SingleRepExplorer: a unified interactive platform for integrated single-cell transcriptomic and immune repertoire analysis
- 1Bioinformatics Analysis Center, Kagawa University
Abstract
Single-cell RNA sequencing coupled with V(D)J repertoire profiling enables simultaneous capture of lymphocyte transcriptomes and paired antigen receptor sequences, yet analysis of these multimodal datasets remains a substantial barrier for experimental immunologists. Existing graphical platforms either focus exclusively on transcriptomics or treat repertoire data as a secondary annotation, lacking the deep integration required for systems immunology. Here, I present SingleRepExplorer, a comprehensive, code-free analytical platform designed for 10x Genomics Cell Ranger output, built with R/Shiny, and distributed as a Docker container. The platform unifies transcriptomic preprocessing with Seurat, immune repertoire profiling with scRepertoire and immunarch, receptor–transcriptome multimodal correlation with CoNGA, cell-cell communication inference with CellChat, B-cell phylogenetic lineage reconstruction, CDR3 sequence-similarity networking, and CDR3 physicochemical characterization within a single interactive interface. Interface features include a Memory-Preserved Quality Control module for iterative threshold adjustment without data reload, JavaScript-based Lasso cell selection for interactive subclustering, and direct export of publication-ready editable vector graphics. I applied SingleRepExplorer to a publicly available pooled PBMC dataset from COVID-19 healthcare workers and vaccinated individuals. Without writing code, I observed a massively expanded cytotoxic T lymphocyte clonotype in the HCW_convalescent condition, characterized its diversity metrics, linked it to a transcriptionally distinct effector cluster expressing GNLY, GZMH, NKG7, and CD8A, and mapped its exploratory intercellular signaling footprint, highlighting Cluster 7-derived MIF and CCL5–CCR4 axes within the HCW_convalescent network. These illustrative results demonstrate that SingleRepExplorer enables an experimental immunologist to carry a dataset from raw sequencing output to systems-level hypotheses entirely through a graphical interface, and benchmarking of the platform’s core modules against independent reference workflows is reported alongside. The platform is freely available at https://github.com/amufaamo/SingleRepExplorer .
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BibTeX
@article{ishikawa2026singlerepexplorer,
title = {{SingleRepExplorer: a unified interactive platform for integrated single-cell transcriptomic and immune repertoire analysis}},
author = {Masakazu Ishikawa},
journal = {Frontiers in Immunology},
year = {2026},
month = oct,
doi = {10.3389/fimmu.2026.1891118},
url = {https://www.frontiersin.org/articles/10.3389/fimmu.2026.1891118/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - SingleRepExplorer: a unified interactive platform for integrated single-cell transcriptomic and immune repertoire analysis
AU - Masakazu Ishikawa
PY - 2026
DA - 2026/10/28
JO - Frontiers in Immunology
DO - 10.3389/fimmu.2026.1891118
UR - https://www.frontiersin.org/articles/10.3389/fimmu.2026.1891118/full
AB - Single-cell RNA sequencing coupled with V(D)J repertoire profiling enables simultaneous capture of lymphocyte transcriptomes and paired antigen receptor sequences, yet analysis of these multimodal datasets remains a substantial barrier for experimental immunologists. Existing graphical platforms either focus exclusively on transcriptomics or treat repertoire data as a secondary annotation, lacking the deep integration required for systems immunology. Here, I present SingleRepExplorer, a comprehensive, code-free analytical platform designed for 10x Genomics Cell Ranger output, built with R/Shiny, and distributed as a Docker container. The platform unifies transcriptomic preprocessing with Seurat, immune repertoire profiling with scRepertoire and immunarch, receptor–transcriptome multimodal correlation with CoNGA, cell-cell communication inference with CellChat, B-cell phylogenetic lineage reconstruction, CDR3 sequence-similarity networking, and CDR3 physicochemical characterization within a single interactive interface. Interface features include a Memory-Preserved Quality Control module for iterative threshold adjustment without data reload, JavaScript-based Lasso cell selection for interactive subclustering, and direct export of publication-ready editable vector graphics. I applied SingleRepExplorer to a publicly available pooled PBMC dataset from COVID-19 healthcare workers and vaccinated individuals. Without writing code, I observed a massively expanded cytotoxic T lymphocyte clonotype in the HCW_convalescent condition, characterized its diversity metrics, linked it to a transcriptionally distinct effector cluster expressing GNLY, GZMH, NKG7, and CD8A, and mapped its exploratory intercellular signaling footprint, highlighting Cluster 7-derived MIF and CCL5–CCR4 axes within the HCW_convalescent network. These illustrative results demonstrate that SingleRepExplorer enables an experimental immunologist to carry a dataset from raw sequencing output to systems-level hypotheses entirely through a graphical interface, and benchmarking of the platform’s core modules against independent reference workflows is reported alongside. The platform is freely available at https://github.com/amufaamo/SingleRepExplorer .
SN - 1664-3224
ER -