71 research outputs found
Supplementary Figures for Repapi et al. 2022
No full textThis repository contains supplementary figures for Repapi et al (2022): Integration of single-cell RNA-Seq and CyTOF data characterises heterogeneity of rare cell subpopulation
Supplementary Figures and Table for Repapi et al. 2022
No full textThis repository contains supplementary figures and table for Repapi et al (2022): Integration of single-cell RNA-Seq and CyTOF data characterises heterogeneity of rare cell subpopulation
Fundamental period of infilled reinforced concrete frame structures
No full textThe fundamental period of vibration appears to be one of the most critical parameters for the seismic design and assessment of structures. In the present paper, the results of a large-scale analytical investigation on the parameters that affect the fundamental period of reinforced concrete structures are presented. The influence of the number of storeys, the number of spans, the span length, the infill wall panel stiffness and the percentage of openings within the infill panel on the fundamental period of infilled RC frames was investigated. Based on these results, a regression analysis is applied in order to propose a new empirical equation for the estimation of the fundamental period. The derived equation is shown to have better predictive power compared with equations available in the literature
An integrated genomic approach for the identification and analysis of single nucleotide polymorphisms that affect cancer in humans
No full textThe identification of genetic variants such as single nucleotide polymorphisms (SNPs), which affect cancer progression, survival and response to treatments could help in the design of better prevention and treatment strategies. Genome-wide association studies (GWAS) have provided the first step of identifying SNPs associating with cancer risk. However, identifying the causal SNPs responsible for the associations has proven challenging, and GWAS have not been successful for time-to-event phenotypes such as cancer progression, due to the insurmountable obstacle of the large sample size needed. The aim of this thesis is to design and implement strategies that combine the identification of SNPs significantly associated with cancer, focusing on time-to-event phenotypes, with detailed bioinformatics analysis to allow for further experimental validation and modelling, to better understand cancer-associated genomic loci and accelerate their incorporation into the clinic. First, a methodology that utilises the Random Survival Forest is developed and combined with a bioinformatics analysis that ranks SNPs according to their potential to result in differential protein levels or activity, in order to identify SNPs that affect the progression of B-cell chronic lymphocytic leukaemia. Next, an analysis that aims to extend our understanding of the role of SNPs in mediating the cellular responses to chemotherapeutic agents is applied. SNPs that could associate with differential cellular growth responses in cancer cell line panels are identified, and their association with the differential survival of cancer patients is explored. Finally, the potential roles of SNPs in affecting the transcriptional regulation of key cancer genes resulting in differential cancer risk are assessed. First, by focusing on SNPs in an important transcription factor binding motif that has been shown to be extremely sensitive to single base pair changes (the E-box) and next, by exploring the possibility that polymorphic transcription factor binding sites could underlie the significant associations noted in cancer GWAS
An integrated genomic approach for the identification and analysis of single nucleotide polymorphisms that affect cancer in humans
No full textThe identification of genetic variants such as single nucleotide polymorphisms (SNPs), which affect cancer progression, survival and response to treatments could help in the design of better prevention and treatment strategies. Genome-wide association studies (GWAS) have provided the first step of identifying SNPs associating with cancer risk. However, identifying the causal SNPs responsible for the associations has proven challenging, and GWAS have not been successful for time-to-event phenotypes such as cancer progression, due to the insurmountable obstacle of the large sample size needed. The aim of this thesis is to design and implement strategies that combine the identification of SNPs significantly associated with cancer, focusing on time-to-event phenotypes, with detailed bioinformatics analysis to allow for further experimental validation and modelling, to better understand cancer-associated genomic loci and accelerate their incorporation into the clinic.
First, a methodology that utilises the Random Survival Forest is developed and combined with a bioinformatics analysis that ranks SNPs according to their potential to result in differential protein levels or activity, in order to identify SNPs that affect the progression of B-cell chronic lymphocytic leukaemia. Next, an analysis that aims to extend our understanding of the role of SNPs in mediating the cellular responses to chemotherapeutic agents is applied. SNPs that could associate with differential cellular growth responses in cancer cell line panels are identified, and their association with the differential survival of cancer patients is explored. Finally, the potential roles of SNPs in affecting the transcriptional regulation of key cancer genes resulting in differential cancer risk are assessed. First, by focusing on SNPs in an important transcription factor binding motif that has been shown to be extremely sensitive to single base pair changes (the E-box) and next, by exploring the possibility that polymorphic transcription factor binding sites could underlie the significant associations noted in cancer GWAS.This thesis is not currently available in ORA
The U2AF1<sup>S34F</sup> mutation induces lineage-specific splicing 1 alterations in myelodysplastic syndromes
No full textMutations of the splicing factor-encoding gene U2AF1 are frequent in the myelodysplastic syndromes (MDS), a myeloid malignancy, and other cancers. Patients with MDS suffer from peripheral blood cytopenias, including anemia, and an increasing percentage of bone marrow myeloblasts. We studied the impact of the common U2AF1 mutation on cellular function and mRNA splicing in the main cell lineages affected in MDS. We demonstrated that U2AF1 expression in human hematopoietic progenitors impairs erythroid differentiation and skews granulomonocytic differentiation toward granulocytes. RNA sequencing of erythroid and granulomonocytic colonies revealed that U2AF1 induced a higher number of cassette exon splicing events in granulomonocytic cells than in erythroid cells. U2AF1 altered mRNA splicing of many transcripts that were expressed in both cell types in a lineage-specific manner. In hematopoietic progenitors, the introduction of isoform changes identified in the U2AF1 target genes H2AFY, encoding an H2A histone variant, and STRAP, encoding serine/threonine kinase receptor-associated protein, recapitulated phenotypes associated with U2AF1 expression in erythroid and granulomonocytic cells, suggesting a causal link. Furthermore, we showed that isoform modulation of H2AFY and STRAP rescues the erythroid differentiation defect in U2AF1 MDS cells, suggesting that splicing modulators could be used therapeutically. These data have critical implications for understanding MDS phenotypic heterogeneity and support the development of therapies targeting splicing abnormalities
Taylor-CCB-Group/SpOOx: First release
No full textSpOOx - Spatial Omics Oxford Analysis Pipeline
Release Notes
Version: 1.0.0
Release Date: 6th September 2023
Overview:
SpOOx is a state-of-the-art pipeline developed in close collaboration with clinicians, computational biologists, laboratory scientists, and mathematicians, offering a comprehensive analysis of spatial proteomics data.
Features:
Automated Processing: SpOOx automatically processes MCD image files, which are generated by the Fluidigm Hyperion Imaging System.
Image Processing with imctools: Integration with imctools provides advanced image processing capabilities.
Segmentation with DeepCell: Utilizes DeepCell for accurate and efficient image segmentation.
Custom Scripts for Data Analysis: Extracts marker intensities, conducts quality control, and performs cell clustering to support cell phenotyping.
Spatial Statistical Analysis: Includes innovative procedures for novel spatial statistical analysis.
Recommendation:
For an enhanced user experience and a more detailed analytics approach, we strongly recommend uploading the outputs of SpOOx to our comprehensive analytics and visualisation platform: MDV Multi-Dimensional View.
Changelog:
Initial Release:
First version of the SpOOx pipeline released to the public.
Introduced automated processing of MCD image files.
Integrated with imctools and DeepCell for image processing and segmentation respectively.
Introduced custom scripts for detailed data analysis.
Embedded novel spatial statistical analysis procedures.
Known Issues and Limitations:
Tested to work on Linux HPC. Untested on Windows / macOS.
Feedback and Support:
We value your feedback. If you have any questions, suggestions, or encounter any issues, please report them in Issues on GitHub
The Inheritance of p53
No full textThe p53 pathway constitutes a major cellular gene network that is crucial in directing the suppression of cancer formation, mediating the response to commonly used cancer therapies, as well as the regulation of germline maintenance, fertility, and reproduction. It has been demonstrated that various cancer predisposition syndromes are caused by low-frequency, highly penetrant inherited mutations in the p53 network, the knowledge of which is already positively affecting patient survival. Mounting evidence from studies utilizing human material, patient cohorts, and mouse models suggests that higher frequency, lesser penetrant genetic variants can also affect p53 signaling, resulting in differences in cancer risk, prognosis, response to therapies, and/or natural selection. Indeed, multiple genes in the p53 network have been shown to harbor functional single nucleotide polymorphisms (SNPs). Comprehensive analyses of two SNPs have demonstrated that their effects on cancer can be modified by factors such as gender, estrogen, and other p53 pathway SNPs. Together these insights suggest that genetic variants in the p53 network could present an excellent opportunity to further define individuals in their abilities to react to stress, suppress tumor formation, and respond to therapies
SCL establishes a transcriptional and epigenetic repressive environment in blood-fated cells to suppress alternative mesodermal lineages
No full textSingle cell spatial analysis reveals inflammatory foci of immature neutrophil and CD8 T cells in COVID-19 lungs
No full textSingle cell spatial interrogation of the immune-structural interactions in COVID −19 lungs is challenging, mainly because of the marked cellular infiltrate and architecturally distorted microstructure. To address this, we develop a suite of mathematical tools to search for statistically significant co-locations amongst immune and structural cells identified using 37-plex imaging mass cytometry. This unbiased method reveals a cellular map interleaved with an inflammatory network of immature neutrophils, cytotoxic CD8 T cells, megakaryocytes and monocytes co-located with regenerating alveolar progenitors and endothelium. Of note, a highly active cluster of immature neutrophils and CD8 T cells, is found spatially linked with alveolar progenitor cells, and temporally with the diffuse alveolar damage stage. These findings offer further insights into how immune cells interact in the lungs of severe COVID-19 disease. We provide our pipeline [Spatial Omics Oxford Pipeline (SpOOx)] and visual-analytical tool, Multi-Dimensional Viewer (MDV) software, as a resource for spatial analysis
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