Development of scGR-seqscGlycan-seq was then combined with scRNA-seq for the simultaneous analysis of glycan and RNA profiles in single cells (scGR-seq) (Figure 1B)1). For scRNA-seq, we used a plate-based method known as RamDA-seq, which is a full-length single-cell total RNA-sequencing method3). We performed scGR-seq on human-induced pluripotent stem cells (hiPSCs) and hiPSC-derived NPCs (11-day differentiation). Using UMAP, a nonlinear dimensional clustering, based on only the mRNA or glycan data, the two cell types (hiPSCs and NPCs) were partially separated (Figure 1D)1). In contrast, when we performed UMAP based on both the mRNA and glycan data, the two cell types were clearly separated (Figure 1D)1). Therefore, the combination of mRNA and glycan profiling techniques has further characterized the cell identities. Simultaneous transcriptome and glycome profiles can associate genes with glycans at the single-cell level. A PLS regression analysis was able to identify a group of mRNAs and lectins that were associated with one another differently per component1). This analysis allowed us to infer Concluding Remarks and Future PerspectivescGR-seq provides lectin-based glycan and gene expression profiles for individual cells, making it possible to obtain detailed glycan information on single cells constituting a tissue. These data will provide insight into complex multicellular communication networks, including tumor microenvironment and neural networks based on lectin-receptor interactions. scGR-seq can also be applied to the development of drug targets for rare cells, such as cancer stem cells and circulating tumor cells. However, there are limitations to the current Glycan-seq and scGR-seq techniques. Similar to flow cytometry and lectin microarray, absolute amounts of glycans cannot be determined. Another limitation of this current system is the throughput. Since scGR-seq is a plate-based platform, processing is currently limited to hundreds of cells, whereas it can perform full-length total RNA sequencing. In contrast, droplet-based methods such as 10x Genomics (CITE-seq) can sequence thousands of cells at once, but target only the 3’ends of poly(A) transcripts (Baran-Gale et al., 2018). Because of this difference, scGR-seq will complement the study of single cells in complex biological systems. To resolve this limitation, we plan to improve scGR-seq and adapt it to a droplet-based high-throughput single-cell technology. We have also adopted Glycan-seq to approach the untapped glycomics of the gut-microbiota, which mediate the direct cross-talk with host4). 1) Minoshima F, Ozaki H, Odaka H, Tateno H. Integrated analysis of glycan and RNA in single cells, iScience 24(8): 102882, 20212) Odaka H, Ozaki H, Tateno H. scGR-seq: Integrated analysis of glycan and RNA in single cells, STAR Protoc. 3(1): 101179, 20223) Hayashi T, Ozaki H, Sasagawa Y, Umeda M, Danno H, Nikaido I. Single-cell full-length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs, Nat Commun. 9(1):619, 20184) Oinam, L., Minoshima, F. & Tateno, H. Glycan profiling of the gut microbiota by Glycan-seq, ISME COMMUN. 2: 1, 2022.ovary mutants, and hiPSCs vs. hiPSC-derived neural progenitor cells (NPCs)1). The results were compared by flow cytometry using fluorescence-labeled lectins as the gold standard. Essentially, the Glycan-seq data were consistent with the flow cytometry data1). Therefore, bulk Glycan-seq can capture distinct and quantitative differences in glycan profiles in various cell populations as confirmed via flow cytometry.Next, we tested the applicability of Glycan-seq in single cells, which we termed single-cell Glycan-seq (scGlycan-seq) (Figure 1C)1). We applied scGlycan-seq for comparative analysis of hiPSCs and hFibs and hiPSCs before and after differentiation into NPCs. The relative quantitative differences in the rBC2LCN signal for hiPSCs before (Day 0) and after differentiation to NPCs (Days 4 and 11) observed by flow cytometry was also captured by scGlycan-seq (Figure 1C, left and middle panels)1). Principal component analysis clearly separated single cells on Days 0, 4, and 11, and the cells were clearly ordered with respect to the progression of differentiation (Figure 1C, right panel)1). Therefore, scGlycan-seq enabled glycan profiling in single cells and revealed cellular heterogeneity in the glycan profiles.Referenceseach glycan’s potential function and role as a marker through the set of genes associated with the glycan. We also established the overall relationship between lectins and glycosylation-related genes1). Therefore, scGR-seq is useful for finding potential relevance between the transcriptome and glycome profiles.In conclusion, we have developed a lectin-based glycan profiling by sequencing and applied this technique to the joint analysis of glycan and RNA in single cells and the glycomic profiling of the gut microbiota. Glycan-seq and scGR-seq have the potential to advance our understanding of cellular heterogeneity and the biological role of glycans across diverse multicellular systems across species and lead to the launch of glycobiology in single cells and microbiomes.57
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