Schisto cartoon

Welcome to the Schistosome single-cell transcriptome atlas

About SchistoCyte
Schistosomes are parasitic flatworms that infect hundreds of millions of the world’s poorest people. This resource seeks to provide molecular details of the cell types that make up the pathogenic adult stage this important parasite and to provide details of phenotypes identified from large-scale RNAi studies.
SchistoCyte: Frequently Asked Questions
  1. What do the scales mean?
    The numbers indicate normalized relative expression levels generated from the standard Seurat workflow (See 'Normalizing the data').
  2. What units are the axes?
    The plots represent UMAP projections that attempt to best represent the relationships between individual cells. As such, the axes and their scales can be considered arbitrary.
  3. Where are the gene models from?
    Gene models are based on version 7 of the Schistosoma mansoni genome, available at https://parasite.wormbase.org/ as "Smansoni_v7". (Direct link)
  4. What are the differences between the Raw and Integrated cluster analyses?
    The short answer is, not much for a vast majority of clusters. In our initial published analyses, we were looking to define cluster markers that were common to all groups (males, mature females, and immature females). Thus, we reported the Integrated or “batch corrected” values to define cluster markers. Obviously, using the batch corrected data to assign cluster markers has the potential to obscure genes that were highly sex-specific. Therefore, we have updated Schistocyte to include a similar cluster analysis using the “Raw” non-batch corrected values. The major differences are largely restricted to male-enriched transcripts present in Neuron Cluster 2 and in the Late Male Germ Cells. Besides those two clusters, the major cluster markers are largely unchanged.
    So which analysis should I use? At this point we feel using the “Raw” analyses will work for most people. However, users should examine whether there are major differences in the levels of expression/number of positive cells between various the sexes on the cluster expression map.
  5. How do I reproduce the images seen here?
    Individual images can be downloaded using the icon in each right-hand corner. The complete procedure for generating the atlas is described in the original paper (PMID: 32973030; also on bioRxiv as a preprint). For the sake of convenience, please see below for how to generate additional plots yourself using the Seurat R package. The Seurat object file from which all plots were generated can be found at the NCBI gene expression omnibus under accession number GSE146736 as "GSE146736_adult_scseq_seurat.rds.gz". Note that gene IDs are described as Smp-###### (note hyphen) in the Seurat object, rather than Smp_###### (note underscore). Images were generated as described below. First prepare the data in R using Seurat v3.2: 
    
library('ggplot2')
library('Seurat')
schistodata <- readRDS('/path/to/GSE146736_adult_scseq_seurat.rds')
    For visualization of gene expression on all cells run: 
    
DefaultAssay(schistodata) = "RNA"
myplot <- FeaturePlot(schistodata, features = geneID, order = TRUE, reduction="umap", cols=c("lavender", "#600000"), pt.size=0.75, min.cutoff=0)
fn = paste(FILENAME, '.png', sep='')
png(fn, width=874, height=825)
print(myplot)
dev.off()
    For visualization of gene expression split by parasite sex and sexual maturity: 
    
DefaultAssay(schistodata) = "RNA"
myplot <- FeaturePlot(schistodata, features = geneID, reduction="umap", order = TRUE, cols=c("lavender", "#600000"), pt.size=0.75, min.cutoff=0, split.by = 'Group')
fn = paste(FILENAME, '.png', sep='')
png(fn, width=2520, height=825)
print(myplot)
dev.off()
Contact the Collins lab
You can visit the CollinsLab.org for more information about the lab.
Email Dr. James Collins.