基于生物学信息的免疫细胞机器学习建模,以揭示生理和病理衰老过程。

Biologically informed machine learning modeling of immune cells to reveal physiological and pathological aging process.

作者信息

Zhang Cangang, Ren Tao, Zhao Xiaofan, Su Yanhong, Wang Qianhao, Zhang Tianzhe, He Boxiao, Chen Yabing, Wu Ling-Yun, Sun Lina, Zhang Baojun, Xia Zheng

机构信息

Department of Pathogenic Microbiology and Immunology, School of Basic Medical Sciences, Xi'an Jiaotong University, Xi'an, Shaanxi, China.

Institute of Infection and Immunity, Translational Medicine Institute, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China.

出版信息

Immun Ageing. 2024 Oct 24;21(1):74. doi: 10.1186/s12979-024-00479-4.

Abstract

The immune system undergoes progressive functional remodeling from neonatal stages to old age. Therefore, understanding how aging shapes immune cell function is vital for precise treatment of patients at different life stages. Here, we constructed the first transcriptomic atlas of immune cells encompassing human lifespan, ranging from newborns to supercentenarians, and comprehensively examined gene expression signatures involving cell signaling, metabolism, differentiation, and functions in all cell types to investigate immune aging changes. By comparing immune cell composition among different age groups, HLA highly expressing NK cells and CD83 positive B cells were identified with high percentages exclusively in the teenager (Tg) group, whereas unknown_T cells were exclusively enriched in the supercentenarian (Sc) group. Notably, we found that the biological age (BA) of pediatric COVID-19 patients with multisystem inflammatory syndrome accelerated aging according to their chronological age (CA). Besides, we proved that inflammatory shift- myeloid abundance and signature correlate with the progression of complications in Kawasaki disease (KD). The shift- myeloid signature was also found to be associated with KD treatment resistance, and effective therapies improve treatment outcomes by reducing this signaling. Finally, based on those age-related immune cell compositions, we developed a novel BA prediction model PHARE ( https://xiazlab.org/phare/ ), which can apply to both scRNA-seq and bulk RNA-seq data. Using this model, we found patients with coronary artery disease (CAD) also exhibit accelerated aging compared to healthy individuals. Overall, our study revealed changes in immune cell proportions and function associated with aging, both in health and disease, and provided a novel tool for successfully capturing features that accelerate or delay aging.

摘要

免疫系统从新生儿期到老年期会经历渐进性的功能重塑。因此,了解衰老如何塑造免疫细胞功能对于精确治疗不同生命阶段的患者至关重要。在此,我们构建了首个涵盖人类寿命(从新生儿到超级百岁老人)的免疫细胞转录组图谱,并全面检查了涉及所有细胞类型的细胞信号传导、代谢、分化和功能的基因表达特征,以研究免疫衰老变化。通过比较不同年龄组的免疫细胞组成,发现HLA高表达的自然杀伤细胞和CD83阳性B细胞仅在青少年(Tg)组中占高比例,而未知_T细胞仅在超级百岁老人(Sc)组中富集。值得注意的是,我们发现患有多系统炎症综合征的儿科COVID-19患者的生物学年龄(BA)根据其实际年龄(CA)加速衰老。此外,我们证明炎症性转变——髓系丰度和特征与川崎病(KD)并发症的进展相关。还发现转变——髓系特征与KD治疗抵抗有关,有效的治疗方法通过减少这种信号传导来改善治疗结果。最后,基于这些与年龄相关的免疫细胞组成,我们开发了一种新型的生物学年龄预测模型PHARE(https://xiazlab.org/phare/),该模型可应用于单细胞RNA测序(scRNA-seq)和批量RNA测序数据。使用该模型,我们发现与健康个体相比,冠心病(CAD)患者也表现出加速衰老。总体而言,我们的研究揭示了健康和疾病状态下与衰老相关的免疫细胞比例和功能变化,并提供了一种成功捕捉加速或延缓衰老特征的新工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c904/11515583/fd2bc418a2fb/12979_2024_479_Fig1_HTML.jpg

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