bioRxivImmunology
Platypus: an open-access software for integrating lymphocyte single-cell immune repertoires with transcriptomes
A. Yermanos, A. Agrafiotis, J. Yates, C. Papadopoulou, D. Robbiani, F. Bieberich, R. Vazquez-Lombardi, D. Neumeier, A. Oxenius, S. T. Reddy
Abstract
High-throughput single-cell sequencing (scSeq) technologies are revolutionizing the ability to molecularly profile B and T lymphocytes by offering the opportunity to simultaneously obtain information on adaptive immune receptor repertoires (VDJ repertoires) and transcriptomes. An integrated quantification of immune repertoire parameters such as germline gene usage, clonal expansion, somatic hypermutation and transcriptional states opens up new possibilities for the high-resolution analysis of lymphocytes and the inference of antigen-specificity. While multiple tools now exist to investigate gene expression profiles from scSeq of transcriptomes, there is a lack of software dedicated to single-cell immune repertoires. Here, we present Platypus, an open-source software platform providing a user-friendly interface to investigate B cell receptor (BCR) and T cell receptor (TCR) repertoires from single-cell sequencing experiments. Platypus provides a framework to automate and ease the analysis of single-cell immune repertoires while also incorporating transcriptional information involving unsupervised clustering, gene expression, and gene ontology. To showcase the capabilities of Platypus, we use it to analyze and visualize single-cell immune repertoires and transcriptomes from B and T cells from convalescent COVID-19 patients, revealing unique insight into the repertoire features and transcriptional profiles of clonally expanded lymphocytes. Platypus will expedite progress by increasing accessibility to the broader immunology community by facilitating the analysis of single-cell immune repertoire and transcriptome sequencing.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- approach: deep learning
- approach: statistical model
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Scirpy: A Scanpy extension for analyzing single-cell T-cell receptor sequencing data
Gregor Sturm, Tamás Géza Szabó, … Francesca Finotello·BioinformaticsPeer-reviewed·Jun 24, 2020
- Single-cell immune repertoire sequencing of B and T cells in murine models of infection and autoimmunity
D. Shlesinger, K.-L. Hong, … A. Yermanos·bioRxiv·Feb 7, 2022
- ePlatypus: an ecosystem for computational analysis of immunogenomics data
V. Kreiner, A. Agrafiotis, … A. Yermanos·bioRxiv·Sep 28, 2022
- Exploring and analysing immune single cell multi-omics data with VDJView
Jerome Samir, Simone Rizzetto, … Fabio Luciani·bioRxiv·Aug 8, 2019
- Echidna: integrated simulations of single-cell immune receptor repertoires and transcriptomes
J. Han, R. Kuhn, … A. Yermanos·bioRxiv·Jul 17, 2021
- scRepertoire 2: Enhanced and Efficient Toolkit for Single-Cell Immune Profiling
Qile Yang, Ksenia R. Safina, … Nicholas C. Borcherding·PLoS Computational BiologyPeer-reviewed·Jun 27, 2025