Deciphering tumor ecosystems at super resolution from spatial transcriptomics with TESLA.

IF 9 1区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Cell Systems Pub Date : 2023-05-17 Epub Date: 2023-05-09 DOI:10.1016/j.cels.2023.03.008

Jian Hu, Kyle Coleman, Daiwei Zhang, Edward B Lee, Humam Kadara, Linghua Wang, Mingyao Li

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引用次数: 0

Abstract

Cell populations in the tumor microenvironment (TME), including their abundance, composition, and spatial location, are critical determinants of patient response to therapy. Recent advances in spatial transcriptomics (ST) have enabled the comprehensive characterization of gene expression in the TME. However, popular ST platforms, such as Visium, only measure expression in low-resolution spots and have large tissue areas that are not covered by any spots, which limits their usefulness in studying the detailed structure of TME. Here, we present TESLA, a machine learning framework for tissue annotation with pixel-level resolution in ST. TESLA integrates histological information with gene expression to annotate heterogeneous immune and tumor cells directly on the histology image. TESLA further detects unique TME features such as tertiary lymphoid structures, which represents a promising avenue for understanding the spatial architecture of the TME. Although we mainly illustrated the applications in cancer, TESLA can also be applied to other diseases.

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利用 TESLA 从空间转录组学的超分辨率解密肿瘤生态系统。

肿瘤微环境（TME）中的细胞群，包括其丰度、组成和空间位置，是决定患者对治疗反应的关键因素。空间转录组学（ST）的最新进展实现了对肿瘤微环境中基因表达的全面描述。然而，目前流行的空间转录组学平台（如 Visium）只能测量低分辨率点的表达，而且有大片组织区域没有被任何点覆盖，这限制了它们在研究 TME 详细结构方面的作用。在此，我们介绍了 TESLA，这是一种用于 ST 中像素级分辨率组织注释的机器学习框架。TESLA 整合了组织学信息和基因表达，可直接在组织学图像上注释异质性免疫细胞和肿瘤细胞。TESLA 还能进一步检测 TME 的独特特征，如三级淋巴结构，这为了解 TME 的空间结构提供了一条很有前景的途径。虽然我们主要说明了 TESLA 在癌症中的应用，但它也可应用于其他疾病。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cell Systems Medicine-Pathology and Forensic Medicine

CiteScore

16.50

自引率

1.10%

发文量

审稿时长

42 days

期刊介绍： In 2015, Cell Systems was founded as a platform within Cell Press to showcase innovative research in systems biology. Our primary goal is to investigate complex biological phenomena that cannot be simply explained by basic mathematical principles. While the physical sciences have long successfully tackled such challenges, we have discovered that our most impactful publications often employ quantitative, inference-based methodologies borrowed from the fields of physics, engineering, mathematics, and computer science. We are committed to providing a home for elegant research that addresses fundamental questions in systems biology.