Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Rapid pathogen detection by metagenomic next-generation sequencing of infected body fluids


We developed a metagenomic next-generation sequencing (mNGS) test using cell-free DNA from body fluids to identify pathogens. The performance of mNGS testing of 182 body fluids from 160 patients with acute illness was evaluated using two sequencing platforms in comparison to microbiological testing using culture, 16S bacterial PCR and/or 28S–internal transcribed ribosomal gene spacer (28S–ITS) fungal PCR. Test sensitivity and specificity of detection were 79 and 91% for bacteria and 91 and 89% for fungi, respectively, by Illumina sequencing; and 75 and 81% for bacteria and 91 and 100% for fungi, respectively, by nanopore sequencing. In a case series of 12 patients with culture/PCR-negative body fluids but for whom an infectious diagnosis was ultimately established, seven (58%) were mNGS positive. Real-time computational analysis enabled pathogen identification by nanopore sequencing in a median 50-min sequencing and 6-h sample-to-answer time. Rapid mNGS testing is a promising tool for diagnosis of unknown infections from body fluids.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Study workflow and sample distribution.
Fig. 2: Accuracy of mNGS testing and relative pathogen burden in body fluid samples.
Fig. 3: Comparison of mNGS and 16S (bacterial) or 28S–ITS (fungal) PCR.
Fig. 4: Comparison of relative pathogen burden in paired body fluid and plasma samples.

Data availability

Metagenomic sequencing data (FASTQ files) after removal of human genomic reads have been deposited in the NCBI Sequence Read Archive (SRA) (PRJNA558701, under umbrella project PRJNA234047).

Code availability

SURPI+ v.1.0 ( and SURPIrt v.1.0 software ( have been deposited on GitHub and are available for download for research use only. Linux (Ubuntu 16.04.6) and Python (python 2.7.12) scripts used for construction of dual-use Illumina and nanopore barcodes are provided in the Supplementary Information, ‘Pipeline for Designing 37mer Barcodes’. Other custom scripts for ROC curve and read length analysis have been deposited on Github (


  1. 1.

    Messacar, K., Parker, S. K., Todd, J. K. & Dominguez, S. R. Implementation of rapid molecular infectious disease diagnostics: the role of diagnostic and antimicrobial stewardship. J. Clin. Microbiol. 55, 715–723 (2017).

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Rea, B., Maisel, J. R., Glaser, L. & Alby, K. Identification of clinically relevant mycobacterial species after extended incubation times in the BACTEC MGIT System. Am. J. Clin. Pathol. 151, 63–67 (2019).

    PubMed  Google Scholar 

  3. 3.

    Janda, J. M. & Abbott, S. L. 16S rRNA gene sequencing for bacterial identification in the diagnostic laboratory: pluses, perils, and pitfalls. J. Clin. Microbiol. 45, 2761–2764 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Petti, C. A., Polage, C. R. & Schreckenberger, P. The role of 16S rRNA gene sequencing in identification of microorganisms misidentified by conventional methods. J. Clin. Microbiol. 43, 6123–6125 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Reuwer, A. Q., van den Bijllaardt, W., Murk, J. L., Buiting, A. G. M. & Verweij, J. J. Added diagnostic value of broad-range 16S PCR on periprosthetic tissue and clinical specimens from other normally sterile body sites. J. Appl. Microbiol. 126, 661–666 (2019).

    CAS  PubMed  Google Scholar 

  6. 6.

    Wilson, M. R. et al. Actionable diagnosis of neuroleptospirosis by next-generation sequencing. N. Engl. J. Med. 370, 2408–2417 (2014).

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Wilson, M. R. et al. Clinical metagenomic sequencing for diagnosis of meningitis and encephalitis. N. Engl. J. Med. 380, 2327–2340 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Glimaker, M. et al. Adult bacterial meningitis: earlier treatment and improved outcome following guideline revision promoting prompt lumbar puncture. Clin. Infect. Dis. 60, 1162–1169 (2015).

    PubMed  Google Scholar 

  9. 9.

    Kumar, A. et al. Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Crit. Care Med. 34, 1589–1596 (2006).

    PubMed  Google Scholar 

  10. 10.

    Weiss, S. L. et al. Delayed antimicrobial therapy increases mortality and organ dysfunction duration in pediatric sepsis. Crit. Care Med. 42, 2409–2417 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Llor, C. & Bjerrum, L. Antimicrobial resistance: risk associated with antibiotic overuse and initiatives to reduce the problem. Ther. Adv. Drug Saf. 5, 229–241 (2014).

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Chiu, C. Y. & Miller, S. A. Clinical metagenomics. Nat. Rev. Genet. 20, 341–355 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Gu, W., Miller, S. & Chiu, C. Y. Clinical metagenomic next-generation sequencing for pathogen detection. Annu. Rev. Pathol. 14, 319–338 (2019).

    CAS  PubMed  Google Scholar 

  14. 14.

    Simner, P. J., Miller, S. & Carroll, K. C. Understanding the promises and hurdles of metagenomic next-generation sequencing as a diagnostic tool for infectious diseases. Clin. Infect. Dis. 66, 778–788 (2018).

    CAS  PubMed  Google Scholar 

  15. 15.

    Blauwkamp, T. A. et al. Analytical and clinical validation of a microbial cell-free DNA sequencing test for infectious disease. Nat. Microbiol. 4, 663–674 (2019).

    CAS  PubMed  Google Scholar 

  16. 16.

    Charalampous, T. et al. Nanopore metagenomics enables rapid clinical diagnosis of bacterial lower respiratory infection. Nat. Biotechnol. 37, 783–792 (2019).

    CAS  PubMed  Google Scholar 

  17. 17.

    Ivy, M. I. et al. Direct detection and identification of prosthetic joint infection pathogens in synovial fluid by metagenomic shotgun sequencing. J. Clin. Microbiol. 56, e00402-18 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Langelier, C. et al. Integrating host response and unbiased microbe detection for lower respiratory tract infection diagnosis in critically ill adults. Proc. Natl Acad. Sci. USA 115, E12353–E12362 (2018).

    CAS  PubMed  Google Scholar 

  19. 19.

    Leo, S. et al. Detection of bacterial pathogens from broncho-alveolar lavage by next-generation sequencing. Int. J. Mol. Sci. 18, 2011 (2017).

    PubMed Central  Google Scholar 

  20. 20.

    Miller, S. et al. Laboratory validation of a clinical metagenomic sequencing assay for pathogen detection in cerebrospinal fluid. Genome Res. 29, 831–842 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Schlaberg, R. et al. Validation of metagenomic next-generation sequencing tests for universal pathogen detection. Arch. Pathol. Lab Med. 141, 776–786 (2017).

    CAS  PubMed  Google Scholar 

  22. 22.

    Schmidt, K. et al. Identification of bacterial pathogens and antimicrobial resistance directly from clinical urines by nanopore-based metagenomic sequencing. J. Antimicrob. Chemother. 72, 104–114 (2017).

    CAS  PubMed  Google Scholar 

  23. 23.

    Thoendel, M. J. et al. Identification of prosthetic joint infection pathogens using a shotgun metagenomics approach. Clin. Infect. Dis. 67, 1333–1338 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Wilson, M. R. et al. Chronic meningitis investigated via metagenomic next-generation sequencing. JAMA Neurol. 75, 947–955 (2018).

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Lucas, M. J., Brouwer, M. C. & van de Beek, D. Neurological sequelae of bacterial meningitis. J. Infect. 73, 18–27 (2016).

    PubMed  Google Scholar 

  26. 26.

    Schlaberg, R. et al. Viral pathogen detection by metagenomics and pan-viral group polymerase chain reaction in children with pneumonia lacking identifiable etiology. J. Infect. Dis. 215, 1407–1415 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Costales, C. & Butler-Wu, S. M. A real pain: diagnostic quandaries and septic arthritis. J. Clin. Microbiol. 56, e01358-17 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    McGill, F., Heyderman, R. S., Panagiotou, S., Tunkel, A. R. & Solomon, T. Acute bacterial meningitis in adults. Lancet 388, 3036–3047 (2016).

    PubMed  Google Scholar 

  29. 29.

    Singal, A. K., Salameh, H. & Kamath, P. S. Prevalence and in-hospital mortality trends of infections among patients with cirrhosis: a nationwide study of hospitalised patients in the United States. Aliment. Pharm. Ther. 40, 105–112 (2014).

    CAS  Google Scholar 

  30. 30.

    Greninger, A. L. et al. Rapid metagenomic identification of viral pathogens in clinical samples by real-time nanopore sequencing analysis. Genome Med. 7, 99 (2015).

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Faria, N. R. et al. Mobile real-time surveillance of Zika virus in Brazil. Genome Med. 8, 97 (2016).

    PubMed  PubMed Central  Google Scholar 

  32. 32.

    Kafetzopoulou, L. E. et al. Metagenomic sequencing at the epicenter of the Nigeria 2018 Lassa fever outbreak. Science 363, 74–77 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Quick, J. et al. Real-time, portable genome sequencing for Ebola surveillance. Nature 530, 228–232 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Raith, E. P. et al. Prognostic accuracy of the SOFA Score, SIRS Criteria, and qSOFA score for in-hospital mortality among adults with suspected infection admitted to the intensive care unit. JAMA 317, 290–300 (2017).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Deng, X. et al. Metagenomic sequencing with spiked primer enrichment for viral diagnostics and surveillance. Nat. Microbiol. 5, 443–454 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Fan, H. C., Blumenfeld, Y. J., Chitkara, U., Hudgins, L. & Quake, S. R. Analysis of the size distributions of fetal and maternal cell-free DNA by paired-end sequencing. Clin. Chem. 56, 1279–1286 (2010).

    CAS  PubMed  Google Scholar 

  37. 37.

    De Vlaminck, I. et al. Temporal response of the human virome to immunosuppression and antiviral therapy. Cell 155, 1178–1187 (2013).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Focosi, D., Antonelli, G., Pistello, M. & Maggi, F. Torquetenovirus: the human virome from bench to bedside. Clin. Microbiol. Infect. 22, 589–593 (2016).

    CAS  PubMed  Google Scholar 

  39. 39.

    Abril, M. K. et al. Diagnosis of Capnocytophaga canimorsus sepsis by whole-genome next-generation sequencing. Open Forum Infect. Dis. 3, ofw144 (2016).

  40. 40.

    Grumaz, S. et al. Next-generation sequencing diagnostics of bacteremia in septic patients. Genome Med. 8, 73 (2016).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Long, Y. et al. Diagnosis of sepsis with cell-free DNA by next-generation sequencing technology in ICU patients. Arch. Med. Res. 47, 365–371 (2016).

    CAS  PubMed  Google Scholar 

  42. 42.

    De Mattos-Arruda, L. et al. Cerebrospinal fluid-derived circulating tumour DNA better represents the genomic alterations of brain tumours than plasma. Nat. Commun. 6, 8839 (2015).

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Dudley, J. C. et al. Detection and surveillance of bladder cancer using urine tumor DNA. Cancer Discov. 9, 500–509 (2019).

    CAS  PubMed  Google Scholar 

  44. 44.

    Pan, W., Gu, W., Nagpal, S., Gephart, M. H. & Quake, S. R. Brain tumor mutations detected in cerebral spinal fluid. Clin. Chem. 61, 514–522 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Springer, S. U. et al. Non-invasive detection of urothelial cancer through the analysis of driver gene mutations and aneuploidy. eLife 7, e32143 (2018).

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Wang, Y. et al. Evaluation of liquid from the Papanicolaou test and other liquid biopsies for the detection of endometrial and ovarian cancers. Sci. Transl. Med. 10, eaap8793 (2018).

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Corless, C. E. et al. Contamination and sensitivity issues with a real-time universal 16S rRNA PCR. J. Clin. Microbiol. 38, 1747–1752 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Salipante, S. J. et al. Performance comparison of Illumina and ion torrent next-generation sequencing platforms for 16S rRNA-based bacterial community profiling. Appl. Environ. Microbiol. 80, 7583–7591 (2014).

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Wang, Y. & Qian, P. Y. Conservative fragments in bacterial 16S rRNA genes and primer design for 16S ribosomal DNA amplicons in metagenomic studies. PLoS ONE 4, e7401 (2009).

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    Hoggard, M. et al. Characterizing the human Mycobiota: a comparison of small subunit rRNA, ITS1, ITS2, and large subunit rRNA genomic targets. Front. Microbiol. 9, 2208 (2018).

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Chan, K. C. et al. Molecular characterization of circulating EBV DNA in the plasma of nasopharyngeal carcinoma and lymphoma patients. Cancer Res. 63, 2028–2032 (2003).

    CAS  PubMed  Google Scholar 

  53. 53.

    Greninger, A. L. et al. Clinical metagenomic identification of Balamuthia mandrillaris encephalitis and assembly of the draft genome: the continuing case for reference genome sequencing. Genome Med. 7, 113 (2015).

    PubMed  PubMed Central  Google Scholar 

  54. 54.

    Mongkolrattanothai, K. et al. Neurobrucellosis: unexpected answer from metagenomic next-generation sequencing. J. Pediatr. Infect. Dis. 6, 393–398 (2017).

    Google Scholar 

  55. 55.

    Sardi, S. I. et al. Coinfections of Zika and Chikungunya viruses in Bahia, Brazil, identified by metagenomic next-generation sequencing. J. Clin. Microbiol. 54, 2348–2353 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Wilson, M. R. et al. Diagnosing Balamuthia mandrillaris encephalitis with metagenomic deep sequencing. Ann. Neurol. 78, 722–730 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    World Medical Association World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 310, 2191–2194 (2013).

    Google Scholar 

  58. 58.

    Naccache, S. N. et al. A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples. Genome Res. 24, 1180–1192 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Zaharia, M. et al. Faster and more accurate sequence alignment with SNAP. Preprint at (2011).

  60. 60.

    Zinter, M. S., Mayday, M. Y., Ryckman, K. K., Jelliffe-Pawlowski, L. L. & DeRisi, J. L. Towards precision quantification of contamination in metagenomic sequencing experiments. Microbiome 7, 62 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    McIntyre, A. B. R. et al. Comprehensive benchmarking and ensemble approaches for metagenomic classifiers. Genome Biol. 18, 182 (2017).

    PubMed  PubMed Central  Google Scholar 

  62. 62.

    Strain, M. C. et al. Highly precise measurement of HIV DNA by droplet digital PCR. PLoS ONE 8, e55943 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Taylor, S. C., Laperriere, G. & Germain, H. Droplet digital PCR versus qPCR for gene expression analysis with low abundant targets: from variable nonsense to publication quality data. Sci. Rep. 7, 2409 (2017).

    PubMed  PubMed Central  Google Scholar 

  64. 64.

    Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Burnham, P. et al. Urinary cell-free DNA is a versatile analyte for monitoring infections of the urinary tract. Nat. Commun. 9, 2412 (2018).

    PubMed  PubMed Central  Google Scholar 

Download references


We thank H. Reyes, A. Chan, D. Ingebrigtsen, L. Dang, W. Lorizio, J. Streithorst, B. Fung and the UCSF clinical laboratory staff for assistance with orthogonal testing. We thank R. Da for assistance with sequencing runs. We thank S. Shiboski for biostatistics feedback. We thank members of the Chiu, Miller and DeRisi laboratories for feedback and support. This work was funded in part by an NIH grant (no. K08-CA230156) and a Burroughs-Wellcome Award to W.G., by Abbott Laboratories (C.Y.C.), a NIH/NHLBI grant (no. R01-HL105704, to C.Y.C.), a NIH/NIAID grant (no. R33-AI120977, to C.Y.C.), the California Initiative to Advance Precision Medicine (C.Y.C.), a UC Center for Accelerated Innovation grant funded by NIH grant U54HL119893 and NIH/NCATS UCSF-CTSI grant UL1TR000004 (C.Y.C.), and the Charles and Helen Schwab Foundation (C.Y.C.).

Author information




W.G., X.D. and C.Y.C. conceived and designed the study. W.G., X.D. and C.Y.C. coordinated the study. W.G., X.D., M.L., Y.D.S., S.A., A.G., K.R., G.Y., B.B., E.D.C., C.W. and E.H. performed experiments. W.G., X.D., M.L., E.H. and C.Y.C. analyzed data. W.G., G.I., E.H. and C.Y.C. reviewed patient electronic medical records. S.F. and D.S. wrote software and performed SURPI bioinformatics analysis of mNGS data. K.Z., H.S. and G.I. enrolled patients in the study and assisted in patient data collection. A.B., M.R.W., S.M., J.L.D. and C.Y.C. provided clinical samples and resources. W.G., X.D., M.L. and C.Y.C. wrote and edited the manuscript. W.G. and C.Y.C. designed the figures. C.Y.C. supervised the study. All authors read the manuscript and agree to its contents.

Corresponding author

Correspondence to Charles Y. Chiu.

Ethics declarations

Competing interests

C.Y.C. is the director of the UCSF-Abbott Viral Diagnostics and Discovery Center and receives research support funding from Abbott Laboratories, Inc. C.Y.C., D.S., S.F. and S.M. are inventors on a patent application on algorithms related to SURPI+ software (no. PCT/US/16/52912, ‘Pathogen Detection using Next-Generation Sequencing’). C.Y.C., X.D. and S.F. are inventors on a patent pending on algorithms related to SURPIrt software (Case no. SF2015-154, ’Methods for Real-Time Sequencing Analysis of Infectious Diseases’). Other coauthors declare no competing interests.

Additional information

Peer review information Alison Farrell is the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Metagenomic sequencing of body fluids.

a, Nanopore time to detection (minutes) across different body fluid types. Each data point represents the time to detection of the organism, if any, in each body fluid sample. b, Nanopore time to detection (minutes) in relation to pathogen DNA abundance in samples (reads per million, RPM). All box plots represent the median (centre), the interquartiles (minima and maxima), and 1.5 x interquartile range (whiskers). c, Precision-recall curves for Illumina and nanopore training fungal datasets. d, Precision-recall curves based on the Illumina training bacterial dataset in comparison with the composite standard. e, Precision-recall curves based on nanopore bacterial training datasets. f, Pie chart showing distribution of bacterial pathogen titers as estimated by semi-quantitative culture. g, Plot of nRPM values versus semi-quantitative bacterial titers. The nRPM corresponding to bacteria cultured in enrichment broth was significantly lower than the other higher-titer cultures (p = 0.006). h, Relative pathogen burden in positive and negative (non-infectious) body fluid samples. i, Log scale plot of the bacterium Achromobacter xylosoxidans from mNGS data, a common background contaminant in sequencing libraries. There is a log-linear relationship between the qPCR cycle threshold (Ct) value and the RPM corresponding to Achromobacter xylosoxidans. The background level of Achromobacter xylosoxidans is inversely correlated with the input concentration and is relatively constant.

Extended Data Fig. 2 ROC curves of mNGS test performance.

ROC curves are plotted from validation set data based on a clinical gold standard or composite standard. Data are presented as median true positive rates± the 95% confidence intervals. The 95% confidence interval was obtained via a bootstrap method with 2000 resampling iterations. a, Illumina dataset, bacterial detection (n = 127 samples). b, Nanopore dataset, bacterial detection (n = 43 samples), c, Illumina dataset, fungal detection (n = 127, 32 fungal organisms). d, Nanopore dataset, fungal detection (n = 43, 11 fungal organisms).

Extended Data Fig. 3 Relationship of external positive control organism titer with mNGS detection signal (expressed in nRPM).

Simple linear regression of normalized reads per million (nRPM) over four replicates per dilution factor, calculated as genome equivalents per mL (GE/mL) for a, Streptococcus uberis, b, Rhodobacter sphaeroides, c, Aspergillus oryzae, and d, Millerozyma farinosa.

Extended Data Fig. 4 Orthogonal testing for Case S31: Klebsiella pneumoniae infection of pleural fluid.

a, Genomic coverage of K. pneumoniae from Illumina mNGS. Sequencing spanned 36,490 base pairs, or 0.65% of the K. pneumoniae genome. b, Orthogonal confirmation of K. pneumoniae by dPCR of the sequencing library. Nine negative controls from other cases were run in parallel. Out of 10 sequencing libraries, only Case S31 had any positive droplets (n = 43 of 12022 total droplets as circled). c, Orthogonal confirmation of K. pneumoniae by dPCR of the DNA extract. Three positive droplets were detected, indicating a low positive result. d, Orthogonal confirmation of K. pneumoniae by dPCR of contralateral pleural fluid (sample C31). 29 and 24 positive droplets were detected out of 2 replicates. Digital PCR targeting Streptococcus mitis on both pleural fluids did not yield any positive droplets. The positive controls for these experiments were from sheared DNA from Klebsiella pneumoniae and Streptococcus mitis respectively, whereas the negative control was water. e, Sanger sequencing of the K. pneumoniae amplicon from dPCR. Shown are sequencing traces confirming the presence of K. pneumoniae.

Extended Data Fig. 5 Orthogonal testing for Cases S88: Klebsiella aerogenes from cerebrospinal fluid and S87: Bartonella henselae from a skin abscess.

a, Orthogonal confirmation of K. aerogenes by dPCR of the DNA extract. The sample was run in parallel with 9 negative controls. Out of 10 sequencing libraries, only Case S88 had positive dPCR droplets (n = 61). b, Genomic coverage of K. aerogenes from Illumina mNGS. The assembled genomic regions spanned 536,461 bp, or 9.9% of the bacterial genome. c, Orthogonal confirmation of Bartonella henselae by dPCR of the DNA extract. Positive dPCR droplets (n = 12) are seen in abscess fluid and the positive control consisting of sheared DNA from Bartonella henselae (ATCC 49882). The negative control was water.

Extended Data Fig. 6 Length distributions of pathogen cfDNA in mNGS data.

Analysis is performed on the 87 body fluid samples sequenced on both Illumina and nanopore platforms. a, Diagram showing how original genomic DNA lengths are recovered. Paired-end sequencing data is aligned to either a human or microbial genome, followed by determination of fragment length from the start and end positions and construction of a read length histogram. b, Histogram of average DNA lengths for human, bacterial, and fungal organisms obtained from mNGS data. Human DNA is observed to peak at the stereotypical 160 bp nucleosome footprint; both bacterial and fungal DNA are most abundant at sizes of <100 bp, but a higher molecular weight tail is observed extending to 500–600 bp. c, Histogram of bacterial read lengths according to sequencing platform. Illumina and nanopore sequencing platforms produce different size distributions. d, Length analysis of mNGS reads for samples with 16S PCR performed. Comparison of the length profiles of the 16S discordant bacteria cases (S31 and S88), 16S concordant bacteria cases (mean of S10, S36, S41, S69, S85, S128), and all bacteria mean. The pathogen cfDNA in cases S31 and S88 are more fragmented, with the vast majority of fragments <300 bp. The relative paucity of longer fragments could hinder 16S PCR amplification.

Extended Data Fig. 7 Comparison of different threshold variables on the training set to calibrate the thresholds for each variable used.

The final thresholds used are circled in each ROC chart. a, Comparison of different minimal read thresholds for bacteria calling. Based on this data and prior selection of minimal reads, we selected a minimal of 3 reads for the validation set. b, Comparison of using or not using a PCR Ct value normalization for bacteria calling. Normalization resulted in higher specificity and was used on the validation set. c, Comparison of using a same-genus/same-family filter to decrease an informatics artifact where a pathogen burden is high and related species would appear at significant lower values. Using this filter improved specificity. d, Comparison of different minimal read thresholds for fungal calling. We selected a minimal of 1 read based on the significantly higher sensitivity at the lowest threshold. e, Comparison of using or not using a PCR Ct value normalization for fungal calling. Normalization resulted in higher specificity and was used on the validation set.

Supplementary information

Supplementary Information

Supplementary Tables 1–13 and discussion.

Reporting Summary

Supplementary Table 1

Clinical characteristics and mNGS results of all cases used in the accuracy study.

Supplementary Table 2

Positive and negative external controls.

Supplementary Table 3

Differences between clinical and composite standards.

Supplementary Table 4

Bacterial false positives and false negatives using Illumina and nanopore sequencing.

Supplementary Table 5

Fungal true positives, false positives and false negatives using Illumina and nanopore sequencing.

Supplementary Table 11

PCR primers and indexes used for sequencing library barcoding.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gu, W., Deng, X., Lee, M. et al. Rapid pathogen detection by metagenomic next-generation sequencing of infected body fluids. Nat Med 27, 115–124 (2021).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing