Objective Quantification of immunity is a challenge in clinical practice due to the complexity and heterogeneity of immune cells. This study aimed to establish comprehensive reference ranges for immune indicators and characterize immune heterogeneity in healthy adults.
Methods A total of 115 healthy adults aged 18–65 years were enrolled. Sixty immune indicators encompassing natural immunity (NK cells, monocytes, dendritic cells, myeloid-derived suppressor cells), cellular immunity (T cells, regulatory T cells, T follicular helper cells, T helper cells), and humoral immunity (B cells), along with nutritional and metabolic indicators, were simultaneously detected. Flow cytometry was used to measure the number, phenotype, and functional subsets of immune cells. Unsupervised k-means clustering was performed to identify immune subtypes. RNA-sequencing was conducted on representative individuals from each cluster for transcriptomic validation.
Results The reference ranges for 60 immune indicators were established, with over half (38/60) exhibiting coefficient of variation > 30%, indicating substantial heterogeneity. Gender differences were minimal, whereas age-related changes were pronounced in adaptive immune cells. Specifically, human leukocyte antigen DR-positive (HLA-DR+) T cells (%) increased from 20.76% ± 7.75% (18–30 years) to 30.06% ± 10.82% (51–65 years, P = 0.001), while CD45RA+ regulatory T (Treg) cells (%) and naive CD8+ T cells (%) decreased progressively with age (P < 0.001). Correlation analysis between immune cells and routine laboratory indicators revealed that nutritional indicators like albumin (ALB) were positively correlated with the number of immune cells such as CD8+ T cells, while lipid metabolism indicators like low-density lipoprotein (LDL) were negatively correlated with T helper cell differentiation (P < 0.01). Clustering analysis identified three distinct immune subtypes: “potential type” (26.1%, n = 30) characterized by high naive T cells (44.91% ± 9.88% CD4+ T cells, 33.86% ± 13.82% CD8+ T cells) and CD1c-positive myeloid dendritic cells (CD1c+ mDCs) (45.17% ± 11.58%); “effector NK type” (34.8%, n = 45) with elevated NK cell count (704.22 ± 280.79 cells/μL) and cytotoxic function (93.16% ± 2.38% perforin+ NK cells); and “effector T type” (39.1%, n = 40) distinguished by increased HLA-DR+ T cells (19.48% ± 7.1% CD4+ T cells, 45.11% ± 10.92% CD8+ T cells) and effector memory (EM) CD4+ T cells (37.85% ± 11.01%). A further RNA-sequencing analysis confirmed the transcriptomic characteristics of different immune subtypes, which was in accordance with phenotype analysis. Specifically, adults in the potential type had strong adaptive immunity; those in the effector NK type showed upregulated NK cell-mediated cytotoxicity; those in the effector T type exhibited enhanced T-helper 1 immune responses.
Conclusion This study provides a systematic framework for immunity quantification by establishing reference ranges and classifying healthy adults into three immune subtypes with distinct metabolic and transcriptomic features. These findings could enhance understanding of immune heterogeneity in healthy individuals and guide personalized immune monitoring and intervention strategies in clinical practice.
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Funding
National Natural Science Foundation of China(82372324)
National Key R&D Program of China(2022YFA1303500)
RIGHTS & PERMISSIONS
The Author(s), under exclusive licence to the Huazhong University of Science and Technology
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