2721a372-c8cb-4190-b280-e3f62edfe8ae - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001007523 | EGA |
REPOSITORIES: EGA
Similar Datasets
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:SORT-seq is a plate-based method of single-cell RNA sequencing (Muraro et al. 2016) is a partially robotized version of the CEL-seq2 protocol (Hashimshony et al. 2016). Synovial membranes from Rheumatoid arthritis' patients yield a relatively robust number of DCs using scRNAseq, we sought to validate this initial finding at protein level. We employed a rigorous gating strategy to sort specific ST DC subsets, developed based on CITEseq analysis, which was then confirmed by index cell plate sort sequencing (SORT-seq).
2024-12-09 | E-MTAB-14198 | biostudies-arrayexpress
Project description:Single-cell RNA-seq of human liver samples
| PRJNA894458 | ENA
Project description:Single cell RNA-seq of brain glioblastoma samples
| PRJNA682432 | ENA
Project description:Methods: Cells isolated from the whole brains of naive wild type, infected wild type, and infected CCR2-DTR mice were sort-purified on live CD45hi cells from five biological replicates and pooled. Sort-purified cells were then processed using the 10X Genomics Chromium Controller. Cell suspensions were loaded onto the Chromium Single Cell A Chip for cell lysis and barcoding. RNA from individual cells was reverse transcribed and sequencing libraries prepared using the Chromium Single Cell 3’ Library Kit v2 following the manufacturers protocol. Samples were sequenced using an Illumina NextSeq 550 with standard 10X Genomics Configuration (26 bp x 98 bp). After sequencing, raw bcl files were processed using the cellranger mkfastq command for sample demultiplexing and conversion to .fastq files, followed by cellranger count for cell barcode and UMI deconvolution as well as mapping to the respective reference genome. Processed digital gene expression matrices were imported into R studio for analysis using the Seurat package. Samples were aligned along common sources of variation and compared using canonical correlation analysis to identify unique clusters of cells within the samples. Marker genes for each sample and cluster were identified and used for generation of downstream plots within the Seurat package. All packages are maintained to be best in class and are regularly updated to their most recent release. Results: 10 distinct cell clusters were identified and we found that two populations were found at increased proportions post-infection but were substantially reduced in the monocyte-depleted animals (CCR2-DTR). We confirmed that these populations to be monocytes and monocyte-derived cells, and valicated their immunoregulatory molecules by RT-qPCR analysis.
2022-08-25 | GSE189957 | GEO
Project description:We performed single-cell RNA sequencing (scRNA-seq) on infrarenal abdominal aortas from C57BL/6J mice after perivascular CaCl2 treatment. Infrarenal abdominal aortas were collected four days after AAA induction and processed for sequencing. These data provide high-resolution insight into the complexity and heterogeneity of mouse AAA.
2021-03-15 | GSE164678 | GEO
Project description:A variety of newly developed next-generation sequencing technologies are making their way rapidly into the research and clinical applications, for which accuracy and cross-lab reproducibility are critical, and reference standards are much needed. However, there is still a lack of well-characterized reference materials which include epigenomic and proteomic data. Our previous multicenter studies under the SEQC-2 umbrella using a breast cancer cell line with paired B-cell line have produced large amount different genomic data including whole genome sequencing (Illumina, PacBio, Nanopore), HiC, and scRNA-seq with detailed analyses on somatic mutations, single-nucleotide variations (SNVs), and structure variations (SVs). Here we further performed ATAC-seq, Methyl-seq, RNA-seq, and proteomic analyses and provided a comprehensive catalog of epigenomic landscape, which overlapped with the transcriptomes and proteomes for the two cell lines. We identified >7,700 peptide isoforms, where the majority (95%) of the genes had a single peptide isoform and found that the protein expression levels of the transcripts overlapping CGIs were much higher than the protein expression levels of the non-CGI transcripts in both cell lines. We observed that open chromatin regions had low methylation while closed chromatin regions had high methylation, which were largely regulated by CG density, where CG-rich regions had more accessible chromatin, low methylation, and higher gene and protein expressions. The CG-poor regions had higher repressive epigenetic regulations (less open chromatin and higher DNA methylation), resulting in a cell line specific methylation and gene expression patterns. Our studies provide well-defined reference materials consisting of two cell lines with genomic, epigenomic, transcriptomic, scRNA-seq and proteomic characterizations which can serve as standards for validating and benchmarking not only on various omics assays, but also on bioinformatics methods. It will be a valuable resource for both research and clinical communities.
2025-10-01 | PXD052353 | Pride
Project description:Single-cell RNA-seq of traumatic brain injury samples
| PRJNA756100 | ENA
Project description:We performed single-cell RNA sequencing (scRNA-seq) on human salivary gland tissues and derived organoids to investigate their cellular composition, differentiation states, and regenerative potential. Four samples were dissociated into single-cell suspensions and processed using the 10X Genomics Chromium platform.
2025-12-19 | GSE303450 | GEO
Project description:Single-cell RNA sequencing of human thymic samples
| PRJNA624123 | ENA