High-throughput single-microbe RNA sequencing reveals adaptive state heterogeneity and host-phage activity associations in human gut microbiome
作者:Yifei Shen, Qinghong Qian, Liguo Ding, Wenxin Qu, Tianyu Zhang, Mengdi Song, Yingjuan Huang, Mengting Wang, Ziye Xu, Jiaye Chen, Ling Dong, Hongyu Chen, Enhui Shen, Shufa Zheng, Yu Chen, Jiong Liu, Longjiang Fan, Yongcheng Wang · 发表于:Protein & Cell · 年份:2024 · DOI:10.1093/procel/pwae027 · 被引用次数:39 · 研究领域:Gut microbiota and health、Bacteriophages and microbial interactions、RNA modifications and cancer
Microbial communities such as those residing in the human gut are highly diverse and complex, and many with important implications for health and diseases. The effects and functions of these microbial communities are determined not only by their species compositions and diversities but also by the dynamic intra- and inter-cellular states at the transcriptional level. Powerful and scalable technologies capable of acquiring single-microbe-resolution RNA sequencing information in order to achieve a comprehensive understanding of complex microbial communities together with their hosts are therefore utterly needed. Here we report the development and utilization of a droplet-based smRNA-seq (single-microbe RNA sequencing) method capable of identifying large species varieties in human samples, which we name smRandom-seq2. Together with a triple-module computational pipeline designed for the bacteria and bacteriophage sequencing data by smRandom-seq2 in four human gut samples, we established a single-cell level bacterial transcriptional landscape of human gut microbiome, which included 29,742 single microbes and 329 unique species. Distinct adaptive response states among species in Prevotella and Roseburia genera and intrinsic adaptive strategy heterogeneity in Phascolarctobacterium succinatutens were uncovered. Additionally, we identified hundreds of novel host-phage transcriptional activity associations in the human gut microbiome. Our results indicated that smRandom-seq2 is a high...