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

scPathoQuant: a tool for efficient alignment and quantification of pathogen sequence reads from 10× single cell sequencing datasets

MOTIVATION

Currently there is a lack of efficient computational pipelines/tools for conducting simultaneous genome mapping of pathogen-derived and host reads from single cell RNA sequencing (scRNAseq) output from pathogen-infected cells. Contemporary options include processes involving multiple steps and/or running multiple computational tools, increasing user operations time.

RESULTS

To address the need for new tools to directly map and quantify pathogen and host sequence reads from within an infected cell from scRNAseq datasets in a single operation, we have built a python package, called scPathoQuant. scPathoQuant extracts sequences that were not aligned to the primary host genome, maps them to a pathogen genome of interest (here as demonstrated for viral pathogens), quantifies total reads mapping to the entire pathogen, quantifies reads mapping to individual pathogen genes, and finally integrates pathogen sequence counts into matrix files that are used by standard single cell pipelines for downstream analyses with only one command. We demonstrate that scPathoQuant provides a scRNAseq viral and host genome-wide sequence read abundance analysis that can differentiate and define multiple viruses in a single sample scRNAseq output.

AVAILABILITY AND IMPLEMENTATION

The SPQ package is available software accessible at https://github.com/galelab/scPathoQuant (DOI 10.5281/zenodo.10463670) with test codes and datasets available https://github.com/galelab/Whitmore_scPathoQuant_testSets (DOI 10.5281/zenodo.10463677) to serve as a resource for the community.

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

The rapidly growing collection of public single-cell sequencing data has become a valuable resource for molecular, cellular, and microbial discovery. Previous studies mostly overlooked detecting pathogens in human single-cell sequencing data. Moreover, existing bioinformatics tools lack the scalability to deal with big public data. We introduce Vulture, a scalable cloud-based pipeline that performs microbial calling for single-cell RNA sequencing (scRNA-seq) data, enabling meta-analysis of host-microbial studies from the public domain. In our benchmarking experiments, Vulture is 66% to 88% faster than local tools (PathogenTrack and Venus) and 41% faster than the state-of-the-art cloud-based tool Cumulus, while achieving comparable microbial read identification. In terms of the cost on cloud computing systems, Vulture also shows a cost reduction of 83% ($12 vs. ${\$}$70). We applied Vulture to 2 coronavirus disease 2019, 3 hepatocellular carcinoma (HCC), and 2 gastric cancer human patient cohorts with public sequencing reads data from scRNA-seq experiments and discovered cell type-specific enrichment of severe acute respiratory syndrome coronavirus 2, hepatitis B virus (HBV), and Helicobacter pylori-positive cells, respectively. In the HCC analysis, all cohorts showed hepatocyte-only enrichment of HBV, with cell subtype-associated HBV enrichment based on inferred copy number variations. In summary, Vulture presents a scalable and economical framework to mine unknown host-microbial interactions from large-scale public scRNA-seq data. Vulture is available via an open-source license at https://github.com/holab-hku/Vulture.

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

Over the last three years, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related health crisis has claimed over six million lives and caused USD 12 trillion losses to the global economy. SARS-CoV-2 continuously mutates and evolves with a high basic reproduction number (R0), resulting in a variety of clinical manifestations ranging from asymptomatic infection to acute respiratory distress syndrome (ARDS) and even death. To gain a better understanding of coronavirus disease 2019 (COVID-19), it is critical to investigate the components that cause various clinical manifestations. Single-cell sequencing has substantial advantages in terms of identifying differentially expressed genes among individual cells, which can provide a better understanding of the various physiological and pathological processes. This article reviewed the use of single-cell transcriptomics in COVID-19 research, examined the immune response disparities generated by SARS-CoV-2, and offered insights regarding how to improve COVID-19 diagnosis and treatment plans.

Skeletal Muscle and COVID-19: The Potential Involvement of Bioactive Sphingolipids

SARS-CoV-2 virus infection is the cause of the coronavirus disease 2019 (COVID-19), which is still spreading over the world. The manifestation of this disease can range from mild to severe and can be limited in time (weeks) or persist for months in about 30–50% of patients. COVID-19 is considered a multiple organ dysfunction syndrome and the musculoskeletal system manifestations are beginning to be considered of absolute importance in both COVID-19 patients and in patients recovering from the SARS-CoV-2 infection. Musculoskeletal manifestations of COVID-19 and other coronavirus infections include loss of muscle mass, muscle weakness, fatigue or myalgia, and muscle injury. The molecular mechanisms by which SARS-CoV-2 can cause damage to skeletal muscle (SkM) cells are not yet well understood. Sphingolipids (SLs) represent an important class of eukaryotic lipids with structural functions as well as bioactive molecules able to modulate crucial processes, including inflammation and viral infection. In the last two decades, several reports have highlighted the role of SLs in modulating SkM cell differentiation, regeneration, aging, response to insulin, and contraction. This review summarizes the consequences of SARS-CoV-2 infection on SkM and the potential involvement of SLs in the tissue responses to virus infection. In particular, we highlight the role of sphingosine 1-phosphate signaling in order to aid the prediction of novel targets for preventing and/or treating acute and long-term musculoskeletal manifestations of virus infection in COVID-19.