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

IF 5.2 2区 医学 Q1 GERIATRICS & GERONTOLOGY
Cangang Zhang, Tao Ren, Xiaofan Zhao, Yanhong Su, Qianhao Wang, Tianzhe Zhang, Boxiao He, Yabing Chen, Ling-Yun Wu, Lina Sun, Baojun Zhang, Zheng Xia
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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.

从生物学角度对免疫细胞进行机器学习建模,以揭示生理和病理衰老过程。
从新生儿期到老年期,免疫系统的功能都在逐步重塑。因此,了解衰老如何影响免疫细胞的功能对于精确治疗不同生命阶段的患者至关重要。在此,我们首次构建了涵盖人类从新生儿到超百岁老人的免疫细胞转录组图谱,并全面检测了所有细胞类型中涉及细胞信号传导、新陈代谢、分化和功能的基因表达特征,以研究免疫衰老的变化。通过比较不同年龄组的免疫细胞组成,我们发现 HLA 高表达的 NK 细胞和 CD83 阳性的 B 细胞在青少年(Tg)组中独占高比例,而未知_T 细胞在超百岁老人(Sc)组中独占高比例。值得注意的是,我们发现患有多系统炎症综合征的小儿 COVID-19 患者的生物年龄(BA)与他们的生理年龄(CA)相比加速了衰老。此外,我们还证明了炎性转变髓系细胞的丰度和特征与川崎病(KD)并发症的进展相关。我们还发现,炎性转变-髓系特征与川崎病的治疗耐药性有关,而有效的疗法可以通过减少这种信号转导来改善治疗效果。最后,基于这些与年龄相关的免疫细胞组成,我们开发了一种新的BA预测模型PHARE ( https://xiazlab.org/phare/ ),该模型可应用于scRNA-seq和大量RNA-seq数据。利用该模型,我们发现与健康人相比,冠状动脉疾病(CAD)患者也表现出加速衰老。总之,我们的研究揭示了与衰老相关的免疫细胞比例和功能的变化,无论是在健康还是疾病中,并为成功捕捉加速或延缓衰老的特征提供了一种新工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Immunity & Ageing
Immunity & Ageing GERIATRICS & GERONTOLOGY-IMMUNOLOGY
CiteScore
10.20
自引率
3.80%
发文量
55
期刊介绍: Immunity & Ageing is a specialist open access journal that was first published in 2004. The journal focuses on the impact of ageing on immune systems, the influence of aged immune systems on organismal well-being and longevity, age-associated diseases with immune etiology, and potential immune interventions to increase health span. All articles published in Immunity & Ageing are indexed in the following databases: Biological Abstracts, BIOSIS, CAS, Citebase, DOAJ, Embase, Google Scholar, Journal Citation Reports/Science Edition, OAIster, PubMed, PubMed Central, Science Citation Index Expanded, SCImago, Scopus, SOCOLAR, and Zetoc.
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