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Dissecting insect cell heterogeneity during influenza VLP production using single-cell transcriptomics
The insect cell-baculovirus expression vector system (IC-BEVS) has been widely used to produce recombinant protein at high titers, including complex virus-like particles (VPLs). However, cell-to-cell variability upon infection is yet one of the least understood phenomena in virology, and little is k...
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Published in: | Frontiers in bioengineering and biotechnology 2023-03, Vol.11, p.1143255 |
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Main Authors: | , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | The insect cell-baculovirus expression vector system (IC-BEVS) has been widely used to produce recombinant protein at high titers, including complex virus-like particles (VPLs). However, cell-to-cell variability upon infection is yet one of the least understood phenomena in virology, and little is known about its impact on production of therapeutic proteins. This study aimed at dissecting insect cell population heterogeneity during production of influenza VLPs in IC-BEVS using single-cell RNA-seq (scRNA-seq). High Five cell population was shown to be heterogeneous even before infection, with cell cycle being one of the factors contributing for this variation. In addition, infected insect cells were clustered according to the timing and level of baculovirus genes expression, with each cluster reporting similar influenza VLPs transgenes (i.e., hemagglutinin and M1) transcript counts. Trajectory analysis enabled to track infection progression throughout pseudotime. Specific pathways such as translation machinery, protein folding, sorting and degradation, endocytosis and energy metabolism were identified as being those which vary the most during insect cell infection and production of Influenza VLPs. Overall, this study lays the ground for the application of scRNA-seq in IC-BEVS processes to isolate relevant biological mechanisms during recombinant protein expression towards its further optimization. |
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ISSN: | 2296-4185 2296-4185 |
DOI: | 10.3389/fbioe.2023.1143255 |