Advancing Single-Cell Multi-Omic Approaches to Biomedical Research

April 14, 2021

By Derek Vargas, Application Scientist, MedGenome Inc.

A fundamental challenge in biomedical research is to identify accurate, early indicators of a disease. Recent advances in sequencing technologies have led to unparalleled efforts to characterize the molecular changes that underlie the development and progression of complex human diseases, including cancer. Scientists have widely used RNA-seq analysis to study the transcriptome in populations of cells. More recently, single-cell RNA seq studies have been used to gain insight on cellular traits and changes in cellular state.

The increasing commercial availability of single-cell sequencing platforms, such as 10x Genomics’ Chromium, has lead to many exciting discoveries. Additionally, researchers can now go beyond single-cell RNA seq analysis and can also capture information on cell-surface proteins, chromatin state, genetic perturbations, and even genome data. Each modality complements RNA data to provide a unique perspective on cell state and identity.

Researchers at Peter MacCallum Cancer Centre have recently outlined a method for SUGAR-seq (SUrface-protein Glycan AND RNA-seq) which enables detection and analysis of N-linked glycosylation, extracellular epitopes, and the transcriptome at the single-cell level. Specifically, they used biotinylated lectins (carbohydrate-binding proteins) to label the complex N-glycan branches on the surface of cells. They then used an anti-biotin monoclonal antibody conjugated to an oligonucleotide tag that is compatible with the 10x Genomics workflow. This allows for easy integration with the Chromium platform, allowing simultaneous profiling of N-glycan levels, cell-surface protein expression, TCR sequences, and the transcriptome. Additionally, this modular approach allows for this protocol to be easily translated to other single-cell platforms.

The SUGAR-seq technique was used to examine tumor-infiltrating T cells (TILs) from multiple tumor sources. Analysis of the data revealed divergent levels of N-glycosylation across distinct TIL populations. Regulatory T cells and exhausted T cell subsets showed high levels of N-glycosylation, whereas memory T cells showed lower levels. Additionally, N-glycosylation levels were significantly increased in the TILS compared to the levels detected in lymph nodes, suggesting the tumor microenvironment modulates high N-glycan levels.

The use of SUGAR-seq has allowed for the simultaneous detection of surface glycans, epitopes, transcripts, and TCR repertoire to characterize TILs. This allows for deeper insights into the cellular environment and can be used to identify cellular phenotypes associated with disease. This demonstrates the power of using multi-omic data to answer scientific questions, and is an example showing how single cell platforms can be modified to maximize the data output.


Kearney CJ, Vervoort SJ, et al. (2021) SUGAR-seq enables simultaneous detection of glycans, epitopes, and the transcriptome in single cells.
Science Advances Vol. 7 (8) DOI: 10.1126/sciadv.abe3610


#Single-Cell Multi-Omic, #Biomedical Research, #RNA-seq analysis, #Single-cell RNA seq, #SUGAR-seq (SUrface-protein Glycan AND RNA-seq), #TCR sequences, #tumor-infiltrating T cells, #TCR repertoire

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