Single cell assesment for a serous carcinoma transcriptomic data.
A single-cell dataset, "Chemotherapy induces myeloid-driven spatial T-cell exhaustion in ovarian cancer", was analyzed in this assesment.
- scripts :Directory, Storing the codes and notebooks.
This repository provides a comprehensive analysis of a single-cell RNA sequencing (scRNA-seq) dataset, focusing on trajectory inference and pseudotime analysis. The primary aim is to explore the tumor microenvironment (TME) dynamics in high-grade serous ovarian cancer (HGSC) through advanced computational tools. This analysis leverages a publicly available dataset, GSE266577, to examine the effects of chemotherapy on the spatial organization and immune dynamics of the TME.
Dataset: GSE266577
Title: Chemotherapy induces myeloid-driven spatial T-cell exhaustion in ovarian cancer
Organism: Homo sapiens
Experiment Type: Expression profiling by high-throughput sequencing
Summary: This dataset characterizes the spatial TME of HGSC at the single-cell level using scRNA-seq from 48 tumor or ascites samples of 29 HGSC patients. The data includes paired scRNA-seq samples from chemo-naïve and post-neoadjuvant chemotherapy (IDS) conditions, focusing on spatial immune dynamics and T-cell exhaustion mechanisms.
Study Design:
Single-cell gene expression profiles were generated from tumor and ascites samples. Data includes chemo-naïve and IDS conditions for 22 patients.
Publication:
Launonen IM, Niemiec I, Hincapié-Otero M, Erkan EP et al. Chemotherapy induces myeloid-driven spatially confined T cell exhaustion in ovarian cancer. Cancer Cell 2024 Dec 9;42(12):2045-2063.e10. PMID: 39658541 DOI: https://doi.org/10.1016/j.ccell.2024.11.005
celldex: Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A, Chak S, Naikawadi RP, Wolters PJ, Abate AR, Butte AJ, Bhattacharya M (2019). “Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage.” Nat. Immunol., 20, 163-172. https://doi:10.1038/s41590-018-0276-y.
dplyr: Wickham H, François R, Henry L, Müller K, Vaughan D (2023). dplyr: A Grammar of Data Manipulation. R package version 1.1.4, https://github.com/tidyverse/dplyr, https://dplyr.tidyverse.org.
GEOquery: Davis S, Meltzer P (2007). “GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor.” Bioinformatics, 14, 1846–1847. https://doi:10.1093/bioinformatics/btm254.
ggplot2: Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.
monocle3: Cao J, Spielmann M, Qiu X, Huang X, Ibrahim DM, Hill AJ, Zhang F, Mundlos S, Christiansen L, Steemers FJ, Trapnell C. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019 Feb 28;566(7745):496-502. https://doi.org/10.1038/s41586-019-0969-x
Seurat: Hao Y, Stuart T, Kowalski MH, Choudhary S, Hoffman P, Hartman A, Srivastava A, Molla G, Madad S, Fernandez-Granda C, Satija R (2023). “Dictionary learning for integrative, multimodal and scalable single-cell analysis.” Nature Biotechnology. doi:10.1038/s41587-023-01767-y, https://doi.org/10.1038/s41587-023-01767-y.
SingleR: Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A, Chak S, Naikawadi RP, Wolters PJ, Abate AR, Butte AJ, Bhattacharya M (2019). “Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage.” Nat. Immunol., 20, 163-172. https://doi:10.1038/s41590-018-0276-y.