bioRixv: Single-cell analysis and machine learning identified coVID-19's main targets
Last Update: 2020-06-17
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, June 6, 2020 /
BiovalleyBIOON/-- Scientists at Yale University School of Medicine are using single-cell RNA sequencing to study how SARS-CoV-2 interacts with host cellsDrDavid van Dijk, Assistant Professor of Medicine, Department of Cardiovascular Medicine and Computer Science, and DrCraig Wilen, Assistant Professor of Medicine and Immunobiology in the Laboratory, used single-cell RNA sequence data from infected human bronchophone epithelial cells (HBECs) to determine how viruses infect and alter healthy cellspublished in the study on the bioRxiv preprint server, the authors identified cilia cells as the primary target sacs for SARS-CoV-2 infectionThe bronchial epithelial is a protective barrier against allergens and pathogensThe cilia removes mucus and other particles from the respiratory tractTheir findings provide insight into how the virus causes diseaseWilen and postdoctoral colleague Dr Mia Alfajaro infected HBECs with SARS-CoV-2 in an air-liquid interfaceOver a three-day period, they used single-cell RNA sequencing to identify the characteristics of infection dynamics, such as the number of infected cells of different cell types and whether SARS-CoV-2 activated the immune response of infected cellsVan Dijk, who specializes in single-cell technology, uses advanced algorithms to develop working assumptionspicture source: Blausen Medical
"Machine learning allows us to make assumptions." This is a different method of scientific researchWe use as few assumptions as possibleMeasure everything we can measure, and the algorithm presents us with assumptions "
researchers, working with Dr Tamas Horvath and Klara Szigetii-buck, used electron microscopes to understand the structural basis of viruses and target cells These observations provide insights into host-virus interactions to measure the tendency of SARS-CoV-2 cells, or the ability to infect different cell types Three days later, thousands of cultured cells were infected The authors analyzed data from infected cells and adjacent bystander cells They observed that 83% of the infected cells were cilia cells Throughout the study, these cells were the first and main source of infection The virus also targets other epithelial cell types, including base cells and club cells Cup-shaped cells, neuroendocrine cells, cluster cells and ion cells are less likely to be infected genetic markers reveal an innate immune response associated with a protein called leukocyte interleukin 6 (IL-6) The analysis also showed a change in the transcriptofs of viruses for polyadenosine Finally, (uninfected) bystander cells also show an immune response, possibly due to signals from infected cells Extracted from thousands of genes, these algorithms locate genetic differences between infected and uninfected cells In the next phase of this study, Wilen and van Dijk will examine the severity of SARS-CoV-2 and other types of coronaviruses and test them in animal models (BioValleyBioon.com) References: Single-cell analysis and machine sydd sym nod-19
Neal G Ravindra et al.
Single-cell longitudinal analysis of SARS-CoV-2 infection human bronchial epithelial cells , (2020) DOI: 10.1101/2020.05.06.081695
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