A theoretical framework of immune cell phenotypic classification and discovery

Abstract

Immune cells are highly heterogeneous and show diverse phenotypes, but the underlying mechanism remains to be elucidated. In this study, we proposed a theoretical framework for immune cell phenotypic classification based on gene plasticity, which herein refers to expressional change or variability in response to conditions. The system contains two core points. One is that the functional subsets of immune cells can be further divided into subdivisions based on their highly plastic genes, and the other is that loss of phenotype accompanies gain of phenotype during phenotypic conversion. The first point suggests phenotypic stratification or layerability according to gene plasticity, while the second point reveals expressional compatibility and mutual exclusion during the change in gene plasticity states. Abundant transcriptome data analysis in this study from both microarray and RNA sequencing in human CD4 and CD8 single-positive T cells, B cells, natural killer cells and monocytes supports the logical rationality and generality, as well as expansibility, across immune cells. A collection of thousands of known immunophenotypes reported in the literature further supports that highly plastic genes play an important role in maintaining immune cell phenotypes and reveals that the current classification model is compatible with the traditionally defined functional subsets. The system provides a new perspective to understand the characteristics of dynamic, diversified immune cell phenotypes and intrinsic regulation in the immune system. Moreover, the current substantial results based on plasticitomics analysis of bulk and single-cell sequencing data provide a useful resource for big-data–driven experimental studies and knowledge discoveries.

FCRL3 expression is upregulated and closely correlates with TIGIT expression in regulatory T cells of patients with systemic lupus erythematosus

“…TIGIT + CD4 + T cells showed less proliferation and cytokine production potential than TIGIT -CD4 + T cells [8]. As for FCRL3, it showed mutually exclusive expression with a number of cytokines and cell cycle-related molecules based on transcriptomic data analysis [9], further supporting that FCRL3 highexpressing T cells should also show low cytokine secretion and low proliferation potential [2].…”

Evaluating imputation methods for single-cell RNA-seq data

“…We also evaluated the impact of imputation on gene-gene correlation. Based on some significantly correlated gene pairs from bulk RNA-seq datasets [ 24 ], we compared their correlation before and after imputation. We found that some imputation algorithms, such as MAGIC, scVI and scScope, were indeed able to improve the correlation.…”

Protocol to examine immune subpopulations in murine conjunctiva and lacrimal gland using flow cytometry

“…Furthermore, aside from the conventional classification system, Hu et al. (2023) 2 proposed alternative theories for immune cell subdivision. Fourthly, it is crucial to optimize antibody panels for the specific flow cytometer being used, as lasers and voltage settings may vary among different instruments.…”