Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Network analyses of single cell-based digital twins for individualized treatment of antigen induced arthritis in mice.

ABSTRACT: 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.

ORGANISM(S): Mus musculus

PROVIDER: GSE193536 | GEO | 2023/07/31

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets

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

A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets

Project description:A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets

| PRJNA749249 | ENA

Single Cell Transcriptomics of Human Native Kidney and Kidney Allografts

Project description:We tested the hypothesis that single-cell RNA-sequencing (scRNA-seq, 10X) and computational analysis of human kidney allograft biopsies will reveal new cell types and cell states and yield insights to personalize the care of transplant recipients.

2022-04-19 | GSE151671 | GEO

Blood, adipose and muscle samples taken from monozygotic twin pairs with age range 32-37

Project description:FITFATTWIN study identified from the FinnTwin16 Cohort, which is a population based, longitudinal study of Finnish twins born between October 1974 and December 1979. The participants had no chronic disease affecting the ability to exercise, no acute disease, and no drug or alcohol abuse.

2016-04-20 | GSE73142 | GEO

Novel Inhibitors of the Histone-Methyltransferase DOT1L Show Potent Antileukemic Activity in Patient-derived Xenografts (RNA-seq dataset)

Project description:Using 3-end RNA-seq followed by digital gene expression analyses we assessed genes that change expression upon treatment with the DOT1L-methyltransferase inhibitors EPZ5676 and the novel drug "compound 10"

2020-06-01 | GSE149184 | GEO

Transcriptome analysis of Cymelarsan resistant and Cymelarsan sensitive strains of Trypanosoma brucei.

Project description:A drug sensitive stock of T.b.brucei (STIB247) was used to generate a drug resistant strain (247MelCyR), expressing 130-fold increase in resistance to the melaminophenyl arsenical Cymelarsan (MelCy) by subcurative treatment in mice. Expression of the Cymelarsan resistant phenotype was maintained in 247MelCyR following adaptation to procyclic form in vitro culture. Here we have compared the transcriptomes of the STIB247 and 247MelCyR strains from procyclic cell culture, using digital gene expression (DGE) analysis. Digital gene expression analysis was performed on RNA derived from 2 biological replicates each of Cymelarsan resistant (247MelCyR) and Cymelarsan sensitive (STIB 247), T.b.brucei procyclic cell culture.

2011-07-28 | E-GEOD-23288 | biostudies-arrayexpress

Resolution of the curse of dimensionality in single-cell RNA-sequencing data analysis

Project description:Single-cell RNA sequencing (scRNA-seq) can determine gene expression in numerous individual cells simultaneously, promoting progress in the biomedical sciences. However, scRNA-seq data are high-dimensional with substantial technical noise, including dropouts. During analysis of scRNA-seq data, such noise engenders a statistical problem known as the curse of dimensionality (COD). Based on high-dimensional statistics, we herein formulate a noise reduction method, RECODE (resolution of the curse of dimensionality), for high-dimensional data with random sampling noise. We show that RECODE consistently resolves COD in relevant scRNA-seq data with unique molecular identifiers. RECODE does not involve dimension reduction and recovers expression values for all genes, including lowly expressed genes, realizing precise delineation of cell-fate transitions and identification of rare cells with all gene information. Compared to representative imputation methods, RECODE employs different principles and exhibits superior overall performance in cell-clustering, expression-value recovery, and single-cell level analysis. The RECODE algorithm is parameter-free, data-driven, deterministic, and high-speed, and its applicability can be predicted based on the variance normalization performance. We propose RECODE as a powerful strategy for preprocessing noisy high-dimensional data.

2022-08-04 | GSE175525 | GEO

Spike-in normalization for single-cell RNA-seq reveals dynamic global transcriptional activity mediating anti-cancer drug response

Project description:The transcriptional plasticity of cancer cells promotes intercellular heterogeneity in response to anti-cancer drugs and facilitates the generation of subpopulation surviving cells. Characterizing single-cell transcriptional heterogeneity after drug treatments can provide mechanistic insights into drug efficacy. Here we used single-cell RNA sequencing (scRNA-seq) to examine transcriptomic profiles of cancer cells treated with chemotherapy drug paclitaxel, anti-inflammatory drug celecoxib, and the combination of the two drugs. By normalizing the expression of endogenous genes to spike-in molecules, we found that global mRNA abundance shows dynamic regulation across single cells after drug treatment. Using a random forest model, we identified gene signatures classifying single cells into three states: transcriptional repression, amplification, and control-like. Treatment with paclitaxel or celecoxib alone generally repressed gene transcription at variable levels across single cells. Interestingly, the combination of the two drugs resulted in transcriptional amplification and hyperactivation of the mitochondrial oxidative phosphorylation pathway, which is linked to enhanced cell killing efficiency. Finally, we identified a regulatory module enriched with metabolism and inflammatory genes that was activated in a subpopulation of paclitaxel-treated cells, the expression of which predicted paclitaxel efficacy across human cancer cell lines and in vivo patient samples. Our study highlights the dynamic global transcriptional activity driving single-cell heterogeneity during drug response. The results also emphasize the importance of adding spike-in molecules to study gene expression regulation using scRNA-seq experiments.

2021-05-25 | GSE162256 | GEO

Stage-specific sensitivity to p53 restoration in lung cancer: cell line data

Project description:Tumorigenesis is a multistep process that results from the sequential accumulation of mutations in key oncogene and tumor-suppressor pathways. The quest to personalize cancer medicine based on targeting these underlying genetic abnormalities presupposes that sustained inactivation of tumor suppressors and activation of oncogenes are required for tumor maintenance. Mutations in the p53 tumor-suppressor pathway are a hallmark of cancer and significant efforts toward pharmaceutical reactivation of mutant p53 pathways are underway1-3. Here we show that restoration of p53 in established murine lung tumors leads to significant but incomplete tumor cell loss specifically in malignant adenocarcinomas but not in adenomas. Also, we define amplification of MAPK signaling as a critical determinant of malignant progression. The differential response to p53 restoration depends on activation of the Arf tumor suppressor downstream of hyperactive MAPK signaling. We propose that p53 naturally limits malignant progression by responding to increased oncogenic signaling, but is unresponsive to low levels of oncogene activity that are sufficient for early stages of lung tumor development. These data suggest that restoration of pathways important in tumor progression, as opposed to initiation, may lead to incomplete tumor regression due to the stage-heterogeneity of tumor cell populations. 3 cell lines each treated with Tamoxifen or EtOH vehicle. Tamoxifen treatment results in reactivation of p53 expression

2010-11-24 | E-GEOD-23873 | biostudies-arrayexpress

Stage-specific sensitivity to p53 restoration in lung cancer: tumor data

Project description:Tumorigenesis is a multistep process that results from the sequential accumulation of mutations in key oncogene and tumor-suppressor pathways. The quest to personalize cancer medicine based on targeting these underlying genetic abnormalities presupposes that sustained inactivation of tumor suppressors and activation of oncogenes are required for tumor maintenance. Mutations in the p53 tumor-suppressor pathway are a hallmark of cancer and significant efforts toward pharmaceutical reactivation of mutant p53 pathways are underway1-3. Here we show that restoration of p53 in established murine lung tumors leads to significant but incomplete tumor cell loss specifically in malignant adenocarcinomas but not in adenomas. Also, we define amplification of MAPK signaling as a critical determinant of malignant progression. The differential response to p53 restoration depends on activation of the Arf tumor suppressor downstream of hyperactive MAPK signaling. We propose that p53 naturally limits malignant progression by responding to increased oncogenic signaling, but is unresponsive to low levels of oncogene activity that are sufficient for early stages of lung tumor development. These data suggest that restoration of pathways important in tumor progression, as opposed to initiation, may lead to incomplete tumor regression due to the stage-heterogeneity of tumor cell populations. 18 tumors of different grade and treatment status. 6 treated grade 3, 4 treated grade2, 4 untreated grade3, 4 untreated grade2

2010-11-24 | E-GEOD-23874 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data