Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA276115 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR1813910.fastq.gz | Fastqsanger.gz | |||
| SRR1813920.fastq.gz | Fastqsanger.gz | |||
| SRR1813930.fastq.gz | Fastqsanger.gz | |||
| SRR1813940.fastq.gz | Fastqsanger.gz | |||
| SRR1813911.fastq.gz | Fastqsanger.gz |
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Project description:mRNA and small RNA associated with Crohn's disease behavior [RNA-Seq]
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Project description:mRNA and small RNA associated with Crohn's disease behavior [RNA-Seq]
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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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Project description:Purpose: We compare small noncoding RNA (ncRNA) profiles between exosomes and whole plasma to test a better source of small ncRNA biomarkers. We hypothesize that exosome is a better source of small ncRNA biomarkers compared to whole plasma. Methods: Small ncRNA sequencings were done using Illumina NextSeq500. Single-end 76 bp reads were explored using FASTQC. GeneGlobe Data Analysis Center, an online platform from Qiagen was used for the small ncRNA-seq data analysis. The mapped read counts for miRNA, piRNA and tRNA were used for differential expression analysis using DESeq2 Bioconductor in R. The small RNA species with logFC > |1| and adj.P-value <0.05 were called differentially expressed. Conclusion: Small ncRNAs extracted from exosomes were found to have the most consistent profiles among healthy individuals. Whole plasma RNA profiles contain high concentrations of blood-derived miRNAs. These miRNA levels can create batch effects among samples due to different levels of hemolysis. Our finding suggests the importance of using purified exosomes as a better source of small ncRNA biomarkers to avoid the batch effect.
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