通过Visium、GeoMx DSP和Chromium对存档肿瘤进行转录组分析揭示了患者的异质性。

Transcriptome analysis of archived tumors by Visium, GeoMx DSP, and Chromium reveals patient heterogeneity.

作者信息

Dong Yixing, Saglietti Chiara, Bayard Quentin, Espin Perez Almudena, Carpentier Sabrina, Buszta Daria, Tissot Stephanie, Dubois Rémy, Kamburov Atanas, Kang Senbai, Haignere Carla, Sarkis Rita, Andre Sylvie, Alexandre Gaveta Marina, Lopez Lastra Silvia, Piazzon Nathalie, Santos Rita, von Loga Katharina, Hoffmann Caroline, Coukos George, Peters Solange, Soumelis Vassili, Durand Eric Yves, de Leval Laurence, Gottardo Raphael, Homicsko Krisztian, Madissoon Elo

机构信息

Biomedical Data Science Center, Lausanne University Hospital; University of Lausanne, Lausanne, Switzerland.

Institute of Pathology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.

出版信息

Nat Commun. 2025 May 12;16(1):4400. doi: 10.1038/s41467-025-59005-9.

DOI:10.1038/s41467-025-59005-9

PMID:40355415

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12069714/

Abstract

Recent advancements in probe-based, full-transcriptome technologies for FFPE tissues, such as Visium CytAssist, Chromium Flex, and GeoMx DSP, enable analysis of archival samples, facilitating the generation of data from extensive cohorts. However, these methods can be labor-intensive and costly, requiring informed selection based on research objectives. We compare these methods on FFPE tumor samples in Breast, NSCLC and DLBCL showing 1) good-quality, highly reproducible data from all methods; 2) GeoMx data containing cell mixtures despite marker-based preselection; 3) Visium and Chromium outperform GeoMx in discovering tumor heterogeneity and potential drug targets. We recommend the use of Visium and Chromium for high-throughput and discovery projects, while the manually more challenging GeoMx platform with targeted regions remains valuable for specialized questions.

摘要

基于探针的FFPE组织全转录组技术的最新进展，如Visium CytAssist、Chromium Flex和GeoMx DSP，能够对存档样本进行分析，有助于从大量队列中生成数据。然而，这些方法可能需要大量人力且成本高昂，需要根据研究目标进行明智的选择。我们在乳腺癌、非小细胞肺癌和弥漫性大B细胞淋巴瘤的FFPE肿瘤样本上对这些方法进行了比较，结果表明：1）所有方法都能产生高质量、高度可重复的数据；2）尽管进行了基于标记的预选，但GeoMx数据仍包含细胞混合物；3）在发现肿瘤异质性和潜在药物靶点方面，Visium和Chromium优于GeoMx。我们建议在高通量和探索性项目中使用Visium和Chromium，而对于专门问题，手动操作更具挑战性的具有靶向区域的GeoMx平台仍然很有价值。