Ontology highlight
ABSTRACT:
PROVIDER: PRJNA595606 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR10689468_1.fastq.gz | Fastqsanger.gz | |||
| SRR10689468_2.fastq.gz | Fastqsanger.gz | |||
| SRR10689469_1.fastq.gz | Fastqsanger.gz | |||
| SRR10689469_2.fastq.gz | Fastqsanger.gz | |||
| SRR10689470_1.fastq.gz | Fastqsanger.gz |
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Project description:This model was reconstructed from single-nucleus RNA-seq (snRNA-seq) data of human postmortem brain and curated using published metabolomics data from human iPSC-derived neurons and cerebrospinal fluid (CSF), together with gene expression data from the Human Protein Atlas. It more accurately simulates human neuronal metabolic flux in neurodegenerative conditions such as Alzheimer's disease (AD).
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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:This database provides TMT-labeled proteomic data of aorta (thoracic aorta), brain, heart, kidney, liver, lung, muscle (gastrocnemius muscle), and skin (abdominal skin) of 6, 15, 24, and 30 months old male C57BL/6 mice. In addition to the whole-tissue lysate, low-soluble protein-enriched fraction was also analyzed for heart, kidney, lung, muscle, and skin. Bulk RNA-Seq data are available for brain, heart, kidney, liver, lung, muscle, and skin. The tissues used for transcriptomic analysis and proteomic analysis of whole-tissue lysate and low-soluble protein-enriched fraction were collected from the same mice. All analyses were conducted with 4 biological replicates.
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Project description:Understanding the extent of genomic transcription and its functional relevance is a central goal in genomics research. However, detailed genome‐wide investigations of transcriptome complexities in major mammalian organs and their underlying cellular sources, transcriptional mechanisms, and functional relevance have been scarce. Here we first show, using extensive RNA‐seq data, that transcription of both functional and nonfunctional genomic elements is substantially more widespread in the testis than in other organs across representative mammals. By scrutinizing the transcriptomes of all main testicular cell types in the mouse, we then reveal that meiotic spermatocytes and especially post‐meiotic round spermatids have remarkably diverse transcriptomes, which explains the high transcriptome complexity of the testis as a whole. The widespread transcriptional activity in spermatocytes and spermatids encompasses protein‐coding genes and long noncoding RNA genes but also poorly conserved intergenic sequences, suggesting that much of it is not of immediate functional relevance. Rather, our analyses of genome‐wide epigenetic data show that this prevalent transcription, which apparently promoted the birth of new genes during evolution, results from a highly permissive chromatin state during and after meiosis that may ultimately facilitate the replacement of histones by protamines during late spermatogenesis. To study the cellular source and mechanisms of high transcriptome complexity in the mammalian testis, we generated strand-specific deep coverage RNA‐Seq data for purified sertoli cells, spermatogonia, spermatocytes, spermatids and spermatozoa as well as for brain, liver and the whole testis from the mouse. We prepared 8 sequencing libraries for the polyadenylated RNA fraction of each sample and sequenced each library in 3 lanes of the Illumina Genome Analyser IIx platform, yielding a total of >60 millions strand-specific reads of 76 base pairs per sample. In addition, we generated ChIP-Seq data for the H3K4me2 modification as well as RRBS data for brain, liver, testis, spermatocytes and spermatids. RNA-seq, ChIP-seq and RRBS data were generated from the same individual or pool of individuals, in the case of purified cells. ChIP-Seq data for the H3K4me2 modification as well as RRBS data for brain, liver, testis, spermatocytes and spermatids
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Project description:<p>The NeuroLINCS Center is part of the NIH Common Fund's Library of Integrated Network-based Cellular Signatures (LINCS) program, which aims to characterize how a variety of human cells, tissues and the entire organism respond to perturbations by drugs and other molecular factors.</p> <p>As Part of the LINCS program, the NeuroLINCS study concentrates on human brain cells, which are far less understood than other cells in the body. Our initial focus is to produce diseased motor neurons from patients by utilizing high-quality induced pluripotent stem cell (iPSC) lines from Amyotrophic Lateral Sclerosis (ALS) and Spinal Muscular Atrophy (SMA) patients in addition to unaffected normal healthy controls. Using state-of-the-art OMICS methods (genomics, epigenomics, transcriptomics, and proteomics), we intend to create a wealth of cellular data that is patient-specific in the context of their baseline genetic perturbations and in the presence of other genetic and environmental perturbagens (e.g. endoplasmic reticulum stress). The primary data will be used to build cell signatures that convey the key features that distinguish the state of a cell and determine its behavior. Ultimately, the analysis of these datasets will lead to the identification of a network of unique signatures relevant to each of these motor neuron diseases. The datasets represented in this study are generated from assays interrogating RNA expression (RNA-seq), chromatin accessibility (ATAC-seq) and whole genome sequencing. </p>
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