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

doi:10.1007/s11684-021-0915-9

Front. Med.

2022, Vol. 16

Issue (2) : 251-262 https://doi.org/10.1007/s11684-021-0915-9

RESEARCH ARTICLE

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

Wei Zhang^1,², Xiaoguang Xu¹, Ziyu Fu¹, Jian Chen³(

), Saijuan Chen¹(

), Yun Tan¹(

)

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. School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
³. Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China

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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.

Keywords scRNA-seq intracellular pathogen microbe COVID-19 SARS-CoV-2

Corresponding Author(s): Jian Chen,Saijuan Chen,Yun Tan

About author:

Mingsheng Sun and Mingxiao Yang contributed equally to this work.

Just Accepted Date: 23 December 2021 Online First Date: 22 February 2022 Issue Date: 26 April 2022

Cite this article:

Wei Zhang,Xiaoguang Xu,Ziyu Fu, et al. PathogenTrack and Yeskit: tools for identifying intracellular pathogens from single-cell RNA-sequencing datasets as illustrated by application to COVID-19[J]. Front. Med., 2022, 16(2): 251-262.

URL:

https://academic.hep.com.cn/fmd/EN/10.1007/s11684-021-0915-9
https://academic.hep.com.cn/fmd/EN/Y2022/V16/I2/251

Fig.1 An overview of the PathogenTrack workflow and the downstream analysis package Yeskit. (A) The input to the PathogenTrack pipeline are sample-demultiplexed FASTQ files, and there are several steps required to process this data and obtain per-cell pathogen species level quantification estimates. The output count matrix is a dense matrix, where rows stand for pathogens and columns stand for barcodes of cells. (B) scRNA-seq reads are processed by cellranger or alevin, and the output barcode file is used as input to the PathogenTrack workflow. (C) The host gene-by-cell count matrix and the pathogen species-by-cell count matrix are taken as input of Yeskit for single-cell integration analysis. Yeskit contains 17 functions for importing, integrating, analyzing, and visualizing the host-pathogen interactions at the single-cell level.

Fig.2 Application of the PathogenTrack and Yeskit in the scRNA-seq analysis of COVID-19. (A) Overview of the cell clusters of 13 138 single cells derived from two severe COVID-19 patients. Clusters were named based on the cluster-specific gene expression patterns. (B) Density plot depicting projection of cells on the 2D map shown in (A). (C) Proportion of subpopulations in each sample. (D) Viral load of SARS-CoV-2 in each cell quantified by PathogenTrack. (E) Proportion of SARS-CoV-2 infected (Pos) and bystander (Neg) cells in each cluster. (F) Bacterial load of Hemophilus parahemolyticus in each cell quantified by PathogenTrack. (G) Volcano plot showing differentially expressed genes between neutrophil cells with or without SARS-CoV-2 RNA detected. Differentially expressed (>0.5 absolute log₂ fold change) and statistically significant (adjusted P value<0.05) are colored in green (downregulated) or purple (upregulated). (H) Enriched Gene Ontology (GO) terms in genes highly expressed in SARS-CoV-2-positive neutrophil cells shown in (G). (I) Scores of the interferon alpha response gene module across all cells, projected on the 2D map shown in (A). Color scale represents the average expression level of the gene module subtracted by the aggregated expression of control feature sets.

Fig.3 Performance of PathogenTrack under various simulation parameters. (A–E) The impact of UMI length, PCR cycles, Sigma, read length, and infection level on the performance of the PathogenTrack. In each panel, only one simulation parameter is varied, as shown on the x-axis. (A) UMI length showed no impact on the performance. 10 bp and 12 bp UMI length were used for simulation. (B) PCR cycles showed no impact on the performance. 4–6 PCR cycles were used for simulation. (C) Sigma showed no impact on the performance. Three different Sigma, the standard deviation of start positions, were used simulation. (D) Longer read length showed better performance. The 50 bp, 100 bp, 150 bp read lengths were used for simulation. (E) Higher infection level showed better performance. Five infection levels (levels 1–5) were used for simulation. (F) Accuracy of the PathogenTrack in pathogen detection. Scatter plot shows the relation of the number of pathogen-infected cells predicted by PathogenTrack and the expected genuine.

Fig.4 Performance evaluation on 13 real scRNA-seq data sets (VT, Viral-Track; PT, PathogenTrack). (A) Venn diagram shows the logical relation between the number of pathogen-infected cells detected by Viral-Track and PathogenTrack. (B) Scatter plot shows the correlation between the UMI counts of pathogen-infected cells generated by Viral-Track and PathogenTrack (UMIs≤100). (C) Time and memory performance of Viral-Track and PathogenTrack on 13 real data sets. Note that sample Perth09_Infected ran out of memory when running on a computer with 180 Gb RAM.

1	GXY Zheng, JM Terry, P Belgrader, P Ryvkin, ZW Bent, R Wilson, SB Ziraldo, TD Wheeler, GP McDermott, J Zhu, MT Gregory, J Shuga, L Montesclaros, JG Underwood, DA Masquelier, SY Nishimura, M Schnall-Levin, PW Wyatt, CM Hindson, R Bharadwaj, A Wong, KD Ness, LW Beppu, HJ Deeg, C McFarland, KR Loeb, WJ Valente, NG Ericson, EA Stevens, JP Radich, TS Mikkelsen, BJ Hindson, JH Bielas. Massively parallel digital transcriptional profiling of single cells. Nat Commun 2017; 8(1): 14049 https://doi.org/10.1038/ncomms14049 pmid: 28091601
2	J Cao, JS Packer, V Ramani, DA Cusanovich, C Huynh, R Daza, X Qiu, C Lee, SN Furlan, FJ Steemers, A Adey, RH Waterston, C Trapnell, J Shendure. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 2017; 357(6352): 661–667 https://doi.org/10.1126/science.aam8940 pmid: 28818938
3	S Aibar, CB González-Blas, T Moerman, VA Huynh-Thu, H Imrichova, G Hulselmans, F Rambow, JC Marine, P Geurts, J Aerts, J van den Oord, ZK Atak, J Wouters, S Aerts. SCENIC: single-cell regulatory network inference and clustering. Nat Methods 2017; 14(11): 1083–1086 https://doi.org/10.1038/nmeth.4463 pmid: 28991892
4	E Papalexi, R Satija. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat Rev Immunol 2018; 18(1): 35–45 https://doi.org/10.1038/nri.2017.76 pmid: 28787399
5	Q Zhang, Y He, N Luo, SJ Patel, Y Han, R Gao, M Modak, S Carotta, C Haslinger, D Kind, GW Peet, G Zhong, S Lu, W Zhu, Y Mao, M Xiao, M Bergmann, X Hu, SP Kerkar, AB Vogt, S Pflanz, K Liu, J Peng, X Ren, Z Zhang. Landscape and dynamics of single immune cells in hepatocellular carcinoma. Cell 2019; 179(4): 829–845.e20 https://doi.org/10.1016/j.cell.2019.10.003 pmid: 31675496
6	S Jin, CF Guerrero-Juarez, L Zhang, I Chang, R Ramos, CH Kuan, P Myung, MV Plikus, Q Nie. Inference and analysis of cell−cell communication using CellChat. Nat Commun 2021; 12(1): 1088 https://doi.org/10.1038/s41467-021-21246-9 pmid: 33597522
7	B Hwang, JH Lee, D Bang. Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp Mol Med 2018; 50(8): 1–14 https://doi.org/10.1038/s12276-018-0071-8 pmid: 30089861
8	AJ Westermann, L Barquist, J Vogel. Resolving host-pathogen interactions by dual RNA-seq. PLoS Pathog 2017; 13(2): e1006033 https://doi.org/10.1371/journal.ppat.1006033 pmid: 28207848
9	P Bost, A Giladi, Y Liu, Y Bendjelal, G Xu, E David, R Blecher-Gonen, M Cohen, C Medaglia, H Li, A Deczkowska, S Zhang, B Schwikowski, Z Zhang, I Amit. Host-viral infection maps reveal signatures of severe COVID-19 patients. Cell 2020; 181(7): 1475–1488.e12 https://doi.org/10.1016/j.cell.2020.05.006 pmid: 32479746
10	A Srivastava, L Malik, T Smith, I Sudbery, R Patro. Alevin efficiently estimates accurate gene abundances from dscRNA-seq data. Genome Biol 2019; 20(1): 65 https://doi.org/10.1186/s13059-019-1670-y pmid: 30917859
11	T Smith, A Heger, I Sudbery. UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Res 2017; 27(3): 491–499 https://doi.org/10.1101/gr.209601.116 pmid: 28100584
12	S Chen, Y Zhou, Y Chen, J Gu. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 2018; 34(17): i884–i890 https://doi.org/10.1093/bioinformatics/bty560 pmid: 30423086
13	A Dobin, CA Davis, F Schlesinger, J Drenkow, C Zaleski, S Jha, P Batut, M Chaisson, TR Gingeras. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 2013; 29(1): 15–21 https://doi.org/10.1093/bioinformatics/bts635 pmid: 23104886
14	DE Wood, J Lu, B Langmead. Improved metagenomic analysis with Kraken 2. Genome Biol 2019; 20(1): 257 https://doi.org/10.1186/s13059-019-1891-0 pmid: 31779668
15	T Stuart, A Butler, P Hoffman, C Hafemeister, E Papalexi, WM Mauck 3rd, Y Hao, M Stoeckius, P Smibert, R Satija. Comprehensive integration of single-cell data. Cell 2019; 177(7): 1888–1902.e21 https://doi.org/10.1016/j.cell.2019.05.031 pmid: 31178118
16	I Korsunsky, N Millard, J Fan, K Slowikowski, F Zhang, K Wei, Y Baglaenko, M Brenner, PR Loh, S Raychaudhuri. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods 2019; 16(12): 1289–1296 https://doi.org/10.1038/s41592-019-0619-0 pmid: 31740819
17	HTN Tran, KS Ang, M Chevrier, X Zhang, NYS Lee, M Goh, J Chen. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol 2020; 21(1): 12 https://doi.org/10.1186/s13059-019-1850-9 pmid: 31948481
18	A Alexa, J Rahnenführer. Gene set enrichment analysis with topGO. Bioconductor Improv 2009; 27: 1–26
19	A Liberzon, A Subramanian, R Pinchback, H Thorvaldsdóttir, P Tamayo, JP Mesirov. Molecular signatures database (MSigDB) 3.0. Bioinformatics 2011; 27(12): 1739–1740 https://doi.org/10.1093/bioinformatics/btr260 pmid: 21546393
20	L Zappia, B Phipson, A Oshlack. Splatter: simulation of single-cell RNA sequencing data. Genome Biol 2017; 18(1): 174 https://doi.org/10.1186/s13059-017-1305-0 pmid: 28899397
21	H Sarkar, A Srivastava, R Patro. Minnow: a principled framework for rapid simulation of dscRNA-seq data at the read level. Bioinformatics 2019; 35(14): i136–i144 https://doi.org/10.1093/bioinformatics/btz351 pmid: 31510649
22	WV Li, JJ Li. A statistical simulator scDesign for rational scRNA-seq experimental design. Bioinformatics 2019; 35(14): i41–i50 https://doi.org/10.1093/bioinformatics/btz321 pmid: 31510652
23	X Zhang, C Xu, N Yosef. Simulating multiple faceted variability in single cell RNA sequencing. Nat Commun 2019; 10(1): 2611 https://doi.org/10.1038/s41467-019-10500-w pmid: 31197158
24	P Dibaeinia, S Sinha. SERGIO: a single-cell expression simulator guided by gene regulatory networks. Cell Syst 2020; 11(3): 252–271.e11 https://doi.org/10.1016/j.cels.2020.08.003 pmid: 32871105
25	J Tian, J Wang, K Roeder. ESCO: single cell expression simulation incorporating gene co-expression. Bioinformatics 2021; 37(16): 2374–2381 https://doi.org/10.1093/bioinformatics/btab116 pmid: 33624750
26	AC Frazee, AE Jaffe, B Langmead, JT Leek. Polyester: simulating RNA-seq datasets with differential transcript expression. Bioinformatics 2015; 31(17): 2778–2784 https://doi.org/10.1093/bioinformatics/btv272 pmid: 25926345
27	B Hie, H Cho, B DeMeo, B Bryson, B Berger. Geometric sketching compactly summarizes the single-cell transcriptomic landscape. Cell Syst 2019; 8(6): 483–493.e7 https://doi.org/10.1016/j.cels.2019.05.003 pmid: 31176620
28	M Liao, Y Liu, J Yuan, Y Wen, G Xu, J Zhao, L Cheng, J Li, X Wang, F Wang, L Liu, I Amit, S Zhang, Z Zhang. Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19. Nat Med 2020; 26(6): 842–844 https://doi.org/10.1038/s41591-020-0901-9 pmid: 32398875
29	AS Le Floch, N Cassir, S Hraiech, C Guervilly, L Papazian, JM Rolain. Haemophilus parahaemolyticus septic shock after aspiration pneumonia, France. Emerg Infect Dis 2013; 19(10): 1694–1695 https://doi.org/10.3201/eid1910.130608 pmid: 24050515
30	P Zhang, M Yang, Y Zhang, S Xiao, X Lai, A Tan, S Du, S Li. Dissecting the single-cell transcriptome network underlying gastric premalignant lesions and early gastric cancer. Cell Rep 2019; 27(6): 1934–1947.e5 https://doi.org/10.1016/j.celrep.2019.04.052 pmid: 31067475
31	C Wang, J Xie, L Zhao, X Fei, H Zhang, Y Tan, X Nie, L Zhou, Z Liu, Y Ren, L Yuan, Y Zhang, J Zhang, L Liang, X Chen, X Liu, P Wang, X Han, X Weng, Y Chen, T Yu, X Zhang, J Cai, R Chen, ZL Shi, XW Bian. Alveolar macrophage dysfunction and cytokine storm in the pathogenesis of two severe COVID-19 patients. EBioMedicine 2020; 57: 102833 https://doi.org/10.1016/j.ebiom.2020.102833 pmid: 32574956
32	Y Tan, W Zhang, Z Zhu, N Qiao, Y Ling, M Guo, T Yin, H Fang, X Xu, G Lu, P Zhang, S Yang, Z Fu, D Liang, Y Xie, R Zhang, L Jiang, S Yu, J Lu, F Jiang, J Chen, C Xiao, S Wang, S Chen, XW Bian, H Lu, F Liu, S Chen. Integrating longitudinal clinical laboratory tests with targeted proteomic and transcriptomic analyses reveal the landscape of host responses in COVID-19. Cell Discov 2021; 7(1): 42 https://doi.org/10.1038/s41421-021-00274-1 pmid: 34103487
33	RM Rodriguez, BY Hernandez, M Menor, Y Deng, VS Khadka. The landscape of bacterial presence in tumor and adjacent normal tissue across 9 major cancer types using TCGA exome sequencing. Comput Struct Biotechnol J 2020; 18: 631–641 https://doi.org/10.1016/j.csbj.2020.03.003 pmid: 32257046
34	GD Poore, E Kopylova, Q Zhu, C Carpenter, S Fraraccio, S Wandro, T Kosciolek, S Janssen, J Metcalf, SJ Song, J Kanbar, S Miller-Montgomery, R Heaton, R Mckay, SP Patel, AD Swafford, R Knight. Microbiome analyses of blood and tissues suggest cancer diagnostic approach. Nature 2020; 579(7800): 567–574 https://doi.org/10.1038/s41586-020-2095-1 pmid: 32214244
35	D Nejman, I Livyatan, G Fuks, N Gavert, Y Zwang, LT Geller, A Rotter-Maskowitz, R Weiser, G Mallel, E Gigi, A Meltser, GM Douglas, I Kamer, V Gopalakrishnan, T Dadosh, S Levin-Zaidman, S Avnet, T Atlan, ZA Cooper, R Arora, AP Cogdill, MAW Khan, G Ologun, Y Bussi, A Weinberger, M Lotan-Pompan, O Golani, G Perry, M Rokah, K Bahar-Shany, EA Rozeman, CU Blank, A Ronai, R Shaoul, A Amit, T Dorfman, R Kremer, ZR Cohen, S Harnof, T Siegal, E Yehuda-Shnaidman, EN Gal-Yam, H Shapira, N Baldini, MGI Langille, A Ben-Nun, B Kaufman, A Nissan, T Golan, M Dadiani, K Levanon, J Bar, S Yust-Katz, I Barshack, DS Peeper, DJ Raz, E Segal, JA Wargo, J Sandbank, N Shental, R Straussman. The human tumor microbiome is composed of tumor type-specific intracellular bacteria. Science 2020; 368(6494): 973–980 https://doi.org/10.1126/science.aay9189 pmid: 32467386
36	MG Gareau, PM Sherman, WA Walker. Probiotics and the gut microbiota in intestinal health and disease. Nat Rev Gastroenterol Hepatol 2010; 7(9): 503–514 https://doi.org/10.1038/nrgastro.2010.117 pmid: 20664519
37	I Cho, MJ Blaser. The human microbiome: at the interface of health and disease. Nat Rev Genet 2012; 13(4): 260–270 https://doi.org/10.1038/nrg3182 pmid: 22411464
38	F Sommer, JM Anderson, R Bharti, J Raes, P Rosenstiel. The resilience of the intestinal microbiota influences health and disease. Nat Rev Microbiol 2017; 15(10): 630–638 https://doi.org/10.1038/nrmicro.2017.58 pmid: 28626231
39	ME Sanders, DJ Merenstein, G Reid, GR Gibson, RA Rastall. Probiotics and prebiotics in intestinal health and disease: from biology to the clinic. Nat Rev Gastroenterol Hepatol 2019; 16(10): 605–616 https://doi.org/10.1038/s41575-019-0173-3 pmid: 31296969
40	D Zheng, T Liwinski, E Elinav. Interaction between microbiota and immunity in health and disease. Cell Res 2020; 30(6): 492–506 https://doi.org/10.1038/s41422-020-0332-7 pmid: 32433595
41	JL Round, SK Mazmanian. The gut microbiota shapes intestinal immune responses during health and disease. Nat Rev Immunol 2009; 9(5): 313–323 https://doi.org/10.1038/nri2515 pmid: 19343057

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