Login / Signup

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

Sophia K LongoMargaret G GuoAndrew L JiPaul A Khavari
Published in: Nature reviews. Genetics (2021)
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.
Keyphrases
  • single cell
  • rna seq
  • high throughput
  • genome wide
  • high resolution
  • stem cells
  • big data
  • machine learning
  • mass spectrometry
  • quality improvement
  • bone marrow
  • fluorescence imaging