IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-40137-9.html
   My bibliography  Save this article

Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq

Author

Listed:
  • Ziye Xu

    (Zhejiang University School of Medicine
    Zhejiang University)

  • Yuting Wang

    (Zhejiang University
    Zhejiang University)

  • Kuanwei Sheng

    (Harvard University
    Harvard Medical School)

  • Raoul Rosenthal

    (Harvard University)

  • Nan Liu

    (Zhejiang University)

  • Xiaoting Hua

    (Zhejiang University School of Medicine)

  • Tianyu Zhang

    (Zhejiang University)

  • Jiaye Chen

    (Harvard Medical School)

  • Mengdi Song

    (Zhejiang University)

  • Yuexiao Lv

    (Zhejiang University)

  • Shunji Zhang

    (Zhejiang University)

  • Yingjuan Huang

    (Zhejiang University)

  • Zhaolun Wang

    (Zhejiang University)

  • Ting Cao

    (Zhejiang University School of Medicine
    Harvard University
    Harvard University)

  • Yifei Shen

    (Zhejiang University School of Medicine)

  • Yan Jiang

    (Zhejiang University School of Medicine)

  • Yunsong Yu

    (Zhejiang University School of Medicine)

  • Yu Chen

    (Zhejiang University School of Medicine)

  • Guoji Guo

    (Zhejiang University)

  • Peng Yin

    (Harvard University
    Harvard Medical School)

  • David A. Weitz

    (Harvard University
    Harvard University)

  • Yongcheng Wang

    (Zhejiang University School of Medicine
    Zhejiang University
    Zhejiang University
    Harvard University)

Abstract

Bacteria colonize almost all parts of the human body and can differ significantly. However, the population level transcriptomics measurements can only describe the average bacteria population behaviors, ignoring the heterogeneity among bacteria. Here, we report a droplet-based high-throughput single-microbe RNA-seq assay (smRandom-seq), using random primers for in situ cDNA generation, droplets for single-microbe barcoding, and CRISPR-based rRNA depletion for mRNA enrichment. smRandom-seq showed a high species specificity (99%), a minor doublet rate (1.6%), a reduced rRNA percentage (32%), and a sensitive gene detection (a median of ~1000 genes per single E. coli). Furthermore, smRandom-seq successfully captured transcriptome changes of thousands of individual E. coli and discovered a few antibiotic resistant subpopulations displaying distinct gene expression patterns of SOS response and metabolic pathways in E. coli population upon antibiotic stress. smRandom-seq provides a high-throughput single-microbe transcriptome profiling tool that will facilitate future discoveries in microbial resistance, persistence, microbe-host interaction, and microbiome research.

Suggested Citation

  • Ziye Xu & Yuting Wang & Kuanwei Sheng & Raoul Rosenthal & Nan Liu & Xiaoting Hua & Tianyu Zhang & Jiaye Chen & Mengdi Song & Yuexiao Lv & Shunji Zhang & Yingjuan Huang & Zhaolun Wang & Ting Cao & Yife, 2023. "Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40137-9
    DOI: 10.1038/s41467-023-40137-9
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-40137-9
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-023-40137-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40137-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.