Measure single virus transcriptomics
It is known that transcription measured by standard methods from a bulk
sample of materials is typically lost upto 20% variants present in a
population[70,71],
thereby causing a strong bias for investigating HIV gene expression
which is known for its stochastic phenotype of
transcription[72,73].
Barcoded HIV thus provides us a power matter to quantitatively measure
insert-specific provirus transcription at a single-virus level. Taking
the B-HIVE technology as an example, the authors first found that the
transcriptional phenotype of stochastic HIV expression falls under the
concept of ‘position effects’ first discovered by Joseph Müller in
1930[74]when he observed that the translocation of the Drosophila white
gene to the centromere caused its expression to fluctuate. This
phenomenon remained elusive until the discovery of histone
modifications. On the basis of the concept of position effects, it turns
out that proviruses that integrate in the proximity of active enhancers
(defined by histone H3 lysine 27 acetylation, H3K27ac) displayed a
better transcriptional level compared with those present far from active
enhancers[25].
This observation was supported by the previous study, showing that HIV
that integrated in different chromosomal regions displayed different
transcription levels in Jurkat clonal cell
lines[75]and the study conducted by Vansant and
colleagues[76],
showing that integration sites retargeted by
‘LEDGINs’[77,78]harbored low HIV transcription measured by B-HIVE. Another example was
given by Russel and
colleagues[67],
who attempted to study how well influenza virus genes are expressed in
hundreds of individual mammalian cells in a single-cell scale. By
employing the three-barcodes approach described in the previous section,
the authors statistically quantified viral transcription from every
infected cell by using the method called the Gini
coefficient[79], a measure of the difference
between a given distribution of variables to represent an inequality
within a group. The authors calculated the Gini coefficient value that
was at least 0.64 from viral mRNA per infected cell, indicating a
detectable variation present in viral gene expression and concluded that
such transcriptional variation was caused by both cellular factors and
inherent stochasticity in addition to viral transcription
itself[80–82],
which only partially explained this phenotype.