Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, United States; Department of Genome Sciences & Medical Scientist Training Program, University of Washington, Seattle, United States
Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, United States
Allison J Greaney
Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, United States; Department of Genome Sciences & Medical Scientist Training Program, University of Washington, Seattle, United States
Nicholas S Heaton
Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, United States; Duke Human Vaccine Institute, Duke University School of Medicine, Durham, United States
Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, United States; Howard Hughes Medical Institute, Chevy Chase, United States
The ultimate success of a viral infection at the cellular level is determined by the number of progeny virions produced. However, most single-cell studies of infection quantify the expression of viral transcripts and proteins, rather than the amount of progeny virions released from infected cells. Here, we overcome this limitation by simultaneously measuring transcription and progeny production from single influenza virus-infected cells by embedding nucleotide barcodes in the viral genome. We find that viral transcription and progeny production are poorly correlated in single cells. The cells that transcribe the most viral mRNA do not produce the most viral progeny and often represent aberrant infections that fail to express the influenza NS gene. However, only some of the discrepancy between transcription and progeny production can be explained by viral gene absence or mutations: there is also a wide range of progeny production among cells infected by complete unmutated virions. Overall, our results show that viral transcription is a relatively poor predictor of an infected cell’s contribution to the progeny population.