E3e69ed5-a2f1-4c2c-8dc5-06f2de243995 - samples
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ABSTRACT:
PROVIDER: EGAD00001007867 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Single-cell multi-omic profiling of COVID19 patients recruited from University College London. Data represent RNA-seq, surface protein measurements (CITE-seq) of 192 antibody targets, along with VDJ-seq profiling of single T cell and B cell receptors. Samples are pooled, with 4 donors per pool. Germ-line genotypes derived from previous single-cell RNA-sequencing are provided (VCF) to aid demultiplexing of single-cell and assignment to specific patient donor samples.
| EGAD00001007865 | EGA
Project description:Single-cell multi-omic profiling of healthy controls, asymptomatic and hosptial-admitted COVID19 patients recruited from Newcastle University hospitals. These data also include healthy control volunteers treated with IV-LPS as inflammatory controls. Data represent RNA-seq, surface protein measurements (CITE-seq) of 192 antibody targets, along with VDJ-seq profiling of single T cell and B cell receptors.
| EGAD00001007866 | EGA
Project description:Eighteen patients admitted to CSMC and diagnosed with COVID19 by RT-PCR were stratified into COVID19 mild/moderate, severe, and recovery groups (n=5-6/group). Venous blood was collected into EDTA coated tubes and centrifuged to separate plasma and buffy coat. Plasma was collected and frozen at -80C, and the buffy coat was collected into cryo-preservation media and frozen at -80C. Recovered cells are sorted for live-dead staining, then fixed with methanol. Fixed single cells were further captured using 10x chromium Next GEM 3 prime v3.1 kit. Two patients samples from the same group were mixed, captured and sequenced together.
2020-09-24 | GSE154567 | GEO
Project description:Objective: COVID19 is caused by the SARS-CoV-2 virus and has been associated with severe inflammation leading to organ dysfunction and mortality. Our aim was to profile the transcriptome in leukocytes from critically ill ICU patients positive for COVID19 vs. those negative for COVID19 to better understand the COVID19 associated host response. Design: Transcriptome profiling of buffy coat cells via ribonucleic acid sequencing (RNAseq) at the time of admission to the ICU. Setting: Tertiary care ICU and academic laboratory. Subjects: All patients admitted to the ICU suspected of being infected with SARS-CoV-2, using standardized hospital screening methodologies, had blood samples collected at the time of admission to the ICU. Interventions: None. Measurement and Main Results: Age- and sex-matched ICU patients that were either COVID19+ (PCR positive, 2 genes) or COVID19- (PCR negative) were enrolled. Cohorts were well-balanced with the exception that COVID19- patients had significantly higher total white blood cell counts and circulating neutrophils and COVID19+ patients were more likely to suffer bilateral pneumonia compared to COVID19- patients. Further, the mortality rate for this cohort of COVID19+ ICU patients was 29%. Transcriptional analysis revealed that when compared to COVID19- patients, the altered transcriptional responses of leukocytes in critically ill COVID19+ ICU patients appeared to be associated with multiple interrelated outcomes, including but not limited to robust interferon (IFN)-associated transcriptional responses, a marked decrease in the transcriptional activity of genes contributing to protein synthesis and the dysregulated expression of genes that contribute to coagulation, platelet activation, Toll-like receptor activation, neurotrophin signaling, and protein SUMOylation/ubiquitination. Conclusions: COVID19+ patients on day 1 of admission to the ICU display a unique leukocyte transcriptional profile that distinguishes them from COVID19- patients. Identification of this profile provides guidance for future targeted studies exploring novel prognostic/therapeutic aspects of COVID19.
2020-10-01 | GSE154998 | GEO
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-nucleus RNA sequencing enables high-resolution profiling of complex tissues, but its high cost limits large-scale studies. Sample pooling with genetic demultiplexing is a scalable solution, yet comparative benchmarks are lacking. We benchmarked four widely used tools using variants from SNP arrays or extracted from matched bulk RNA-Seq. Performance, including accuracy, runtime, robustness, and scalability, was evaluated. Real-world application to 10x RNA-Seq from human and multi-species heart tissue demonstrates the tools' utility for demultiplexing and doublet removal.
2025-07-30 | GSE298266 | GEO
Project description:Single-nucleus RNA sequencing enables high-resolution profiling of complex tissues, but its high cost limits large-scale studies. Sample pooling with genetic demultiplexing is a scalable solution, yet comparative benchmarks are lacking. We benchmarked four widely used tools using variants from SNP arrays or extracted from matched bulk RNA-Seq. Performance, including accuracy, runtime, robustness, and scalability, was evaluated. Real-world application to 10x RNA-Seq from human and multi-species heart tissue demonstrates the tools' utility for demultiplexing and doublet removal.
2025-07-30 | GSE298265 | GEO
Project description:Motivation Recently, single-cell DNA sequencing (scDNA-seq) and multi-modal profiling with the addition of cell-surface antibodies (scDAb-seq) have provided key insights into cancer heterogeneity. Scaling these technologies across large patient cohorts, however, is frequently cost and time prohibitive. Multiplexing, in which cells from unique patients and pooled into a single experiment, offers a possible solution. While multiplexing methods are available for scRNAseq, accurate demultiplexing in scDNAseq remains an unmet need. Results Here, we introduce SNACS: Single-Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting. SNACS relies on a combination of patient-level cell-surface identifiers and natural variation in genetic polymorphisms to demultiplex scDNAseq data. We demonstrated the performance of SNACS on a dataset consisting of multi-sample experiments from patients with leukemia where we knew truth from single-sample experiments from the same patients. Relative to demultiplexing methods derived from the scRNAseq literature, SNACS offered superior accuracy. Availability Implementation SNACS is available at https://github.com/olshena/SNACS.
2024-02-09 | GSE255224 | GEO
Project description:Background and Objectives Recently accumulating evidence demonstrates the rare occurrence of COVID19 vaccination- induced inflammation in the central nervous system. However, the precise immune dysregulation related to the COVID19 vaccination-associated autoimmunity remains elusive. Here we report a case of COVID19 vaccination-associated encephalitis, where single-cell RNA sequencing (scRNA-seq) analysis identified a distinct myeloid cell population in the blood during the acute phase of the disease. Methods Peripheral blood mononuclear cells (PBMCs) were analyzed by scRNA-seq to clarify the cellular components of the patients at both the acute and remission phases of the disease. The data obtained were compared to those acquired from a healthy cohort. Results In this study, scRNA-seq analysis of PBMCs identified a distinct myeloid cell population observed at the acute phase of encephalitis. The specific myeloid population was not detected both at the remission phase of the disease and the samples fromof a healthy cohort. Discussion Our findings illustrate the induction of a unique myeloid subset in COVID19 vaccination-associated encephalitis. Further accumulation of reports investigating the dysregulated immune signature of COVID19 vaccination-associated autoimmunity is warranted to clarify the pathogenic role of the myeloid subset observed in our study.
2023-02-20 | GSE205606 | GEO
Project description:Background and Objectives Recently accumulating evidence demonstrates the rare occurrence of COVID19 vaccination- induced inflammation in the central nervous system. However, the precise immune dysregulation related to the COVID19 vaccination-associated autoimmunity remains elusive. Here we report a case of COVID19 vaccination-associated encephalitis, where single-cell RNA sequencing (scRNA-seq) analysis identified a distinct myeloid cell population in the blood during the acute phase of the disease. Methods Peripheral blood mononuclear cells (PBMCs) were analyzed by scRNA-seq to clarify the cellular components of the patients at both the acute and remission phases of the disease. The data obtained were compared to those acquired from a healthy cohort. Results In this study, scRNA-seq analysis of PBMCs identified a distinct myeloid cell population observed at the acute phase of encephalitis. The specific myeloid population was not detected both at the remission phase of the disease and the samples fromof a healthy cohort. Discussion Our findings illustrate the induction of a unique myeloid subset in COVID19 vaccination-associated encephalitis. Further accumulation of reports investigating the dysregulated immune signature of COVID19 vaccination-associated autoimmunity is warranted to clarify the pathogenic role of the myeloid subset observed in our study.
2023-02-20 | GSE205607 | GEO