| RESEARCH HIGHLIGHTS |


He collaborated with an international


team to engineer mammalian cells that express a range of malarial antigens on their surfaces. The team exposed the cells to blood samples taken from two groups of a total of 14 individuals: those who had been treated for long-lasting immunity and those who had not. The immunized individuals produced antibodies that recognized three malaria antigens, which were generally absent in the non-immunized group.


The researchers then tested these


antigens’ potential as vaccine targets. They introduced one of the antigens to human liver cells growing in a dish and then exposed the cells to rabbit antibodies that recognize and block the protein’s activity. The antibodies protected the liver cells against parasitic invasion. During an infection, the malaria parasite


first incubates and amplifies in the liver, before flooding the bloodstream and attacking red


blood cells. Blocking the infection at this early stage could save lives. Rénia now wants to replicate the experiment


on a larger group to see if the same three proteins resurface as provokers of an immune response.


1. Peng, K., Goh, Y. S., Siau, A., Franetich, J.-F., Chia, W. N. et al. Breadth of humoral response and antigenic targets of sporozoite-inhibitory antibodies associated with sterile protection induced by controlled human malaria infection. Cellular Microbiology 18, 1739–1750 (2016).


Bioinformatics


A NEW KIT FOR CYTOMETRY ANALYSIS


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A new software package offers easier analysis and interpretation of experiments that use mass cytometry, a sophisticated method for determining the properties of cells. The tool — called cytofkit — enables scientists to identify different subpopulations of cells within a sample of immune cells, cancer cells or other tissue types. Flow cytometry remains the go-to method


−20 −20 cluster


CD4 CM CD4 Eff CD4 EM CD4 Naive CD8 Eff


CD8 EM CD8 Naive


gamma delta Vd2− gamma delta Vd2+ late CD4 Eff


Less diff gamma delta


MAIT NK


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for biological investigations that require sin- gle-cell resolution. But because the technology relies on fluorescent tags to detect different markers within the cell, only a limited number of labels can be applied before the light signals start to bleed into one another. Mass cytometry helps solve this problem.


Cytofkit allows users to visualize the different subtypes of cells in their sample, as above.


36 A*STAR RESEARCH


By using metal labeling, the technique allows scientists to measure many more characteristics simultaneously within individual cells. But sorting through all the data it produces can be challenging, and most researchers agree that better analytic tools are needed. Jinmiao Chen and her colleagues at the


A*STAR Singapore Immunology Network made cytofkit in response to this need.


CYTOFKIT HELPS RESEARCHERS MAKE SENSE OF MASS CYTOMETRY DATASETS TO UNCOVER CELL SUBSETS


The package combines state-of-the-art bioinformatics methods and in-house novel algorithms to help anyone make sense of mass cytometry data. “It provides a very user- friendly graphical interface and interactive visualization of analysis results,” says Chen. “Anybody, including bench scientists and non-bioinformaticians, can use it without any training.” The software involves four main steps: first,


cytofkit performs data preprocessing according to the users’ specifications; second, the software automatically identified different matching subsets of cells; third, it allows visualization of the data with color-labeled cell types; and lastly, it infers the relatedness between cell groups. Chen’s team tested the tool’s performance


on mass spectrometry results collected from a sample of white blood cells. As they reported in PLOS Computational Biology, the software correctly identified known subpopulations of cells and further segregated these subsets to reveal additional cell types. In collaboration with A*STAR immunologist Evan Newell, the researchers also showed that cytofkit revealed


ISSUE 6 | JANUARY – MARCH 2017


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© 2017 A*STAR Singapore Immunology Network


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