Single-cell RNA sequencing of proliferative stem cell population from juvenile Schistosoma mansoni worms

The rise of single-cell RNA sequencing (scRNAseq) technologies has enabled researchers to classify cell types, delineate cell developmental trajectories, and measure molecular responses to external perturbations. These technologies all rely on the premise that cell-to-cell variations arising from the biological processes of interest are clearly distinguishable from the intrinsic transcriptional and technical noise. However, for datasets in which the biologically relevant differences between cells are subtle, this assumption does not always hold. Here we present the self-assembling manifold (SAM) algorithm, which iteratively rescales gene expression to extract these subtle signals in a robust and unsupervised manner. We demonstrate its advantages over other state-of-the-art methods with experimental validation in identifying novel stem cell populations of Schistosoma, one of the most prevalent parasites that infects hundreds of millions of people worldwide. Extending our analysis to a total of 56 datasets, we show that SAM is generalizable and consistently outperforms other methods in a variety of biological and quantitative benchmarks. [N402Barcode samples] We applied Smart-Seq2 protocol to sequence 370 single stem cells isolated from 2.5 weeks old Schistosoma mansoni worms, which infect millions of people worldwide. We discovered new stem cell sub-populations using a novel single-cell RNA sequencing analysis method (Self-assembling manifolds, SAM). We found differential expressions in a few transcription factors and marker genes that distinguish these sub-populations. We hope this will be a useful resource for the community interested in the developmental biology of Schistosoma at large. Transcriptome sequencing of 370 single stem cells of Schistosoma [YX samples] We applied Smart-Seq2 protocol to sequence 367 single stem cells isolated from 3.5 weeks old Schistosoma mansoni worms, which infect millions of people worldwide. We discovered new stem cell sub-populations using a novel single-cell RNA sequencing analysis method (Self-assembling manifolds, SAM). We found differential expressions in a few transcription factors and marker genes that distinguish these sub-populations. We hope this will be a useful resource for the community interested in the developmental biology of Schistosoma at large. Transcriptome sequencing of 367 stem cells of Schistosoma