VirtualCytometry: A webserver for evaluating immune cell differentiation using single-cell RNA sequencing data

Kyungsoo Kim, Sunmo Yang, Sang Jun Ha, Insuk Lee

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


The immune system has diverse types of cells that are differentiated or activated via various signaling pathways and transcriptional regulation upon challenging conditions. Immunophenotyping by flow and mass cytometry are the major approaches for identifying key signaling molecules and transcription factors directing the transition between the functional states of immune cells. However, few proteins can be evaluated by flow cytometry in a single experiment, preventing researchers from obtaining a comprehensive picture of the molecular programs involved in immune cell differentiation. Recent advances in single-cell RNA sequencing (scRNA-seq) have enabled unbiased genome-wide quantification of gene expression in individual cells on a large scale, providing a new and versatile analytical pipeline for studying immune cell differentiation. Results: We present VirtualCytometry, a web-based computational pipeline for evaluating immune cell differentiation by exploiting cell-to-cell variation in gene expression with scRNA-seq data. Differentiating cells often show a continuous spectrum of cellular states rather than distinct populations. VirtualCytometry enables the identification of cellular subsets for different functional states of differentiation based on the expression of marker genes. Case studies have highlighted the usefulness of this subset analysis strategy for discovering signaling molecules and transcription factors for human T-cell exhaustion, a state of T-cell dysfunction, in tumor and mouse dendritic cells activated by pathogens. With more than 226 scRNA-seq datasets precompiled from public repositories covering diverse mouse and human immune cell types in normal and disease tissues, VirtualCytometry is a useful resource for the molecular dissection of immune cell differentiation.

Original languageEnglish
Pages (from-to)546-551
Number of pages6
Issue number2
Publication statusPublished - 2020 Jan 15

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) [NRF-2018M3C9A5064709, NRF-2018R1A5A2025079 and NRF-2019M3A9B6065192].

Publisher Copyright:
© 2019 The Author(s) 2019. Published by Oxford University Press.

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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