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Single microorganism RNA sequencing of microbiomes using smRandom-Seq.

Ziye Xu1,2, Yuting Wang3, Wenjie Cai1

  • 1Department of Laboratory Medicine of The First Affiliated Hospital and Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, China.

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We developed single-microorganism RNA sequencing (smRandom-seq) to reveal bacterial diversity. This high-throughput method analyzes individual microbial cells, uncovering heterogeneity missed by traditional approaches.

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Area of Science:

  • Microbiology
  • Genomics
  • Molecular Biology

Background:

  • Bacteria exhibit significant diversity across various environments.
  • Traditional transcriptomics methods average gene expression, masking individual microbial behaviors and heterogeneity.
  • Understanding microbial heterogeneity is crucial for fields like microbiome research and infectious disease.

Purpose of the Study:

  • To introduce a novel droplet-based, high-throughput single-microorganism RNA sequencing (smRandom-seq) method.
  • To provide detailed protocols for implementing smRandom-seq for microbial sample analysis.
  • To enable in-depth investigation of microbial heterogeneity at the single-cell level.

Main Methods:

  • Developed a droplet-based, high-throughput single-microorganism RNA sequencing (smRandom-seq) protocol.
  • Procedures include microbial sample preprocessing, in situ preindexed cDNA synthesis, in situ poly(dA) tailing, droplet barcoding, ribosomal RNA depletion, and library preparation.
  • The entire workflow, from sample processing to library construction, is completed in approximately 2 days.

Main Results:

  • smRandom-seq achieves highly species-specific and sensitive gene detection.
  • The method demonstrates enhanced RNA coverage, reduced doublet rates, and minimized ribosomal RNA contamination.
  • Successfully applied to both laboratory cultures and complex microbial community samples.

Conclusions:

  • smRandom-seq offers a powerful tool for constructing single-microorganism transcriptomic atlases.
  • This method facilitates detailed analysis of microbial heterogeneity in diverse settings.
  • It holds significant potential for research in bacterial resistance, microbiome complexity, and host-microorganism interactions.