Table 1 Comparison of instrument shown in this paper
scRNA-seq (10x Genomics) | CyTOF (Fluidigm) | scATAC-seq (10xGenomics) | scGEX-ATAC (10xGenomics) | Visium | |
|---|---|---|---|---|---|
Basic information | Single-cell RNA sequencing | Cytometry by Time-Of-Flight (elemental mass spectometry + flow cytometry) | Single-cell assay for transposase-accessible chromatin | Single-cell RNA-seq and ATAC-seq | Spatial Transcriptome |
Required input | 870–17,400 cell (after wash/5'GEX) | 1,000,000–3,000,000 cells | 775–15,400 nuclei (after wash) | 775–15,400 nuclei (after wash) | |
Expected output | 500–10,000 cells | ~ 200,000 | 500–10,000 cells | 500–10,000 nuclei | Up to 5000 data spot |
Number of markers can be analyzed | ~ 500–~ 5000 (depend on sample type) | 30–40 proteins | ~ 500–~ 5000 (depend on sample type) | depend on sample type | Transcriptome |
Tool for infomatic analysis | Cell Ranger, R package | Cytobank, Pathsetter, R packages | Cell Ranger, R packages | Cell Ranger ARC, R packages | Cell Ranger, R packages |
Pros | ・Reveal whole transcriptome dataset as long as the gene has enough expression level | ・Reveal expression of targeted protein levels of each cell ・Low cost compare to scRNA-seq and scATAC-seq ・Identify > 30 immune cell type easily (pre-set) ・Have advantage in highly-target cell analysis | ・Reveal cell heterogeneity in chromatin level | ・Reveal cell heterogeneity in transcriptome and chromatin level simultaneously | ・Gene expression can be obtained while maintaining spatial information. |
Cons | ・Low cell number of output compared to CyTOF ・Low depth reads compared to conventional bulk RNA-seq ・Unable to use cell after processing | ・Can use only pre-determined markers ・Unable to use cell after processing ・Affected by sensitivity of ion used in the staining | ・Difficult to get comprehensive coverage of open chromatin sites ・Unable to use cell after processing | ・Cannot detect transcriptome use nuclei, not cell cytoplasm | ・At this stage, the resolution is not at the level of a single cell |