Placing RNA in context and space – methods for spatially resolved transcriptomics. (31st March 2018)

• Record Type: Journal Article
• Authors: Strell, Carina; Hilscher, Markus M.; Laxman, Navya; Svedlund, Jessica; Wu, Chenglin; Yokota, Chika; Nilsson, Mats
• Is Part Of: FEBS journal. Volume 286:Number 8(2019)
• Page Start: 1468
• Page End: 1481
• Publication Date: 2018-03-31
• Subjects: cell profiling -- gene expression maps -- in situ sequencing -- spatial transcriptomics -- spatially resolved transcriptomics -- tissue heterogeneity -- tissue maps; Biochemistry -- Periodicals; Molecular biology -- Periodicals; Pathology, Molecular -- Periodicals; 572
• DOI: 10.1111/febs.14435
• Languages: English
• ISSNs: 1742-464X

Abstract

Abstract : Single‐cell transcriptomics provides us with completely new insights into the molecular diversity of different cell types and the different states they can adopt. The technique generates inventories of cells that constitute the building blocks of multicellular organisms. However, since the method requires isolation of discrete cells, information about the original location within tissue is lost. Therefore, it is not possible to draw detailed cellular maps of tissue architecture and their positioning in relation to other cells. In order to better understand the cellular and tissue function of multicellular organisms, we need to map the cells within their physiological, morphological, and anatomical context and space. In this review, we will summarize and compare the different methods of in situ RNA analysis and the most recent developments leading to more comprehensive and highly multiplexed spatially resolved transcriptomic approaches. We will discuss their highlights and advantages as well as their limitations and challenges and give an outlook on promising future applications and directions both within basic research as well as clinical integration. Abstract : Spatially resolved transcriptomic techniques have become important tools to systematically map gene expression data to the spatial distribution of heterogeneous cell populations within both perturbed and unperturbed tissues. Currently, a broad variety of different methods to extract information from the spatial tissue context are available. This review discusses their advantages or limitations and gives an outlook on promising future applications.