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| RESEARCH HIGHLIGHTS |


A new class of drugs was released in the


1990s called selective serotonin reuptake inhibitors (SSRIs). These drugs, including Prozac and Zoloft, increase serotonin levels in the brain by blocking the proteins that pump them into neurons. But scientists have grave doubts about their effectiveness. Claridge-Chang’s group at A*STAR studies


anxiety in the vinegar fly, a powerful genetic model. When they turned to the mouse and rat literature for guidance, they found many contradictory results. This lack of consensus was especially striking, as preclinical studies of rodents typically form the basis for psychiatric drugs entering clinical trials.


To make sense of the background, team


members Farhan Mohammad and Joses Ho analyzed more than 300 mouse and rat studies published between 1985 and 2015 for ten types of anxiety drug targets, including the targets of SSRIs. Eight of the interventions were found to have strong effects on anxiety in the animals. However, when the researchers plotted


the published data on a graph, they found an unexpectedly skewed pattern. “Where dots should have been, they weren’t,” explains Ho. Medical statisticians show that such skewed distributions usually indicate that researchers are shelving statistically


insignificant results, a phenomenon called publication bias. This was not the only inconsistency:


mutant mice lacking the SSRI target protein had higher anxiety levels, even though SSRIs are prescribed as anti-anxiety medications. Yet the literature did not reflect this. “This is a direct contradiction, but about half of the authors didn’t even mention it in their papers,” says Claridge-Chang.


1. Mohammad, F., Ho, J., Woo, J. H., Lim, C. L., Poon, D. J. et al. Concordance and incongruence in preclinical anxiety models: Systematic review and meta- analyses. Neuroscience & Biobehavioral Reviews 68, 504–529 (2016).


Bioinformatics


NEW TOOL TO CLEAN FLOW CYTOMETRY DATA


A*STAR researchers have developed a new bioinformatics tool called flowAI, which provides a more objective, efficient and intuitive solution to the quality control of data


acquired via a common biological technique called flow cytometry1. Flow cytometry is the first-choice tech-


20 0


nology in immunology and other biological fields to characterize physical and functional properties of cells. Beyond separating cells according to their size and granularity, flow cytometry can distinguish specific cells by the proteins present on their membrane, which are recognized by antibodies labeled with different fluorescence colors. Although this technique allows up to


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20 parameters to be analyzed simultaneously, its inefficient data analysis is often performed manually, which is time consuming and relies on high expertise and subjective interpretation. “Being actively involved in several activities


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The complexity of the immune cells in human whole blood analyzed by flow cytometry.


www.astar-research.com


of the International Society for the Advancement of Cytometry (ISAC), we have realized that one growing demand is to improve the automatic analysis of flow cytometry data,” explains


A MORE INTUITIVE AND EFFICIENT


SOLUTION TO GET RID OF ANOMALIES IN IMMUNOLOGICAL STUDIES


Anis Larbi, principal investigator at the Singa- pore Immunology Network. “We believe that high-quality data lead to more-accurate results and better downstream computational analyses.” FlowAI is a software package, which uses the


statistical language known as R, and is available on the open-source project Bioconductor. It allows users to discard poor-quality data either automatically via an algorithm or manually using a graphical user interface. FlowAI eliminates anomalies caused by debris, air intrusion in the fluidic system, technical issues, voltage instability and so on, which create abrupt changes in the speed of the fluid, instability of signal acquisition over time and data outliers. The analysis gener- ates a report that indicates the percentage of cells that did not pass the quality checks and graphs showing where the anomalies were detected. A*STAR scientists tested flowAI with 4,469


flow cytometry files from 11 different datasets and also compared the flowAI automatic


A*STAR RESEARCH 21


© 2016 A*STAR Singapore Immunology Network


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