ScRNA_singleron
Ontology highlight
ABSTRACT:
TISSUE(S): ['lung', 'leptomeninge', 'brain Parenchyma']
SUBMITTER: shenbing shan
PROVIDER: OEX00027254 | NODE | 2024-01-25
REPOSITORIES: NODE
Similar Datasets
Project description:Single-cell RNA sequencing
2026-08-12 | OEX00033509 | NODE
Project description:Kilian2024 - Immune cell dynamics in Cue-Induced Extended Human Colitis Model
Single-cell technologies such as scRNA-seq and flow cytometry provide critical insights into immune cell behavior in inflammatory bowel disease (IBD). However, integrating these datasets into computational models for dynamic analysis remains challenging. Here, Kilian et al., (2024) developed a deterministic ODE-based model that incorporates these technologies to study immune cell population changes in murine colitis. The model parameters were optimized to fit experimental data, ensuring an accurate representation of immune cell behavior over time. It was then validated by comparing simulations with experimental data using Pearson’s correlation and further tested on independent datasets to confirm its robustness. Additionally, the model was applied to clinical bulk RNA-seq data from human IBD patients, providing valuable insights into immune system dynamics and potential therapeutic strategies.
Figure 4c, obtained from the simulation of human colitis model is highlighted here.
This model is described in the article:
Kilian, C., Ulrich, H., Zouboulis, V.A. et al. Longitudinal single-cell data informs deterministic modelling of inflammatory bowel disease. npj Syst Biol Appl 10, 69 (2024). https://doi.org/10.1038/s41540-024-00395-9
Abstract:
Single-cell-based methods such as flow cytometry or single-cell mRNA sequencing (scRNA-seq) allow deep molecular and cellular profiling of immunological processes. Despite their high throughput, however, these measurements represent only a snapshot in time. Here, we explore how longitudinal single-cell-based datasets can be used for deterministic ordinary differential equation (ODE)-based modelling to mechanistically describe immune dynamics. We derived longitudinal changes in cell numbers of colonic cell types during inflammatory bowel disease (IBD) from flow cytometry and scRNA-seq data of murine colitis using ODE-based models. Our mathematical model generalised well across different protocols and experimental techniques, and we hypothesised that the estimated model parameters reflect biological processes. We validated this prediction of cellular turnover rates with KI-67 staining and with gene expression information from the scRNA-seq data not used for model fitting. Finally, we tested the translational relevance of the mathematical model by deconvolution of longitudinal bulk mRNA-sequencing data from a cohort of human IBD patients treated with olamkicept. We found that neutrophil depletion may contribute to IBD patients entering remission. The predictive power of IBD deterministic modelling highlights its potential to advance our understanding of immune dynamics in health and disease.
This model was curated during the Hackathon hosted by BioMed X GmbH in 2024.
2025-03-07 | MODEL2502180001 | BioModels
Project description:Single-cell RNA sequencing (10X)
2021-02-05 | OEX00002235 | NODE
Project description:Single cell-based studies have revealed tremendous cellular heterogeneity in stem cell and progenitor compartments, suggesting continuous differentiation trajectories with intermixing of cells at various states of lineage commitment and notable degree of plasticity during organogenesis. The hepato-pancreato-biliary organ system relies on a small endoderm progenitor compartment that gives rise to a variety of different adult tissues, including liver, pancreas, gallbladder, and extra-hepatic bile ducts. Experimental manipulation of various developmental signals in the mouse embryo underscored important cellular plasticity in this embryonic territory. This is also reflected in the existence of human genetic syndromes as well as congenital or environmentally-caused human malformations featuring multiorgan phenotypes in liver, pancreas and gallbladder. Nevertheless, the precise lineage hierarchy and succession of events leading to the segregation of an endoderm progenitor compartment into hepatic, biliary, and pancreatic structures are not yet established. Here, we combine computational modelling approaches with genetic lineage tracing to assess the tissue dynamics accompanying the ontogeny of the hepato-pancreato-biliary organ system. We show that a multipotent progenitor domain persists at the border between liver and pancreas, even after pancreatic fate is specified, contributing to the formation of several organ derivatives, including the liver. Moreover, using single-cell RNA sequencing we define a specialized niche that possibly supports such extended cell fate plasticity.
2021-07-01 | MODEL2105030001 | BioModels
Project description:Intervention type:DRUG. Intervention1:Huaier, Dose form:GRANULES, Route of administration:ORAL, intended dose regimen:20 to 60/day by either bulk or split for 3 months to extended term if necessary. Control intervention1:None.
Primary outcome(s): For mRNA libraries, focus on mRNA studies. Data analysis includes sequencing data processing and basic sequencing data quality control, prediction of new transcripts, differential expression analysis of genes. Gene Ontology (GO) and the KEGG pathway database are used for annotation and enrichment analysis of up-regulated genes and down-regulated genes.
For small RNA libraries, data analysis includes sequencing data process and sequencing data process QC, small RNA distribution across the genome, rRNA, tRNA, alignment with snRNA and snoRNA, construction of known miRNA expression pattern, prediction New miRNA and Study of their secondary structure Based on the expression pattern of miRNA, we perform not only GO / KEGG annotation and enrichment, but also different expression analysis.. Timepoint:RNA sequencing of 240 blood samples of 80 cases and its analysis, scheduled from June 30, 2022..
| 2612481 | ecrin-mdr-crc
Project description:Single cell RNA sequencing
| PRJEB104697 | ENA