loading page

A unique human cord blood CD8 + CD45RA + CD27 + CD161 + T cell subset identified by flow cytometric data analysis using Seurat
  • +10
  • Duan Ni,
  • Julen Gabirel Araneta Reyes,
  • Brigitte Santner-Nanan,
  • Gabriela Veronica Pinget,
  • Lucie Kraftova,
  • Thomas M. Ashhurst,
  • Felix Marsh-Wakefield,
  • Claire Leana Wishart,
  • Jian Tan,
  • Peter Hsu,
  • Nicholas Jonathan Cole King,
  • Laurence Macia,
  • Ralph Nanan
Duan Ni
The University of Sydney Charles Perkins Centre
Author Profile
Julen Gabirel Araneta Reyes
The University of Sydney Charles Perkins Centre
Author Profile
Brigitte Santner-Nanan
The University of Sydney Charles Perkins Centre
Author Profile
Gabriela Veronica Pinget
The University of Sydney Charles Perkins Centre
Author Profile
Lucie Kraftova
The University of Sydney Charles Perkins Centre
Author Profile
Thomas M. Ashhurst
The University of Sydney
Author Profile
Felix Marsh-Wakefield
The University of Sydney
Author Profile
Claire Leana Wishart
The University of Sydney Charles Perkins Centre
Author Profile
Jian Tan
The University of Sydney Charles Perkins Centre
Author Profile
Peter Hsu
The Children's Hospital at Westmead
Author Profile
Nicholas Jonathan Cole King
The University of Sydney Charles Perkins Centre
Author Profile
Laurence Macia
The University of Sydney Charles Perkins Centre
Author Profile
Ralph Nanan
The University of Sydney Charles Perkins Centre

Corresponding Author:[email protected]

Author Profile

Abstract

Advances in single-cell level analytical techniques, especially cytometric approaches, have led to profound innovation in biomedical research, particularly in the field of clinical immunology. This has resulted in an expansion of high-dimensional data, posing great challenges for comprehensive and unbiased analysis. Conventional manual analysis is thus becoming untenable to handle these challenges. Furthermore, most newly developed computational methods lack flexibility and interoperability, hampering their accessibility and usability. Here, we adapted Seurat, an R package originally developed for single-cell RNA sequencing (scRNA-seq) analysis, for high-dimensional flow cytometric data analysis. Based on a 20-marker antibody panel and analyses of T cell profiles in both adult blood and cord blood, we showcased the robust capacity of Seurat in flow cytometric data analysis, which was further validated by Spectre, another high-dimensional cytometric data analysis package, and conventional manual analysis. Importantly, we identified a unique CD8 + T cell population defined as CD8 +CD45RA +CD27 +CD161 + T cell, that was predominantly present in cord blood. We characterized its IFN-γ-producing and potential cytotoxic properties using flow cytometry experiments and scRNA-seq analysis from a published dataset. Collectively, we identified a unique human cord blood CD8 +CD45RA +CD27 +CD161 + T cell subset and demonstrated that Seurat, a widely used package for scRNA-seq analysis, possesses great potential to be repurposed for cytometric data analysis. This facilitates an unbiased and thorough interpretation of complicated high-dimensional data using a single analytical pipeline and opens a novel avenue for data-driven investigation in clinical immunology.