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相关概念视频

The Tumor Microenvironment02:17

The Tumor Microenvironment

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Every normal cell or tissue is embedded in a complex local environment called stroma, consisting of different cell types, a basal membrane, and blood vessels. As normal cells mutate and develop into cancer cells, their local environment also changes to allow cancer progression. The tumor microenvironment (TME) consists of a complex cellular matrix of stromal cells and the developing tumor. The cross-talk between cancer cells and surrounding stromal cells is critical to disrupt normal tissue...
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相关实验视频

Updated: Jan 16, 2026

Enrichment and Characterization of the Tumor Immune and Non-immune Microenvironments in Established Subcutaneous Murine Tumors
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Enrichment and Characterization of the Tumor Immune and Non-immune Microenvironments in Established Subcutaneous Murine Tumors

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解读瘤微环境组合使用批量转录组学:最近进展和公开挑战的指南.

Sotiris Ouzounis1, Donya Zojaji2, Sandra García-Mulero3

  • 1Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece.

Methods in molecular biology (Clifton, N.J.)
|September 30, 2025
PubMed
概括

瘤微环境 (TME) 解卷分析从批量转录组学估计了细胞类型. 对三阴性乳腺癌 (TNBC) 数据的基准测试CIBERSORTx和BayesPrism揭示了影响准确性和可重复性的因素.

关键词:
大量批量货物 批量货物癌症 癌症 癌症 癌症细胞类型 细胞类型面临的挑战 挑战解体解体是一种解体.免疫系统的免疫系统.微观环境是一个微观环境.在TME中,它是最重要的.文字转录学 (Transcriptomics) 是一个学科.

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Expanding the Comprehension of the Tumor Microenvironment using Mass Spectrometry Imaging of Formalin-Fixed and Paraffin-Embedded Tissue Samples
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Expanding the Comprehension of the Tumor Microenvironment using Mass Spectrometry Imaging of Formalin-Fixed and Paraffin-Embedded Tissue Samples

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Author Spotlight: Exploring Strategies for Successful Immune Response Against Tumors
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相关实验视频

Last Updated: Jan 16, 2026

Enrichment and Characterization of the Tumor Immune and Non-immune Microenvironments in Established Subcutaneous Murine Tumors
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科学领域:

  • 癌症生物学 癌症生物学
  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 瘤是复杂的生态系统,具有多样化的细胞类型,影响癌症的进展和治疗反应.
  • 了解瘤微环境 (TME) 是至关重要的,尤其是在免疫治疗的临床应用中.
  • 单细胞和空间技术提供高分辨率,但成本高且复杂.

研究的目的:

  • 从批量转录组学引入解卷分析,以估计细胞类型的流行率.
  • 介绍解卷分析中的进展,挑战和基准分析方法.
  • 为优化解卷质量和解释结果提供实际指导.

主要方法:

  • 对解卷分析步骤,进展和挑战的审查.
  • 强调强大的基准测试,参数定义和指标选择.
  • 使用CIBERSORTx和BayesPrism对三阴性乳腺癌 (TNBC) 数据集 (TCGA) 的实践分析.

主要成果:

  • 演示如何预处理,参考数据集和软件选择影响解卷结果.
  • 强调质量控制对于可靠的解卷分析的重要性.
  • 确定影响解卷方法准确性和可重复性的因素.

结论:

  • 解卷分析是利用批量转录学探索TME复杂性的宝贵工具.
  • 需要进一步的研究来提高解卷方法的准确性和可重复性.
  • 优化解卷分析需要仔细考虑方法和数据质量.