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By integrating three GEO single-cell RNA-seq groups from 17 patients (90,168 cells) using Seurat, the study clusters in UMAP and identifies marker genes to categorize cell types.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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is the main goal of the study?","Question",{"text":63,"@type":64},"To investigate and compare heterogeneity landscapes in gliomas and brain metastasis from different origins using integrated single-cell RNA sequencing data.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How were the single-cell datasets integrated?",{"text":68,"@type":64},"Three groups of single-cell RNA-sequencing datasets from GEO were integrated using the R package Seurat, followed by clustering in UMAP space and differential gene analysis for cell categorization.",{"name":70,"@type":61,"acceptedAnswer":71},"Which cell populations differ across metastasis origins?",{"text":72,"@type":64},"Lung-to-brain metastases show elevated AT2, cytotoxic CD4+ T, and exhausted CD8+ T cells, while breast-to-brain metastases are characterized by epithelial and myCAF cell 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integrated single-cell RNA sequencing analysis of brain metastasis and glioma microenvironment: Contrasting heterogeneity landscapes. PLoS ONE 19(7):  \ne0306220 . [https://doi.org/10.1371/journal](https://doi.org/10.1371/journal). pone.0306220  \nEditor: Syed M. Faisal, University of Michigan Medical School, UNITED STATES  \nReceived: January 31, 2024  \nAccepted: June 12, 2024  \nPublished: July 26, 2024  \nCopyright: © 2024 Sajjadi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: The gene expression dataset analyzed in this study was extracted from Gene Expression Omnibus (GEO, [https://www.ncbi](https://www.ncbi). [nlm.nih.gov/geo/](nlm.nih.gov/geo/)) database and is available under Accession Numbers GSE186344, GSE143423, GSE186344, GSE131907, GSE143423, GSE117891, GSE202371, [and GSE135045. data:](and GSE135045. data:)[ ](and GSE135045. data:)[https://github.com/sajadi1985/brain-metastasis](https://github.com/sajadi1985/brain-metastasis)glioma/tree/main.  \nRESEARCH ARTICLE  \nComprehensive integrated single-cell RNA sequencing analysis of brain metastasis and glioma microenvironment: Contrasting heterogeneity landscapes  \nSeyedeh Fatemeh Sajjadi1, Najmeh Salehi1, Mehdi Sadeghi1,2 *  \n1 School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran,  \n2 National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran  \n* [sadeghi@nigeb.ac.ir](sadeghi@nigeb.ac.ir)  \nAbstract  \nUnderstanding the specific type of brain malignancy, source of brain metastasis, and underlying transformation mechanisms can help provide better treatment and less harm to patients. The tumor microenvironment plays a fundamental role in cancer progression and affects both primary and metastatic cancers. The use of single-cell RNA sequencing to gain insights into the heterogeneity profiles in the microenvironment of brain malignancies is useful for guiding treatment decisions. To comprehensively investigate the heterogeneity in gliomas and brain metastasis originating from different sources (lung and breast), we integrated data from three groups of single-cell RNA-sequencing datasets obtained from GEO. We gathered and processed single-cell RNA sequencing data from 90,168 cells obtained from 17 patients. We then employed the R package Seurat for dataset integration. Next, we clustered the data within the UMAP space and acquired differentially expressed genes for cell categorization. Our results underscore the significance of macrophages as abundant and pivotal constituents of gliomas. In contrast, lung-to-brain metastases exhibit elevated numbers of AT2, cytotoxic CD4+ T, and exhausted CD8+ T cells. Conversely, breast-to-brain metastases are characterized by an abundance of epithelial and myCAF cells. Our study not only illuminates the variation in the TME between brain metastasis with different origins but also opens the door to utilizing established markers for these cell types to differentiate primary brain metastatic cancers.  \nIntroduction  \nThe brain tissue environment exhibits significant distinctions from other tissues, making it uniquely poised to impact the low incidence of glioma brain metastasis, while also providing amore conducive environment for invasive cancers that reach the brain [ 1, 2] . Brain malignancies encompass two distinct categories of tumors: gliomas (GM) and brain metastasis (BM) . GM refers to primary tumors that originate within the brain, whereas BM refers to secondary  \nFunding: The author(s) received no specific funding for this work.  \nCompeting interests: The authors have declared that no competing interests exist.  \ntumors that develop from primary tumors originatin","cbCaig7oIDHmvPKu","https://ap.wps.com/l/cbCaig7oIDHmvPKu","pdf",3542690,"English","# Abstract\n## Methods and integration approach\n## Key findings and cell-type heterogeneity\n# Introduction\n## Classification of brain malignancies\n## Clinical challenges and diagnosis\n## Treatment context and therapeutic variability\n## Tumor microenvironment role in progression","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To investigate and compare heterogeneity landscapes in gliomas and brain metastasis from different origins using integrated single-cell RNA sequencing data.\"},{\"question\":\"How were the single-cell datasets integrated?\",\"answer\":\"Three groups of single-cell RNA-sequencing datasets from GEO were integrated using the R package Seurat, followed by clustering in UMAP space and differential gene analysis for cell categorization.\"},{\"question\":\"Which cell populations differ across metastasis origins?\",\"answer\":\"Lung-to-brain metastases show elevated AT2, cytotoxic CD4+ T, and exhausted CD8+ T cells, while breast-to-brain metastases are characterized by epithelial and myCAF cell abundance.\"}]","Comprehensive integrated single-cell RNA sequencing analysis of brain metastasis and glioma microenvironment - Contrasting heterogeneity landscapes | PDF",1789991182]