Lost in space: what single-cell RNA sequencing cannot tell you

• Published in: Trends in plant science Vol. 29; no. 9; pp. 1018 - 1028
• Main Authors: Adema, Kelvin, Schon, Michael A., Nodine, Michael D., Kohlen, Wouter
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.09.2024
• Subjects: cellular ontogeny; dimension reduction; Plants - genetics; RNA, Plant - genetics; RNA-seq; Sequence Analysis, RNA - methods; single-cell; Single-Cell Analysis - methods; spatial transcriptomics; Transcriptome; single-cell; cellular ontogeny; RNA-seq; dimension reduction; spatial transcriptomics
• ISSN: 1360-1385; 1878-4372; 1878-4372
• DOI: 10.1016/j.tplants.2024.03.010

Tissue dissociation results in single-cell datasets with limited spatiotemporal context.The predictive power of single-cell RNA sequencing (scRNA-seq) can be improved by complementing with methods that ‘anchor’ cells in space and time.We assess how the information content produced by various spatial technologies can be used to integrate spatial information into scRNA-seq datasets.We reflect on the initial promises of scRNA-seq and whether complementary spatial data can bring us closer to truly observing plant biology at cellular resolution. Plant scientists are rapidly integrating single-cell RNA sequencing (scRNA-seq) into their workflows. Maximizing the potential of scRNA-seq requires a proper understanding of the spatiotemporal context of cells. However, positional information is inherently lost during scRNA-seq, limiting its potential to characterize complex biological systems. In this review we highlight how current single-cell analysis pipelines cannot completely recover spatial information, which confounds biological interpretation. Various strategies exist to identify the location of RNA, from classical RNA in situ hybridization to spatial transcriptomics. Herein we discuss the possibility of utilizing this spatial information to supervise single-cell analyses. An integrative approach will maximize the potential of each technology, and lead to insights which go beyond the capability of each individual technology. Plant scientists are rapidly integrating single-cell RNA sequencing (scRNA-seq) into their workflows. Maximizing the potential of scRNA-seq requires a proper understanding of the spatiotemporal context of cells. However, positional information is inherently lost during scRNA-seq, limiting its potential to characterize complex biological systems. In this review we highlight how current single-cell analysis pipelines cannot completely recover spatial information, which confounds biological interpretation. Various strategies exist to identify the location of RNA, from classical RNA in situ hybridization to spatial transcriptomics. Herein we discuss the possibility of utilizing this spatial information to supervise single-cell analyses. An integrative approach will maximize the potential of each technology, and lead to insights which go beyond the capability of each individual technology.