{"title":"Integrating microarray-based spatial transcriptomics and RNA-seq reveals tissue architecture in colorectal cancer","authors":"Zheng Li, Xiaojie Zhang, Chongyuan Sun, Zefeng Li, He Fei, Dongbing Zhao","doi":"10.1186/s40537-024-00992-9","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background</h3><p>The tumor microenvironment (TME) provides a region for intricate interactions within or between immune and non-immune cells. We aimed to reveal the tissue architecture and comprehensive landscape of cells within the TME of colorectal cancer (CRC).</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>Fresh frozen invasive adenocarcinoma of the large intestine tissue from 10× Genomics Datasets was obtained from BioIVT Asterand. The integration of microarray-based spatial transcriptomics (ST) and RNA sequencing (RNA-seq) was applied to characterize gene expression and cell landscape within the TME of CRC tissue architecture. Multiple R packages and deconvolution algorithms including MCPcounter, XCELL, EPIC, and ESTIMATE methods were performed for further immune distribution analysis.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The subpopulations of immune and non-immune cells within the TME of the CRC tissue architecture were appropriately annotated. According to ST and RNA-seq analyses, a heterogeneous spatial atlas of gene distribution and cell landscape was comprehensively characterized. We distinguished between the cancer and stromal regions of CRC tissues. As expected, epithelial cells were located in the cancerous region, whereas fibroblasts were mainly located in the stroma. In addition, the fibroblasts were further subdivided into two subgroups (F1 and F2) according to the differentially expressed genes (DEGs), which were mainly enriched in pathways including hallmark-oxidative-phosphorylation, hallmark-e2f-targets and hallmark-unfolded-protein-response. Furthermore, the top 5 DEGs, SPP1, CXCL10, APOE, APOC1, and LYZ, were found to be closely related to immunoregulation of the TME, methylation, and survival of CRC patients.</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p>This study characterized the heterogeneous spatial landscape of various cell subtypes within the TME of the tissue architecture. The TME-related roles of fibroblast subsets addressed the potential crosstalk among diverse cells.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"26 1","pages":""},"PeriodicalIF":8.6000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Big Data","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1186/s40537-024-00992-9","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Background
The tumor microenvironment (TME) provides a region for intricate interactions within or between immune and non-immune cells. We aimed to reveal the tissue architecture and comprehensive landscape of cells within the TME of colorectal cancer (CRC).
Methods
Fresh frozen invasive adenocarcinoma of the large intestine tissue from 10× Genomics Datasets was obtained from BioIVT Asterand. The integration of microarray-based spatial transcriptomics (ST) and RNA sequencing (RNA-seq) was applied to characterize gene expression and cell landscape within the TME of CRC tissue architecture. Multiple R packages and deconvolution algorithms including MCPcounter, XCELL, EPIC, and ESTIMATE methods were performed for further immune distribution analysis.
Results
The subpopulations of immune and non-immune cells within the TME of the CRC tissue architecture were appropriately annotated. According to ST and RNA-seq analyses, a heterogeneous spatial atlas of gene distribution and cell landscape was comprehensively characterized. We distinguished between the cancer and stromal regions of CRC tissues. As expected, epithelial cells were located in the cancerous region, whereas fibroblasts were mainly located in the stroma. In addition, the fibroblasts were further subdivided into two subgroups (F1 and F2) according to the differentially expressed genes (DEGs), which were mainly enriched in pathways including hallmark-oxidative-phosphorylation, hallmark-e2f-targets and hallmark-unfolded-protein-response. Furthermore, the top 5 DEGs, SPP1, CXCL10, APOE, APOC1, and LYZ, were found to be closely related to immunoregulation of the TME, methylation, and survival of CRC patients.
Conclusions
This study characterized the heterogeneous spatial landscape of various cell subtypes within the TME of the tissue architecture. The TME-related roles of fibroblast subsets addressed the potential crosstalk among diverse cells.
期刊介绍：
The Journal of Big Data publishes high-quality, scholarly research papers, methodologies, and case studies covering a broad spectrum of topics, from big data analytics to data-intensive computing and all applications of big data research. It addresses challenges facing big data today and in the future, including data capture and storage, search, sharing, analytics, technologies, visualization, architectures, data mining, machine learning, cloud computing, distributed systems, and scalable storage. The journal serves as a seminal source of innovative material for academic researchers and practitioners alike.
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