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SORC：癌症综合空间组学资源。

SORC: an integrated spatial omics resource in cancer.

机构信息

College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, Heilongjiang Province, China.

School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin 150081, Heilongjiang Province, China.

出版信息

Nucleic Acids Res. 2024 Jan 5;52(D1):D1429-D1437. doi: 10.1093/nar/gkad820.

DOI:10.1093/nar/gkad820
PMID:37811897
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10768140/
Abstract

The interactions between tumor cells and the microenvironment play pivotal roles in the initiation, progression and metastasis of cancer. The advent of spatial transcriptomics data offers an opportunity to unravel the intricate dynamics of cellular states and cell-cell interactions in cancer. Herein, we have developed an integrated spatial omics resource in cancer (SORC, http://bio-bigdata.hrbmu.edu.cn/SORC), which interactively visualizes and analyzes the spatial transcriptomics data in cancer. We manually curated currently available spatial transcriptomics datasets for 17 types of cancer, comprising 722 899 spots across 269 slices. Furthermore, we matched reference single-cell RNA sequencing data in the majority of spatial transcriptomics datasets, involving 334 379 cells and 46 distinct cell types. SORC offers five major analytical modules that address the primary requirements of spatial transcriptomics analysis, including slice annotation, identification of spatially variable genes, co-occurrence of immune cells and tumor cells, functional analysis and cell-cell communications. All these spatial transcriptomics data and in-depth analyses have been integrated into easy-to-browse and explore pages, visualized through intuitive tables and various image formats. In summary, SORC serves as a valuable resource for providing an unprecedented spatially resolved cellular map of cancer and identifying specific genes and functional pathways to enhance our understanding of the tumor microenvironment.

摘要

肿瘤细胞与微环境的相互作用在癌症的发生、进展和转移中起着关键作用。空间转录组学数据的出现为揭示癌症中细胞状态和细胞间相互作用的复杂动态提供了机会。在此，我们开发了一个癌症综合空间组学资源（SORC，http://bio-bigdata.hrbmu.edu.cn/SORC），该资源可交互式地可视化和分析癌症中的空间转录组学数据。我们手动整理了目前可用于 17 种癌症的空间转录组学数据集，涵盖了 269 个切片中的 722,899 个点。此外，我们在大多数空间转录组学数据集中匹配了参考单细胞 RNA 测序数据，涉及 334,379 个细胞和 46 种不同的细胞类型。SORC 提供了五个主要的分析模块，满足了空间转录组学分析的主要需求，包括切片注释、鉴定空间可变基因、免疫细胞和肿瘤细胞的共现、功能分析和细胞间通讯。所有这些空间转录组学数据和深入分析都已整合到易于浏览和探索的页面中，通过直观的表格和各种图像格式进行可视化。总之，SORC 是一个有价值的资源，提供了一个前所未有的癌症空间分辨率细胞图谱，并确定了特定的基因和功能途径，以增强我们对肿瘤微环境的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94b9/10768140/bd093ede64d1/gkad820fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94b9/10768140/a701e9179c73/gkad820figgra1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94b9/10768140/3a83305bb63a/gkad820fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94b9/10768140/351e907ec4f5/gkad820fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94b9/10768140/bd093ede64d1/gkad820fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94b9/10768140/a701e9179c73/gkad820figgra1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94b9/10768140/3a83305bb63a/gkad820fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94b9/10768140/351e907ec4f5/gkad820fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94b9/10768140/bd093ede64d1/gkad820fig3.jpg

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本文引用的文献

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A global database for modeling tumor-immune cell communication.用于肿瘤免疫细胞通讯建模的全球数据库。
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SODB facilitates comprehensive exploration of spatial omics data.SODB 有助于全面探索空间组学数据。
Nat Methods. 2023 Mar;20(3):387-399. doi: 10.1038/s41592-023-01773-7. Epub 2023 Feb 16.
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Single-cell RNA binding protein regulatory network analyses reveal oncogenic HNRNPK-MYC signalling pathway in cancer.单细胞 RNA 结合蛋白调控网络分析揭示癌症中致癌的 HNRNPK-MYC 信号通路。
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Brief Bioinform. 2025 Mar 4;26(2). doi: 10.1093/bib/bbaf136.
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HISSTA: a human in situ single-cell transcriptome atlas.HISSTA：一个人类原位单细胞转录组图谱。
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SPathDB: a comprehensive database of spatial pathway activity atlas.SPathDB：一个空间通路活性图谱的综合数据库。
Nucleic Acids Res. 2025 Jan 6;53(D1):D1205-D1214. doi: 10.1093/nar/gkae1041.
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Pairpot: a database with real-time lasso-based analysis tailored for paired single-cell and spatial transcriptomics.Pairpot：一个专为配对单细胞和空间转录组学量身定制的、基于实时套索分析的数据库。
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ArXiv. 2024 Jun 20:arXiv:2406.06393v2.
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