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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.
2025-03-07 | MODEL2502180001 | BioModels
Project description:Inflammatory breast cancer (IBC) is a difficult-to-treat disease with poor clinical outcomes due to high risk of metastasis and resistance to treatment. We previously described a CD44+CD24-pSTAT3+ cancer cell subpopulation with stem cell-like features in breast cancer that is dependent on JAK/STAT3 signaling. Here we report that CD44+CD24- cells are the most frequent cell-type in IBC and are commonly pSTAT3+. Combination of JAK/STAT3 inhibition with paclitaxel decreased IBC xenograft growth more than either agent alone. We developed and characterized IBC cell lines resistant to paclitaxel and doxorubicin to mimic therapeutic resistance in patients. Multi-omic profiling of parental and resistant cells revealed genes associated with lineage identity and inflammation were enriched in chemotherapy resistant derivatives. Integrated pSTAT3 ChIP-seq and RNA-seq analyses showed pSTAT3 regulates genes related to inflammation and epithelial to mesenchymal transition (EMT) in resistant cells, as well as PDE4A, a cAMP-specific phosphodiesterase. Metabolomic characterization identified elevated cAMP signaling and CREB as a candidate therapeutic target in IBC. We also investigated cellular dynamics and heterogeneity at the single cell level during chemotherapy and acquired resistance by CyTOF and single cell RNA-seq. We identified mechanisms of resistance including a shift from luminal to basal/mesenchymal cell states through selection for rare pre-existing subpopulations or an acquired change. Lastly, we showed that combination treatment with paclitaxel and JAK/STAT3 inhibition prevented the emergence of this more mesenchymal chemo-resistant subpopulation. Our results provide mechanistic rational for combination of chemotherapy with inhibition of JAK/STAT3 signaling as a new more effective therapeutic strategy in IBC.
2022-11-04 | MSV000090647 | GNPS