PathogenTrack and Yeskit: tools for identifying intracellular pathogens from single-cell RNA-sequencing datasets as illustrated by application to COVID-19

Abstract

Pathogenic microbes can induce cellular dysfunction, immune response, and cause infectious disease and other diseases including cancers. However, the cellular distributions of pathogens and their impact on host cells remain rarely explored due to the limited methods. Taking advantage of single-cell RNA-sequencing (scRNA-seq) analysis, we can assess the transcriptomic features at the single-cell level. Still, the tools used to interpret pathogens (such as viruses, bacteria, and fungi) at the single-cell level remain to be explored. Here, we introduced PathogenTrack, a python-based computational pipeline that uses unmapped scRNA-seq data to identify intracellular pathogens at the single-cell level. In addition, we established an R package named Yeskit to import, integrate, analyze, and interpret pathogen abundance and transcriptomic features in host cells. Robustness of these tools has been tested on various real and simulated scRNA-seq datasets. PathogenTrack is competitive to the state-of-the-art tools such as Viral-Track, and the first tools for identifying bacteria at the single-cell level. Using the raw data of bronchoalveolar lavage fluid samples (BALF) from COVID-19 patients in the SRA database, we found the SARS-CoV-2 virus exists in multiple cell types including epithelial cells and macrophages. SARS-CoV-2-positive neutrophils showed increased expression of genes related to type I interferon pathway and antigen presenting module. Additionally, we observed the Haemophilus parahaemolyticus in some macrophage and epithelial cells, indicating a co-infection of the bacterium in some severe cases of COVID-19. The PathogenTrack pipeline and the Yeskit package are publicly available at GitHub. Electronic Supplementary Material Supplementary material is available in the online version of this article at 10.1007/s11684-021-0915-9 and is accessible for authorized users.

Vulture: Cloud-enabled scalable mining of microbial reads in public scRNA-seq data

“…We also tested Vulture local command line tools against Venus and PathogenTrack [15], [16] using three COVID-19 scRNA-seq run with different file sizes: a 1GB small sample (SRR12570205) from Bost et al [14], a 67 GB medium sample (SRR11537951) and a 141 GB large sample (SRR11181956) from Liao et al [35]. Fig.…”

“…Viral-Track [14] is an existing computational pipeline that detects viral-host interactions in droplet scRNA-seq data by scanning host-unmapped reads for the presence of viral RNA. Based on a similar schema, Zhang et al and Lee et al developed PathogenTrack [15] and Venus [16] which have added capabilities. PathogenTrack can quantify bacteria in addition to viruses; the tool was benchmarked to be mostly correlated in microbial unique molecular identifiers (UMIs) called as and faster in run time than Viral-Track [15].…”

“…Based on a similar schema, Zhang et al and Lee et al developed PathogenTrack [15] and Venus [16] which have added capabilities. PathogenTrack can quantify bacteria in addition to viruses; the tool was benchmarked to be mostly correlated in microbial unique molecular identifiers (UMIs) called as and faster in run time than Viral-Track [15]. Venus identifies viruses only but has another module to discover viral integration sites.…”

The role of tumor-infiltrating B cells in the tumor microenvironment of hepatocellular carcinoma and its prognostic value: a bioinformatics analysis

“…Dataset integration was performed by Seurat software (14) in R, using the Harmony method (15) for integrating different batches of samples. Then, data were visualized using the Yeskit tool (16). The transcriptome data on The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) were processed using the Cell-type Identification be Estimating Relative Subsets of RNA Transcripts (CIBERSORT) tool (17) for calculating each proportion of immune cell subtype.…”

Development of Single-Cell Transcriptomics and Its Application in COVID-19

“…Wei Zhang’s team developed an algorithm called PathogenTrack that is based on unsupervised identification of features of the intracellular microbiota extracted from scRNA-seq data. Using this algorithm, they found that a small percentage of cells in the bronchoalveolar lavage fluid (BALF) samples of a COVID-19 patient, including neutrophils and macrophages, were infected with Haemophilus parahemolyticus [ 67 ], a bacterium that is usually associated with ARDS and septic shock [ 68 ].…”