Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics

By Sophia K. Longo, M. Guo, A. Ji, P. Khavari. Nature reviews genetics, 2021

830 citations recorded by the source corpus.

Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but does not capture their spatial distribution nor reveal local networks of intercellular communication acting in situ. A suite of recently developed techniques that localize RNA within tissue, including multiplexed in situ hybridization and in situ sequencing (here defined as high-plex RNA imaging) and spatial barcoding, can help address this issue. However, no method currently provides as complete a scope of the transcriptome as does scRNA-seq, underscoring the need for approaches to integrate single-cell and spatial data. Here, we review efforts to integrate scRNA-seq with spatial transcriptomics, including emerging integrative computational methods, and propose ways to effectively combine current methodologies. Combining single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics can localize transcriptionally characterized single cells within their native tissue context. This Review discusses methodologies and tools to integrate scRNA-seq with spatial transcriptomics approaches, and illustrates the types of insights that can be gained.

Subfields: Cancer Research, Immunology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/be0211442f9187fd099936c4c1f8e37f75a531cf

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In Cancer Research

1. Qing Nie

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2. Zixuan Cang

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1. Maksim V. Plikus
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Citations in the corpus

• Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues (2023, neutral): “…to be dependent on the cell type 13,23 and non-random co-expression of the LR pair among neighbouring cells within a range of 200 μ m 43,44 . stLearn also differs from other methods (e.g. DIALOGUE 54 ) that focus on identifying coordinated gene expression programs in different cells…”
• Screening cell–cell communication in spatial transcriptomics via collective optimal transport (2023, background): “With the foreseeable availability of temporal sequences of spatial transcriptomics data 68 , CCC dynamics may be elucidated, for example by extending collective optimal transport into a dynamic optimal transport formulation.”
• Multimodal machine learning in precision health: A scoping review (2022, background): “We also excluded papers where the fusion already occurred at the data generation stage, such as spatial transcriptomics producing integrated tissue imaging and transcriptomics data 199 – 201 .”