Single-cell RNA-seq profiling of Pseudomyxoma Peritonei
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ABSTRACT:
PROVIDER: PRJNA949595 | ENA |
REPOSITORIES: ENA
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Project description:Through single-cell transcriptomic profiling of five samples with Pseudomyxoma Peritonei (PMP) , we identified ten distinct cell type-specific clusters, including an epithelial cell community with significantly increased expression of canonical epithelial molecules, and a mesenchymal cell community with notably higher expression of established mesenchymal molecules, supporting the presence of both epithelial and mesenchymal molecular characteristics in PMP disease.
2024-10-09 | GSE228377 | GEO
Project description:The purpose of this study is to evaluate gene expression changes and identify characteristics in patients with Pseudomyxoma Peritonei (PMP). We analyzed RNA-seq data to identify gene signatures associated with PMP development using 19 fresh frozen tissue samples including 12 PMPs (discovery cohort). Through analyzing the differences in gene expression between normal tissues and PMP, we identified statistically significant gene groups, and confirmed the biological functions and pathways associated with these gene groups.
2024-10-09 | GSE228376 | GEO
Project description:Bacterial species associated with pseudomyxoma peritonei
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Project description:The purpose of this study is to evaluate gene expression changes and identify characteristics in patients with Pseudomyxoma Peritonei (PMP). We analyzed RNA-seq data from 34 formalin-fixed paraffin-embedded (FFPE) samples, including 25 PMPs (validation cohort), to identify gene signatures associated with the development of PMP. We confirmed protein expression levels using a tissue microarray (TMA) cohort of PMP patients. Through analyzing the differences in gene expression between normal tissues and PMP, we identified statistically significant gene groups, and confirmed the biological functions and pathways associated with these gene groups.
2024-10-09 | GSE228375 | GEO
Project description:A major challenge in developmental biology is to understand the genetic and cellular processes/programs driving organ formation and differentiation of the diverse cell types that comprise the embryo. While recent studies using single cell transcriptome analysis illustrate the power to measure and understand cellular heterogeneity in complex biological systems, processing large amounts of RNA-seq data from heterogeneous cell populations creates the need for readily accessible tools for the analysis of single-cell RNA-seq (scRNA-seq) profiles. The present study presents a generally applicable analytic pipeline (SINCERA: a computational pipeline for SINgle CEll RNA-seq profiling Analysis) for processing scRNA-seq data from a whole organ or sorted cells. The pipeline supports the analysis for: 1) the distinction and identification of major cell types; 2) the identification of cell type specific gene signatures; and 3) the determination of driving forces of given cell types. We applied this pipeline to the RNA-seq analysis of single cells isolated from embryonic mouse lung at E16.5. Through the pipeline analysis, we distinguished major cell types of fetal mouse lung, including epithelial, endothelial, smooth muscle, pericyte, and fibroblast-like cell types, and identified cell type specific gene signatures, bioprocesses, and key regulators. SINCERA is implemented in R, licensed under the GNU General Public License v3, and freely available from CCHMC PBGE website, https://research.cchmc.org/pbge/sincera.html.
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Project description:<p>Droplet-based single-cell RNA-seq has emerged as a powerful technique for massively parallel cellular profiling. While these approaches offer the exciting promise to deconvolute cellular heterogeneity in diseased tissues, the lack of cost-effective, reliable, and user-friendly instrumentation has hindered widespread adoption of droplet microfluidic techniques. To address this, we have developed a microfluidic control instrument that can be easily assembled from 3D printed parts and commercially available components costing approximately $575. We adapted this instrument for massively parallel scRNA-seq and deployed it in a clinical environment to perform single-cell transcriptome profiling of disaggregated synovial tissue from 5 rheumatoid arthritis patients. We sequenced 20,387 single cells from synovectomies, revealing 13 transcriptomically distinct clusters. These encompass a comprehensive and unbiased characterization of the autoimmune infiltrate, including inflammatory T and NK subsets that contribute to disease biology. Additionally, we identified fibroblast subpopulations that are demarcated via THY1 (CD90) and CD55 expression. Further experiments confirm that these represent synovial fibroblasts residing within the synovial intimal lining and subintimal lining, respectively, each under the influence of differing microenvironments. We envision that this instrument will have broad utility in basic and clinical settings, enabling low-cost and routine application of microfluidic techniques, and in particular single-cell transcriptome profiling.</p> <p>Reprinted from [Stephenson et al., Nature Communications, 2018], with permission from the Nature Publishing Group.</p>
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Project description:Single-cell RNA sequencing has enabled the decomposition of complex tissues into functionally distinct cell types. Often, investigators wish to assign cells to cell types through unsupervised clustering followed by manual annotation or via 'mapping' to existing data. However, manual interpretation scales poorly to large datasets, mapping approaches require purified or pre-annotated data and both are prone to batch effects. To overcome these issues, we present CellAssign, a probabilistic model that leverages prior knowledge of cell-type marker genes to annotate single-cell RNA sequencing data into predefined or de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while controlling for batch and sample effects. We demonstrate the advantages of CellAssign through extensive simulations and analysis of tumor microenvironment composition in high-grade serous ovarian cancer and follicular lymphoma.
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