PathogenTrack and Yeskit: tools for identifying intracellular pathogens from single-cell RNA-sequencing datasets as illustrated by application to COVID-19.
10.1007/s11684-021-0915-9
- Author:
Wei ZHANG
1
;
Xiaoguang XU
1
;
Ziyu FU
1
;
Jian CHEN
2
;
Saijuan CHEN
3
;
Yun TAN
4
Author Information
1. Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
2. Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China. yufuchenjian@163.com.
3. Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. sjchen@stn.sh.cn.
4. Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. ty12260@rjh.com.cn.
- Publication Type:Journal Article
- Keywords:
COVID-19;
SARS-CoV-2;
intracellular pathogen;
microbe;
scRNA-seq
- MeSH:
COVID-19;
Humans;
RNA;
SARS-CoV-2/genetics*;
Single-Cell Analysis/methods*;
Transcriptome
- From:
Frontiers of Medicine
2022;16(2):251-262
- CountryChina
- Language:English
-
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.
