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A complete picture of the immune status of a patient could be useful for diagnosing a range of diseases.

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Classifying and counting immune cells with an app

17 Sep 2019

Researchers at A*STAR have developed a method to obtain a holistic view of the identities and proportions of immune cells in patient blood samples.

Bombarded daily with threats both external and internal, our immune system has the remarkable ability to adapt to virtually any situation thanks to its diverse array of cells. However, certain conditions (e.g. autoimmune disease, hypo-responsiveness) result from an imbalance of the types and reactivity of immune cells. Being able to reliably identify which immune cell subsets are involved in disease could facilitate the development of better diagnostic tools and treatments.

“Most of the time, the biomarkers that arise from blood gene expression data are not interpreted from an immunological point of view, even though the cells being interrogated are immune cells,” explained Anis Larbi, a Principal Investigator at the Singapore Immunology Network (SIgN), A*STAR. To enable researchers to link gene expression data to specific cell types, Larbi and his team have developed a method to identify different immune cell subsets in patient blood samples.

Using a combination of high dimension flow cytometry, fluorescence-assisted cell sorting, RNA sequencing and microarray data, the researchers were able to definitively characterize 29 different immune cell types. Beyond simply assigning a molecular ‘fingerprint’ to immune cell types, the researchers were also able to infer the proportions of each immune cell population by using a statistical model known as the ‘robust LM’ method, which allowed them to obtain absolute gene expression values.

“By adding a deconvolution tool to other existing tools to understand gene expression data, one can now have a very precise idea of which cells were present in the studied samples, without the need to perform additional analysis such as high dimension flow cytometry, which requires special skills and equipment,” Larbi explained.

The researchers demonstrated the power of their method by using it to examine the immune response of individuals after influenza vaccination, showing how the composition of immune cell populations shifted in response to vaccination.

These findings have gone towards the development of an app for use by the broader scientific community. “This work should enable us to predict immune cell subset frequencies from previous studies where only gene expression data were available,” Larbi pointed out.

Moving forward, the group is planning on using single-cell data for the deconvolution of more cell subsets than ever performed. Hinting at potential clinical applications, Larbi suggested, “Studies focusing on immune cell composition in situ will help us accelerate the progress in the field of cancer immunotherapy.”

The A*STAR-affiliated researchers contributing to this research are from the Singapore Immunology Network (SIgN).

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References

Monaco, G., Lee, B., Xu, W., Mustafah, S., Hwang, Y.Y. et al. RNA-Seq Signatures Normalized by mRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types. Cell Reports 26, 1627-1640.e7 (2019) | article

About the Researcher

Anis Larbi

Adjunct Investigator

Singapore Immunology Network
Anis Larbi graduated from the University of Sherbrooke, Canada, in 2005 at the Clinical Research Center, Faculty of Medicine. He performed his immunological studies at the Research Center on Aging at the Laboratory of Immunity/Signaling. He pursued his interest in immunological aging at the Center for Medical Research at the University of Tubingen, Germany, from 2006 where he focused on the impact of chronic infectious agents such as cytomegalovirus on the immunity of the elderly. There he also developed his skills in multi-parametric flow cytometry. He joined A*STAR in February 2010 to fulfill two missions: develop a program on immunity in aging humans, and set up a competitive flow cytometry facility at the Singapore Immunology Network (SIgN). He has published more than 160 papers in the field of immunological analysis, especially via flow cytometry, and is an emeritus International Society for Advancement of Cytometry (ISAC) Scholar.

This article was made for A*STAR Research by Wildtype Media Group