In recent years, the development of technologies to obtain omics information for each single cell, rather than obtaining information on a cell population basis, has been rapidly advancing for the elucidation of heterogeneity within tissues and the development of new medical technologies. The scRNA-seq method, which analyzes gene expression in each single cell, is already widely used as an essential technology in life medical science research. However, conventional scRNA-seq methods can only analyze gene expression within cells and cannot directly analyze the glycome localized on the cell surface. On the other hand, technologies such as mass spectrometry (MS), high-performance liquid chromatography (HPLC), capillary electrophoresis (CE), and lectin microarray (LMA) have been used to analyze the glycome. However, these techniques required at least several thousand cells for analysis, and it was not possible to obtain glycome information for each single cell. In this context, the single-cell glycan-RNA sequencing (scGR-seq) method, which simultaneously analyzes the glycome and transcriptome of each single cell using DNA barcode-labeled lectin and next-generation sequencer, has been developed (Fig. 9.1) [1, 2]. Recently, technologies to predict the glycome from single-cell RNA-seq data [3] and to analyze single-cell glycome using CE-MS [4] have also been reported.