Mar 28, 2025
PBMC/CSF single-cell RNAseq CD4+ T cell reference mapping
- 1Yale University
External link: https://github.com/yyoshiaki/screfmapping
Protocol Citation: Yoshiaki Yasumizu 2025. PBMC/CSF single-cell RNAseq CD4+ T cell reference mapping. protocols.io https://dx.doi.org/10.17504/protocols.io.q26g7mqj1gwz/v1
Manuscript citation:
Yasumizu, Yoshiaki, Daiki Takeuchi, Reo Morimoto, Yusuke Takeshima, Tatsusada Okuno, Makoto Kinoshita, Takayoshi Morita, et al. 2024. “Single-Cell Transcriptome Landscape of Circulating CD4+ T Cell Populations in Autoimmune Diseases.” Cell Genomics 4 (2): 100473.
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: March 12, 2025
Last Modified: March 28, 2025
Protocol Integer ID: 124279
Keywords: ASAPCRN, single-cell RNAseq, CD4+ T cells, PBMC, CSF
Abstract
The pipeline for detailed cluster assignment of CD4+ T cells from PBMC or CSF. The pipeline utilize Azimuth and Symphony for the CD4+ T cell extraction and the label transfer. The pipeline was originally developed and used by Yasumizu et al., 2024, Cell Genomics. This pipeline has been used for various studies, contributing to identifying disease-associated T-cell populations in PBMCs and CSFs (Mori et al., Cell, 2024; Yata et al., Cell Genomics, 2025; Wei et al., JCI, 2025).
Materials
The pipeline is tested on Linux OS (Ubuntu, Red Hat) and not tested on MacOS or Windows.
Background
Background
This pipeline will composed of two steps; 1. extraction of CD4+ T cells, 2. Label transfer from pre-defined CD4+ T cells.
Preparation
Preparation
Prepare single-cell data. Whole PBMC/CSF data can be used. PBMC or CSF datasets generated by 10x platforms, Seq-Well, or SPLiT-seq (Parse Biosciences WT Mega) were tested. Another platform can also be used. Test data can be downloaded at https://cf.10xgenomics.com/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz.
A docker container can be used. Alternatively, a local environment can be used. We recommend Docker.
Use Docker (Recommended)5 steps
Alternatively, you can use singularity/Apptainer
Install Docker
Pull a docker image
bash
docker pull yyasumizu/screfmapping:0.0.1
Make a script
Make a script
screfmapping/example.R can be used as the template. Update this example.R as follows.
1. Change parameters for the output. Updating project.name is fine.
example.R
## ----parameter setting (change here)---------------------------------------------------------------
project.name <- "example"
prefix <- paste0("./output/", project.name, "/", project.name)
dir.create(paste0("./output/", project.name), recursive = T)
2. Load single-cell RNAseq object
In the example, the codes are importing raw 10x data. Instead, users can use their own separate objects. The Seurat object needs to include raw counts.
example.R
## ----reading data (change here)--------------------------------------------------------------------
# Load the PBMC dataset
pbmc.data <- Read10X(data.dir = "/filtered_gene_bc_matrices/hg19/")
q <- CreateSeuratObject(counts = pbmc.data,
project = project.name,
assay = "RNA",
min.cells = 3,
min.features = 200)
Run the script
Run the script
Run the script. Change /home/rstudio/autoimmune_10x to the working directory and example.R to the updated script.
bash
docker run --rm -it -v ${PWD}:/home/rstudio/autoimmune_10x yyasumizu/screfmapping:0.0.1 Rscript example.R
Interpretation of the outputs
Interpretation of the outputs
The two files are major outputs from the pipeline.
- ${prefix}_predict_clusterL1L2_Reference_Mapping.pdf
- ${prefix}_Reference_Mapping.csv : Assigned CD4+T clusters per cell.
example_Reference_Mapping.csv
,clusterL1,clusterL1_prob,clusterL2,clusterL2_prob
AAACATTGATCAGC-1,Tcm,1,TcmTfh,0.8
AAACGCACTGGTAC-1,Treg,1,TregEff,1
AAACGCTGTAGCCA-1,Temra,0.8,TemraTh1,0.8
AAACTTGATCCAGA-1,Tnaive,1,Tnaive,1
AAAGAGACGAGATA-1,Tcm,0.8,TcmTh0,0.6
AAAGAGACGGCATT-1,Tnaive,1,TnaiveMX1,0.8
AAAGCCTGTATGCG-1,Tem,1,TemTh1pre,1
AAAGTTTGTAGAGA-1,Tnaive,1,Tnaive,0.6
AAATCAACACCAGT-1,Tnaive,0.8,Tnaive,0.4
Protocol references
Reference
Yasumizu, Yoshiaki, Daiki Takeuchi, Reo Morimoto, Yusuke Takeshima, Tatsusada Okuno, Makoto Kinoshita, Takayoshi Morita, et al. 2024. “Single-Cell Transcriptome Landscape of Circulating CD4+ T Cell Populations in Autoimmune Diseases.” Cell Genomics 4 (2): 100473.
Code