. Author manuscript; available in PMC: 2020 Nov 1.

Published in final edited form as: J Hepatol. 2020 Jun 10;73(5):1219–1230. doi: 10.1016/j.jhep.2020.06.004

Table 1. Spatial transcriptomics and strategies to match scRNA-seq data with spatial information.

Methods	Required input data other than scRNA-seq	Pros/Cons
Spatially-resolved RNA-seq¹⁶	Accurate spatial pattern of two or more marker genes	High resolution and accurate.
Paired-scRNA-seq¹⁹	Spatial pattern of one cell forming strong cell-to-cell interactions with the cell of interest	High resolution and accurate.
Spatial sorting analysis²⁰	Known extracellular marker proteins to be used for FACS	Known extracellular marker proteins are not always available. Can be used for multi-omics analysis.
DPT analysis²¹	None	Cell diversity needs to be correlated with cell position in the tissue. Validation by histology, smRNA-FISH or other imaging techniques is needed.
Gene cartography (novoSpaRc)²²	Optional Marker genes and general tissue organization	Cell diversity needs to be correlated with cell position in the tissue. Marker genes are optional inputs to refine the analysis. Validation by histology, smRNA-FISH or other imaging techniques is needed.
In situ spatial transcriptomics^23–25	Slide-based system	Lower sequencing depth than classical scRNA-seq but higher spatial resolution. High costs. Not data available yet on human liver tissue.

DPT, diffusion pseudo-time; scRNA-seq, single-cell RNA-sequencing; smRNA-FISH, single-molecule RNA fluorescent in situ hybridization.