Single-cell metagenomics: challenges and applications

doi:10.1007/s13238-018-0544-5

Protein Cell

2018, Vol. 9

Issue (5) : 501-510 https://doi.org/10.1007/s13238-018-0544-5

REVIEW

Single-cell metagenomics: challenges and applications

Yuan Xu¹, Fangqing Zhao^1,²(

)

¹. Computational Genomics Lab, Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
². University of Chinese Academy of Sciences, Beijing 100049, China

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Abstract

With the development of high throughput sequencing and single-cell genomics technologies, many uncultured bacterial communities have been dissected by combining these two techniques. Especially, by simultaneously leveraging of single-cell genomics and metagenomics, researchers can greatly improve the efficiency and accuracy of obtaining whole genome information from complex microbial communities, which not only allow us to identify microbes but also link function to species, identify subspecies variations, study host-virus interactions and etc. Here, we review recent developments and the challenges need to be addressed in single-cell metagenomics, including potential contamination, uneven sequence coverage, sequence chimera, genome assembly and annotation. With the development of sequencing and computational methods, single-cell metagenomics will undoubtedly broaden its application in various microbiome studies.

Keywords metagenomics bioinformatics single-cell genomics

Corresponding Author(s): Fangqing Zhao

Issue Date: 08 June 2018

Cite this article:

Yuan Xu,Fangqing Zhao. Single-cell metagenomics: challenges and applications[J]. Protein Cell, 2018, 9(5): 501-510.

URL:

https://academic.hep.com.cn/pac/EN/10.1007/s13238-018-0544-5
https://academic.hep.com.cn/pac/EN/Y2018/V9/I5/501

1	Albanese D, Donati C (2017) Strain profiling and epidemiology of bacterial species from metagenomic sequencing. Nat Commun 8:2260 https://doi.org/10.1038/s41467-017-02209-5
2	Avital G, Avraham R, Fan A, Hashimshony T, Hung DT, Yanai I (2017) scDual-Seq: mapping the gene regulatory program of Salmonella infection by host and pathogen single-cell RNAsequencing. Genome Biol 18:200 https://doi.org/10.1186/s13059-017-1340-x
3	Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski ADet al. (2012) SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 19:455–477 https://doi.org/10.1089/cmb.2012.0021
4	Becraft ED, Dodsworth JA, Murugapiran SK, Ohlsson JI, Briggs BR, Kanbar J, De Vlaminck I, Quake SR, Dong H, Hedlund BPet al. (2015) Single-cell-genomics-facilitated read binning of candidate phylum EM19 genomes from geothermal spring metagenomes. Appl Environ Microbiol 82:992–1003 https://doi.org/10.1128/AEM.03140-15
5	Blainey PC (2013) The future is now: single-cell genomics of bacteria and archaea. FEMS Microbiol Rev 37:407–427 https://doi.org/10.1111/1574-6976.12015
6	Blanco L, Bernad A, Lazaro JM, Martin G, Garmendia C, Salas M (1989) Highly efficient DNA synthesis by the phage phi 29 DNA polymerase. Symmetrical mode of DNA replication. J Biol Chem 264:8935–8940
7	Boisvert S, Raymond F, Godzaridis E, Laviolette F, Corbeil J (2012) Ray Meta: scalable de novo metagenome assembly and profiling. Genome Biol 13:R122 https://doi.org/10.1186/gb-2012-13-12-r122
8	Brown CT (2015) Strain recovery from metagenomes. Nat Biotechnol 33:1041–1043 https://doi.org/10.1038/nbt.3375
9	Chaisson MJ, Pevzner PA (2008) Short read fragment assembly of bacterial genomes. Genome Res 18:324–330 https://doi.org/10.1101/gr.7088808
10	Champlot S, Berthelot C, Pruvost M, Bennett EA, Grange T, Geigl EM (2010) An efficient multistrategy DNA decontamination procedure of PCR reagents for hypersensitive PCR applications. PLoS ONE 5:e13042 https://doi.org/10.1371/journal.pone.0013042
11	Chen M, Song P, Zou D, Hu X, Zhao S, Gao S, Ling F (2014) Comparison of multiple displacement amplification (MDA) and multiple annealing and looping-based amplification cycles (MALBAC) in single-cell sequencing. PLoS ONE 9:e114520 https://doi.org/10.1371/journal.pone.0114520
12	Chitsaz H, Yee-Greenbaum JL, Tesler G, Lombardo MJ, Dupont CL, Badger JH, Novotny M, Rusch DB, Fraser LJ, Gormley NAet al. (2011) Efficient de novo assembly of single-cell bacterial genomes from short-read data sets. Nat Biotechnol 29:915–921 https://doi.org/10.1038/nbt.1966
13	Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, Brown CT, Porras-Alfaro A, Kuske CR, Tiedje JM (2014) Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res 42:D633–D642 https://doi.org/10.1093/nar/gkt1244
14	de la Cruz Peña MJ, Martinez-Hernandez F, Garcia-Heredia I, Lluesma Gomez M, Fornas Ò, Martinez-Garcia M (2018) Deciphering the human virome with single-virus genomics and metagenomics. Viruses 10:113 https://doi.org/10.3390/v10030113
15	De Smet J, Hendrix H, Blasdel BG, Danis-Wlodarczyk K, Lavigne R (2017) Pseudomonas predators: understanding and exploiting phage-host interactions. Nat Rev Microbiol 15:517–530 https://doi.org/10.1038/nrmicro.2017.61
16	Dean FB, Nelson JR, Giesler TL, Lasken RS (2001) Rapid amplification of plasmid and phage DNA using Phi 29 DNA polymerase and multiply-primed rolling circle amplification. Genome Res 11:1095–1099 https://doi.org/10.1101/gr.180501
17	Delcher AL, Bratke KA, Powers EC, Salzberg SL (2007) Identifying bacterial genes and endosymbiont DNA with Glimmer. Bioinformatics 23:673–679 https://doi.org/10.1093/bioinformatics/btm009
18	Delcher AL, Harmon D, Kasif S, White O, Salzberg SL (1999) Improved microbial gene identification with GLIMMER. Nucleic Acids Res 27:4636–4641 https://doi.org/10.1093/nar/27.23.4636
19	DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL (2006) Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72:5069–5072 https://doi.org/10.1128/AEM.03006-05
20	Dodsworth JA, Blainey PC, Murugapiran SK, Swingley WD, Ross CA, Tringe SG, Chain PS, Scholz MB, Lo CC, Raymond Jet al. (2013) Single-cell and metagenomic analyses indicate a fermentative and saccharolytic lifestyle for members of the OP9 lineage. Nat Commun 4:1854 https://doi.org/10.1038/ncomms2884
21	Dupont CL, Rusch DB, Yooseph S, Lombardo MJ, Richter RA, Valas R, Novotny M, Yee-Greenbaum J, Selengut JD, Haft DHet al. (2012) Genomic insights to SAR86, an abundant and uncultivated marine bacterial lineage. ISME J 6:1186–1199 https://doi.org/10.1038/ismej.2011.189
22	Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R (2011) UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27:2194–2200 https://doi.org/10.1093/bioinformatics/btr381
23	Erkel C, Kube M, Reinhardt R, Liesack W (2006) Genome of Rice Cluster I archaea—the key methane producers in the rice rhizosphere. Science 313:370–372 https://doi.org/10.1126/science.1127062
24	Garcia Martin H, Ivanova N, Kunin V, Warnecke F, Barry KW, McHardy AC, Yeates C, He S, Salamov AA, Szeto Eet al. (2006) Metagenomic analysis of two enhanced biological phosphorus removal (EBPR) sludge communities. Nat Biotechnol 24:1263–1269 https://doi.org/10.1038/nbt1247
25	Hasegawa M, Hashimoto T (1993) Ribosomal RNA trees misleading? Nature 361:23 https://doi.org/10.1038/361023b0
26	Hosono S, Faruqi AF, Dean FB, Du Y, Sun Z, Wu X, Du J, Kingsmore SF, Egholm M, Lasken RS (2003) Unbiased whole-genome amplification directly from clinical samples. Genome Res 13:954–964 https://doi.org/10.1101/gr.816903
27	Huerta-Cepas J, Szklarczyk D, Forslund K, Cook H, Heller D, Walter MC, Rattei T, Mende DR, Sunagawa S, Kuhn Met al. (2016) eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res 44:D286–D293 https://doi.org/10.1093/nar/gkv1248
28	Ji P, Zhang Y, Wang J, Zhao F (2017) MetaSort untangles metagenome assembly by reducing microbial community complexity. Nat Commun 8:14306 https://doi.org/10.1038/ncomms14306
29	Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu Tet al. (2008) KEGG for linking genomes to life and the environment. Nucleic Acids Res 36:D480–D484 https://doi.org/10.1093/nar/gkm882
30	Kang DD, Froula J, Egan R, Wang Z (2015) MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3:e1165 https://doi.org/10.7717/peerj.1165
31	Kashtan N, Roggensack SE, Rodrigue S, Thompson JW, Biller SJ, Coe A, Ding H, Marttinen P, Malmstrom RR, Stocker Ret al. (2014) Single-cell genomics reveals hundreds of coexisting subpopulations in wild Prochlorococcus. Science 344:416–420 https://doi.org/10.1126/science.1248575
32	Koren S, Treangen TJ, Pop M (2011) Bambus 2: scaffolding metagenomes. Bioinformatics 27:2964–2971 https://doi.org/10.1093/bioinformatics/btr520
33	Kvist T, Ahring BK, Lasken RS, Westermann P (2007) Specific single-cell isolation and genomic amplification of uncultured microorganisms. Appl Microbiol Biotechnol 74:926–935 https://doi.org/10.1007/s00253-006-0725-7
34	Labonte JM, Swan BK, Poulos B, Luo H, Koren S, Hallam SJ, Sullivan MB, Woyke T, Wommack KE, Stepanauskas R (2015) Single-cell genomics-based analysis of virus-host interactions in marine surface bacterioplankton. ISME J 9:2386–2399 https://doi.org/10.1038/ismej.2015.48
35	Lasken RS, Stockwell TB (2007) Mechanism of chimera formation during the multiple displacement amplification reaction. BMC Biotechnol 7:19 https://doi.org/10.1186/1472-6750-7-19
36	Lin H-H, Liao Y-C (2016) Accurate binning of metagenomic contigs via automated clustering sequences using information of genomic signatures and marker genes. Sci Reports 6:24175 https://doi.org/10.1038/srep24175
37	Liu J, Wang H, Yang H, Zhang Y, Wang J, Zhao F, Qi J (2013) Composition-based classification of short metagenomic sequences elucidates the landscapes of taxonomic and functional enrichment of microorganisms. Nucleic Acids Res 41:e3 https://doi.org/10.1093/nar/gks828
38	Marcy Y, Ishoey T, Lasken RS, Stockwell TB, Walenz BP, Halpern AL, Beeson KY, Goldberg SM, Quake SR (2007) Nanoliter reactors improve multiple displacement amplification of genomes from single cells. PLoS Genet 3:1702–1708 https://doi.org/10.1371/journal.pgen.0030155
39	Marshall IP, Blainey PC, Spormann AM, Quake SR (2012) A singlecell genome for Thiovulum sp. Appl Environ Microbiol 78:8555–8563 https://doi.org/10.1128/AEM.02314-12
40	Martinez-Garcia M, Santos F, Moreno-Paz M, Parro V, Anton J (2014) Unveiling viral-host interactions within the ‘microbial dark matter’. Nat Commun 5:4542 https://doi.org/10.1038/ncomms5542
41	Munson-McGee JH, Peng S, Dewerff S, Stepanauskas R, Whitaker RJ, Weitz JS, Young MJ (2018) A virus or more in (nearly) every cell: ubiquitous networks of virus–host interactions in extreme environments. ISME J. https://doi.org/10.1038/s41396-018-0071-7
42	Namiki T, Hachiya T, Tanaka H, Sakakibara Y (2012) MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads. Nucleic Acids Res 40:e155 https://doi.org/10.1093/nar/gks678
43	Nobu MK, Narihiro T, Rinke C, Kamagata Y, Tringe SG, Woyke T, Liu WT (2015) Microbial dark matter ecogenomics reveals complex synergistic networks in a methanogenic bioreactor. ISME J 9:1710–1722 https://doi.org/10.1038/ismej.2014.256
44	Nurk S, Meleshko D, Korobeynikov A, Pevzner PA (2017) metaSPAdes: a new versatile metagenomic assembler. Genome Res 27:824–834 https://doi.org/10.1101/gr.213959.116
45	Ochman H, Lawrence JG, Groisman EA (2000) Lateral gene transfer and the nature of bacterial innovation. Nature 405:299–304 https://doi.org/10.1038/35012500
46	Overbeek R, Olson R, Pusch GD, Olsen GJ, Davis JJ, Disz T, Edwards RA, Gerdes S, Parrello B, Shukla Met al. (2014) The SEED and the rapid annotation of microbial genomes using subsystems technology (RAST). Nucleic Acids Res 42:D206–D214 https://doi.org/10.1093/nar/gkt1226
47	Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S, Holden MT, Fookes M, Falush D, Keane JA, Parkhill J (2015) Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics 31:3691–3693 https://doi.org/10.1093/bioinformatics/btv421
48	Peng Y, Leung HC, Yiu SM, Chin FY (2011) Meta-IDBA: a de Novo assembler for metagenomic data. Bioinformatics 27:i94–i101 https://doi.org/10.1093/bioinformatics/btr216
49	Peng Y, Leung HC, Yiu SM, Chin FY (2012) IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28:1420–1428 https://doi.org/10.1093/bioinformatics/bts174
50	Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glockner FO (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41:D590–D596 https://doi.org/10.1093/nar/gks1219
51	Quince C, Delmont TO, Raguideau S, Alneberg J, Darling AE, Collins G, Eren AM (2017) DESMAN: a new tool for de novo extraction of strains from metagenomes. Genome Biol 18:181 https://doi.org/10.1186/s13059-017-1309-9
52	Raghunathan A, Ferguson HR Jr, Bornarth CJ, Song W, Driscoll M, Lasken RS (2005) Genomic DNA amplification from a single bacterium. Appl Environ Microbiol 71:3342–3347 https://doi.org/10.1128/AEM.71.6.3342-3347.2005
53	Rinke C, Lee J, Nath N, Goudeau D, Thompson B, Poulton N, Dmitrieff E, Malmstrom R, Stepanauskas R, Woyke T (2014) Obtaining genomes from uncultivated environmental microorganisms using FACS-based single-cell genomics. Nat Protoc 9:1038–1048 https://doi.org/10.1038/nprot.2014.067
54	Rodrigue S, Malmstrom RR, Berlin AM, Birren BW, Henn MR, Chisholm SW (2009) Whole genome amplification and de novo assembly of single bacterial cells. PLoS ONE 4:e6864 https://doi.org/10.1371/journal.pone.0006864
55	Roux S, Hawley AK, Torres Beltran M, Scofield M, Schwientek P, Stepanauskas R, Woyke T, Hallam SJ, Sullivan MB (2014) Ecology and evolution of viruses infecting uncultivated SUP05 bacteria as revealed by single-cell- and meta-genomics. Elife 3: e03125 https://doi.org/10.7554/eLife.03125
56	Seemann T (2014) Prokka: rapid prokaryotic genome annotation. Bioinformatics 30:2068–2069 https://doi.org/10.1093/bioinformatics/btu153
57	Shaw K, Sesardic I, Bristol N, Ames C, Dagnall K, Ellis C, Whittaker F, Daniel B (2008) Comparison of the effects of sterilisation techniques on subsequent DNA profiling. Int J Legal Med 122:29–33 https://doi.org/10.1007/s00414-007-0159-5
58	Shi W, Ji P, Zhao F (2017) The combination of direct and paired link graphs can boost repetitive genome assembly. Nucleic Acids Res 45:e43 https://doi.org/10.1093/nar/gkw1191
59	Spencer SJ, Tamminen MV, Preheim SP, Guo MT, Briggs AW, Brito IL, Weitz A, PitkänenVigneault LK, Virta MPet al. (2015) Massively parallel sequencing of single cells by epicPCR links functional genes with phylogenetic markers. ISME J 10:427 https://doi.org/10.1038/ismej.2015.124
60	Stepanauskas R, Fergusson EA, Brown J, Poulton NJ, Tupper B, Labonte JM, Becraft ED, Brown JM, Pachiadaki MG, Povilaitis Tet al. (2017) Improved genome recovery and integrated cell-size analyses of individual uncultured microbial cells and viral particles. Nat Commun 8:84 https://doi.org/10.1038/s41467-017-00128-z
61	Swan BK, Martinez-Garcia M, Preston CM, Sczyrba A, Woyke T, Lamy D, Reinthaler T, Poulton NJ, Masland ED, Gomez MLet al. (2011) Potential for chemolithoautotrophy among ubiquitous bacteria lineages in the dark ocean. Science 333:1296–1300 https://doi.org/10.1126/science.1203690
62	Szollosi GJ, Boussau B, Abby SS, Tannier E, Daubin V (2012) Phylogenetic modeling of lateral gene transfer reconstructs the pattern and relative timing of speciations. Proc Natl Acad Sci USA 109:17513–17518 https://doi.org/10.1073/pnas.1202997109
63	Truong DT, Tett A, Pasolli E, Huttenhower C, Segata N (2017) Microbial strain-level population structure and genetic diversity from metagenomes. Genome Res 27:626–638 https://doi.org/10.1101/gr.216242.116
64	Wang J, Gao Y, Zhao F (2016) Phage-bacteria interaction network in human oral microbiome. Environ Microbiol 18:2143–2158 https://doi.org/10.1111/1462-2920.12923
65	Wang Y, Leung HC, Yiu SM, Chin FY (2012) MetaCluster 5.0: a tworound binning approach for metagenomic data for low-abundance species in a noisy sample. Bioinformatics 28:i356–i362 https://doi.org/10.1093/bioinformatics/bts397
66	Woese CR, Achenbach L, Rouviere P, Mandelco L (1991) Archaeal phylogeny: reexamination of the phylogenetic position of Archaeoglobus fulgidus in light of certain composition-induced artifacts. Syst Appl Microbiol 14:364–371 https://doi.org/10.1016/S0723-2020(11)80311-5
67	Woyke T, Sczyrba A, Lee J, Rinke C, Tighe D, Clingenpeel S, Malmstrom R, Stepanauskas R, Cheng JF (2011) Decontamination of MDA reagents for single cell whole genome amplification. PLoS ONE 6:e26161 https://doi.org/10.1371/journal.pone.0026161
68	Woyke T, Xie G, Copeland A, Gonzalez JM, Han C, Kiss H, Saw JH, Senin P, Yang C, Chatterji Set al. (2009) Assembling the marine metagenome, one cell at a time. PLoS ONE 4:e5299 https://doi.org/10.1371/journal.pone.0005299
69	Wright ES, Yilmaz LS, Noguera DR (2012) DECIPHER, a searchbased approach to chimera identification for 16S rRNA sequences. Appl Environ Microbiol 78:717–725 https://doi.org/10.1128/AEM.06516-11
70	Wu D, Hugenholtz P, Mavromatis K, Pukall R, Dalin E, Ivanova NN, Kunin V, Goodwin L, Wu M, Tindall BJet al. (2009) A phylogenydriven genomic encyclopaedia of Bacteria and Archaea. Nature 462:1056–1060 https://doi.org/10.1038/nature08656
71	Wu Y-W, Tang Y-H, Tringe SG, Simmons BA, Singer SW (2014) MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm. Microbiome 2:26 https://doi.org/10.1186/2049-2618-2-26
72	Yilmaz S, Singh AK (2012) Single cell genome sequencing. Curr Opin Biotechnol 23:437–443 https://doi.org/10.1016/j.copbio.2011.11.018
73	Yoon HS, Price DC, Stepanauskas R, Rajah VD, Sieracki ME, Wilson WH, Yang EC, Duffy S, Bhattacharya D (2011) Single-cell genomics reveals organismal interactions in uncultivated marine protists. Science 332:714–717 https://doi.org/10.1126/science.1203163
74	Yu FB, Blainey PC, Schulz F, Woyke T, Horowitz MA, Quake SR (2017) Microfluidic-based mini-metagenomics enables discovery of novel microbial lineages from complex environmental samples. Elife. https://doi.org/10.7554/eLife.26580
75	Zaneveld JR, Lozupone C, Gordon JI, Knight R (2010) Ribosomal RNA diversity predicts genome diversity in gut bacteria and their relatives. Nucleic Acids Res 38:3869–3879 https://doi.org/10.1093/nar/gkq066
76	Zerbino DR, Birney E (2008) Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res 18:821–829 https://doi.org/10.1101/gr.074492.107
77	Zhang K, Martiny AC, Reppas NB, Barry KW, Malek J, Chisholm SW, Church GM (2006) Sequencing genomes from single cells by polymerase cloning. Nat Biotechnol 24:680–686 https://doi.org/10.1038/nbt1214
78	Zhang Y, Ji P, Wang J, Zhao F (2016) RiboFR-Seq: a novel approach to linking 16S rRNA amplicon profiles to metagenomes. Nucleic Acids Res 44:e99 https://doi.org/10.1093/nar/gkw165

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