A unique human cord blood CD8⁺CD45RA⁺CD27⁺CD161⁺T cell subset identified by flow cytometric data analysis using Seurat

Abstract

Advances in single cell analysis, especially cytometric approaches, have profoundly innovated immunological research. This has resulted in an expansion of high dimensional data, posing great challenges for comprehensive and unbiased analysis. Conventional manual analysis thus becomes untenable, while most computational methods lack flexibility and interoperability, hampering usability. Here, for the first time, we adapted Seurat, a single cell RNA sequencing (scRNA-seq) analysis package, for end-to-end flow cytometric data analysis. We showcased its robust analytical capacity by analyzing the adult blood and cord blood T cell profiles, which was validated by Spectre, another cytometric data analysis package, and manual analysis. Importantly, a unique CD8+CD45RA+CD27+CD161+T cell subset, was identified in cord blood and characterized using flow cytometry and scRNA-seq analysis from a published dataset. Collectively, Seurat possesses great potential for cytometric data analysis. It facilitates thorough interpretations of high dimensional data using a single pipeline, implementing data-driven investigation in clinical immunology.

High‐Dimensional Methods of Single‐Cell Microglial Profiling to Enhance Understanding of Neuropathological Disease

“…Other visualization approaches include, but are not limited to, heatmaps, bar graphs, bubble plots, and pie charts. High-dimensional analysis can be performed in R with packages such as Spectre (Ashhurst et al, 2021) or Seurat (Reyes et al, 2023) or using browser-based platforms such as the OMIC software from Dotmatics (www.omiq.ai, www.dotmatics.com) and Cytobank (Kotecha et al, 2010).…”