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

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, Yifei Shen, Yan Jiang, Yunsong Yu, Yu Chen, Guoji Guo, Peng Yin (), David A. Weitz () and Yongcheng Wang ()
Additional contact information
Ziye Xu: Zhejiang University School of Medicine
Yuting Wang: Zhejiang University
Kuanwei Sheng: Harvard University
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
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
David A. Weitz: Harvard University
Yongcheng Wang: Zhejiang University School of Medicine

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-40137-9

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