Advanced ScienceMicrobiologyPeer-reviewed journal article
Single-cell and spatial transcriptomics unveils key regulators governing cell differentiation for Schistosoma japonicum sexual development
Zhigang Lu, X WangiD, Shun LiiD, Xuxin Li, Pengfei Du, Yameng Zheng, Lu LiuiD, Yanyan ZhangiD, Omri WurtzeliD, Guofeng ChengiD
Abstract
Schistosomiasis remains a major global health challenge, primarily due to the large number of egg production by sexually mature parasites. However, the cellular basis and regulatory programs governing sexual development and egg production are poorly characterized. Here, we constructed a dynamic single-cell atlas of Schistosoma japonicum (S. japonicum) covering key development stages of sexual maturation and egg production, and integrated it for the first time with spatial transcriptomics to map tissue-resolved cellular niches. Through bioinformatic analysis and experimental validation, we identified several critical cell populations and regulatory networks that drive parasite development and egg laying. By mapping de novo identified transcription factors (TFs) onto these atlases, we generated a comprehensive spatiotemporal expression profile of TFs and define the regulatory programs of several tissue-specific TFs including Zfp, Fbp3, and Lim. Integrated re-clustering further revealed and validated key regulators of germline stem-cell differentiation in S. japonicum. Finally, comparative analyses with S. mansoni single-cell datasets revealed both conserved and species-divergent cellular plasticity, providing unprecedented insights into their distinct biological traits. Collectively, our comprehensive study establishes a foundational resource for understanding schistosome biology, deciphers regulatory programs underlying cell differentiation and pathogenicity, and accelerates the identification of key molecules involved in sexual development against schistosomiasis.
- organism: human
- organism: mouse
- organism: rat
- assay: scRNA-seq
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- 49th percentile
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