Ontology highlight
ABSTRACT:
PROVIDER: PRJNA983851 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR24959100.fastq.gz | Fastqsanger.gz | |||
| SRR24959200.fastq.gz | Fastqsanger.gz | |||
| SRR24959300.fastq.gz | Fastqsanger.gz | |||
| SRR24959400.fastq.gz | Fastqsanger.gz | |||
| SRR24959500.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 1786 |
Similar Datasets
Project description:Single-cell RNA sequencing of Drosophila embryonic heart
| PRJNA713868 | ENA
Project description:single-cell RNA-seq of embryonic Drosophila
| PRJNA650396 | ENA
Project description:Single cell RNA-seq of neonatal heart regeneration
| PRJNA642814 | ENA
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:Single cell RNA-seq identifies cell diversity in embryonic salivary glands [Single cell RNA-seq]
| PRJNA524860 | ENA
Project description:Non-human primates (NHP) are attractive laboratory animal models that accurately reflect both developmental and pathological features of humans. Here we present a compendium of cell types from the cynomolgus monkey Macaca fascicularis (denoted as ‘Monkey Atlas’) using both single-cell chromatin accessibility (scATAC-seq) and RNA sequencing (scRNA-seq) data at the organism-wide level. The integrated cell map enables in-depth dissection and comparison of molecular dynamics, cell-type composition and cellular heterogeneity across multiple tissues and organs. Using single-cell transcriptomic data, we inferred pseudotime cell trajectories and cell-cell communications to uncover key molecular signatures underlying their cellular processes. Furthermore, we identified various cell-specific cis-regulatory elements and constructed organ-specific gene regulatory networks at the single-cell level. Finally, we performed a comparative analysis of single-cell landscapes among mouse, cynomolgus monkey and human, and we showed that cynomolgus monkey has significantly higher degree of cell-type similarity to human than mouse. Taken together, our study provides a valuable resource for NHP cell biology.
2022-06-27 | GSE196791 | GEO
Project description:Single-cell RNA sequencing was performed on embryonic Drosophila heart cells. Analysis of single-cell RNA sequence (scRNA-seq) data at timepoints prior to migration of cardiac progenitor cells through to heart tube closure (embryonic stages 13, 14-early, 14-late, 15 and 16) revealed several interesting findings. We found specification of cardiac cell types takes place early, before stage 13, with biggest changes in transcriptomic profiles detected once cells had settled at the midline for further cardioblast maturation. Throughout development, our data identified multiple cell types, covering cardioblasts and five types of pericardial cells including a neural cardiac cell type. The scRNA-seq data further revealed a combination of first and second heart fields during heart development in fly. Further, we uncovered new cell type-specific markers discerning the different cardiac cell types. And we identified signaling pathways key to heart cell maturation, which are conserved from fly to human.
2023-08-07 | GSE168774 | GEO
Project description:single-cell RNA-seq of embryonic Drosophila motoneurons
| PRJNA650412 | ENA
Project description:We used single-embryo metabolomics to characterize early developmental metabolism in Drosophila. We employed a multi-omics approach where samples were collected, homogenized in 80% methanol and the soluble fraction recover to perform targeted metabolomic whle RNA-seq was performed on the insoluble fraction to accurately stage each embryo. Then, this RNA-based staging was used to place single embryo metabolomes across the developmental trajectory. Thus, we are able to construct a highly detailed metabolomic map of embryonic development. Importantly, we validated our single-embryo metabolomics results in pools of 10 embryos. The data provide a continuous timeline of metabolite levels (and gene expression) during early development (0-3 hours) in Drosophila melanogaster. We used two genetically different lines from the Drosophila Genetic Reference Panel (DGRP) with known genetic variations in our crosses (males/DGRP_352, females/DGRP_737). RNA-seq data related to this dataset can be accessed at GEO under accession number GSE263568.
2024-05-14 | MSV000094752 | MassIVE