Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Yijun Ruan mailto:ruanyj@gis.a-star.edu.sg). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track was produced as part of the ENCODE Project. It shows the locations of protein factor mediated chromatin interactions determined by Chromatin Interaction Analysis with Paired-End Tag (ChIA-PET) data (Fullwood et al., 2010) extracted from five different human cancer cell lines (K562 (chronic myeloid leukemia), HCT116 (colorectal cancer), HeLa-S3 (cervical cancer), MCF-7 (breast cancer), and NB4 (promyelocytic)). A chromatin interaction is defined as the association of two regions of the genome that are far apart in terms of genomic distance, but are spatially proximate to each other in the 3-dimensional cellular nucleus. Additionally, ChIA-PET experiments generate transcription factor binding sites. A binding site is defined as a region of the genome that is highly enriched by specific Chromatin ImmunoPrecipitation (ChIP) against a transcription factor, which indicates that the transcription factor binds specifically to this region. The protein factors displayed in the track include estrogen receptor alpha (ERa), RNA polymerase II (RNAPII), and CCCTC binding factor (CTCF). For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Chromatin interaction analysis with paired-end tag sequencing (ChIA-PET) is a global de novo high-throughput method for characterizing the 3-dimensional structure of chromatin in the nucleus. In the ChIA-PET protocol, samples were cross-linked and fragmented, then subjected to chromatin immunoprecipitation. The DNA fragments that were brought together by the chromatin interactions were then proximity-ligated. During this proximity-ligation step, the half-linkers (created by the fragmentation) containing flanking MmeI sites (type IIS restriction enzymes) were first ligated to the DNA fragments and then ligated to each other to form full linkers. Full linkers bridge either two ends of a self-circularized fragment, or two ends of two different chromatin fragments. The material was then reverse cross-linked, purified and digested with MmeI. MmeI cuts 20 base pairs away from its recognition site. Tag-linker-tag (paired-end tag, PET) constructs were sequenced by ultra-high-throughput methods (Illumina or SOLiD paired-end sequencing). ChIA-PET reads were processed with the ChIA-PET Tool (Li et al., 2010) by the following steps: linker filtering, short reads mapping, PET classification, binding site identification, and interaction cluster identification. The high-confidence binding sites and chromatin interaction clusters were reported. Chromatin interactions identified by ChIA-PET have been validated by 3C, ChIP-3C, 4C and DNA-FISH (Fullwood et al., 2009).

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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Homo sapiens

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