SIgN scientists develop the first universal toolbox for dendritic cell analysis

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Singapore Immunology Network’s standardised analysis framework can be used across tissues and species

The image depicts two major dendritic cell types, coloured using a scanning electron micrograph (SEM). The cells coloured blue in the background represent human plasmacytoid dendritic cells, while the purple cell in the foreground represents a myeloid CD1c dendritic cell. Image by Dr Benoit Malleret, Singapore Immunology Network (SIgN), A*STAR
The image depicts two major dendritic cell types, coloured using a scanning electron micrograph (SEM). The cells coloured blue in the background represent human plasmacytoid dendritic cells, while the purple cell in the foreground represents a myeloid CD1c dendritic cell. Image by Dr Benoit Malleret, Singapore Immunology Network (SIgN), A*STAR

Dendritic cells are antigen-presenting cells in the mammalian immune system that hold great therapeutic potential. However, because there are many different types of dendritic cells, it has been challenging to align them across tissues and species to analyse and compare their functions.

Recognising this as a problem hindering translational research capabilities in the field, Dr Florent Ginhoux and his colleagues at A*STAR’s Singapore Immunology Network (SIgN), have come up with a solution. Using unsupervised analysis of flow cytometry and mass cytometry data obtained from multiple mouse, macaque, and human tissues, the SIgN team and their external collaborators developed a universal, high-throughput and standardised methodology for analysing dendritic cell complexity across tissues and species.

Dr Ginhoux, Senior Principal Investigator at SIgN, said: “For years, dendritic cell research has been plagued by the fact that the scientific community lacked a common language. Every lab used different markers for dendritic cell analysis, and it was difficult to compare results. Now, we finally have a universal toolbox for the automated identification of dendritic cells. This can be broadly utilized by students, scientists, and clinicians alike, to generate easily comparable results and analyses.”

The researchers hope that the standardised framework they have established will help in the identification of the best dendritic cell subsets to target for specific therapeutic applications, such as the development of next-generation vaccines.

For more information, please refer to the paper “Unsupervised High-Dimensional Analysis Aligns Dendritic Cells across Tissues and Species”, published in Immunity on 20 September, 2016.