A5011372787- Single-cell and spatial transcriptomics
- Cancer Genomics and Diagnostics
- COVID-19 Clinical Research Studies
- Silicon Effects in Agriculture
- Yersinia bacterium, plague, ectoparasites research
- Mechanisms of cancer metastasis
- Adrenal Hormones and Disorders
- Monoclonal and Polyclonal Antibodies Research
- Immune Response and Inflammation
- CRISPR and Genetic Engineering
- Cardiovascular Disease and Adiposity
- Mycobacterium research and diagnosis
- Sepsis Diagnosis and Treatment
- Gut microbiota and health
- Diabetes and associated disorders
- Plant nutrient uptake and metabolism
- Immune Cell Function and Interaction
- Extracellular vesicles in disease
- Systemic Lupus Erythematosus Research
- CAR-T cell therapy research
- Inflammatory Biomarkers in Disease Prognosis
- Immune cells in cancer
- Long-Term Effects of COVID-19
- Leprosy Research and Treatment
- SARS-CoV-2 and COVID-19 Research
Centre for Human Genetics
2020-2024
University of Oxford
2020-2024
Dysregulated host responses to infection can lead organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication biomarkers response are urgently needed. We investigated the use whole-blood transcriptomics for stratification patients with severe by integrating data from 3149 samples sepsis due community-acquired pneumonia or fecal peritonitis admitted intensive care healthy individuals into a gene expression reference map. used...
Objective Rituximab, a CD20+ B cell depletion therapy, is frequently used in the treatment of systemic lupus erythematosus (SLE). However, variability patient response highlights need for deeper understanding underlying immune dynamics and repopulation. Methods In this study, we conducted longitudinal single-cell profiling nine SLE patients treated with rituximab from pretreatment to up 15 months post-treatment. These were compared eight healthy controls. We profiled 169,513 cells via RNA,...
Abstract Epstein-Barr virus (EBV) reactivation is common in sepsis patients but the extent and nature of this remains unresolved. We sought to determine incidence correlates EBV-positivity a large cohort. also hypothesised that EBV would be increased whom relative immunosuppression was major feature their response. To identify such we aimed use knowledge response subphenotypes based on transcriptomic studies circulating leukocytes, specifically with Sepsis Response Signature endotype (SRS1)...
The computational detection and exclusion of cellular doublets and/or multiplets is a cornerstone for the identification true biological signals from single-cell RNA sequencing (scRNA-seq) data. Current methods do not sensitively identify both heterotypic homotypic multiplets. Here, we describe machine learning approach doublet/multiplet utilizing VDJ-seq CITE-seq data to predict their presence based on transcriptional features associated with identified hybrid droplets. This highlights...
<h3>Background:</h3> In the R4RA biopsy-driven randomized clinical trial[1] rheumatoid arthritis (RA) patients were randomised to rituximab or tocilizumab based on their synovial tissue B cell rich/poor signature. Response, defined as 50% improvement of disease activity index (CDAI50), was assessed at 16 weeks. Samples from this cohort utilised improve understanding pathogenic mechanisms determining response rituximab. This is still a major issue with up 5-20% RA not responding all current...
<title>Abstract</title> CELLeBT is a novel machine learning tool to decipher B cell-T cell interactions from single multi-omics data through leveraging droplets containing both receptor (BCR) and T (TCR), thus cell. provides multiple types of information about interactions, including the relative frequency stable cellular clonal nature network molecular drivers these as demonstrated analysis RCC PDAC datasets, with unprecedented detail.
The computational detection and exclusion of cellular doublets/multiplets is a cornerstone for the identification true biological signals from single-cell RNAseq (scRNA-seq) data. Current methods do not sensitively identify both heterotypic homotypic doublets/multiplets. Here, we describe novel machine learning approach doublet/multiplet utilising VDJ-seq and/or CITE-seq information to predict their presence based on transcriptional features associated with identified hybrid droplets. Our...