Simultaneous profiling of host expression and microbial abundance by spatial metatranscriptome sequencing
- Lin Lyu1,8,
- Xue Li1,8,
- Ru Feng1,8,
- Xin Zhou2,8,
- Tuhin K. Guha2,
- Xiaofei Yu3,
- Guo Qiang Chen4,
- Yufeng Yao5,
- Bing Su1,
- Duowu Zou6,
- Michael P. Snyder2 and
- Lei Chen1,7
- 1Shanghai Institute of Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;
- 2Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA;
- 3State Key Laboratory of Genetic Engineering, School of Life Sciences, Shanghai Engineering Research Center of Industrial Microorganisms, Fudan University, Shanghai 20082, China;
- 4Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, State Key Laboratory of Oncogenes and Related Genes and Chinese Academy of Medical Sciences, Shanghai Cancer Institute, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;
- 5Laboratory of Bacterial Pathogenesis, Department of Microbiology and Immunology, Institutes of Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;
- 6Department of Gastroenterology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;
- 7Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
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↵8 These authors contributed equally to this work.
Abstract
We developed an analysis pipeline that can extract microbial sequences from spatial transcriptomic (ST) data and assign taxonomic labels, generating a spatial microbial abundance matrix in addition to the default host expression matrix, enabling simultaneous analysis of host expression and microbial distribution. We called the pipeline spatial metatranscriptome (SMT) and applied it on both human and murine intestinal sections and validated the spatial microbial abundance information with alternative assays. Biological insights were gained from these novel data that showed host–microbe interaction at various spatial scales. Finally, we tested experimental modification that can increase microbial capture while preserving host spatial expression quality and, by use of positive controls, quantitatively showed the capture efficiency and recall of our methods. This proof-of-concept work shows the feasibility of SMT analysis and paves the way for further experimental optimization and application.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.277178.122.
- Received August 4, 2022.
- Accepted January 31, 2023.
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