Category: Technology

A group from State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, China has reproted about Lectin microarray based glycan profiling of exosomes.
https://www.sciencedirect.com/science/article/abs/pii/S0003267024006202?via%3Dihub

In this styudy, the differential analysis of glycosylation on the surface of exosomes between from three olorectal cancer cell lines (SW480, SW620, HCT116) and from one normal colon epithelial cell line (NCM460) has been done by using a lectin microarray. As a result, it has shown that UEA-I lectin could be used to detect aberant glycosylation of exosomes derived from SW480 cells.

And further, in this experiment, the limit of detection (LOD) of UEA-I lectin microarray was calculated to be as low as 2.7 × 105 extracellular vehicles/mL.

A group from Department of Biotechnology, Graduate School of Engineering, Osaka University, Osaka, Japan has reported about glycosylation of recombinant adeno-associated virus serotype 6 (rAAV6) which is used for Gene therapy.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11107246/

Glycosylation of recombinant adeno-associated viruses (rAAVs) that are used for gene theraphy has been evaluated by Lectin microarrays.
It was found that rAAV6 was mainly O-glycosylated with the mucin-type glycans, O-GalNAc (Tn antigen), and mono- and di-sialylated Galβ1-3GalNAc (T antigen).

The purpose of this study was to see if glycosylation of rAAV6 can affect gene therapy safety in terms of immunogenecity and overall transduction efficacy of this method.
However, unfortunately, this study still could not evaluate the direct influence of mucin-type O-glycans on transduction efficiency.

A group from Laboratory for Functional Glycomics, College of Life Sciences, Northwest University, China has reported about employing a combination of lectin microarrays and machine learning to identify alterations in serum glycopatterns with a special emphasis on its early detection.
https://pubmed.ncbi.nlm.nih.gov/38698681/

In recent years, the number of Chinese papers has increased rapidly, and the probability of finding Japanese papers has decreased considerably when searching for papers.
This work from China proposes a method that uses the glycan profile of blood glycoproteins as a serum biomarker combining machine learning with lectin microarrays for the early detection of non-alcoholic fatty liver disease.

However, this type of method was already developed by us about six years ago, and although the target was different from the above paper, its excellent effectiveness has been demonstrated.
The following website explains that it is possible to characterize target cells quite accurately by using deep learning and lectin microarrays for glycoproteins secreted by cells into the culture media.
Combining Deep Learning and Lectin microarrays

A group from Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA, etc. has reported a lectin and AI-based approach to predict N-glycan structures and determine their relative abundance in purified proteins based on lectin-binding patterns.
https://www.biorxiv.org/content/10.1101/2024.03.27.587044v1

This method can be used when the number of glycanss to be evaluated is limited, but there are a lot of problems when applying it generally.

A similar software named “SA/DL easy” had been created by Mx using Deep Learning as a core technology 5 years ago. By using this software, you can quickly do the same thing.
The problem lies in the tedious work of creating training data, or preparing a large number of expressed glycan structures whose structures have been properly identified, and obtaining glycan profiles.
https://www.emukk.com/SADL-Easy_Eng/index.html

A group from Beijing Engineering Research Center for BioNanotechnology, CAS Key Laboratory of Standardization and Measurement for Nanotechnology, National Center for Nanoscience and Technology, Beijing, China, etc. has reported about glycan profiling of extracellular vesicles (EVs) for detecting triple-negative breast cancer (TNBC).
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10937950/

A panel of 3 lectins (ConA, WGA and RCA I) was used to detect the EV surface glycan profiles unique to TNBC.
As a result, they succeeded in getting an area under ROC curve (AUC) of 0.91 with using the weighted sum of 3 lectins (ConA, WGA and RCA I) for discriminatiing TNBC from other BCs and HDs.

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A group from Institute of Chemistry, Slovak Academy of Sciences, Bratislava, etc. has reported about a measurement using a sandwich scheme with CD63, exosomes and SNA lectin for detecting prostate cancer.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10892626/

In this experment, exosomes produced by prostate cancer cells are examined as a new biomarker for detecting prostate cancer.

Comparing with exosomes produced by benign (control) cell line RWPE1 and carcinoma cell line 22Rv1, it was showen that
(1) the control exosomes mainly interacted with SNA and MAAII lectins; however, they exhibited a lower affinity than the carcinoma exosomes, and also
(2) PHA-L and PHA-E were only able to bind poorly to control-derived exosomes, while there were no interactions to carcinoma exosomes.
This result is quite reasonable because usually the signal intensity of PHA-L and PHA-E disapper with fully sialylated N-glycans suggesting that sialylation is stronger in carcinoma exsosomes than that of control exosomes.

However, blog author is skeptical about their conclusion that it is possible to perform measurements in a sandwich configuration, i.e., antibody/exosomes/lectin, because exsosmes are generally strongly sialylated and CD63 can not discriminate exsosomes produced by prostate cancer cells from other exosomes.