Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR7183107_1.fastq.gz | Fastqsanger.gz | |||
| SRR7183107_2.fastq.gz | Fastqsanger.gz |
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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:The identification and characterisation of Circulating Tumour Cells (CTCs) is important to get insights into the biology of metastatic cancers, monitoring disease progression, and potential use in liquid biopsy-based personalised cancer treatment. The major limitation of CTC isolation is due to their heterogeneous nature, altered phenotype from primary tumour and availability in limited numbers. In the past years, several techniques have been developed to detect CTCs from peripheral blood, based on canonical markers. These methods are, however, prone to miss a larger set of CTCs due to variable or no expression of these markers. Furthermore, CTC enrichment processes are not free from White Blood Cell (WBC) contamination. Single cell RNA sequencing (scRNA-Seq) of CTCs provides a wealth of information about their tumors of origin as well as their fate. The first and most important roadblock encountered in CTC scRNA-Seq data analysis is confirmation of CTC capture. We present unCTC, an R software for unsupervised and semi-supervised characterisation of CTC transcriptomes, in contrast with WBCs. unCTC features various standard and novel computational/statistical modules for clustering, Copy Number Variation (CNV) inference, and marker based verification of the malignant phenotypes. Notably, we propose Deep Dictionary Learning using K-means clustering cost (DDLK) that robustly clusters scRNA-Seq profiles of CTCs and WBC contaminates to characterise heterogeneity among the concerned cell population. Interestingly, DDLK performs better as gene expression data is transformed into pathway enrichment profiles. To demonstrate the utility of unCTC, we produce scRNA-Seq profiles of breast CTCs enriched by the integrated ClearCell® FX/PolarisTM workflow that works on the principles of size selection and negative enrichment for CD45, a pan leukocyte marker.
2022-11-09 | GSE186288 | GEO
Project description:The identification and characterisation of Circulating Tumour Cells (CTCs) is important to get insights into the biology of metastatic cancers, monitoring disease progression, and potential use in liquid biopsy-based personalised cancer treatment. The major limitation of CTC isolation is due to their heterogeneous nature, altered phenotype from primary tumour and availability in limited numbers. In the past years, several techniques have been developed to detect CTCs from peripheral blood, based on canonical markers. These methods are, however, prone to miss a larger set of CTCs due to variable or no expression of these markers. Furthermore, CTC enrichment processes are not free from White Blood Cell (WBC) contamination. Single cell RNA sequencing (scRNA-Seq) of CTCs provides a wealth of information about their tumors of origin as well as their fate. The first and most important roadblock encountered in CTC scRNA-Seq data analysis is confirmation of CTC capture. We present unCTC, an R software for unsupervised and semi-supervised characterisation of CTC transcriptomes, in contrast with WBCs. unCTC features various standard and novel computational/statistical modules for clustering, Copy Number Variation (CNV) inference, and marker based verification of the malignant phenotypes. Notably, we propose Deep Dictionary Learning using K-means clustering cost (DDLK) that robustly clusters scRNA-Seq profiles of CTCs and WBC contaminates to characterise heterogeneity among the concerned cell population. Interestingly, DDLK performs better as gene expression data is transformed into pathway enrichment profiles. To demonstrate the utility of unCTC, we produce scRNA-Seq profiles of breast CTCs enriched by the integrated ClearCell® FX/PolarisTM workflow that works on the principles of size selection and negative enrichment for CD45, a pan leukocyte marker.
2022-11-09 | GSE210651 | GEO
Project description:Circulating tumor cells (CTCs) play a fundamental role in cancer progression. However, in mice, limited blood volume and the rarity of CTCs in the bloodstream preclude longitudinal, in-depth studies of these cells using existing liquid biopsy techniques. Here, we present an optofluidic system that continuously collects fluorescently-labeled CTCs from a genetically-engineered mouse model for several hours per day over multiple days or weeks. The system is based on a microfluidic cell-sorting chip connected serially to an un-anesthetized mouse via an implanted arteriovenous shunt. Pneumatically-controlled microfluidic valves capture CTCs as they flow through the device and CTC-depleted blood is returned back to the mouse via the shunt. To demonstrate the utility of our system, we profile CTCs isolated longitudinally from animals over a four-day treatment with the BET inhibitor JQ1 using single-cell RNA-Seq (scRNA-Seq) and show that our approach eliminates potential biases driven by inter-mouse heterogeneity that can occur when CTCs are collected across different mice. The CTC isolation and sorting technology presented here provides a research tool to help reveal details of how CTCs change over time, allowing studies to credential changes in CTCs as biomarkers of drug response and facilitating future studies to understand the role of CTCs in metastasis.
2018-12-31 | GSE122233 | GEO
Project description:Pancreatic cancer is a complex disease with a desmoplastic stroma, extreme hypoxia, and inherent resistance to therapy. Understanding the signaling and adaptive response of such an aggressive cancer is key to making advances in therapeutic efficacy and understanding disease progression. Redox factor-1 (Ref-1), a redox signaling protein, regulates the DNA binding activity of several transcription factors, including HIF-1. The conversion of HIF-1 from an oxidized to reduced state leads to enhancement of its DNA binding. In our previously published work, knockdown of Ref-1 under normoxia resulted in altered gene expression patterns on pathways including EIF2, protein kinase A, and mTOR. In this study, single cell RNA sequencing (scRNA-seq) and proteomics were used to explore the effects of Ref-1 on metabolic pathways under hypoxia.Results: We also integrated the scRNA data analysis with the proteomic analysis and found that the differentially expressed genes and pathways identified from the scRNA-seq data are highly consistent to the significant proteins observed in the proteomics data, especially for the upregulated cell cycle and transcription pathways and downregulated metabolic, apoptosis and signaling pathways under hypoxia. Conclusion: The scRNA-seq and proteomics data consistently demonstrated down-regulated central metabolism pathways in APE1/Ref-1 knockdown vs scrambled control under both normoxia and hypoxia conditions. Experimental Methods: scRNA-seq comparing pancreatic cancer cells expressing less than 20% of the Ref-1 protein was analyzed using left truncated mixture Gaussian model. Matched samples were also collected for bulk proteomic analysis of the four conditions. scRNA-seq data was validated using proteomics and qRT-PCR. Ref-1’s role in mitochondrial function was confirmed using mitochondrial function assays and qRT-PCR. Results: We also integrated the scRNA data analysis with the proteomic analysis and found that the differentially expressed genes and pathways identified from the scRNA-seq data are highly consistent to the significant proteins observed in the proteomics data, especially for the upregulated cell cycle and transcription pathways and downregulated metabolic, apoptosis and signaling pathways under hypoxia. Conclusion: The scRNA-seq and proteomics data consistently demonstrated down-regulated central metabolism pathways in APE1/Ref-1 knockdown vs scrambled control under both normoxia and hypoxia conditions.
2021-09-09 | PXD020515 | Pride
Project description:Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease characterized by repetitive alveolar injuries with excessive deposition of extracellular matrix (ECM) proteins. A crucial need in understanding IPF pathogenesis is identifying cell types associated with histopathological regions, particularly local fibrosis centers known as fibroblast foci. To address this, we integrated published spatial transcriptomics and single-cell RNA sequencing (scRNA-seq) transcriptomics and adopted the Query method and the Overlap method to determine cell type enrichments in histopathological regions. Distinct fibroblast cell types are highly associated with fibroblast foci, and transitional alveolar type 2 and aberrant KRT5-/KRT17+ epithelial cells are associated with morphologically normal alveoli in human IPF lungs. Furthermore, we employed laser capture microdissection directed mass spectrometry to profile proteins. By comparing with another published similar dataset, common differentially expressed proteins and enriched pathways related to ECM structure organization and collagen processing were identified in fibroblast foci. Importantly, cell type enrichment results from innovative spatial proteomics and scRNA-seq data integration accord with those from spatial transcriptomics and scRNA-seq data integration, supporting the capability and versatility of the entire approach. In summary, we integrated spatial multi-omics with scRNA-seq data to identify disease-associated cell types and potential targets for novel therapies in IPF intervention. The approach can be further applied to other disease areas characterized by spatial heterogeneity.
2025-01-27 | PXD058805 | Pride
Project description:Energy metabolism and extracellular matrix function are closely connected to orchestrate and maintain tissue organization, but the crosstalk is poorly understood. Here, we used scRNA-seq analysis to uncover the importance of respiration for extracellular matrix homeostasis in mature cartilage. Genetic inhibition of respiration in cartilage results in the expansion of a central area of 1-month-old mouse femur head cartilage showing disorganized chondrocytes and increased deposition of extracellular matrix material. scRNA-seq analysis identified a cluster-specific decrease in mitochondrial DNA-encoded respiratory chain genes and a unique regulation of extracellular matrix-related genes in nonarticular chondrocyte clusters. These changes were associated with alterations in extracellular matrix composition, a shift in the collagen/non-collagen protein content and an increase of collagen crosslinking and ECM stiffness. The results demonstrate, based on findings of the scRNA-seq analysis, that respiration is a key factor contributing to ECM integrity and mechanostability in cartilage and presumably also in many other tissues.
2022-02-17 | PXD027109 | Pride
Project description:Aging is a universal biological phenomenon linked to many diseases, such as cancer or neurodegeneration. However, the molecular mechanisms underlying aging, or how lifestyle interventions such as cognitive stimulation can ameliorate this process, are yet to be clarified. Here, we performed a multi-omic profiling, including RNA-seq, ATAC-seq, ChIP-seq, EM-seq, SWATH-MS and single cell Multiome scRNA and scATAC-seq, in the dorsal hippocampus of young and old mouse subjects which were subject to cognitive stimulation using the paradigm of environmental enrichment. In this study we were able to describe the epigenomic landscape of aging and cognitive stimulation.
2023-09-29 | PXD045567 | Pride