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.