RNA Sequencing for Glyco-code Based Diagnostics

With the rapid development of next-generation sequencing (NGS) technologies in recent years, the important status of RNA sequencing (RNA-Seq) in glyco-code based diagnostics has been gradually uncovered. Creative Biolabs has extensive experience in NGS and glycoprotein researches. Thus we are capable of offering reliable RNA-seq services to help promote our customers' researches for glyco-code based diagnostics.

Overview of Glyco-code in Diagnostics

Protein glycosylation is a highly prevalent posttranslational modification. Glycosylation is a highly regulated process with more than 300 enzymes involved in their biosynthesis and processing in a non-template-driven manner. This process is done by a series of sequential reactions involving a wide array of glycosyltransferases that attach single monosaccharides from nucleotide sugar donors, as well as different glycosidases that remove individual monosaccharides. The expression, activity, and localization of these enzymes are sensitive to the physiologic state of the cell. Alterations in N-linked glycosylation have been found in a variety of diseases, including but not limited to cancer, inflammatory arthritis, liver fibrosis/ cirrhosis, schizophrenia, type 2 diabetes, ischemic stroke, and Parkinson's disease. Particularly for cancers, most US Food and Drug Administration-approved cancer biomarkers comprise circulating glycoproteins or carbohydrate antigens for measurement in blood. Glycoproteins can be ideal biomarkers because they enter circulation from tissues or blood cells through active secretion or leakage, making them assessable for analysis through the serum.

Introduction of RNA-Seq

RNA-Seq workflow is divided into several steps: RNA extraction, library construction, sequencing, and data analysis. RNA-Seq offers several distinct advantages over previous hybridization-based microarray technology. In general, microarray-based gene expression profiling is limited to hybridizing gene probes to known genomic sequences within target genes of tumor specimens. Preferably, RNA-Seq enables the detection and discovery of novel gene transcripts and ncRNAs. RNA-Seq can also detect transcription boundaries with a single nucleotide resolution, reveal alternative splicing, allele-specific expression, and SNPs in the transcribed regions. RNA-Seq has remarkably low background signaling owing to its precisely mapped sequenced reads of unique genomic regions. Depending on the detected sequence, RNA-Seq provides a more precise and quantitative profile of gene expression in diverse groups or treatments.

Fig.1 Schematic representation of glyco-biomarker identification using transcriptome analysis.^{1, 2}

RNA-Seq for Glyco-code Based Diagnostics

Expression of glycoproteins on the cell surface and into extracellular space depends on the cellular conditions that are evidenced by the altered expression profiles of glycoproteins in conditions such as acute or chronic diseases and cancers, as compared to normal conditions. The expression of glycoproteins can be determined by analyzing the glycogenes, including glycosyltransferases, nucleotide-sugar transporters, and glycosidases as well as certain proteins that act as glycan acceptors. Evaluating the expression levels of glycogenes is a useful step in studying the biological phenomena involving biomarkers. Currently, there are multiple gene expression profiling techniques such as DNA microarray that can be used to analyze genome-wide gene expression. However, since the expression levels of several glycogenes are generally low, some of them were barely detectable in DNA microarray analysis. Thus, it was necessary to obtain accurate quantification and comprehensive expression profiling of as many glycogenes involved in the glycan synthesis pathway as possible. In this context, RNA-Seq with a more detailed and unbiased view of the complex and dynamic nature of the transcriptome becomes a better choice for glyco-code unscramble.

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References

1. Angata, Kiyohiko, et al. "Glycogene expression profiling of hepatic cells by RNA-Seq analysis for Glyco-biomarker identification." Frontiers in Oncology 10 (2020): 1224.
2. Under Open Access license CC BY 4.0, without modification.

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