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Selitsky, S. R.

Publications and source records attributed to Selitsky, S. R..

2 recordsLinked to original sources

Epigenomic characterization of latent HIV infection identifies latency regulating transcription factors.

Transcriptional silencing of HIV generates a reservoir of latently infected cells, but the mechanisms that lead to this outcome are not well understood. We characterized a primary cell model of HIV latency, and observed that latency is a stable, heritable viral state that is rapidly reestablished after stimulation. Using Assay of Transposon-Accessible Chromatin sequencing (ATACseq) we found that latently infected cells exhibit reduced proviral accessibility, elevated activity of Forkead and Kruppel-like factor transcription factors (TFs), and reduced activity of AP-1, RUNX and GATA TFs. Latency reversing agents caused distinct patterns of chromatin reopening across the provirus. Furthermore, depletion of a chromatin domain insulator, CTCF inhibited HIV latency, identifying this factor as playing a key role in the initiation or enforcement of latency. These data indicate that HIV latency develops preferentially in cells with a distinct pattern of TF activity that promotes a closed proviral structure and inhibits viral gene expression.

microbiology

FastPG: Fast clustering of millions of single cells

Current single-cell experiments can produce datasets with millions of cells. Unsupervised clustering can be used to identify cell populations in single-cell analysis but often leads to interminable computation time at this scale. This problem has previously been mitigated by subsampling cells, which greatly reduces accuracy. We built on the graph-based algorithm PhenoGraph and developed FastPG which has the same cell assignment accuracy but is on average 27x faster in our tests. FastPG also has higher cell assignment accuracy than two other fast clustering methods, FlowSOM and PARC. AvailabilityFastPG is available here: https://github.com/sararselitsky/FastPG

bioinformatics