Single-cell sequencing techniques from individual to multiomics analyses

Abstract

Here, we review single-cell sequencing techniques for individual and multiomics profiling in single cells. We mainly describe single-cell genomic, epigenomic, and transcriptomic methods, and examples of their applications. For the integration of multilayered data sets, such as the transcriptome data derived from single-cell RNA sequencing and chromatin accessibility data derived from single-cell ATAC-seq, there are several computational integration methods. We also describe single-cell experimental methods for the simultaneous measurement of two or more omics layers. We can achieve a detailed understanding of the basic molecular profiles and those associated with disease in each cell by utilizing a large number of single-cell sequencing techniques and the accumulated data sets.

Fig. 1. Comparison of scRNA-seq platforms.

Characteristics of two major scRNA-seq platforms, C1 and Chromium.

Fig. 2. Integration of scRNA-seq and scATAC-seq in mouse lung cells.

a The workflow for the integration of scRNA-seq and sATAC-seq. b 2D visualization of scRNA-seq clusters from mouse lungs. The UMAP figure was created with Seurat v3.1.2. The cell types in each cluster were identified on the basis of the expression levels of cell type-specific markers. The clusters with the same cell type annotation were merged. In this figure, clusters of epithelial cells with Epcam and B cells with Cd19 were the focus. c 2D visualization of scATAC-seq clusters (left). The UMAP figure was created by using Signac v0.1.6. Coverage plots are shown for two marker genes (right). d UMAP visualization of scATAC-seq with Seurat Label Transfer from scRNA-seq data. The cell types in the scATAC-seq clusters were predicted by scRNA-seq annotation.

Fig. 3. Multilayered single-cell sequencing.

Representative single-cell multimodal sequencing methods. Genomic, epigenomic, and proteomic information can be simultaneously profiled with the transcriptome. Spatial information for a tissue section can also be obtained with gene expression data at the level of one to tens of cells. ST spatial transcriptomics (Visium).