizing and analyzing cellulome- and genome-wide data in digital twins.
2022-04-12 | GSE180697 | GEO
Project description:BackgroundMedical digital twins are computational disease models for drug discovery and treatment. Unresolved problems include how to organize and prioritize between disease-associated changes in digital twins, on cellulome- and genome-wide scales. We present a dynamic framework that can be used to model such changes and thereby prioritize upstream regulators (URs) for biomarker- and drug discovery.MethodsWe started with seasonal allergic rhinitis (SAR) as a disease model, by analyses of in vitro allergen-stimulated peripheral blood mononuclear cells (PBMC) from SAR patients. Time-series a single-cell RNA-sequencing (scRNA-seq) data of these cells were used to construct multicellular network models (MNMs) at each time point of molecular interactions between cell types. We hypothesized that predicted molecular interactions between cell types in the MNMs could be traced to find an UR gene, at an early time point. We performed bioinformatic and functional studies of the MNMs to develop a scalable framework to prioritize UR genes. This framework was tested on a single-cell and bulk-profiling data from SAR and other inflammatory diseases.ResultsOur scRNA-seq-based time-series MNMs of SAR showed thousands of differentially expressed genes (DEGs) across multiple cell types, which varied between time points. Instead of a single-UR gene in each MNM, we found multiple URs dispersed across the cell types. Thus, at each time point, the MNMs formed multi-directional networks. The absence of linear hierarchies and time-dependent variations in MNMs complicated the prioritization of URs. For example, the expression and functions of Th2 cytokines, which are approved drug targets in allergies, varied across cell types, and time points. Our analyses of bulk- and single-cell data from other inflammatory diseases also revealed multi-directional networks that showed stage-dependent variations. We therefore developed a quantitative approach to prioritize URs: we ranked the URs based on their predicted effects on downstream target cells. Experimental and bioinformatic analyses supported that this kind of ranking is a tractable approach for prioritizing URs.ConclusionsWe present a scalable framework for modeling dynamic changes in digital twins, on cellulome- and genome-wide scales, to prioritize UR genes for biomarker and drug discovery.
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Project description:Computational treatment of digital twins of individual patients has been proposed as a strategy to personalize treatment. Systematic solutions are though currently lacking for inflammatory diseases. Here, we propose a strategy (scDrugPrio) that creates scRNA-seq based multicellular disease models that incorporate the key biological and pharmacological properties such as gene interactions within and between cell types, varying gene expression levels between cells and individuals, and inhibitory or enhancing drug effects. scDrugPrio was constructed based on joint scRNA-seq data from a mouse model of antigen induced arthritis. It was and validated by improved precision/recall for known drug targets and in vitro studies of predicted but yet unknown drug targets. When applied to human multiple sclerosis and Crohn’s disease, scDrugPrio was able to stratify predicted treatments based on interindividual variations. We hence propose scDrugPrio as a solution to personalize treatment by projecting network-based drug screening to digital twins of individual patients on cellulome-, genome-and drugome-wide scales.
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Project description:Recent advancements in metabolic flux estimations in vivo are limited to preclinical models, primarily due to challenges in tissue sampling, tumor microenvironment heterogeneity, and non-steady state conditions. To address these limitations and enable flux estimation in human patients, we developed two machine learning-based frameworks. First, the digital twin framework integrates first-principles stoichiometric and isotopic simulations with convolutional neural networks to estimate fluxes in patient bulk samples. Second, the 13C-scMFA framework combines patient scRNA-seq data with 13C-isotope tracing, allowing single-cell-level flux quantification. These studies allow quantification of metabolic activity in neoplastic glioma cells, revealing frequently elevated purine synthesis and serine uptake compared to non-malignant cells. Our models also identify metabolic heterogeneity among patients and mice with brain cancer, in turn predicting treatment responses to metabolic inhibitors. Our frameworks advance in vivo metabolic flux analysis, may lead to novel metabolic therapies, and identify biomarkers for metabolism-directed therapies in patients.
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Project description:Digital twins for in vivo metabolic flux estimations in patients with brain cancer
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Project description:Recent advancements in metabolic flux estimations in vivo are limited to preclinical models, primarily due to challenges in tissue sampling, tumor microenvironment heterogeneity, and non-steady state conditions. To address these limitations and enable flux estimation in human patients, we developed two machine learning-based frameworks. First, the digital twin framework integrates first-principles stoichiometric and isotopic simulations with convolutional neural networks to estimate fluxes in patient bulk samples. Second, the 13C-scMFA framework combines patient scRNA-seq data with 13C-isotope tracing, allowing single-cell-level flux quantification. These studies allow quantification of metabolic activity in neoplastic glioma cells, revealing frequently elevated purine synthesis and serine uptake compared to non-malignant cells. Our models also identify metabolic heterogeneity among patients and mice with brain cancer, in turn predicting treatment responses to metabolic inhibitors. Our frameworks advance in vivo metabolic flux analysis, may lead to novel metabolic therapies, and identify biomarkers for metabolism-directed therapies in patients.
2025-11-29 | GSE311464 | GEO
Project description:Digital twins for in vivo metabolic flux estimations in patients with brain cancer II
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Project description:Study of changes in DNA Methylation in monozygotic Rheumatoid Arthritis discordant twins and ACPA+/- discordant twins
2016-11-24 | GSE72582 | GEO
Project description:IntroductionA digital twin is a virtual representation of a patient's disease, facilitating real-time monitoring, analysis, and simulation. This enables the prediction of disease progression, optimization of care delivery, and improvement of outcomes.MethodsHere, we introduce a digital twin framework for type 2 diabetes (T2D) that integrates machine learning with multiomic data, knowledge graphs, and mechanistic models. By analyzing a substantial multiomic and clinical dataset, we constructed predictive machine learning models to forecast disease progression. Furthermore, knowledge graphs were employed to elucidate and contextualize multiomic-disease relationships.Results and discussionOur findings not only reaffirm known targetable disease components but also spotlight novel ones, unveiled through this integrated approach. The versatile components presented in this study can be incorporated into a digital twin system, enhancing our grasp of diseases and propelling the advancement of precision medicine.
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