Dataset Information

E-MTAB-3358

ABSTRACT: RNA-Seq CAGE (Cap Analysis of Gene Expression) analysis of human tissues in RIKEN FANTOM5 project

TISSUE(S): Heart, Cerebellum, Mitral Valve, Brain, Prostate Gland, Tongue, Throat, Amygdala, Zone Of Skin, Lung, Pulmonary Valve, Eye, Middle Temporal Gyrus, Ovary, Epididymis, Hippocampal Formation, Occipital Lobe, Spleen, Globus Pallidus, Olfactory Apparatus, Gall Bladder, Vagina, Heart Left Ventricle, Vas Deferens, Artery, Occipital Cortex, Temporal Lobe, Placenta, Rectum, Caudate Nucleus, Dura Mater, Brain Meninx, Testis, Umbilical Cord, Left Cardiac Atrium, Pineal Body, Lymph Node, Vermiform Appendix, Parietal Lobe, Smooth Muscle Tissue, Kidney, Uterine Cervix, Submandibular Gland, Uterus, Colon, Pancreas, Seminal Vesicle, Trachea, Bone Marrow, Stomach, Duodenum, Skeletal Muscle Tissue, Locus Ceruleus, Substantia Nigra, Parotid Gland, Dorsal Thalamus, Thyroid Gland, Putamen, Tricuspid Valve, Spinal Cord, Middle Frontal Gyrus, Penis, Medulla Oblongata, Breast, Diaphragm, Diencephalon, Pituitary Gland, Small Intestine

PROVIDER: E-MTAB-3358 | ExpressionAtlas | 2025-01-23

REPOSITORIES: ExpressionAtlas

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Dataset's files

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			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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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.

Project description:Transcription profiling by high throughput sequencing of polyA+ RNAs from eight different human tissues to test a set of de novo transcript models (GENCODE PCR-Seq Batch IX) As part of the ENCODE consortium the GENCODE project is producing a reference gene set through manual and automated gene prediction. Selected transcript models are verified experimentally by RT-PCR amplification followed by sequencing. In batch IX, a set of de novo transcript models was tested aiming to incorporate new long non-coding RNA models into the GENCODE annotation. The original set was built with Cufflinks from ENCODE RNAseq data derived from 15 cell lines by the Gingeras (CSHL) and Wold (CalTech) labs. A subset of multiexonic transcripts not overlapping the GENCODE v10 annotation was selected for this experiment. ArrayExpress Release Date: 2012-10-01 Person Roles: submitter Person Last Name: Gonzalez Person First Name: Jose Person Mid Initials: M Person Email: jmg@sanger.ac.uk Person Phone: -498006 Person Address: Wellcome Trust Genome Campus, Hinxton, UK Person Affiliation: Wellcome Trust Sanger Institute Person Roles: investigator Person Last Name: Hubbard Person First Name: Tim Person Mid Initials: Person Email: th@sanger.ac.uk Person Phone: -498055 Person Address: Wellcome Trust Genome Campus, Hinxton, UK Person Affiliation: Wellcome Trust Sanger Institute Person Roles: investigator Person Last Name: Reymond Person First Name: Alexandre Person Mid Initials: Person Email: Alexandre.Reymond@unil.ch Person Phone: Person Address: Lausanne, Switzerland Person Affiliation: University of Lausanne Person Roles: investigator Person Last Name: Guigo Person First Name: Roderic Person Mid Initials: Person Email: roderic.guigo@crg.cat Person Phone: Person Address: Barcelona, Spain Person Affiliation: Centre for Genomic Regulation (CRG) For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf

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E-MTAB-3358

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