Speaker
Description
Detection of HIV-1-infected cells in single-cell sequencing studies presents a significant challenge partially due to the high variability of viral genomes. Conventional alignment-based tools frequently fail to identify these cells accurately, as they rely on perfect sequence matches. We have developed a bioinformatic pipeline that integrates multiple B-clade viral genome references and employs Kraken2 (K2) for taxonomic identification, circumventing the requirement for personalized sequence alignment.
We analyzed PBMC from 15 samples: five HIV-negative (HIV-), five HIV-positive (HIV+) and five HIV+ on antiretroviral therapy (ART). Transcriptomic (GEX) and epigenetic (ATAC) data were generated using Chromium Multiome (10x Genomics). To construct a B-clade reference dataset, we downloaded 1,312 HXB2 genomes from LANL database. Samples were then compared against this dataset using K2. To minimize false positives, we calibrated K2 using HIV- samples and subsequently analyzed HIV+ and ART samples with the optimized K2 parameters. For comparative purposes, cells were analyzed with Cell Ranger ARC (CR) with a reference combining hg38 and HXB2 genomes.
Out of 150,060 cells, our novel pipeline identified 32 HIV-infected cells: 26 in the five HIV+ samples and 6 in three out of the five ART samples. Of these, 13 were identified in ATAC-seq data, 18 in GEX data, and 1 cell in both. The HIV-infected cells comprised 21 CD4 T cells, 7 CD8 T cells, 2 monocytes, and 2 B cells. CR only identified 11 HIV-infected cells, and exclusively from GEX data: 10 in HIV+ and 1 in an HIV- sample (7 CD4 T cells, 3 monocytes and 1 B cell).
Our novel bioinformatics pipeline enhances the detection of HIV-infected cells compared to CR, across both ATAC-seq and GEX datasets. Notably, our K2-based pipeline identified infected cells exclusively in HIV+ samples. This pipeline has the potential to complement studies aimed at identifying cellular markers characteristic of HIV-infected cells.
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