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

Authors :
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
Yongcheng Wang
Source :
Nature Communications, Vol 14, Iss 1, Pp 1-12 (2023)
Publication Year :
2023
Publisher :
Nature Portfolio, 2023.

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.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
14
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.f9796ee4d62c4bb5ac272ab0616f7bd1
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-023-40137-9