E-MTAB-3358
Ontology highlight
ABSTRACT:
PROVIDER: E-MTAB-3358 | ExpressionAtlas | 2025-01-23
REPOSITORIES: ExpressionAtlas
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| E-MTAB-3358-analysis-methods.tsv | Tabular | |||
| E-MTAB-3358-coexpressions.tsv.gz | Tabular | |||
| E-MTAB-3358-configuration.xml | Xml | |||
| E-MTAB-3358-factors.xml | Xml | |||
| E-MTAB-3358-factors.xml~ | Xml |
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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.
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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.
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2015-05-29 | GSE69360 | GEO
Project description:The main goal of the project is to develop a new generation of bioinformatics resources for the integrative analysis of multiple types of 'omics data. These resources include both novel statistical methodologies as well as user-friendly software implementations. STATegra methods address many aspects of the 'omics data integration problem such as the design of multi-omics experiments, integrative transcriptional and regulatory networks, integrative variable selection, data fusion, integration of public domain data, and integrative pathway analysis. To support method development STATegra uses a model biological system, namely the differentiation process of mouse pre-B-cells. The STATegra consortium generated data focused on a critical step in the differentiation of B lymphocytes, which are key components of the adaptive immune system. Transcription factors of the Ikaros family are central to the normal differentiation of B cell progenitors and their expression increases in response to developmental stage-specific signals to terminate the proliferation of B cell progenitors and to initiate their differentiation. In particular, a novel biological system that models the transition from the pre-BI stage to the pre-BII subsequent stage, where B cell progenitors undergo growth arrest and differentiation, was used. The approach involves a pre-B cell line, B3, and an inducible version of the Ikaros transcription factor, Ikaros-ERt2. Ikaros factors act to down-regulate genes that drive proliferation and to simultaneously up-regulate the expression of genes that promote the differentiation of B cell progenitors. Hence, in the B3 system, before induction of Ikaros, cells proliferate and their gene expression pattern is similar to proliferating B cell progenitors in vivo. Following Ikaros induction, B3 cells undergo gene expression changes that resemble those that occur in vivo during the transition from cycling to resting pre-B cells, followed by a marked reduction in cellular proliferation and by G1 arrest. On this system the consortium has created a high-quality data collection consisting of a replicated time course using seven different 'omics platforms: RNA-seq, miRNA-seq, ChIP-seq, DNase-seq, Methyl-seq, proteomics and metabolomics, which is used to assess and to validate STATegra methods. The STATegra experimental design consists of a 6 point time course that captures the differentiation of B3 cells containing Ikaros-ERt2 upon Ikaros induction within a 24 hr period. The process was sampled at 0, 2, 6, 12, 18, and 24 hrs respectively after Ikaros induction by Tamoxifen. As control, B3 cells transfected with an empty vector were treated and sampled in the same way as the inducible line. Eight different 'omic technologies were measured on this system: RNA-seq, miRNA-seq, DNase-seq, RRBS-seq, ChIP-seq, scRNA-seq, Proteomics and Metabolomics. Generally, three biological replicates were obtained per platform and were processed as independent batches. Metabolomics analysis was performed using 4 biological batches (7, 8, 9 and 10) and at times 0, 2, 6, 12, 18 and 24 hrs respectively. Each sample was analyzed using gas chromatography mass spectrometry (GC-MS) and liquid chromatography mass spectrometry (LC-MS).
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